AI駆動型サステナブルファイナンスの進化：ESGパフォーマンスと投資戦略に関する計量書誌学的・体系的レビュー

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AI×ESG分野の研究動向と手法選択（ツリー系・LLM・GNN・XAI）の全体像を把握できる。

🏢実務担当者:XAI検証済みESGシグナルをポートフォリオ最適化やグリーンウォッシュ抑制にどう組み込むかの指針を得られる。

🏛政策担当者:ESG評価におけるAI透明性要件やXAI活用の規制枠組み設計の参考になる。

The exponential growth of Artificial Intelligence (AI) has revolutionized sustainable finance, transforming how investors evaluate Environmental, Social, and Governance (ESG) criteria. However, a significant gap persists regarding the practical integration and transparency of these complex algorithmic models into reliable investment signals, as traditional approaches are often hindered by data lags and greenwashing risks. To address this evolving challenge and bridge the divide between technical innovation and portfolio application, this study employs a rigorous hybrid research design. The proposed study integrates a bibliometric mapping of 388 Scopus-indexed documents published between 2020 and February 2026 alongside an in-depth systematic thematic review of 39 high-impact, implementation-focused studies. The analysis identifies a transformative shift in model dominance driven by data structure: tree-based ensemble models currently dominate structured corporate sustainability assessment (observed in 8 key studies), while Transformer and Large Language Model (LLM) architectures are emerging to extract narrative signals from unstructured text. Additionally, Graph Neural Networks (GNNs) are increasingly adopted to map hidden, non-linear dependencies in credit risk and complex supply chains. Furthermore, the evaluation highlights a critical need for model interpretability; while Explainable AI (XAI) frameworks like SHAP and LIME are increasingly utilized to meet strict regulatory transparency requirements and clarify ESG predictions, many existing models still fail to provide actionable validation for investors. From a practical perspective, this synthesis offers a strategic roadmap for fund managers. By translating raw ESG signals into decision-grade intelligence, it facilitates the integration of transparent, XAI-verified outputs into active portfolio optimization, risk management, and the mitigation of greenwashing risks, directly supporting strategies like best-in-class screening and thematic tilting. Finally, the integrity and scholarly reliability of these insights are reinforced by the strict adherence to the PRISMA protocol, ensuring the reproducibility and long-term validity of the synthesized evidence.

• semanticscholar https://doi.org/10.31893/multirev.2027155first seen 2026-10-11 05:15:07 · last seen 2026-10-14 05:40:52