Department of Economics and Management, North China Electric Power University, Hebei, 071003, People's Republic of China.
Environ Sci Pollut Res Int. 2022 Sep;29(43):64983-64998. doi: 10.1007/s11356-022-20393-w. Epub 2022 Apr 28.
Grasping the dynamics of carbon emission in time plays a key role in formulating carbon emission reduction policies. In order to provide more accurate carbon emission prediction results for planners, a novel short-term carbon emission prediction model is proposed. In this paper, the secondary decomposition technology combining ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is used to process the original data, and the partial autocorrelation function (PACF) is applied to select the optimal model input. Then, the long short-term memory network (LSTM) is chosen for prediction. The secondary decomposition algorithm is innovatively introduced into the field of carbon emission prediction, and the empirical results illustrate that the secondary decomposition technology can further improve the prediction accuracy. Combined with the secondary decomposition, the R, MAPE, and RMSE of the model are improved by 2.20%, 43.08%, and 36.92% on average. And the proposed model shows excellent prediction accuracy (R = 0.9983, MAPE = 0.0031, RMSE = 118.1610) compared with other 12 comparison models. Therefore, this model not only has potential value in the formulation of carbon emission reduction plans, but also provides a valuable reference for future carbon emission forecasting research.
把握碳排放的动态变化对于制定减排政策至关重要。为了为规划者提供更准确的碳排放预测结果，提出了一种新的短期碳排放预测模型。本文采用了集合经验模态分解（EEMD）和变分模态分解（VMD）相结合的二次分解技术对原始数据进行处理，并应用偏自相关函数（PACF）选择最佳模型输入。然后，选择长短期记忆网络（LSTM）进行预测。将二次分解算法创新性地引入到碳排放预测领域，实证结果表明，二次分解技术可以进一步提高预测精度。与二次分解相结合，模型的 R、MAPE 和 RMSE 平均提高了 2.20%、43.08%和 36.92%。与其他 12 个对比模型相比，所提出的模型具有出色的预测精度（R=0.9983，MAPE=0.0031，RMSE=118.1610）。因此，该模型不仅在减排计划的制定方面具有潜在价值，而且为未来的碳排放预测研究提供了有价值的参考。
