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.

本文选用六种模型（灰色预测模型、ARIMA 模型、SARIMAX 模型、人工神经网络模型、随机森林模型和长短时记忆模型）对中国所有部门（电力、工业、地面交通、住宅、国内航空、国际航空）在 2020 年 1 月 1 日至 2022 年 9 月 30 日的日期间序列数据进行预测，结果表明长短时记忆模型具有最佳的预测性能。