Dynamic Carbon Quota Gap Prediction and Rolling Optimization Based on Temporal Fusion Transformer and Reinforcement Learning

As global efforts to mitigate climate change intensify, efficient carbon quota management has become critical for enterprises to meet emission reduction targets. Traditional carbon quota allocation models often struggle to adapt to dynamic market conditions and diverse enterprise needs, especially for those transitioning to clean energy. This study addresses the challenge by proposing a novel approach that integrates the Temporal Fusion Transformer (TFT) model with reinforcement learning to predict carbon quota gaps and optimize quota management strategies. The method leverages time series forecasting to accurately predict future carbon emissions and employs reinforcement learning to dynamically adjust production strategies based on carbon quota gaps. The main contribution of this research lies in its ability to not only forecast emissions but also adaptively optimize quota management in response to real-time conditions. Experimental results demonstrate the method’s effectiveness in reducing carbon emissions and managing quota gaps, particularly in industries facing variable market demands and regulatory environments. This approach offers a flexible, data-driven solution for enterprises, including those with advanced electrification, aiming to enhance carbon quota efficiency and transition towards carbon neutrality.