Forecasting the Potential Scenarios of CO2 Emissions in Kazakhstan Using Deep Learning (DL) Predictive Models

Abstract

Carbon dioxide (CO 2 ) emissions remain a critical factor in global energy transformation and environmental sustainability. Fossil fuel combustion is the primary source of CO 2 emissions, and as one of the world’s major oil producers, Kazakhstan faces significant challenges in mitigating its environmental impact. This study applies deep learning (DL) predictive models, including recurrent neural networks (RNN), bidirectional LSTM (BiLSTM), and attention mechanisms, to forecast Kazakhstan’s CO 2 emission scenarios for the period 2019–2023 using historical national emission data. The models achieved high predictive accuracy (MSE = 0.031, MAE = 0.18, R 2 = 0.97), and the results, supported by both survey data and simulations, indicate a potential downward trend in future CO 2 emissions, reflecting growing public awareness and technological advancements. These findings highlight the potential of AI-based forecasting tools for supporting sustainable energy policy. Nonetheless, the relatively short forecasting horizon and small survey sample size represent limitations that should be addressed in future work.

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