Exploring on the Identification of Green Transition Risks Based on Big Data-driven Approaches
ビッグデータ駆動型アプローチに基づくグリーン移行リスクの識別に関する探索的研究 (AI 翻訳)
Shuying Gao, Meifang Zhou
🤖 gxceed AI 要約
日本語
本論文は、グリーン移行リスクのモニタリングに必要なデータ要件が不明確であると指摘し、AI大規模モデルとデジタルツイン技術を用いた能動的診断・精密識別・知的政策立案を提案する。従来の経験的リスク分析からデータ駆動型意思決定へのパラダイムシフトを促す。
English
This paper highlights the unclear data requirements for monitoring green transition risks and proposes using AI large models and digital twin technology for active diagnosis, precise identification, and intelligent policy formulation. It advocates shifting from empirical risk analysis to data-driven decision-making.
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GX推進に伴い、移行リスクの早期発見・定量化が課題。本稿のAI・デジタルツイン活用提案は、企業のリスク管理体制や開示（SSBJ等）への応用可能性を示唆するが、実証が伴わない点に留意。
In the global GX context
Globally, the paper contributes to the discourse on using AI for climate risk management, aligning with TCFD/ISSB frameworks that emphasize forward-looking risk assessment. However, it remains conceptual without empirical validation.
👥 読者別の含意
🔬研究者:Explores a novel AI-driven framework for green transition risk identification, offering a conceptual foundation for future empirical studies.
🏢実務担当者:Provides a high-level vision for integrating AI and digital twins into corporate risk management, but lacks actionable implementation details.
🏛政策担当者:Suggests potential for data-driven policy formulation in green transition, but requires further evidence to inform regulatory design.
📄 Abstract（原文）
The green transition is closely intertwined with the rise of sustainable development and green development concepts. Current research on green transition risks primarily focuses on key areas such as energy, finance, trade, and social justice, exhibiting highly interdisciplinary and systemic characteristics. The data requirements for monitoring green transition risks remain unclear. Big data-driven behavioral analysis and situational awareness break away from traditional empirical risk analysis models, bringing fundamental changes to risk situational awareness and analytical paradigms. This paper proposes utilizing AI large models and digital twin technology for the active diagnosis, precise identification, and intelligent policy formulation regarding green transition risks. It further advocates for the active diagnosis of potential risks to achieve accurate identification, promoting a shift in risk analysis from "empirical induction" to "data-driven decision making".
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.54254/2754-1169/2025.cau28201first seen 2026-05-05 19:07:13
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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。