ESTIMATIONS OF GREEN HOUSE GASES EMISSIONS OF TURKEY BY STATISTICAL METHODS

Year 2024, Volume: 12 Issue: 1, 138 - 149, 01.03.2024

Suat Öztürk , Ahmet Emir

https://doi.org/10.36306/konjes.1267008

Abstract

The way of life, consumption habits, urbanization rate, type of energy production and increasing energy need with growing economies and population progressively promote the GHGs emissions to Earth’s atmosphere. GHGs consisting of CH4, N2O, CO2, H2O and HFCs cause the climate change, disrupting ecological balance, melting glaciers with global warming in the last decades. Therefore, the issues of future prediction and reduction of GHGs emissions became crucial for policy makers of Turkey and other countries under the international protocols and agreements. This article aims to present the prediction and 8-year future forecasting of CH4, N2O and CO2 emissions of Turkey using past annual data between years 1970 and 2018 with grey, autoregressive integrated moving average and double exponential smoothing models. Based on the results, the best prediction performance is reached by DES model followed by ARIMA and GM for all the emissions. MAPEs calculated from the available data and prediction by DES model from 1970 to 2018 are 0.285, 0.355 and 0.408 for CH4, N2O and CO2 in turn. DES future estimations of CH4, N2O and CO2 at 2026 year are determined as 50700 kiloton of CO2 eq., 38100 thousand metric ton of CO2 eq., and 512000 kilotons.

