The Role of Artificial Intelligence in Advancing ESG Integration and Sustainable Finance: A Secondary Data Analysis
ESG統合とサステナブルファイナンスにおける人工知能の役割：二次データ分析 (AI 翻訳)
D. R, Santhosh Kumar A G
🤖 gxceed AI 要約
日本語
本論文は、2020-2025年の機関報告書や学術文献等の二次データを用いて、AI（特にNLP・ML）がESGデータ集約、グリーンウォッシング検出、気候リスクモデリング、ポートフォリオ最適化に与える影響を分析。ESGデータカバレッジが最大40%向上し、気候関連金融リスクの予測精度が向上する一方、データバイアスやモデル透明性の課題も指摘。責任あるAI導入の枠組みを提案。
English
This secondary data analysis (2020-2025 sources including Bloomberg, MSCI, World Bank) examines AI's role in ESG integration and sustainable finance. It finds that NLP and ML improve ESG data coverage by up to 40% and enhance climate risk prediction accuracy, but challenges like data bias and model transparency remain. The paper proposes a framework for responsible AI adoption in sustainable finance.
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基準や有報でのESG開示が進む中、AIを用いたESGデータの信頼性向上やグリーンウォッシング検出は実務上の関心が高い。本論文の提案する枠組みは、日本企業の非財務情報開示の高度化に示唆を与える。
In the global GX context
Globally, as ISSB and CSRD drive mandatory ESG disclosure, AI tools for data aggregation and greenwashing detection are increasingly critical. This paper's findings on improved ESG data coverage and predictive accuracy directly support the transition finance agenda and TCFD-aligned risk management.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of AI applications in ESG and identifies key research gaps in data bias and model transparency.
🏢実務担当者:Offers insights on how AI can improve ESG data quality and greenwashing detection for corporate reporting and portfolio management.
🏛政策担当者:Highlights regulatory fragmentation and data quality issues that need attention for credible AI adoption in sustainable finance.
📄 Abstract（原文）
The rapid growth of Environmental, Social, and Governance (ESG) investing has exposed limitations in traditional data collection, scoring, and risk assessment methods. This study examines how Artificial Intelligence (AI) is transforming ESG and sustainable finance using secondary data from institutional reports, academic literature, and market databases from 2020- 2025. Through systematic review and thematic analysis of secondary sources including Bloomberg ESG data, MSCI ESG Ratings methodology, World Bank sustainable finance reports, and peer-reviewed articles, this paper identifies key AI applications in ESG data aggregation, greenwashing detection, climate risk modeling, and portfolio optimization. Findings indicate that AI technologies, particularly Natural Language Processing (NLP) and Machine Learning (ML), improve ESG data coverage by up to 40% and enhance predictive accuracy for climate-related financial risks. However, challenges related to data bias, model transparency, and regulatory fragmentation persist. The paper proposes a conceptual framework for responsible AI adoption in sustainable finance and outlines future research directions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.22214/ijraset.2026.84367first seen 2026-07-26 06:26:59
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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。