Empirically assessing corporate adaptation and resilience disclosure using AI
Roberto Spacey Martín, Nicola Ranger, T. Schimanski, Markus Leippold
🤖 gxceed AI 要約
日本語
本論文は、大規模言語モデル（LLM）を用いてS&P500企業のサステナビリティ報告書から適応とレジリエンス（A&R）関連情報を抽出・分析した。開示フレームワークを構築し、リスクや目標に関する情報が不足していることを実証した。投資家や規制当局が企業の気候変動適応力を評価する際の課題を明らかにした。
English
This paper uses large language models (LLMs) to assess adaptation and resilience (A&R) disclosure in S&P 500 sustainability reports. It develops an A&R framework and finds significant gaps, especially regarding risks, metrics, and targets. The study highlights the need for supplementary data sources for firm-level climate adaptation assessment.
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SSBJが気候関連開示基準を策定中であり、適応・レジリエンス情報の位置づけが重要になる。本論文は、現状の開示の不十分さを定量的に示し、日本の企業や規制当局に示唆を与える。
In the global GX context
Global frameworks like TCFD and ISSB emphasize climate resilience, yet this paper empirically demonstrates that actual corporate disclosure remains inadequate. The findings support the need for stronger regulatory guidance and alternative data sources for financial institutions.
👥 読者別の含意
🔬研究者:Provides empirical evidence of A&R disclosure gaps using state-of-the-art NLP methods.
🏢実務担当者:Highlights the need to improve A&R disclosure to meet investor and regulatory expectations.
🏛政策担当者:Suggests that current voluntary disclosure may insufficiently capture adaptation risks, warranting mandatory frameworks.
📄 Abstract（原文）
The extent to which firms are adapting and building resilience to environmental change is crucial information for financial institutions, regulators and governments. While corporates’ physical climate risk exposure of their assets to environmental change can be calculated using models, additional information is needed to evaluate their vulnerability to physical climate change, how well they are adapting and broader alignment with societal adaptation and resilience (A&R) goals. This paper empirically evaluates the extent of A&R-related information in current corporate sustainability reports to provide such insights. We build on established sustainability disclosure frameworks and develop an A&R disclosure framework that we combine with the latest advances in large language models to assess S&P 500 company sustainability reports. We prove that corporate A&R information in sustainability reports is lacking, particularly around risks, metrics and targets, underlining the need to consider other data sources when assessing firm-level risks and contributions to societal A&R goals.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1038/s44168-025-00321-7first seen 2026-06-29 08:27:31
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