Carbon Emission Right Price Prediction Model Based on CEEMDAN-IWOA-LSTM

Based on the background of greenhouse gas emissions caused by industrialization, regional energy saving and emission reduction will be promoted through regulated carbon trading, such as carbon trading quota and carbon tax, to further deepen the green environmental goal of sustainable development. Predicting the price of carbon emission allowances is a pivotal aspect for managing the risks faced by entities engaged in the carbon market. Therefore, this paper proposes a carbon emission right price prediction model based on CEEMDAN-IWOA-LSTM. Firstly, CEEMDAN method is used to implement the decomposition of the original data, followed by the reconstruction operation. Then the IWOA is used to improve the LSTM model and the reconstructed data is predicted and the final prediction results are obtained. Finally, the study contrasts the outcomes with those from alternative models employing distinct decomposition and forecasting techniques. The findings illustrate that the proposed model excels in prediction accuracy relative to its counterparts.