CaStNet: A Causality-Guided Decomposition and Cell-State-Driven Attention Framework for Carbon Price Forecasting
CaStNet: 因果関係に基づく分解と細胞状態駆動型注意機構を用いた炭素価格予測フレームワーク (AI 翻訳)
Zhenchen Sun, Min Xiao, D Zhang, Mingyue Liu, Yingxiu Zhao, Ying Liu
🤖 gxceed AI 要約
日本語
本研究は、炭素価格予測のためのCaStNetフレームワークを提案。Granger因果関係を用いた2次分解モジュールと、LSTMのセル状態を活用した注意機構を導入。上海市場でRMSE=0.8326を達成し、湖北市場でも高い一般化性能を示した。
English
This paper proposes CaStNet, a causality-guided decomposition and cell-state-driven attention framework for carbon price forecasting. It uses Granger causality for residual decomposition and cell-state differential velocity for adaptive attention sparsity. Achieves RMSE=0.8326 and R2=0.9777 on Shanghai market, outperforming 12 benchmarks. Cross-market validation on Hubei yields R2=0.9487.
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
中国炭素市場での検証だが、枠組みは日本のJ-クレジット市場や東京都排出量取引にも適用可能。因果関係に基づく分解手法は、日本の政策変動を含む炭素価格予測に有用。
In the global GX context
While validated on Chinese markets, the causality-guided decomposition and adaptive attention framework is applicable to any carbon market, including EU ETS. The use of Granger causality to incorporate policy shocks is relevant for understanding carbon price drivers globally.
👥 読者別の含意
🔬研究者:This paper provides a novel causal approach to carbon price forecasting combining Granger causality with deep learning, which can be extended to other climate-finance time series.
🏢実務担当者:The method could be used by carbon trading firms and compliance teams for short-term price risk management and hedging decisions.
🏛政策担当者:Demonstrates the role of energy-carbon causal linkages; regulators can use similar analysis to monitor market efficiency and assess policy impact.
📄 Abstract（原文）
Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical complexity rather than domain-informed causality, and standard Long Short-Term Memory (LSTM)-Transformer architectures discard the cell state that encodes long-term temporal memory. These limitations are particularly pronounced where energy-driven causal structures and regime-switching volatility coexist. This study proposes Causal State-driven Network (CaStNet), an intelligent forecasting framework with two core innovations. A Policy-Causality-guided Residual Secondary Decomposition (PCRSD) module replaces entropy-based criteria with Granger causality to select intrinsic mode functions (IMFs) exhibiting significant energy-carbon causal linkages for targeted variational mode decomposition (VMD). A Cell-State-Driven Dual-function Attention (CSDA) mechanism repurposes the LSTM cell state for simultaneously injecting long-term memory into the Transformer and employing the cell-state differential velocity as a volatility proxy to adaptively regulate Top-k attention sparsity. The Artificial Lemming Algorithm (ALA) globally co-optimizes decomposition dimensions and attention boundaries. A Shapley Additive exPlanations (SHAP)–Local Interpretable Model-agnostic Explanations (LIME) interpretability analysis reveals horizon-dependent driver transitions from short-term autoregressive momentum to long-term energy fundamentals, uncovering threshold nonlinearities in energy-carbon transmission channels. Validation on the Shanghai market (2013–2025) achieves point-forecast RMSE = 0.8326 and R2 = 0.9777, outperforming all twelve benchmark models. Cross-market testing on the Hubei market yields R2 = 0.9487, and expanding-window five-fold cross-validation on the Shanghai dataset yields mean R2 = 0.9704, jointly confirming generalization robustness.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/math14132399first seen 2026-07-08 05:23:39
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