Dynamic forecasting of China's carbon market prices by the coupling of macroeconomic indicators and LSTM model.

ABSTRACT: Accurately forecasting carbon prices in China's carbon market remains a critical challenge for both low-carbon development and market regulation. This study proposes a hybrid Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) model that combines the strengths of CNN and LSTM networks. The coupling between the two networks was optimized using a dynamic weight allocation mechanism, while incorporating macroeconomic indicators such as GDP growth rate, Producer Price Index for Industrial Products, coal futures prices, interbank offered rates, and installed renewable energy capacity. Multi-source data were processed using Z-score standardization, min-max scaling, and related techniques. A three-dimensional input structure was constructed via a sliding window approach, and the model'

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12630664 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature