Intelligent data-driven models for the accurate multi-factor prediction of carbon credit prices
炭素クレジット価格の高精度多因子予測のためのインテリジェントデータ駆動モデル (AI 翻訳)
Najlaa Alshatri, Safa Ghannam, Farookh Khadeer Hussain
🤖 gxceed AI 要約
日本語
本研究は、炭素クレジット価格の短期予測のために、22の外部要因を考慮した多因子予測モデルを開発。ランダムフォレストとSHAPで重要因子を特定し、ICAとSVRを組み合わせたモデルが最高精度（R²=0.99）を達成。オーストラリアのACCU実データで検証し、97%超の予測精度を示した。
English
This study develops multi-factor prediction models for short-term carbon credit price forecasting, integrating 22 external factors. Using random forest and SHAP for key factor identification, and combining ICA with SVR, the best model achieves R²=0.99 and over 97% accuracy on Australian ACCU data.
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の本格導入が進む中、炭素クレジット価格の高精度予測は市場参加者の信頼向上に直結する。本モデルは日本のJ-クレジット市場にも応用可能であり、GX推進における実務的示唆を与える。
In the global GX context
Accurate carbon credit pricing is critical for transparent carbon markets globally. This data-driven approach, validated on Australian ACCUs, offers a transferable methodology for other compliance and voluntary markets, supporting the integrity of carbon credits as a climate finance tool.
👥 読者別の含意
🔬研究者:Provides a robust machine learning framework for carbon credit price prediction that can be adapted to other markets.
🏢実務担当者:Offers a practical model for traders and investors to forecast carbon credit prices and manage financial risk.
🏛政策担当者:Demonstrates how advanced analytics can enhance market transparency and support effective carbon pricing mechanisms.
📄 Abstract（原文）
Accurately pricing carbon credits is essential for maintaining a transparent and effective carbon market. However, carbon credit price time series data are non-stationary, nonlinear, and multicollinear. This study addresses these challenges by developing advanced multi-factor prediction models for short-term price forecasting. The proposed models integrate key factor identification and optimised prediction algorithms to manage complex interactions among 22 external factors. This paper proposes a carbon credit multi-factor identification (CCMFI) model to study the importance of each factor. The CCMFI model combines the random forest (RF) model and SHapley Additive exPlanations (SHAP). The selected key factors, were then used as input to the carbon credit multi-factor prediction (CCMFP) models. The CCMFP applies feature extraction and reduction techniques, including independent component analysis (ICA), nonlinear independent component analysis (NLICA), and principal component analysis (PCA). The extracted components then serve as input variables for the support vector regression (SVR) and multilayer perceptron (MLP) neural networks. The study used real daily price data for Australian Carbon Credit Units (ACCUs) to validate the proposed model. The experiment results demonstrate that the ICA_SVR model with $$K = 10$$ factors outperformed other models, achieving an MSE value of 1.039, a $$R^2$$ value of 0.99, and a computational efficiency of 1.33 s. All proposed models exhibited superior prediction performance, with accuracy exceeding 97%. The developed models improve confidence among carbon credit traders and investors, helping mitigate the financial risks associated with price fluctuations. This research supports global efforts by promoting carbon credits as an effective tool for sustainable practices.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s44163-026-00894-0first seen 2026-05-05 19:11:44
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。