Greenhouse gas increases and climate change are both influenced by human-caused carbon dioxide emissions. A key component of the fight against climate change and global warming is the regulation and reduction of carbon dioxide emissions. The transition to renewable energy sources and the reduction of emissions of greenhouse gases are topics of active discussion on a global and national scale. This is why it's critical to predict future greenhouse gases emissions in order to plan accordingly.  This research uses two separate machine learning algorithms to effectively predict Uzbekistan's CO₂ emissions. R², MSE, and MAE were the three statistical metrics used to assess the study's efficacy. Artificial neural networks had an R² of 93.8 percent, an MSE of 0.007, and an MAE of 0.005. Decision trees had an R² of 90.1 percent, an MSE of 0.011, and an MAE of 0.009. By comparing the two models, we find that ANN outperforms decision trees and produces more accurate predictions.

Carbon emission, ANN, The Decision tree, Uzbekistan

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

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

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.

Khalimjonov, N. (2024). Evaluating the environmental sustainability of Uzbek firms in the green economy, Yashil Iqtisodiyot va Taraqqiyot, 6(1), 142–147.

Khalimjonov, N. (2023). Foreign direct investment and electricity consumption during Uzbekistan’s green transition, Yashil Iqtisodiyot va Taraqqiyot, 1(11–12).

Khalimjonov, N. (2023). Ways to improve the green recovery in Uzbekistan by investment and trade, Yashil Iqtisodiyot va Taraqqiyot, 1(10), 97–101.

Khalimjonov, N., Allayarov, P., & Tajibaeva, K. (2023). Analysis and evaluation of worldwide carbon emission topics in economic journals: A review on articles published during 2016–2021, E3S Web of Conferences, 419, 01027.

Khalimjonov, N., & Rikhsimbaev, O. (2023). The analysis of economic growth and energy use and CO₂ emission in Uzbekistan, Economics and Innovative Technologies, 11(5), 247–257.

Khalimjonov, N., & Sharipov, B. (2024). An econometric analysis of FDI and trade in Uzbekistan, Mehnat Iqtisodiyoti va Inson Kapitali, 8(2), 163–171.

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.

Kwak, S.K., & Kim, J.H. (2017). Statistical data preparation: Management of missing values and outliers, Korean Journal of Anaesthesiology, 70(4), 407–411.

Kuhn, M., & Johnson, K. (2013). Data pre-processing, Applied Predictive Modeling, 27–59.

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