Carbon Price Prediction With Public Social Media Big Data and an Interpretable Multi‐Objective Intelligent Feature Optimization Strategy
公開ソーシャルメディアビッグデータと解釈可能な多目的知的特徴最適化戦略による炭素価格予測 (AI 翻訳)
Honggang Guo, Shuang Bi, Yu Jin, Houhang Zhao, Yutong Ai
🤖 gxceed AI 要約
日本語
この研究は、炭素価格予測の精度向上を目的として、ソーシャルメディアビッグデータを活用し、改良型多目的秘書鳥最適化アルゴリズムに基づく解釈可能な特徴最適化戦略を開発した。投資家の感情と注意を反映する公開ソーシャルメディアデータを初めて導入し、予測のタイムリー性を向上させた。実証実験では、提案手法が予測精度と頑健性を有意に向上させることが示された。
English
This study develops an interpretable feature optimization strategy using an improved multi-objective secretary bird optimization algorithm for carbon price prediction. It incorporates public social media big data capturing investor sentiment and attention for the first time. Empirical results show significant improvement in prediction accuracy and robustness, offering methodological innovations for carbon market analysis.
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2023年度からカーボンプライシング（有償オークション）の本格導入が検討されており、炭素価格の正確な予測は企業の排出削減投資判断に直結する。本研究はソーシャルメディアデータを活用したリアルタイム予測手法を提示しており、日本の炭素市場運営や投資家の意思決定に示唆を与える。
In the global GX context
This paper is globally relevant as carbon pricing mechanisms expand under the Paris Agreement. The integration of social media sentiment into price forecasting offers a novel approach that can improve market efficiency and transparency. It contributes to the growing literature on AI-driven climate finance and carbon market analytics.
👥 読者別の含意
🔬研究者:This paper provides a novel feature optimization method with interpretability for carbon price forecasting, which can be extended to other environmental markets.
🏢実務担当者:Carbon trading teams can use social media sentiment as a leading indicator for price movements to optimize trading strategies.
🏛政策担当者:Regulators overseeing carbon markets may consider incorporating alternative data sources to monitor market dynamics and prevent manipulation.
📄 Abstract（原文）
Accurate forecasts of carbon prices are essential for optimizing resource allocation in the carbon market and guiding corporate emissions reduction decisions. However, carbon prices are influenced by a variety of factors, and traditional forecasting methods often fail to account for the complex interrelationships among these factors, making it difficult to extract effective features and reduce the forecasting accuracy. To address these challenges, this study innovatively develops an interpretable intelligent feature optimization strategy based on an improved multi‐objective secretary bird optimization algorithm. This strategy effectively addresses the issue of feature masking by introducing the discrete optimization and bidirectional propagation technique, thereby enabling the precise identification and quantification of useful features. The innovative development of embedded interpretable mechanisms can provide a transparent and interpretable basis for carbon price forecasting in the feature optimization process. Furthermore, this study is the early attempt to incorporate public social media big data, which responds to investor sentiment and attention, into carbon price forecasts, whose unique real‐time and interactive nature can keenly capture social dynamics, further optimizing the timeliness of carbon price prediction. Empirical studies show the proposed feature optimization strategy and the introduction of multimodal public social media big data can significantly improve the precision and robustness of the prediction. The study offers methodological innovations for carbon price forecasting and serves as a valuable reference for investors to optimize their trading decisions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/for.70108first seen 2026-05-15 17:20:32
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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。