Ma, Zhonglin, Wang, Chao, Qi, Hong, and Wood, Jacob (2025) A carbon price prediction model based on decomposition and dual-channel attention network. Environment, Development and Sustainability. (In Press)

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Abstract

Accurate carbon price forecasting is important for informed policy-making and investment decisions in the expanding carbon trading market. This study proposes an improved deep learning model, TCN-LSTM-Self-Attention, that integrates decomposition and error correction to enhance predictive precision. We first decompose the close price and error series into subsequences, which are then separately predicted using the TCN-LSTM-Self-Attention framework. Next, the initial predictions are refined through error predictions, resulting in final corrected forecasts. Empirical results demonstrate notable accuracy improvements compared to traditional methods, achieving a coefficient of determination of 0.982, a root mean square error of 0.646, and a mean absolute percentage error of 0.777%. These findings demonstrate the model’s ability to capture complex short-term and long-term dynamics of carbon price fluctuations. Validation across Shenzhen, Guangdong, and Hubei carbon trading pilots further indicates its robustness and applicability. We recommend this integrated model as a useful tool for policymakers and market participants seeking accurate and stable carbon price forecasts.

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