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中国の全国炭素許容価格は予測可能か？2025年の市場拡大を巡る解釈可能な機械学習とボラティリティ分析

Is China’s National Carbon-Allowance Price Predictable? An Interpretable Machine Learning and Volatility Analysis Around the 2025 Market Expansion (原題)

Shichao Li, Heng Wu, A. S. Abu Bakar

Sustainability📚 査読済 / ジャーナル2026-09-01#炭素価格Origin: CN経営インパクト: 調達リスク対象セクター: power
DOI: 10.3390/su18178967
原典: https://doi.org/10.3390/su18178967
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🤖 gxceed AI 要約

日本語

中国の全国排出権取引制度が2025年3月に電力から鉄鋼・セメント・アルミへ拡大したことを踏まえ、日次炭素価格の予測可能性を検証。11モデルと26予測因子を用いた入れ子拡張ウィンドウ検証で、ランダムウォークが最も低いRMSEを示し、機械学習モデルの安定した点予測優位は確認されなかった。統計的依存は存在するが、予測ゲインには結びつかない。

English

This study examines the predictability of China's national carbon allowance prices around the March 2025 market expansion to steel, cement, and aluminum. Using 1,203 daily prices and 11 models with nested expanding-window validation, the random walk achieves the lowest RMSE (1.242 CNY/t), while machine learning models fail to outperform consistently. Statistical dependence exists but does not translate into stable point-forecast gains.

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ETSの市場拡大と価格形成メカニズムの実証分析は、日本のカーボンプライシング導入検討や、中国市場への事業展開を図る日本企業のリスク管理に示唆を与える。ただし、中国特有の政策・市場構造に依存するため、日本への直接適用には注意が必要。

In the global GX context

This empirical study of China's ETS expansion provides valuable evidence on carbon price behavior under policy shocks, relevant to global carbon pricing research and international investors exposed to Chinese carbon markets. It underscores the difficulty of predicting carbon prices, informing expectations for similar markets like the EU ETS and emerging carbon pricing mechanisms.

👥 読者別の含意

🔬研究者:Provides rigorous evidence on carbon price predictability using ML, contributing to carbon market efficiency literature.

🏢実務担当者:Highlights challenges in forecasting carbon prices, useful for risk management in carbon-intensive sectors.

🏛政策担当者:Informs on market stability and information efficiency, relevant for designing carbon pricing mechanisms.

📄 Abstract（原文）

China’s national Emissions Trading System expanded from power to steel, cement and aluminum in March 2025. We examine daily carbon emission allowance price predictability using 1203 trading-day prices. Eleven models and a 26-predictor baseline undergo nested expanding-window validation. Separate common-sample sensitivities add pre-open GFS weather, an official ten-day coal price, a conservatively lagged national generation proxy and official macroeconomic first releases. Across 952 forecasts, the random walk has the lowest RMSE (1.242 CNY/t); the stabilized neural network reaches 1.251. The exact-release/first-public macro specification lowers random-forest RMSE from 1.289 to 1.266, whereas the public energy/weather specification records 1.275; neither beats the benchmark. Technical variables retain the largest model attribution, but even a technical-only forest records 1.253. Ljung–Box and BDS tests detect dependence, while sign runs do not reject sign independence and the variance-ratio null is rejected only at the two-day horizon. Rolling and multiple-break tests find no expansion-date shift. Volatility rankings remain loss-dependent. Statistical dependence therefore exists without stable point-forecast gains. The added energy measures do not represent observed national daily load, and execution returns are not inferred from daily OHLC data.

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