Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive...

In light of global climate change and the objective of carbon neutrality, the carbon market has become an important tool for the international community to combat climate change. Nonetheless, due to the complexity and non-linear nature of the carbon price, its accurate prediction has always been a research difficulty. This work presents a hybrid model incorporating comprehensive feature screening, optimized quadratic decomposition, and the Optuna-Attention-LSTM prediction method, aiming to improve the accuracy and stability of carbon price prediction. First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the carbon price time series once, extract high-frequency and low-frequency components, and denoise the high-frequency components using stacked denoising autoencoder (SDAE). Then, the variational mode decomposition (VMD) method is subsequently employed to execute a secondary decomposition on the reconstructed signal, with the decomposition hyperparameters optimized via crested porcupine optimization (CPO). Subsequently, Boruta and least absolute shrinkage and selection operator (Lasso) regression are employed to identify significant external features; finally, a long short-term memory (LSTM) model integrated with an attention mechanism is utilized for prediction, and optuna is introduced to optimize the hyperparameters. This paper evaluates the performance of the proposed model using the carbon markets of Guangdong, Hubei, and Shanghai in China as examples. The experimental results indicate that compared with the traditional model, the proposed model achieves average reductions of 67.30%, 47.68%, 48.42%, and 48.79% in the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), respectively, demonstrating higher prediction accuracy and robustness. Shapley additive explanations (SHAP) analysis results indicate that carbon prices in the Guangdong carbon market are dominated by macroeconomic and regional environmental factors, while those in the Hubei carbon market are mainly driven by changes in the energy market. The Shanghai carbon market, on the other hand, is more significantly influenced by global carbon market dynamics and international trade activities. The research not only verifies the efficacy of the decomposition ensemble prediction framework, but also provides a scientific basis for decision-making for carbon market participants and policymakers.