Artificial Intelligence and Corporate Sustainability Disclosure: Evidence from Corporate Climate Risk Disclosure in China
人工知能と企業のサステナビリティ開示：中国における企業の気候リスク開示からのエビデンス (AI 翻訳)
Weiting Qin, Liying Song, Qi Zhang, Zewen Yuan
🤖 gxceed AI 要約
日本語
本研究は、中国の国家AI革新発展パイロットゾーン政策が企業の気候リスク開示に与える影響を、2014～2023年の中国A株上場企業データと差分の差分法を用いて分析。政策導入後、気候リスク開示が増加し、その効果は情報処理能力、リスクガバナンス、外部監視圧力の3つの経路を通じることが示唆された。開示の増加はESGパフォーマンス（特に環境・ガバナンス）と正の相関を持つが、実質的な変革との区別が必要と指摘。
English
This study examines the impact of China's National AI Innovation and Development Pilot Zone policy on corporate climate risk disclosure, using a multi-period difference-in-differences approach on A-share listed firms from 2014 to 2023. It finds that the policy is associated with higher climate risk disclosure, with mechanism analysis suggesting channels of improved information-processing capability, risk governance, and external monitoring. The increase in disclosure is positively correlated with ESG performance, especially environmental and governance scores, but cautions against conflating disclosure with substantive change.
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、AI戦略とGX政策の連携が進む中、本稿の知見はAI技術活用が気候関連開示を促進する可能性を示唆する。ただし、中国と日本の制度差に留意が必要。SSBJ基準や有報での気候リスク開示義務化の議論にも示唆を与える。
In the global GX context
This paper provides novel empirical evidence on how AI-oriented public policies can influence corporate climate risk disclosure, relevant to global frameworks like TCFD and ISSB. It highlights the potential for AI-driven governance improvements while emphasizing the need to distinguish disclosure quality from actual sustainability performance—a key issue in global disclosure scholarship.
👥 読者別の含意
🔬研究者:Provides robust empirical evidence on the link between AI policy and climate disclosure, with mechanism analysis useful for future research on policy effectiveness.
🏢実務担当者:Suggests that firms in AI pilot zones may face increased climate disclosure expectations; useful for benchmarking against Chinese peers.
🏛政策担当者:Demonstrates that AI-focused policies can indirectly boost climate disclosure, offering insights for designing integrated AI and sustainability strategies.
📄 Abstract（原文）
As artificial intelligence becomes more deeply embedded in corporate sustainability governance, it remains unclear whether public policies centered on AI development are linked to changes in corporate sustainability disclosure. Focusing on corporate climate risk disclosure as an important dimension of sustainability disclosure, this study examines how China’s National Artificial Intelligence Innovation and Development Pilot Zone policy, referred to as the AI Pilot Zone policy, is associated with firms’ disclosure of climate-related risks. Using Chinese A-share listed firms from 2014 to 2023 and a multi-period difference-in-differences approach, we find that the AI Pilot Zone policy is associated with higher levels of corporate climate risk disclosure. A series of robustness checks further confirms the stability of this finding. Mechanism analysis provides suggestive evidence consistent with three channels: climate-related information-processing capability, climate risk governance capability, and external monitoring pressure. The subsample estimates reveal that the association is more pronounced among firms under greater environmental pressure, firms with higher levels of institutional ownership, and firms whose managers exhibit stronger green awareness. Additional analysis suggests that climate risk disclosure is positively associated with ESG performance, especially environmental and governance performance. This study contributes firm-level empirical evidence on the association between AI-oriented public policies and corporate climate risk disclosure, while highlighting the need to distinguish disclosure improvement from substantive sustainability transformation.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.3390/su18147313first seen 2026-07-21 06:02:47
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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。