Carbon price fluctuation forecasting using an adaptive dual-channel residual attention neural network optimized with white shark optimizer and blockchain-based data provenance
ホワイトシャーク最適化とブロックチェーンベースのデータ出所を活用した適応型デュアルチャネル残差注意ニューラルネットワークによる炭素価格変動予測 (AI 翻訳)
S. Biswal, K. Kotecha, Neha Munjal
🤖 gxceed AI 要約
日本語
本論文は、炭素価格予測のための統合フレームワークADRGPNN-WSOを提案。ノイズ除去にFKMV、時系列・クロス特徴量のモデリングにADRGPNN、最適化にWSO、データ改ざん防止にRB-DCHFを採用。中国3市場のデータで検証し、従来モデルを上回る精度（R²=0.942）を達成。予測手法の信頼性向上に貢献。
English
This paper proposes ADRGPNN-WSO, an integrated framework for carbon price forecasting. It uses FKMV for noise reduction, ADRGPNN for temporal and cross-feature modeling, WSO for optimization, and RB-DCHF for data provenance. Validated on three Chinese carbon exchanges, it outperforms conventional models with R²=0.942, enhancing forecast reliability for trading and policy.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の炭素市場（TSXなど）への直接適用は限定的だが、価格予測の高度化やデータガバナンス手法は、今後の排出量取引制度設計やリスク管理に参考となる可能性がある。
In the global GX context
While focused on Chinese markets, the model's advanced forecasting and blockchain-based data integrity offer insights for global carbon market participants, especially as carbon pricing expands under Article 6 and compliance schemes like EU ETS.
👥 読者別の含意
🔬研究者:Provides a novel hybrid model combining neural networks, metaheuristic optimization, and blockchain for carbon price prediction with rigorous empirical validation.
🏢実務担当者:Offers a high-accuracy forecasting tool usable by trading firms for risk management and arbitrage in regulated carbon markets.
🏛政策担当者:Highlights how reliable price forecasts and data provenance can support market monitoring and regulatory oversight.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1038/s41598-026-43184-6first seen 2026-05-15 17:18:06
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。