CEEMDAN-SE-Transformerモデルによる炭素価格予測
Forecasting carbon price with CEEMDAN-SE-transformer model (原題)
Shiju Li, Hongjie Lan, Hanying Jiang, Fangqiu Xu, Shouyang Wang, Pengfei Du, Xinlu Li, Kaiye Gao
🤖 gxceed AI 要約
日本語
本論文は、CEEMDAN-SE-Transformerモデルを用いて炭素排出権価格の予測精度を向上させる手法を提案する。中国広東省の炭素市場の2015〜2022年のデータを用い、原油価格やEU炭素価格などの影響要因を考慮する。比較実験の結果、提案モデルは他のモデルより誤差が小さく、高精度な予測が可能であることを示した。炭素市場の取引者や規制当局の意思決定に有用な知見を提供する。
English
This paper proposes a CEEMDAN-SE-Transformer model to improve carbon price forecasting accuracy. Using data from China's Guangdong carbon market (2015-2022) and incorporating factors like oil prices and EU carbon prices, the model outperforms benchmarks in reducing forecast error. The findings support decision-making for carbon market traders and regulators.
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
日本ではカーボンプライシング導入が進む中、炭素価格予測の高度化は排出量取引制度設計や企業のリスク管理に示唆を与える。ただし、中国市場特有のデータに基づくため、日本市場への適用には調整が必要。
In the global GX context
As global carbon markets expand, advanced forecasting models like this contribute to market efficiency and risk management. The methodology can be adapted to other emissions trading systems, supporting the development of robust carbon pricing mechanisms worldwide.
👥 読者別の含意
🔬研究者:AIと炭素市場予測の融合研究の最新手法を学ぶことができる。
🏢実務担当者:炭素価格変動リスクの管理や取引戦略の策定に活用できる。
🏛政策担当者:炭素市場の安定化と価格発見機能の向上に資する予測技術の重要性を認識できる。
📄 Abstract（原文）
The consensus regarding the impact of carbon dioxide emissions on the environment has been widely acknowledged, with market-based mechanisms highlighted in international frameworks like the Paris Agreement. China has actively advanced carbon market development by establishing transparent carbon trading mechanisms to address emission challenges. Accurately forecasting carbon emission rights prices is crucial for supporting enterprise and government decision-making and promoting a low-carbon economy. However, the non-stationary and nonlinear nature of carbon price time series makes precise forecasting difficult. To address this issue, this paper proposes a new method for forecasting the prices of carbon emission rights with the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which provides a reconstruction method from the original signal and a better spectral separation of the Intrinsic Mode Functions (IMFs). Based on correlation analysis, this study identified Brent crude oil prices, EU carbon prices, natural gas prices, thermal coal prices, and the Frankfurt DAX index as key influencing factors. To demonstrate and validate the model, simulations were conducted utilizing empirical data. The Guangdong carbon trading market serves as the research object, with historical data from 2015 to 2022 used for model training. Several comparative models—CEEMDAN-Transformer, CEEMDAN-SE-BPNN, and CEEMDAN-SE-LSTM—were also developed using the same dataset. Empirical results indicate that the CEEMDAN-SE-Transformer combined model outperforms other models, exhibiting lower forecast error and higher accuracy of each evaluation index. The results of this study can serve as an effective reference for carbon market traders' decision-making and for related regulatory departments.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.sftr.2026.102115first seen 2026-09-05 05:15:32
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