Modeling the CO2 Emissions of Turkey Dependent on Various Parameters Employing ARIMAX and Deep Learning Methods

Abstract

CO2 emission is a big problem, not only for current living beings, but also for nature and the upcoming generations. Therefore, modeling CO2 emissions is the first step in reducing these emissions. In this work, the CO2 emissions of Turkey are modeled depending on the gross domestic product, amount of hydroelectric electricity generated, amount of energy generated from coal and the electricity obtained from natural gas power plants. The conventional autoregressive integrated moving average with exogenous variables (ARIMAX) and nonlinear deep learning methods are utilized to model CO2 emissions for the period of 1960–2020 using yearly data obtained from official sources. The modeling results of the ARIMAX and the deep learning methods are quantitatively assessed using four key figures of merit, namely the coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE) and the root mean square error (RMSE). Considering the coefficient of determination and the other performance parameters, it is observed that the deep learning model provides better performance compared with the ARIMAX model in modeling the annual CO2 emission data, especially in the pandemic period of 2019–2020. The results show that both the conventional ARIMAX and the nonlinear deep learning methods can be utilized to model CO2 emissions, therefore providing a crucial step for reducing CO2 emissions and the carbon footprint.