Keywords

Greenhouse gases, Emission, Forecasting, Environment, Turkey

References

• Ü. Ağbulut, İ. Ceylan, A. E. Gürel, and A. Ergün, “The history of greenhouse gas emissions and relation with the nuclear energy policy for Turkey,” International Journal of Ambient Energy, vol. 42, no. 12. pp. 1447–1455, 2021, doi: 10.1080/01430750.2018.1563818.
• B. Wu and C. Mu, “Effects on greenhouse gas (CH4, CO2, N2O) emissions of conversion from over-mature forest to secondary forest and Korean pine plantation in Northeast China,” Forests, vol. 10, no. 9, 2019, doi: 10.3390/f10090788.
• K. Kumaş ve A. Ö. Akyüz, “Theoretical nitrous oxide, methane, carbon dioxide emissions calculations to the atmosphere in Niğde, Turkey,” Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 10, sayı. 2, ss. 209-220, Ara. 2021
• G. Moiceanu and M. N. Dinca, “Climate change-greenhouse gas emissions analysis and forecast in Romania,” Sustain., vol. 13, no. 21, 2021, doi: 10.3390/su132112186.
• R. Sivaprasada, B. Meenakshi, Amruta R, Kowsalya S, Nivetthini Ag, “Forecasting of greenhouse gases and air quality prediction using matlab analytics,” Turkish Journal of Computer and Mathematics Education, 12, 13, 7226-7231, 2021.
• Q. R. Ollivier, D. T. Maher, C. Pitfield, and P. I. Macreadie, “Winter emissions of CO2, CH4, and N2O from temperate agricultural dams: fluxes, sources, and processes,” Ecosphere, 10 (11), e02914, 2019.
• M. A. Budihardjo, I. Faadhilah, N. G. Humaira, M. Hadiwidodo, I. W. Wardhana and B. S. Ramadan, “Forecasting greenhouse gas emissions from heavy vehicles: a case study of Semarang city,” Vol 18, Jurnal Presipitasi, 2, 254-260, 2021.
• U. Şahin, “Forecasting of Turkey's greenhouse gas emissions using linear and nonlinear rolling metabolic grey model based on optimization,” Journal of Cleaner Production, 239, 118079, 2019, doi: 10.1016/j.jclepro.2019.118079.
• A. Hamrani, A. Akbarzadeh and C. A. Madramootoo, “Machine learning for predicting greenhouse gas emissions from agricultural soils,” Science of the Total Environment, 741, 140338, 2020, doi: 10.1016/j.scitotenv.2020.140338.
• Ö. Eren, O. Gökdoğan and M. F. Baran, “Determination of greenhouse gas emissions (GHG) in the production of different aromatic plants in Turkey,” Türk Tarım ve Doğa Bilimleri Dergisi, 6(1), 90–96, 2019.
• Sadorsky, P., “Renewable energy consumption, CO2 emissions and oil prices in the G7 countries. Energy Economics,” 31(3), 456-462, 2009).
• M. Muhadinovic, G. Djurovic, M. M. Bojaj, “Forecasting greenhouse gas emissions and sustainable growth in montenegro: a SVAR approach”, Pol. J. Environ. Stud., 30, 5, 4115-4129, 2021, doi: 10.15244/pjoes/132625.
• Ö. K. Albayrak, “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”, KAUJEASF, 12, 24, 2021, doi: 10.36543/kauiibfd.2021.033.
• C. Hamzacebi and I. Karakurt, “Forecasting the energy-related CO2 emissions of Turkey using a grey prediction model,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 37, no. 9, pp. 1023–1031, 2015, doi: 10.1080/15567036.2014.978086.
• E. Uzlu, “Estimates of greenhouse gas emission in Turkey with grey wolf optimizer algorithm-optimized artificial neural networks,” Neural Comput. Appl., vol. 33, no. 20, pp. 13567–13585, 2021, doi: 10.1007/s00521-021-05980-1.
• M. S. Bakay and Ü. Ağbulut, “Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms,” J. Clean. Prod., vol. 285, 2021, doi: 10.1016/j.jclepro.2020.125324.
• D. Radojević, V. Pocajt, I. Popović, A. Perić-Grujić, and M. Ristić, “Forecasting of greenhouse gas emissions in serbia using artificial neural networks,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 35, no. 8, pp. 733–740, 2013, doi: 10.1080/15567036.2010.514597.
• Mardani, A., Streimikiene, D., Nilashi, M., Arias Aranda, D., Loganathan, N., & Jusoh, A., “Energy consumption, economic growth, and CO2 emissions in G20 countries: application of adaptive neuro-fuzzy inference system,” Energies, 11(10), 2771, 2018.
