The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Research on the Decision-Making Model of Carbon Quota Trading Based on Deep Reinforcement Learning
|
|
Author(s): Jiawen Liu (Grid Planning and Research Center of Guangdong Power Grid Co., Ltd., China), Jiajia Huan (Grid Planning and Research Center of Guangdong Power Grid Co., Ltd., China)and Xiaodong Lan (Grid Planning and Research Center of Guangdong Power Grid Co., Ltd., China)
Copyright: 2025
Volume: 21
Issue: 1
Pages: 12
Source title:
International Journal of e-Collaboration (IJeC)
Editor(s)-in-Chief: Jingyuan Zhao (University of Toronto, Canada)
DOI: 10.4018/IJeC.368042
Purchase
|
Abstract
This study addresses the complexities of carbon quota trading markets amidst global warming concerns, proposing a deep reinforcement learning (DRL)-based decision-making model to enhance trading strategies. Acknowledging the limitations of conventional methods in navigating volatile carbon prices, policy shifts, and informational disparities, the research integrates DRL's advanced capabilities. It commences with an overview of DRL principles and its successful applications, followed by an analysis of market dynamics and trading nuances. A DRL model is then formulated, delineating state-action spaces and a tailored reward function for optimized learning within the carbon trading context. Model refinement involves hyperparameter tuning for superior performance. The summary concludes with an evaluation of the model's efficacy, highlighting its adaptability and computational demands, while outlining avenues for further enhancement and real-world implementation to combat climate change through improved carbon market operations.
Related Content
|
Runhe Xue.
© 2025.
27 pages.
|
|
Hua Liu.
© 2025.
19 pages.
|
|
Weiwei Tian.
© 2025.
20 pages.
|
|
Jiawen Liu, Jiajia Huan, Xiaodong Lan.
© 2025.
12 pages.
|
|
Liming Cui, Lemin Li.
© 2025.
21 pages.
|
|
Anqi Dou, Wei Xu, Liwen Xu.
© 2025.
16 pages.
|
|
Hongjuan Sun, Yuankun Du.
© 2025.
16 pages.
|
|
|