A comparative study of statistical and machine learning models on carbon dioxide emissions prediction of China

The escalating levels of carbon dioxide (CO₂) emissions represent the primary driver of global warming, and addressing them is of paramount importance. Timely and accurate prediction, as well as effective control of CO₂ emissions, are pivotal for guiding mitigation measures. This paper aims to select the best prediction model for near-real-time daily CO₂ emissions in China. The prediction models are based on univariate daily time-series data spanning January 1st, 2020, to September 30st, 2022. Six models are proposed, including three statistical models: grey prediction (GM(1,1)), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average with exogenous factors (SARIMAX), and three machine learning models: artificial neural network (ANN), random forest (RF), and long short-term memory (LSTM). The performance of these six models is evaluated using five criteria: mean squared error (MSE), root-mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²). Our findings reveal that the three machine learning models consistently outperform the three statistical models across all five criteria. Among them, the LSTM model demonstrates exceptional performance for daily CO₂ emission prediction, boasting an impressively low MSE value of 3.5179e-04, an RMSE value of 0.0187, an MAE value of 0.0140, an MAPE value of 14.8291%, and a high R² value of 0.9844. This underscores the robustness of the LSTM model in capturing and predicting complex emission patterns, positioning it as the most suitable option for near-real-time daily CO₂ emission prediction based on the provided daily time series data. Moreover, our study's results provide valuable insights into emissions forecasting, enabling data-driven decision-making for policymakers and stakeholders. The accurate and timely predictions offered by the LSTM model can aid in the formulation of effective strategies to mitigate carbon emissions, contributing to a more sustainable future. Furthermore, the findings of this study can enhance our understanding of the dynamics of CO₂ emissions, leading to more informed environmental policies and actions aimed at reducing carbon emissions.