AI for Sustainable Finance: A Bibliometric Map of Emerging Technologies, ESG Integration, and Systemic Risk
持続可能な金融のためのAI：新興技術、ESG統合、システミックリスクのビブリオメトリックマップ (AI 翻訳)
Imen Jellouli, Emna Mnif
🤖 gxceed AI 要約
日本語
本論文は、2006年から2025年までの3,180件の文献を対象にビブリオメトリクス分析を実施し、AIと持続可能な金融の交差点における研究の知的構造とテーマの進化を明らかにした。2020年以降の急増と、気候・信用リスクの意思決定支援、ESG報告の透明性向上、サステナブル投資、責任あるAIフレームワークへのテーマの再編成を確認した。また、AIがリスク測定や開示処理を改善する一方で、モデルの不透明性やグリーンウォッシングなどの脆弱性を増幅する二重の効果を指摘し、今後の研究課題を提示している。
English
This bibliometric analysis of 3,180 publications (2006-2025) maps the intellectual structure and thematic evolution of AI in sustainable finance. It reveals a post-2020 surge and thematic reordering toward AI-driven decision support for climate and credit risk, ESG reporting transparency, sustainable investing, and responsible AI frameworks. The study identifies a dual effect: AI improves risk measurement and disclosure processing while amplifying systemic vulnerabilities such as model opacity, algorithmic bias, and greenwashing. A research agenda prioritizing stress-tested responsible AI and interoperable disclosure standards is proposed.
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
本論文はAIがESG評価・開示に与える影響を俯瞰する。日本でもSSBJ対応や有報でのESG情報拡充が進む中、AI活用のリスクと機会を整理した点が有用。特に、AIによるグリーンウォッシング検出や開示例の自動処理は、統合報告書やTCFD/ISSB準拠開示の実務に示唆を与える。
In the global GX context
This paper provides a systematic overview of the AI–sustainable finance nexus, highly relevant for global standard-setters (ISSB, ESRS, SEC) and financial regulators. It highlights how AI can enhance ESG data processing and risk analysis but also introduces new systemic risks such as model opacity and greenwashing, underscoring the need for responsible AI frameworks and interoperable disclosure standards.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of research themes and gaps at the AI–sustainable finance nexus, useful for identifying future research directions.
🏢実務担当者:Highlights how AI can improve ESG data processing, risk measurement, and fraud detection, but warns about algorithmic bias and greenwashing; can guide technology adoption and vendor evaluation.
🏛政策担当者:Emphasizes the need for responsible AI governance and interoperable sustainability disclosure standards to mitigate systemic risks from AI in finance.
📄 Abstract（原文）
The accelerated diffusion of artificial intelligence (AI), including machine learning, generative AI, and agentic systems, alongside FinTech platforms and blockchain infrastructures, is reshaping financial decision making and the sustainability transition. Yet evidence on how AI enabled innovation advances sustainable finance remains conceptually fragmented. This study maps the intellectual structure and thematic evolution of research at the intersection of AI and sustainability in finance through a bibliometric analysis of 3,180 Scopus indexed publications (2006-2025). The results show a pronounced post 2020 surge and a clear thematic reordering toward (i) AI driven decision support and predictive analytics for climate and credit risk, (ii) digital infrastructures that enhance transparency and traceability in ESG reporting, (iii) sustainable investing and green innovation analytics, and (iv) governance and responsible AI frameworks. Importantly, the mapped literature converges on a dual effect: AI strengthens sustainability outcomes by improving risk measurement, disclosure processing, fraud detection, and inclusion, while simultaneously amplifying systemic vulnerabilities through model opacity, algorithmic bias, cybersecurity exposure, and greenwashing enabled information asymmetries. Building on these findings, we propose a research agenda that prioritizes stress tested responsible AI, interoperable sustainability disclosure standards, and resilience by design in digital sustainable finance architectures.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/ibi68858.2026.11604080first seen 2026-07-25 05:54:02
🔔 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。