Artificial Intelligence and Climate Risk in Finance: A Bibliometric Review of Emerging Trends and Analytical Frontiers
金融における人工知能と気候リスク：新興トレンドと分析フロンティアの文献計量学的レビュー (AI 翻訳)
Triana Arias Abelaira, María Jesús Guillén Palomino, Lázaro Rodríguez Ariza, Carlos Díaz Caro
🤖 gxceed AI 要約
日本語
本研究は、気候リスクに関する金融文献の進化を分析し、AI技術の統合状況を文献計量学的手法で明らかにする。NLPやデジタルトランスフォーメーションが中心的テーマであり、AIイノベーション政策が企業のグリーンウォッシュ抑制と情報透明性向上に関連することを示す。また、新興市場と先進国との間のデータインフラ格差や、二重機械学習などの高度な因果推計手法のニッチな存在も指摘する。
English
This study uses a bibliometric approach to analyze 221 articles on climate risk in finance, focusing on the integration of AI techniques. It finds that NLP and digital transformation are key themes, linking AI innovation policies to greenwashing mitigation and transparency. The review reveals a shift away from traditional financial performance metrics and highlights a data infrastructure gap between emerging and advanced markets, along with niche econometric methods like double machine learning.
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
日本の金融機関や企業は、TCFD/ISSB/SSBJ対応において気候リスクの定量化が急務である。本レビューは、AIを用いたリスク評価手法の国際的な研究動向を俯瞰し、日本におけるAI活用の方向性を検討する際のベンチマークとなる。特に、グリーンウォッシュ検出や情報透明性向上への示唆は、日本の開示実務にも応用可能である。
In the global GX context
As global climate disclosure standards (ISSB, CSRD, SEC) demand robust risk quantification, this review maps how AI—especially NLP and ML—is being operationalized for climate transition risk pricing and portfolio valuation. It identifies research gaps such as the data infrastructure disparity in emerging economies, which is critical for global regulators and investors seeking consistent risk assessment across markets.
👥 読者別の含意
🔬研究者:Provides a structured map of the AI-for-climate-risk literature, highlighting dominant methods (NLP, ML) underexplored areas (double ML) and regional disparities.
🏢実務担当者:Helps identify which AI tools (e.g., NLP for disclosure analysis) are gaining traction for climate risk management and their implications for greenwashing detection.
🏛政策担当者:Highlights the data infrastructure gap in emerging markets that may hinder global climate risk comparability and suggests policy attention to AI-driven transparency.
📄 Abstract（原文）
This study analyses the evolution of the financial literature on climate risk, examining the integration of artificial intelligence techniques into its measurement and management. To this end, a bibliometric approach is employed based on 221 articles indexed in the Web of Science Core Collection, using the Bibliometrix package. Moving beyond existing descriptive bibliometric reviews on ESG and green finance, the novelty of this paper lies in its analytical focus on how financial science operationalises quantitative AI mechanisms to price and integrate climate transition risk into asset and portfolio valuation. The structural analysis reveals that natural language processing (NLP) and digital transformation acting as driving motor themes, suggesting that the reviewed literature associates AI innovation policies with the mitigation of corporate greenwashing and enhance information transparency. Furthermore, while machine learning algorithms establish the cross-cutting predictive foundation for risk assessment, empirical evidence unveils a critical academic shift of traditional ‘financial performance’ towards a declining quadrant, indicating that empirical studies frequently find that that multi-phase investments in risk technologies do not yield immediate financial returns. Finally, the study maps a persistent geographical gap where emerging markets lack the data infrastructure of advanced economies, alongside isolated high-dimensional causal econometric niches like double machine learning. This analytical mapping provides key implications for global risk management and future quantitative research avenues.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.mdpi.com/1911-8074/19/7/537/pdf?version=1784528409first seen 2026-07-24 06:32:30
🔔 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。