高汚染産業における曖昧な環境開示と資本市場の反応：大規模言語モデルによる実証
Vague Environmental Disclosure and Capital Market Responses in High-Pollution Industries: Evidence from Large Language Models (原題)
Xiaoli Li, Yiliang Song, Baicun Shen
🤖 gxceed AI 要約
日本語
中国の高汚染A株企業1016社のMD&Aテキストから、LLMを用いた曖昧開示指数（GFIX）を構築。GFIXが高い企業は発表四半期の株価リターンが有意に低く、機関投資家保有もその後減少することを示す。曖昧な環境ナラティブは情報リスクのシグナルとして投資家に割り引かれ、環境規制の節目後にGFIXが上昇する点も確認。開示の検証可能性がグリーン移行リスクの価格形成を左右することを示唆する。
English
Using an LLM-based fuzzy disclosure index (GFIX) built from Chinese MD&A texts of 1,016 high-polluting A-share firms, the study finds higher narrative fuzziness predicts significantly lower announcement-quarter returns and subsequent declines in institutional ownership. The discount concentrates in high-polluting manufacturing and firms with more non-institutional ownership, and major green-regulatory milestones are followed by rising GFIX. Narrative verifiability emerges as a first-order determinant of how green-transition risk is priced.
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
SSBJ・有報の記述開示が拡充される日本では、ナラティブの「検証可能性」が株価・資本コストに影響するという示唆は、統合報告書や任意開示の質的評価、投資家対話の設計に直結する。LLMによる開示品質の定量化は、日本企業の移行リスク開示の実効性を測る手法として応用可能。
In the global GX context
Amid ISSB/CSRD and SEC climate disclosure, this paper quantifies narrative fuzziness with LLMs and prices it, offering a scalable method to assess whether mandatory environmental disclosure is verifiable rather than boilerplate. It links disclosure quality directly to transition-risk pricing and ownership reallocation, informing debates on assurance and enforcement.
👥 読者別の含意
🔬研究者:LLMで開示の曖昧さを指標化し資産価格・所有構造と接続する識別設計は、開示研究とAI×ESGの手法参照になる。
🏢実務担当者:環境ナラティブの曖昧さが株価ディスカウントと機関投資家離れを招くため、開示の具体性・検証可能性の確保が投資家対応の実務課題となる。
🏛政策担当者:義務的環境開示の検証可能性を高めることが情報リスクと移行リスクの価格形成を改善しうる点は、開示規制・保証制度の設計に示唆を与える。
📄 Abstract（原文）
A key question at the interface of sustainable finance and market efficiency is whether capital markets discipline firms not only for what they do—emissions, violations, or green investment—but also for how they communicate their environmental positions. Disclosure research suggests that the quality and verifiability of environmental narratives matter for investors, yet the pricing of narrative fuzziness itself remains underexplored. This study examines whether capital markets respond to the way high-polluting firms narrate green constraints in their annual reports. We construct a large-language-model-based fuzzy disclosure index (GFIX) from Chinese MD&A texts. Using only pre-2008 annual reports for scale learning, we train a two-stage comparator–scorer system from sentence-level pairwise judgments and apply it to MD&A sentences from 2008 to 2025. The resulting firm–year GFIX is matched with announcement-quarter stock returns and ownership data for 1016 high-polluting A-share firms. Double/debiased machine learning and panel fixed-effects estimates show that higher GFIX is systematically associated with significantly lower announcement-quarter returns, suggesting that investors treat fuzzy green narratives as a signal of information risk under green transition. The discount is concentrated in high-polluting manufacturing industries and firms with higher non-institutional ownership, while higher GFIX also predicts subsequent declines in institutional ownership. In addition, major Chinese green-regulatory milestones are followed by systematic increases in GFIX. These findings indicate that narrative clarity is an economically meaningful channel through which green transition and environmental regulation are transmitted to asset prices and ownership reallocations. These findings carry direct implications for sustainable finance: disclosure verifiability appears to be a first-order determinant of how green-transition risks are priced, and strengthening the verifiability of mandatory environmental disclosure can lower information risk, reduce the discount applied to opaque narrators, and support the orderly pricing of transition risks, in line with SDG 12 (responsible consumption and production) and SDG 13 (climate action).
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.3390/su18189585first seen 2026-11-05 04:35:59
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