CTr²L: A Novel Carbon Trading Transfer Learning Framework for the Price Volatility Forecasting

Abstract

Carbon trading is a market-based mechanism for reducing greenhouse gas emissions, providing economic incentives for mitigating climate change and promoting the development of a low-carbon economy. However, China’s carbon market is still in its early stages of development, leading to limited data availability for deep neural network modeling. Consequently, accurately predicting price volatility in China’s carbon market is a challenging task. To address this issue, we propose a transfer learning framework based on the hybrid GARCH-GRU model, called CTr²L, to predict carbon price volatility. The CTr²L framework achieves comparable prediction accuracy to ordinary deep learning but with a significant reduction in required training data, and the effectiveness of CTr²L is verified through the ablation study. Furthermore, we propose a metric factor of the transferability of CTr²L, enabling us to verify the effectiveness of CTr²L before actual modeling and provide relevant guidance for time series data selection of source domains. Finally, we present the empirical results based on actual data to demonstrate the superiority of the proposed transfer learning framework in predicting carbon price volatility as well as the effectiveness of the proposed metric factor of the transferability.

Keywords

Carbon trading / volatility / time series forecasting / transfer learning / machine learning

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