← 論文一覧に戻る

<b>AI-Driven ESG Scoring Model for Sustainable Investment in Digital Markets</b>

デジタル市場における持続可能な投資のためのAI駆動型ESGスコアリングモデル (AI 翻訳)

Yan Luo

Journal of Neuromorphic Intelligence📚 査読済 / ジャーナル2026-08-03#AI×ESGOrigin: CN経営インパクト: 資金調達対象セクター: finance
DOI: 10.63382/kxev7y63
原典: https://doi.org/10.63382/kxev7y63

🤖 gxceed AI 要約

日本語

本研究は、スライムモールドアルゴリズム（SMA）による特徴選択とランダムフォレスト回帰（RFR）を用いたAI駆動型ESGスコア予測フレームワークを提案。データ前処理を徹底し、RMSE 0.3649、MAE 0.2538、R² 0.9912と既存手法を上回る精度を達成。持続可能な投資判断を支援する意思決定ツールとしての有効性を示した。

English

This study proposes an AI-driven ESG scoring framework using Slime Mould Algorithm (SMA) for feature selection and Random Forest Regressor (RFR) for prediction. With rigorous preprocessing, it achieves RMSE 0.3649, MAE 0.2538, and R² 0.9912, outperforming existing methods. The model serves as a decision-support tool for sustainable investment in digital markets.

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SSBJ開示や投資家対応が進む中、AIによるESG評価の精度向上は実務ニーズに直結。本手法は日本企業のESGデータ分析にも応用可能で、開示情報の信頼性向上に寄与する可能性がある。

In the global GX context

Globally, as ISSB and CSRD frameworks demand robust ESG data, AI-driven scoring models like this enhance transparency and consistency. The proposed framework offers a scalable approach for sustainable investment analysis, aligning with global trends toward data-driven ESG assessment.

👥 読者別の含意

🔬研究者:AI×ESG評価の新たな手法として、特徴選択と予測精度の向上に寄与する知見を提供。

🏢実務担当者:ESGスコアリングの自動化と精度向上に活用でき、投資判断や開示対応の効率化に役立つ。

🏛政策担当者:AIを活用したESG評価の標準化に向けた技術的根拠を提供。

📄 Abstract（原文）

Sustainable investment has become a critical focus area, with Environmental, Social, and Governance (ESG) criteria gaining significant importance in financial decision-making. Traditional ESG evaluation methods often struggle with accuracy, transparency, and consistency, which can hinder effective sustainable investment analysis. This study proposed an AI-driven framework designed to enhance the accuracy of ESG score predictions, utilizing the Slime Mould Algorithm (SMA) for feature selection and Random Forest Regressor (RFR) for model training. The framework optimizes feature selection by identifying the most relevant ESG factors, improving prediction accuracy and reducing model complexity. Key preprocessing steps, including missing value imputation, duplicate removal, outlier handling, and data normalization, are incorporated to ensure data quality and reliability. Experimental results demonstrate improved predictive performance, with the proposed model achieving a Root Mean Square Error (RMSE) of 0.3649, Mean Absolute Error (MAE) of 0.2538, and of 0.9912, outperforming existing approaches. These results confirm the effectiveness of the proposed framework provided for ESG score prediction and highlight its applicability as a decision-support tool for sustainable investment in digital market environments. By providing reliable and transparent ESG score predictions, the proposed model supports data-driven sustainable investment analysis. Future work may explore real-time ESG data integration, model scalability, and explainable Artificial Intelligence (AI) techniques to further enhance ESG assessment across diverse sectors.

🔗 Provenance — このレコードを発見したソース

🔔 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。