Estimation Of Turkey's Carbon Dioxide Emission with Machine Learning


  • Ahmet ÇOŞGUN Dr.Öğr.Üyesi



ANN, CO2 emission, AI


Carbon dioxide emissions are an important factor in the increase of greenhouse gases in the atmosphere and climate change. Controlling and reducing carbon dioxide emissions plays an important role in combating global warming and climate change. Various national and international efforts are being carried out to reduce greenhouse gas emissions and switch to sustainable energy sources. For this reason, estimating carbon dioxide emissions in the coming years is important for determining the measures to be taken. 

In this study, Turkey's carbon dioxide emissions are successfully estimated using two different machine learning models. The success of the study was evaluated using three different statistical measures: R2, MSE and MAE. The R2 of decision trees was 89.4%, MSE was 0.013 and MAE was 0.011; the R2 of artificial neural networks was 92.7%, MSE was 0.009 and MAE was 0.006. When we compare the two models, it is seen that ANN is more successful than decision trees and predicts with less error.


Kunt, F. (2007). Hava Kirliliğinin Yapay Sinir Ağları Yöntemiyle Modellenmesi ve Tahmini, Selçuk University Graduate School of Natural and Applied Sciences, M.Sc. Thesis, Environmental Engineering Department, Konya.

Aydınlar, B., Güveni H. ve Kırksekiz, S. (2009). Hava Kirliliği ve Modellenmesi, Sakarya Üniversitesi, Fen Bilimleri Enstitüsü, Çevre Mühendisliği Bölümü Yüksek Lisans Rapor.

Alimissis, A., Philippopoulos, K., Tzanis, C.G., and Deligiorgi, D. (2018). Spatial estimation of urban air pollution with the use of artificial neural network models, Atmospheric Environment, 191, 205-213, 2018. DOI: 10.1016/j.atmosenv.2018.07.058

Hu, K. & Rahman, A. (2017). HazeEst: Machine Learning Based Metropolitan Air Pollution Estimation From Fixed and Mobile Sensors, IEEE Sensors, 17(11): 3571-3525. DOI: 10.1109/JSEN.2017.2690975

Huang, C-J., & Kuo, P-H. (2018). A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities, Sensors

Martínez-Espaňa, R., Bueno-Crespo, A., Timón, I., Soto, J., Muňoz, A. & Cecilia, J.M. (2018). Air-Pollution Prediction in Smart Cities through Machine Learning Methods: A Case of Study in Murcia, Spain.

Tamas, W., Notton, G., Paoli, C., Nivet, M-L. & Voyant, C. (2016). Hybridization of Air Quality Frecasting Models Using Machine Learning and Clustering: An Orginal Approach to Detect Pollutant Peaks, Aerosol and Air Qaulity Research, 16: 405-416.

Zaree, T. & Honarvar, A.R. (2018). Improvement of Air Pollution Prediction in a Smart City and its Correction with Weather Conditions using Metrological Big Data, Turkish Journal of Electrical Engineering & Computer Sciences, 26: 1302-1313.

Kuhn, M., Johnson, K., Kuhn, M., & Johnson, K. (2013). Data pre-processing. Applied predictive modeling, 27-59.

Onoz, B., & Oguz, B. (2003). Assessment of outliers in statistical data analysis. Integrated technologies for environmental monitoring and information production, 173-180.

Kwak, S. K., & Kim, J. H. (2017). Statistical data preparation: management of missing values and outliers. Korean journal of anaesthesiology, 70(4), 407-411.

Nayak, S. C., Misra, B. B., & Behera, H. S. (2014). Impact of data normalization on stock index forecasting. International Journal of Computer Information Systems and Industrial Management Applications, 6(2014), 257-269.

Yu, L., Wang, S., & Lai, K. K. (2005). An integrated data preparation scheme for neural network data analysis. IEEE Transactions on Knowledge and Data Engineering, 18(2), 217-230.

Mazziotta, M., & Pareto, A. (2022). Normalization methods for spatio‐temporal analysis of environmental performance: Revisiting the Min–Max method. Environmetrics, 33(5), e2730.

Kabas, O., Kayakus, M., Ünal, İ., & Moiceanu, G. (2023). Deformation Energy Estimation of Cherry Tomato Based on Some Engineering Parameters Using Machine-Learning Algorithms. Applied Sciences, 13(15), 8906.

Kayakuş, M., Terzioğlu, M., Erdoğan, D., Zetter, S. A., Kabas, O., & Moiceanu, G. (2023). European Union 2030 carbon emission target: The case of Turkey. Sustainability, 15(17), 13025. DOI: 10.3390/su151713025

Geurts, P., Irrthum, A., & Wehenkel, L. (2009). Supervised learning with decision tree-based methods in computational and systems biology. Molecular Biosystems, 5(12), 1593-1605. DOI: 10.1039/b907946g

Thomas, T., P. Vijayaraghavan, A., Emmanuel, S. (2020). Applications of decision trees. Machine learning approaches in cyber security analytics, 157-184.

Kayakuş, M., & Açikgöz, F. Y. (2022). Classification of News Texts by Categories Using Machine Learning Methods. Alphanumeric Journal, 10(2), 155-166.

Kriegeskorte, N. (2015). Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science, 1, 417-446.

Ünal U., G. F., Terzioğlu, M., Kayakuş, M., Tutcu, B., Çoşgun, A., Tonguç, G., & Kaplan Yildirim, R. (2023). Estimation of Methane Gas Production in Turkey Using Machine Learning Methods. Applied Sciences, 13(14), 8442.

Kayakus, M., Tutcu, B., Terzioglu, M., Talaş, H., & Ünal Uyar, G. F. (2023). ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability. Sustainability, 15(9), 7389. DOI:10.3390/su15097389

Huang, Y., Kangas, L. J., & Rasco, B. A. (2007). Applications of artificial neural networks (ANNs) in food science. Critical reviews in food science and nutrition, 47(2), 113-126.

Yağmur, A., Kayakuş, M., & Terzioğlu, M. (2022). House price prediction modeling using machine learning techniques: a comparative study. Aestimum, 81.

Piepho, H. P. (2019). A coefficient of determination (R2) for generalized linear mixed models. Biometrical Journal, 61(4), 860-872.

Wang, Z., & Bovik, A. C. (2009). Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE signal processing magazine, 26(1), 98-117.

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE). Geoscientific model development discussions, 7(1), 1525-1534.

Xu, X., He, X., Ai, Q., & Qiu, R. C. (2015). A correlation analysis method for power systems based on random matrix theory. IEEE Transactions on smart grid, 8(4), 1811-1820.

Hu, Q., Che, X., Zhang, L., Zhang, D., Guo, M., & Yu, D. (2011). Rank entropy-based decision trees for monotonic classification. IEEE Transactions on Knowledge and Data Engineering, 24(11), 2052-2064.

Kingsford, C., & Salzberg, S. L. (2008). What are decision trees?. Nature biotechnology, 26(9), 1011-1013.

Ağbulut, Ü. (2022). Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustainable Production and Cons. 29, 141-157.

Kayakuş, M., & Açikgöz, F Y. (2023). Fake News Detection on Twitter with Machine Learning Methods. Journal of Abant Social Sciences, 23(2), 1017-1027.




How to Cite

ÇOŞGUN, A. (2024). Estimation Of Turkey’s Carbon Dioxide Emission with Machine Learning. International Journal of Computational and Experimental Science and Engineering, 10(1).



Research Article