arbiter
JournalsPricingAccount

Carbon Credit Market Pricing in a Big Data Financial Environment

Luping Yu et al.

Journal of Organizational and End User Computing2026https://doi.org/10.4018/joeuc.405808article
AJG 1ABDC B
Weight
0.50

What the paper says

Global carbon markets face challenges in forecasting prices and aligning policies due to volatility, regulatory constraints, and uncertainty. While existing models improve predictions, they fail to incorporate uncertainty into decision-making. To address this, the authors propose a Bayesian Deep Reinforcement Learning framework for Carbon Pricing, which combines probabilistic price modeling, uncertainty propagation, and constraint-based reinforcement learning. Experiments across five datasets show that the model reduces error by 28%, improves performance by 25%, and boosts trading profit by 22%, while maintaining high emissions compliance. Stress-test results confirm the model's robustness, demonstrating that uncertainty-aware learning enhances the stability and efficiency of carbon credit markets.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.4018/joeuc.405808

Or copy a formatted citation

@article{luping2026,
  title        = {{Carbon Credit Market Pricing in a Big Data Financial Environment}},
  author       = {Luping Yu et al.},
  journal      = {Journal of Organizational and End User Computing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/joeuc.405808},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Carbon Credit Market Pricing in a Big Data Financial Environment

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.