人工知能・持続可能な開発目標・グリーン移行：中央銀行における金融政策と気候リスク評価の再定義

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

🔬研究者:中央銀行の気候リスク評価とデジタル化を制度論的に整理した枠組みとして、SDG指標・NGFSシナリオ・AI能力の統合的分析の出発点になる。

🏢実務担当者:金融機関のサステナビリティ担当は、中央銀行が気候シナリオやストレステストをどう監督に組み込むかを理解し、開示・リスク管理態勢の準備に活用できる。

🏛政策担当者:規制当局は、AI活用時のガバナンス・データ品質・透明性・規制上の保護措置の必要性と、持続可能性目標と物価安定のトレードオフを政策設計に反映すべき。

The accelerating digital transformation of finance is reshaping the institutional and analytical environment in which central banks address climate-related financial risks while maintaining price and financial stability. This paper examines the interplay between Artificial Intelligence (AI), Sustainable Development Goals (SDGs), and the green transition, with particular attention to their implications for monetary policy and climate-risk assessment in central banking. The study does not apply AI or machine-learning techniques empirically; rather, it adopts a qualitative and institutional perspective to examine the potential role of AI and broader digital capabilities in strengthening the identification, assessment, and monitoring of physical and transition risks within central banks’ mandates. The study adopts a conceptual and policy-oriented analytical approach based on evidence from Eurostat Sustainable Development Goal indicators, the Network for Greening the Financial System (NGFS) climate scenarios, European Central Bank (ECB) reports, and European Systemic Risk Board (ESRB) analyses. These institutional frameworks are comparatively examined to assess how climate scenarios, stress-testing methodologies, sustainability indicators, and digital analytical capabilities can support macro-financial risk assessment and climate-aware monetary policy. Particular attention is given to the institutional mechanisms through which these approaches can contribute to understanding the transmission of climate-related risks to inflation, financial stability, and banking-sector resilience. The findings suggest that the integration of SDG indicators, climate scenarios, climate-risk assessment frameworks, and emerging digital capabilities provides a more comprehensive basis for understanding the implications of climate-related risks for monetary and financial stability. The ECB’s Climate and Nature Plan 2024–2025 illustrates how digital technologies, enhanced data capabilities, and climate-risk modelling are increasingly incorporated into monetary-policy implementation, prudential supervision, and portfolio management. Within this broader digital transformation, AI represents an emerging analytical capability that may support the processing of complex datasets and the monitoring of climate-related risks. However, its effective use requires appropriate governance, data quality, transparency, institutional coordination, and regulatory safeguards. The analysis also highlights potential trade-offs between sustainability objectives and traditional central-bank mandates. This paper contributes to the emerging literature on sustainable finance, climate-aware monetary policy, and the digital transformation of central banking by developing an integrated institutional framework linking AI, SDG indicators, climate scenarios, and climate-risk assessment. The findings provide policy-relevant insights into how central banks can enhance their analytical and institutional capacities to address climate-related financial risks and support the green transition while maintaining price and financial stability.

• openalex https://doi.org/10.3390/fintech5040089first seen 2026-10-16 04:38:31