• F. Çemrek ve Ö. Demir , “Estimating CO2 emission time series with support vector machines regression, artificial neural networks, and classic time series analysis,” Turkish Journal of Forecasting, c. 05, sayı. 2, ss. 36-44, Ara. 2021, doi:10.34110/forecasting.1035912.
• M. Akhshik, A. Bilton, J. Tjong, C. V. Singh, O. Faruk, and M. Sain, “Prediction of greenhouse gas emissions reductions via machine learning algorithms: Toward an artificial intelligence-based life cycle assessment for automotive lightweighting”, Sustainable Materials and Technologies, 31, e00370, 2022, doi: 10.1016/j.susmat.2021.e00370.
• T. Li et al., “Prediction of CH4 emissions from potential natural wetlands on the Tibetan Plateau during the 21st century,” Sci. Total Environ., vol. 657, pp. 498–508, 2019, doi: 10.1016/j.scitotenv.2018.11.275.
• A. Rahman and M. M. Hasan, “Modelling and Forecasting of Carbon Dioxide Emissions in Bangladesh Using Autoregressive Integrated Moving Average (ARIMA) Models,” Open J. Stat., vol. 07, no. 04, pp. 560–566, 2017, doi: 10.4236/ojs.2017.74038.
• K. Li, P. Xiong, Y. Wu, and Y. Dong, “Forecasting greenhouse gas emissions with the new information priority generalized accumulative grey model,” Sci. Total Environ., vol. 807, 2022, doi: 10.1016/j.scitotenv.2021.150859.
• World Bank Open Data, http://data.worldbank.org, 12.03.2022.
• T. Abbasi, T. Abbasi, C. Luithui, and S. A. Abbasi, “A model to forecast methane emissions from tropical and subtropical reservoirs on the basis of artificial neural networks,” Water (Switzerland), vol. 12, no. 1, 2020, doi: 10.3390/w12010145.
• H. Ammar et al., “Estimation of Tunisian greenhouse gas emissions from different livestock species,” Agric., vol. 10, no. 11, pp. 1–17, 2020, doi: 10.3390/agriculture10110562.
• S. U. Rehman, I. Husain, M. Z. Hashmi, E. E. Elashkar, J. A. Khader, and M. Ageli, “Forecasting and modeling of atmospheric methane concentration,” Arab. J. Geosci., vol. 14, no. 16, 2021, doi: 10.1007/s12517-021-07998-0.
• H. T. H. Xuyen, N. T. M. Tram, N. T. H. Tram and N. T. H. Quyen, “Forecasting carbon dioxide emissions, total energy consumption and economic growth in Asian countries based on grey theory,” International Research Journal of Advanced Engineering and Science, Volume 6, Issue 2, pp. 77-81, 2021
• H. Yilmaz and M. Yilmaz, “Forecasting CO2 emissions for Turkey by using the grey prediction method,” J. Eng. Nat. Sci., vol. 31, pp. 141–148, 2013.
• J. H. Yousif, N. N. Alattar, and M. A. Fekihal, “Forecasting models based CO2 emission for sultanate of Oman,” Int. J. Appl. Eng. Res., vol. 12, no. 1, pp. 95–100, 2017.
• P. R. Jena, S. Managi, and B. Majhi, “Forecasting the CO2 emissions at the global level: A multilayer artificial neural network modelling,” Energies, vol. 14, no. 19, 2021, doi: 10.3390/en14196336.
• F. Cemrek. Modelling of CO2 emission statistics in turkey by fuzzy time series analysis, 17 January 2022, Preprint (Version 1) available at Research Square, doi: 10.21203/rs.3.rs-1261965/v1.
• P. K. Singh, A. K. Pandey, S. Ahuja, and R. Kiran, “Multiple forecasting approach: a prediction of CO2 emission from the paddy crop in India,” Environ. Sci. Pollut. Res., vol. 29, no. 17, pp. 25461–25472, 2022, doi: 10.1007/s11356-021-17487-2.
• Ü. A. Şahin, B. Onat, N. Sivri, and E. Yalçin, “The potential effect of the regulation for the end of life vehicles (ELV) on greenhouse gas emission sourced from cars,” J. Fac. Eng. Archit. Gazi Univ., vol. 26, no. 3, pp. 677–682, 2011.
• A. Rakhmatova, A. Sergeev, A. Shichkin, A. Buevich, and E. Baglaeva, “Three-day forecasting of greenhouse gas CH4 in the atmosphere of the Arctic Belyy Island using discrete wavelet transform and artificial neural networks,” Neural Comput. Appl., vol. 33, no. 16, pp. 10311–10322, 2021, doi: 10.1007/s00521-021-05792-3.
