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With the increasingly serious problem of global warming, reducing greenhouse gas emissions has become a major task facing all countries (Sun, 2020). As an effective means of carbon emission control, carbon quota trading is gradually becoming a key policy tool to deal with climate change (Wu et al., 2022). However, the complexity and volatility of the carbon quota trading market make it difficult to accurately grasp the accuracy of trading decisions (X. Guo et al., 2024). Traditional trading decision-making methods often rely on a large number of historical data or simple mathematical statistical models, which have limitations in capturing market trends and predicting price trends, and are difficult to adapt to the rapidly changing characteristics of the carbon market (Zhang & Chen, 2024). Therefore, it is very important for the healthy development of this market to study a new method that can improve the accuracy and efficiency of carbon quota trading decisions (Zhou,Y. et al., 2024).
Drawing lessons from the deep reinforcement learning (DRL) technology formed by combining the powerful feature representation ability of deep learning with the decision-making optimization ability of reinforcement learning, this paper constructs a carbon quota trading decision-making model, uses a large number of historical trading data to learn trading strategies, and continuously enriches and optimizes the strategies by simulating the real trading environment, which is helpful to effectively offset the problems caused by the complexity and uncertainty of the carbon quota trading market and improve the accuracy and efficiency of trading decisions.
This paper aims to study the decision-making model of carbon quota trading based on DRL and discuss its core principle, construction process, and other aspects, aiming at solving the complex and changeable decision-making problems in the carbon quota trading market and providing efficient and accurate decision-making support for trading subjects. In the process of model construction, the feature extraction ability of deep learning and the decision-making optimization ability of reinforcement learning are fully combined, and the model parameters are trained and adjusted through a large number of data, so that the model can accurately predict the trend of carbon price and make reasonable trading decisions. Although some achievements have been made in this study on the decision-making model of carbon quota trading based on DRL, there are still many problems worthy of in-depth exploration and potential improvement, including introducing more advanced deep learning algorithms and structures to improve the feature extraction ability and decision-making accuracy of the model, researching more effective reinforcement learning algorithms to speed up the training speed and convergence performance of the model, and considering integrating the model with other related technologies, such as natural language processing technology, to mine and analyze news policies related to the carbon market and provide additional input features for the model. In the future, we will continue to deepen the research on this model, promote its practical application and development in the field of carbon trading, and contribute more wisdom and strength to coping with global climate change.