Comparison and Optimization of Carbon Emission Trading Price Prediction Models in China—Based on Time Series Analysis and Machine Learning
中国における炭素排出権取引価格予測モデルの比較と最適化—時系列分析と機械学習に基づいて (AI 翻訳)
Bingyan Fan, Yuan Xue, Mingyue Dai, Yu Ming, Muchen Lin
🤖 gxceed AI 要約
日本語
中国の五つの炭素取引パイロット市場（深セン、広東、湖北、北京、上海）を対象に、ARIMAX、CNN、GRU、Transformerの四モデルで価格予測を比較。LASSOで共通因子を特定し、SVR-ARIMAXとGA-GRUのハイブリッド最適化により予測精度を向上。12ヶ月先の予測では深セン市場の高変動性が示された。
English
This study compares four models (ARIMAX, CNN, GRU, Transformer) for carbon price prediction across five Chinese pilot markets. LASSO identifies key factors, and hybrid models SVR-ARIMAX and GA-GRU improve accuracy. Forecasts show Shenzhen has high volatility while Guangdong remains stable.
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
中国の炭素市場を対象とした価格予測モデルの比較・最適化研究。日本ではまだ本格的な炭素市場が稼働していないが、今後の市場設計や予測手法の参考となる可能性がある。
In the global GX context
This study compares multiple models for carbon price prediction, including hybrid optimization, across five Chinese pilots. It offers methodological insights applicable to other emerging carbon markets and highlights the importance of model selection for accurate forecasting.
👥 読者別の含意
🔬研究者:This paper provides a comprehensive comparison of time series and machine learning models for carbon price prediction, with hybrid optimization that can be applied to other markets.
🏢実務担当者:Trading firms and carbon market participants can use the optimized models for price forecasting and risk management.
🏛政策担当者:Regulators can evaluate the effectiveness of different modeling approaches for market monitoring and policy assessment.
📄 Abstract（原文）
Against the backdrop of the “dual carbon” goals, carbon emission trading prices serve as a core signal of market operational efficiency. Accurately predicting carbon prices facilitates scientific decision-making, and model optimization is key to improving prediction accuracy. This study takes five major carbon trading pilots in China—Shenzhen, Guangdong, Hubei, Beijing, and Shanghai—as the research objects. An indicator system is constructed from four dimensions: macroeconomy, energy prices, climate and environment, and international markets. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm is employed to identify the key influencing factors of carbon prices across different markets. Among them, “WTI crude oil price” and “EUA futures closing price” are consistently significant factors common to all five pilots. On this basis, four models—Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Transformer—are constructed for multi-method prediction comparison. The results show that ARIMAX and GRU achieve the best prediction performance among the four models. To further enhance prediction accuracy, hybrid optimization models are respectively developed: Support Vector Regression (SVR) is used to optimize the nonlinear residuals of ARIMAX (SVR-ARIMAX), and Genetic Algorithm (GA) is used to optimize the key hyperparameters of GRU (GA-GRU). The hybrid models significantly reduce prediction errors in most markets. Specifically, SVR-ARIMAX shows particularly notable improvements in Beijing and Hubei, while GA-GRU outperforms standard GRU in Guangdong, Shenzhen, Shanghai, and Hubei. Based on the optimized models, 12-month-ahead forecasts indicate that the Shenzhen market exhibits high volatility and greatest uncertainty; Guangdong remains relatively stable; Hubei, Beijing, and Shanghai are characterized by narrow-range fluctuations. The findings provide empirical support for corporate emission reduction decision-making, carbon market risk management, and price mechanism improvement.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18115450first seen 2026-06-04 04:53:01
🔔 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。