Carbon price fluctuation forecasting using an adaptive dual-channel residual attention neural network optimized with white shark optimizer and blockchain-based data provenance

Carbon markets play a critical role in emission reduction and sustainable economic transition; however, carbon price series exhibit high volatility, nonlinear dynamics, and complex cross-feature interactions, making accurate forecasting challenging. Current forecasting models cannot effectively address both time dependence, cross-indicator relationships, and optimization efficiency, as well as data control reliability. To address these shortcomings, this research paper proposes an integrated forecasting approach, named ADRGPNN-WSO. The framework uses Fuzzy K-Top Matching Value (FKMV) to reduce noise, an Adaptive Dual-Channel Residual Group Pulse-Coupled Neural Network (ADRGPNN) to model nonlinear temporal and cross-feature behaviors, and the White Shark Optimizer (WSO) to adaptively optimize weights, a redactable Blockchain with Decentralized Chameleon Hash Functions (RB-DCHF) to provide secure and verifiable data provenance. The experiment with carbon trading data at the Hubei, Shanghai, and Shenzhen exchanges shows that the proposed model has better forecasting performance, with an MSE of 0.0189, MAE of 0.0987, RMSE of 0.1375, MAPE of 5.88, and R 2 of 0.942, compared with the conventional and transformer-based models. Statistical significance tests further demonstrate the strength of the performance improvements. The suggested framework offers a predictive solution based on reliable, governance-conscious forecasting to aid trading companies, risk management, and policy-making in regulated carbon markets.