AI Article Synopsis
- - The carbon trading market, crucial in combating global warming, requires efficient methods for companies and individuals to find investment opportunities and make intelligent trading decisions, which is addressed by a new deep reinforcement learning strategy called Deep Recurrent Q-Network (DRQN).
- - The research demonstrates that the DRQN model yields optimal trading strategies, showing impressive annualized returns of 15.43% in the Guangdong market and 34.75% in Hubei, surpassing other trading strategies.
- - An analysis of the impacts of discount factors and trading costs reveals that an optimal discount factor of 0.4 can clarify expectations for traders, while government regulation of trading costs can help maintain fair practices in carbon trading and reduce speculation. *
Article Abstract
Against the backdrop of global warming, the carbon trading market is considered as an effective means of emission reduction. With more and more companies and individuals participating in carbon markets for trading, it is of great theoretical and practical significance to help them automatically identify carbon trading investment opportunities and achieve intelligent carbon trading decisions. Based on the characteristics of the carbon trading market, we propose a novel deep reinforcement learning (DRL) trading strategy - Deep Recurrent Q-Network (DRQN). The experimental results show that the carbon allowance trading model based on the DRQN algorithm can provide optimal trading strategies and adapt to market changes. Specifically, the annualized returns for the DRQN algorithm strategy in the Guangdong (GD) and Hubei (HB) carbon markets are 15.43% and 34.75%, respectively, significantly outperforming other strategies. To better meet the needs of the actual implementation scenarios of the model, we analyze the impacts of discount factors and trading costs. The research results indicate that discount factors can provide participants with clearer expectations. In both carbon markets (GD and HB), there exists an optimal discount factor value of 0.4, as both excessively small or large values can have adverse effects on trading. Simultaneously, the government can ensure the fairness of carbon trading by regulating the costs of carbon trading to limit the speculative behavior of participants.
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| http://dx.doi.org/10.1016/j.jenvman.2024.123308 | DOI Listing |
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