  The rapid ascent in carbon dioxide emissions is a major cause of global
warming and climate change, which pose a huge threat to human survival and
impose far-reaching influence on the global ecosystem. Therefore, it is very
necessary to effectively control carbon dioxide emissions by accurately
predicting and analyzing the change trend timely, so as to provide a reference
for carbon dioxide emissions mitigation measures. This paper is aiming to
select a suitable model to predict the near-real-time daily emissions based on
univariate daily time-series data from January 1st, 2020 to September 30st,
2022 of all sectors (Power, Industry, Ground Transport, Residential, Domestic
Aviation, International Aviation) in China. We proposed six prediction models,
which including three statistical models: Grey prediction (GM(1,1)),
autoregressive integrated moving average (ARIMA) and seasonal autoregressive
integrated moving average with exogenous factors (SARIMAX); three machine
learning models: artificial neural network (ANN), random forest (RF) and long
short term memory (LSTM). To evaluate the performance of these models, five
criteria: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean
Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of
Determination () are imported and discussed in detail. In the results, three
machine learning models perform better than that three statistical models, in
which LSTM model performs the best on five criteria values for daily emissions
prediction with the 3.5179e-04 MSE value, 0.0187 RMSE value, 0.0140 MAE value,
14.8291% MAPE value and 0.9844 value.
