Carbon Credit Market Pricing in a Big Data Financial Environment

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.