Chen, Shengxin, Agarwal, Vaibhav ORCID: https://orcid.org/0000-0001-5362-2936, Lele, Atharva and Tewari, Hitesh (2025) CarbonChainRL - A Blockchain Oracle-Based Reinforcement Learning System for Dynamic Carbon Trading Markets. In: 2025 IEEE International Conference on Distributed Ledger Technologies (ICDLT). IEEE

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Abstract

As global initiatives for carbon neutrality accelerate, existing Emissions Trading Systems (ETS) are increasingly challenged by volatile pricing, limited transparency, and slow policy responsiveness. This paper introduces CarbonChainRL, a novel blockchain-based framework augmented with a Deep Reinforcement Learning (DRL) agent to enable adaptive and decentralized market regulation. Leveraging a Deep Q-Network (DQN) integrated with Ethereum smart contracts, CarbonChainRL facilitates dynamic supply-demand adjustment policies that indirectly influence carbon pricing. The model operates within an 11dimensional state space, capturing real-time market indicators such as supply-demand imbalances, tax signals, and Market Stability Reserve (MSR) balances. A carefully designed reward function incentivizes price stability, supply-demand equilibrium, and prompt regulatory intervention. The framework employs a hierarchical oracle mechanism that fuses RL-driven predictions with historical market data, utilizing confidence scoring to inform on-chain policy triggers. Simulation results demonstrate rapid convergence of the model and highlight the effectiveness of the MSR module-powered by dynamic TNAC-based calculations-in stabilizing market conditions. Additionally, the system supports multi-sector registration, quota allocation, and real-time auctioning. Preliminary findings indicate that CarbonChainRL enhances market responsiveness and efficiency, outperforming conventional ETS architectures.

Item Type:	Book Section
Status:	Published
DOI:	10.1109/icdlt66400.2025.11466602
Subjects:	T Technology > T Technology (General)
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/15768

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