Climate Risk Disclosure and the Green Innovation Premium of Manufacturing Firms: Does More Disclosure Always Pay Off?
気候リスク開示と製造企業のグリーンイノベーションプレミアム：開示の増加は常に報われるのか？ (AI 翻訳)
M. Wang, Zichen Liu
🤖 gxceed AI 要約
日本語
本稿は中国A株市場の製造業上場企業を対象に、気候リスク開示がグリーンイノベーションプレミアム（GIP）に与える影響を実証分析した。結果、気候リスク開示はGIPを有意に高め、その効果は移行リスクによって主に駆動されること、また地域の気候リスク暴露が低い企業や情報開示品質の高い企業で顕著であることが示された。メカニズム分析から、開示が内部資源統合と外部市場注目を促進することでGIPを強化することが明らかになった。
English
This study uses data from Chinese A-share listed manufacturing firms to empirically examine the impact of climate risk disclosure on firms' green innovation premium (GIP). The findings show that climate risk disclosure significantly enhances GIP, primarily driven by transition risks, and this effect is more pronounced for firms in low climate risk exposure regions, high disclosure quality, and innovative firms. Mechanism analysis reveals that disclosure strengthens GIP by promoting internal resource integration and external market attention.
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
この論文は中国の文脈に基づくが、日本企業にとっても示唆に富む。SSBJに対応する気候リスク開示の戦略的活用が、グリーンイノベーションへの投資を促進する可能性を示唆している。特に、製造業において開示の質と内部資源統合が重要である点は、日本の統合報告書や有報での開示実務に参考となる。
In the global GX context
Although focused on Chinese manufacturing firms, this paper provides valuable insights for global climate disclosure scholarship by demonstrating that climate risk disclosure can enhance firms' green innovation premium through market mechanisms. It supports the TCFD/ISSB rationale that disclosure drives capital allocation toward sustainable investments, and highlights the moderating roles of information quality and market attention.
👥 読者別の含意
🔬研究者:Provides empirical evidence on the causal link between climate risk disclosure and green innovation premium, with mechanism analysis on internal resource integration and market attention.
🏢実務担当者:Suggests that manufacturing firms should upgrade from compliance-oriented to strategic disclosure to leverage market premiums for green innovation.
🏛政策担当者:Supports the institutionalization of climate risk disclosure and incorporating disclosure quality into regulatory frameworks to guide green investment.
📄 Abstract（原文）
With the accelerating progress of global climate governance, regulators and investors increasingly require firms to disclose climate‐related risks. For high‐carbon manufacturing firms, how such disclosure translate into a green innovation premium (GIP) through market mechanisms has become a key policy issue for achieving an effective low‐carbon transition. This study uses data from listed manufacturing firms in China's A‐share market to empirically examine the impact of climate risk disclosure on firms' GIP and its underlying mechanisms. The findings indicate that climate risk disclosure significantly enhances the GIP, with the effect primarily driven by transition risks. This positive influence is more pronounced for firms located in regions with low climate risk exposure, those with high information disclosure quality, and non‐specialized, refined, distinctive, and innovative firms. Mechanism analysis reveals that climate risk disclosure strengthens the GIP by promoting internal resource integration and enhancing external market attention. From the perspective of management practice, this study suggests that governments should promote the institutionalization of climate risk disclosure. High‐quality disclosure should be incorporated into the regulatory framework to guide resources toward manufacturing firms with strong green innovation potential. Meanwhile, firms should shift from compliance‐oriented disclosure to strategic disclosure by strengthening internal resource integration and enhanceing external market attention through multiple channels. These efforts will contribute to the sustainable development of the manufacturing sector and further enhance the green innovation premium.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/sd.70764first seen 2026-06-29 08:13:32
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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。