• S. Akcan, Y. Kuvvetli, and H. Kocyigit, “Time series analysis models for estimation of greenhouse gas emitted by different sectors in Turkey,” Hum. Ecol. Risk Assess., vol. 24, no. 2, pp. 522–533, 2018, doi: 10.1080/10807039.2017.1392233.
• Özgünoğlu, K. and Uygur, N., “Kahramanmaraş havalimanı için uçaklardan kaynaklanan emisyonların belirlenmesi,” Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 20 (3), 24-30, 2017, doi: 10.17780/ksujes.335226.
• Y. Yang, J. Zhang, and C. Wang, “Forecasting China’s carbon intensity: Is China on track to comply with its copenhagen commitment?,” Energy J., vol. 39, no. 2, pp. 147–171, 2018, doi: 10.5547/01956574.39.2.yyan.
• M. M. Yatarkalkmaz and M. B. Özdemir, “The calculation of greenhouse gas emissions of a family and projections for emission reduction,” J. Energy Syst., vol. 3, no. 3, pp. 96–110, 2019, doi: 10.30521/jes.566516.
• C. Tudor and R. Sova, “Benchmarking ghg emissions forecasting models for global climate policy,” Electron., vol. 10, no. 24, 2021, doi: 10.3390/electronics10243149.
• M. Akyol and E. Uçar, “Carbon footprint forecasting using time series data mining methods: the case of Turkey,” Environ. Sci. Pollut. Res., vol. 28, no. 29, pp. 38552–38562, 2021, doi: 10.1007/s11356-021-13431-6.
• S. M. Hosseini, A. Saifoddin, R. Shirmohammadi, and A. Aslani, “Forecasting of CO2 emissions in Iran based on time series and regression analysis,” Energy Reports, vol. 5, pp. 619–631, 2019, doi: 10.1016/j.egyr.2019.05.004.
• X. Pan, H. Xu, M. Song, Y. Lu, and T. Zong, “Forecasting of industrial structure evolution and CO2 emissions in Liaoning Province,” J. Clean. Prod., vol. 285, 2021, doi: 10.1016/j.jclepro.2020.124870.
• M. Tong, H. Duan, and L. He, “A novel Grey Verhulst model and its application in forecasting CO2 emissions,” Environ. Sci. Pollut. Res., vol. 28, no. 24, pp. 31370–31379, 2021, doi: 10.1007/s11356-020-12137-5.
• G. Moiceanu and M. N. Dinca, “Climate change-greenhouse gas emissions analysis and forecast in Romania,” Sustain., vol. 13, no. 21, 2021, doi: 10.3390/su132112186.
• Z. X. Wang, Q. Li, and L. L. Pei, “A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors,” Energy, 2018, doi: 10.1016/j.energy.2018.04.155.
• K. Li and T. Zhang, “Forecasting electricity consumption using an improved grey prediction model,” Inf., vol. 9, no. 8, 2018, doi: 10.3390/info9080204.
• M. Cheng and G. Shi, “Improved methods for parameter estimation of gray model GM(1,1) based on new background value optimization and model application,” Commun. Stat. Simul. Comput., vol. 51, no. 2, pp. 647–669, 2022, doi: 10.1080/03610918.2019.1657450.
• J. Guo, X. Xiao, J. Yang, and Y. Sun, “GM(1,1) model considering the approximate heteroscedasticity,” J. Grey Syst., vol. 29, no. 2, pp. 53–66, 2017.
• Z. X. Wang, D. D. Li, and H. H. Zheng, “Model comparison of GM(1,1) and DGM(1,1) based on Monte-Carlo simulation,” Phys. A Stat. Mech. its Appl., vol. 542, 2020, doi: 10.1016/j.physa.2019.123341.
• Box GEP, Jenkins GM (1976) Times series analysis-forecasting and control. Prentice-Hall, Englewood Cliffs.
• Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis: forecasting and control, 3rd edn. Prentice Hall, Englewood Cliffs.
• L. Wang, H. Zou, J. Su, L. Li, and S. Chaudhry, “An ARIMA-ANN hybrid model for time series forecasting,” Syst. Res. Behav. Sci., vol. 30, no. 3, pp. 244–259, 2013, doi: 10.1002/sres.2179.
• W. C. Wang, K. W. Chau, D. M. Xu, and X. Y. Chen, “Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition,” Water Resour. Manag., vol. 29, no. 8, pp. 2655–2675, 2015, doi: 10.1007/s11269-015-0962-6.
• T. Booranawong and A. Booranawong, “Simple and double exponential smoothing methods with designed input data for forecasting a seasonal time series: In an application for lime prices in Thailand,” Suranaree J. Sci. Technol. 2017,24(3):301-310.