A Bayesian-tuned Gaussian process approach to forecasting carbon market prices: A case study of China's emissions trading scheme
ベイズ最適化ガウス過程回帰による炭素市場価格予測：中国排出量取引制度の事例研究 (AI 翻訳)
Bingzi Jin, Xiaojie Xu
🤖 gxceed AI 要約
日本語
本研究は、中国の排出量取引制度（CHNTS）における炭素価格を、ベイズフレームワークでハイパーパラメータを最適化したガウス過程回帰（GPR）を用いて予測する革新的なモデルを提案する。2021年7月16日から2025年4月9日までの日次データを用いた検証では、RRMSE 1.0771%、相関係数98.604%と高い予測精度を示し、GPRを中国の炭素市場に初めて適用した点が新規性である。
English
This study proposes a novel Gaussian process regression (GPR) model optimized via Bayesian framework to forecast carbon prices in China's Emissions Trading Scheme (CHNTS). Using daily settlement data from July 2021 to April 2025, the model achieves high accuracy (RRMSE 1.0771%, correlation coefficient 98.604%) on out-of-sample data. It is the first application of GPR to China's carbon trading market, offering a flexible template for other cap-and-trade systems.
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
日本でも2026年度の排出量取引制度（GX-ETS）本格稼働が予定されており、本手法は価格予測・政策設計の参考となる。中国ETSの市場成熟過程や制度変更への適応性は日本の制度設計にも示唆を与える。
In the global GX context
As carbon pricing expands globally, advanced forecasting models like the one proposed are critical for market stability and policy refinement. This work provides a rigorous analytical framework applicable to emerging ETS markets, including those under the Paris Agreement.
👥 読者別の含意
🔬研究者:Presents the first GPR application to China's carbon market with Bayesian hyperparameter tuning, advancing forecasting methodology.
🏢実務担当者:Offers a high-accuracy price prediction tool for carbon traders and risk managers in ETS markets.
🏛政策担当者:Demonstrates a robust approach for monitoring market efficiency and setting allowance price floors/cellings.
📄 Abstract（原文）
Precise forecasting of fluctuations in carbon allowance valuations is critical for shaping environmental policy and for bolstering the effectiveness of market-based regulatory mechanisms. Advanced statistical and machine-learning techniques afford regulators the capacity to fine-tune carbon taxation schemes, enhance the operational efficiency of emissions trading frameworks, and steer financial resources toward low-carbon development projects with greater assurance. This study examines the China Emissions Trading Scheme (CHNTS)-one of China's pioneering carbon markets established under the broader national decarbonization strategy-and presents an innovative predictive model based on Gaussian process regression (GPR) whose hyperparameters are optimized through a Bayesian framework. By dynamically adjusting to latent market behaviors and unobserved structural shifts, this method adapts more responsively to evolving trading patterns. Our empirical investigation utilizes daily settlement data for China Emission Allowances spanning July 16, 2021, through April 9, 2025-a timeframe marked by key regulatory amendments, market maturation phases, and changing participant conduct as the scheme integrated into the wider national carbon pricing system. Model validation is performed on an out-of-sample window from June 28, 2024, to April 9, 2025, yielding notable performance metrics: a relative root-mean-square error (RRMSE) of 1.0771%, root-mean-square error (RMSE) of 1.0212, mean absolute error (MAE) of 0.6773, and a correlation coefficient (CC) reaching 98.604%. To the best of our knowledge, this represents the first deployment of GPR in the context of China's carbon trading exchanges. Beyond enriching theoretical understanding of price discovery in emergent emissions markets, the proposed approach provides a flexible analytical template that could readily be applied to analogous cap-and-trade systems worldwide.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1142/s1752890926500030first seen 2026-05-05 22:39:14
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。