The Impact of AI-Driven ESG Compliance Monitoring on Corporate Sustainability Risk: Evidence from Publicly Listed Corporations (2016-2024)
AI駆動型ESGコンプライアンス監視が企業のサステナビリティリスクに与える影響：上場企業の証拠（2016-2024年） (AI 翻訳)
Nabeela Ehsan
🤖 gxceed AI 要約
日本語
本研究は、AIによるESGコンプライアンス監視が企業のサステナビリティリスクを低減するかを、2016-2024年の上場企業320社のパネルデータを用いて検証した。GLS回帰の結果、AI監視の導入はリスクを有意に低減（係数-6.84）し、ESGパフォーマンスも負の関連を示した。企業規模やレバレッジも影響し、結果は頑健性チェックで安定していた。AI統合は規制対応の戦略的必須事項であり、政策立案者は中小企業へのAI導入支援が推奨される。
English
This study examines whether AI-driven ESG compliance monitoring reduces corporate sustainability risk using panel data from 320 listed firms (2016-2024). GLS regression shows AI monitoring significantly lowers risk (coefficient -6.84), and ESG performance is negatively associated. Firm size and leverage also matter; results are robust. AI integration is a strategic imperative for regulatory alignment, and policymakers should support AI adoption, especially among smaller firms.
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開示が始まり、AIを活用したESG情報の正確性・適時性向上は有報や統合報告書の品質向上に直結する。本研究成果は、日本企業がAI監視を導入する際のエビデンスとなり、投資家対応や開示リスク管理に示唆を与える。
In the global GX context
Globally, with ISSB and CSRD raising disclosure standards, AI-driven monitoring offers a scalable solution for accuracy and timeliness. This study provides empirical evidence that AI adoption reduces sustainability risk, supporting the business case for AI in ESG reporting and informing regulators on facilitating AI uptake, particularly for SMEs.
👥 読者別の含意
🔬研究者:Provides empirical evidence on AI-ESG monitoring effectiveness, useful for further research on AI in sustainability risk management.
🏢実務担当者:Supports investment in AI-driven ESG monitoring to reduce sustainability risk and improve disclosure quality.
🏛政策担当者:Highlights the need to facilitate AI adoption among smaller firms to enhance ESG compliance and risk management.
📄 Abstract（原文）
This study investigates how Artificial Intelligence (AI) can improve Environmental, Social, and Governance (ESG) compliance monitoring and its effect on corporate sustainability risks. Specifically, it examines the extent to which AI enhances the accuracy, timeliness, and reliability of ESG disclosures, thereby reducing sustainability risk exposure. By using a quantitative research design, data was gathered from 320 publicly listed corporations across North America, Europe, and Asia-Pacific between 2016 and 2024, yielding a balanced panel dataset of 2,880 firm-year observations. The study used the Generalized Least Squares (GLS) regression model to examine the relationship between AI-driven ESG compliance monitoring and corporate sustainability risks. Results indicated a highly significant negative relationship: the AI_ESG coefficient was −6.84 (p<0.01), suggesting that a one-unit increase in AI-driven ESG monitoring adoption is associated with a 6.84-unit reduction in corporate sustainability risk, equivalent to approximately 0.53 standard deviations of the CSRISK distribution. Similarly, ESG performance scores were negatively associated with sustainability risk (coefficient = −0.39, p<0.01). Factors such as firm size and financial leverage were also found to have significant effects on levels of sustainability risks. Robustness checks, including lagged independent variables and subsample analyses, confirmed the stability of these findings. The implications of the findings are that AI integration in ESG reporting processes represents a strategic imperative for corporations seeking alignment with evolving regulations. Policymakers are encouraged to facilitate AI adoption, particularly among smaller firms, to promote broader ESG compliance and risk management across the corporate landscape.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.32479/ijefi.23359first seen 2026-05-15 18:23:35 · last seen 2026-08-02 06:20:53
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