Research on Carbon Emission Trading Price Predictions with the ICEEMDAN-CNN-LSTM Method

Against the backdrop of worldwide sustainability and low-carbon development, carbon emission trading prices serve as an important signal for carbon reduction and green economic regulation. However, they are influenced by quota policies, energy markets, and macroeconomics, and exhibit pronounced non-stationary, high-noise, and nonlinear dynamics that challenge traditional forecasting models. This research aims to improve carbon price prediction accuracy by proposing a hybrid ICEEMDAN-CNN-LSTM model. The Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method adaptively decomposes the original carbon price series, suppressing mode aliasing and noise interference, and producing stable Intrinsic Mode Function (IMF) components; each IMF is then processed by CNN-LSTM, where the Convolutional Neural Network (CNN) extracts local features and the Long Short-Term Memory (LSTM) captures long short-term dependencies, with the final results obtained by linear combination. This research uses historical closing prices of the Hubei carbon emission trading market with multiple economic indicators as inputs. Model performance is evaluated against LSTM and CNN-LSTM benchmarks. The results show that the proposed model significantly outperforms benchmarks, achieving a test-set MAE of 1.140 yuan, representing reductions of 59.1% and 65.2% compared to LSTM and CNN-LSTM, respectively, and the RMSE is reduced by 57.2% and 62.9%, respectively. At the same time, the proposed model maintains strong robustness under different data splitting ratios. Through the “decomposition–extraction–fitting” framework, the proposed model effectively handles complex carbon price dynamics, offering a reliable forecasting tool that helps stabilize carbon markets, guide emission–reduction behaviors, and advance global sustainability and low-carbon transition.