Research on Carbon Emission Trading Price Predictions with the ICEEMDAN-CNN-LSTM Method
ICEEMDAN-CNN-LSTM法を用いた炭素排出権取引価格予測の研究 (AI 翻訳)
Jiancheng Wang, P. Guo, Peng Hao, Dan Wang
🤖 gxceed AI 要約
日本語
本研究は、炭素排出権取引価格の予測精度向上を目的に、ICEEMDAN-CNN-LSTMモデルを提案。湖北省の炭素市場データを用いて評価し、LSTMやCNN-LSTMと比較してMAEで59.1%改善。分解・抽出・適合の枠組みで非定常な価格動態を効果的に捉え、炭素市場安定化と排出削減行動の促進に貢献する。
English
This paper proposes a hybrid ICEEMDAN-CNN-LSTM model for carbon emission trading price prediction. Using data from Hubei carbon market, the model outperforms LSTM and CNN-LSTM benchmarks with MAE reduction of 59.1%. The decomposition-extraction-fitting framework effectively handles non-stationary and noisy carbon price dynamics, offering a reliable tool for carbon market stabilization.
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
日本では東京都のキャップ＆トレードやJ-クレジット市場があるが、本手法は炭素価格予測の精度向上に寄与し、企業の排出削減投資やリスク管理に応用可能。ただし、データは中国市場に基づくため、日本の市場特性を考慮した調整が必要。
In the global GX context
Carbon price forecasting is crucial for market participants and policymakers. This hybrid model demonstrates significant improvements in prediction accuracy, which can enhance market efficiency and support the global low-carbon transition. The methodology can be adapted to other carbon markets.
👥 読者別の含意
🔬研究者:Methodological contribution for time series forecasting in carbon markets, particularly the combination of ICEEMDAN with CNN-LSTM.
🏢実務担当者:Can be used for carbon price risk assessment and trading strategy development in carbon markets.
🏛政策担当者:Provides insights for monitoring carbon market stability and evaluating the effectiveness of carbon pricing mechanisms.
📄 Abstract（原文）
Against the backdrop of worldwide sustainability and low-carbon development, carbon emission trading prices serve as an important signal for carbon reduction and green economic regulation. However, they are influenced by quota policies, energy markets, and macroeconomics, and exhibit pronounced non-stationary, high-noise, and nonlinear dynamics that challenge traditional forecasting models. This research aims to improve carbon price prediction accuracy by proposing a hybrid ICEEMDAN-CNN-LSTM model. The Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method adaptively decomposes the original carbon price series, suppressing mode aliasing and noise interference, and producing stable Intrinsic Mode Function (IMF) components; each IMF is then processed by CNN-LSTM, where the Convolutional Neural Network (CNN) extracts local features and the Long Short-Term Memory (LSTM) captures long short-term dependencies, with the final results obtained by linear combination. This research uses historical closing prices of the Hubei carbon emission trading market with multiple economic indicators as inputs. Model performance is evaluated against LSTM and CNN-LSTM benchmarks. The results show that the proposed model significantly outperforms benchmarks, achieving a test-set MAE of 1.140 yuan, representing reductions of 59.1% and 65.2% compared to LSTM and CNN-LSTM, respectively, and the RMSE is reduced by 57.2% and 62.9%, respectively. At the same time, the proposed model maintains strong robustness under different data splitting ratios. Through the “decomposition–extraction–fitting” framework, the proposed model effectively handles complex carbon price dynamics, offering a reliable forecasting tool that helps stabilize carbon markets, guide emission–reduction behaviors, and advance global sustainability and low-carbon transition.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18104738first seen 2026-05-15 17:03:22
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