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.

翻译：二氧化碳排放量的快速上升是全球变暖和气候变化的主要原因，对人类生存构成巨大威胁，并对全球生态系统产生深远影响。因此，通过及时准确预测和分析变化趋势来有效控制二氧化碳排放量，从而为减排措施提供参考，显得十分必要。本文旨在基于中国所有部门（电力、工业、地面交通、住宅、国内航空、国际航空）2020年1月1日至2022年9月30日的单变量日时间序列数据，选择合适模型预测近实时日排放量。我们提出了六种预测模型，包括三种统计模型：灰色预测（GM(1,1)）、自回归综合移动平均（ARIMA）和含外生变量的季节性自回归综合移动平均（SARIMAX）；三种机器学习模型：人工神经网络（ANN）、随机森林（RF）和长短期记忆网络（LSTM）。为评估这些模型的性能，引入并详细讨论了五个评价标准：均方误差（MSE）、均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和决定系数（R²）。结果表明，三种机器学习模型的表现优于三种统计模型，其中LSTM模型在日排放量预测中表现最佳，其MSE值为3.5179e-04，RMSE值为0.0187，MAE值为0.0140，MAPE值为14.8291%，R²值为0.9844。