Investor AI Monitoring Capability and ESG Disclosure Granularity: Evidence from East African Banking
投資家のAIモニタリング能力とESG開示の粒度：東アフリカ銀行業からのエビデンス (AI 翻訳)
Lydia Nyongesa, Christine Osinde, Brian Wakasala
🤖 gxceed AI 要約
日本語
本論文は、機関投資家のAIモニタリング能力が東アフリカ商業銀行のESG開示の粒度に与える影響を検証。手動コーディングした47項目のESG開示粒度指標と投資家調査を用いた分析の結果、AI能力は開示粒度を強く予測するが、実際のESGパフォーマンスは予測しないことが判明。特にESGパフォーマンスの低い銀行で効果が強く、「アルゴリズム・グリーンウォッシング」概念を提示。規制当局は独立検証付きの粒度基準を導入すべきと提言。
English
This paper examines whether institutional investors' AI monitoring capability causes East African commercial banks to disclose ESG information more granularly. Using hand-coded ESG disclosure data from 31 banks and surveys of 418 investors, the study finds that investor AI capability strongly predicts disclosure granularity, but actual ESG performance does not. Effects are strongest among poor ESG performers, suggesting strategic impression management. The paper introduces the concept of 'algorithmic greenwashing' and recommends regulators mandate granularity standards with independent verification.
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基準や有報でのESG開示が進む中、AIによる開示監視が逆に「アルゴリズム・グリーンウォッシング」を促進するリスクを示唆。日本の投資家や規制当局は、AIが開示粒度を高める一方で実質的な改善を伴わないケースに注意すべき。
In the global GX context
Globally, as AI tools for ESG monitoring proliferate, this paper provides the first causal evidence that investor AI capability can drive granular disclosure without genuine sustainability progress, introducing 'algorithmic greenwashing' to the disclosure scholarship. It challenges the assumption that machine-readable, granular disclosures equate to accountability, with implications for TCFD/ISSB frameworks and SEC climate rules.
👥 読者別の含意
🔬研究者:Introduces 'algorithmic greenwashing' and provides causal identification strategies for studying AI-driven disclosure behavior.
🏢実務担当者:Highlights the risk that AI monitoring may incentivize impression management over real ESG improvements; caution against treating granular disclosure as a proxy for performance.
🏛政策担当者:Regulators should consider independent verification of granular disclosures to prevent algorithmic greenwashing under emerging global standards.
📄 Abstract（原文）
The rapid proliferation of artificial intelligence tools among institutional investors is reshaping corporate governance and accountability, yet its consequences for ESG disclosure quality in frontier markets remain poorly understood. This study examined one precisely bounded question: does investor AI monitoring capability cause East African commercial banks to disclose ESG information more granularly? The analysis was grounded in agency, signaling, and institutional theories, which together position AI capability as a governance mechanism that conditions the depth — not merely the breadth — of ESG reporting by altering the strategic cost of disclosure imprecision. Using hand-coded ESG disclosure data from 31 commercial banks — 23 domestic private and 8 globally affiliated — across Kenya, Tanzania, Uganda, Rwanda, and Ethiopia (2018–2024), and surveys of 418 institutional investors, the study constructs an ownership-weighted AI capability measure and a comprehensive ESG disclosure granularity index comprising 47 items across environmental, social, and governance dimensions. The empirical analysis employed OLS, instrumental variable estimation exploiting EU SFDR mandates, staggered difference-in-differences, and event studies, controlling for firm size, profitability, leverage, ownership structure, and board characteristics. Investor AI capability is a strong, robust predictor of disclosure granularity (β = 0.52, p < 0.001, ΔR² = 0.22), with a one-standard-deviation increase associating with a 14.9-point granularity rise. Results were consistent across all five identification strategies (β range: 0.49–0.68). Crucially, actual ESG performance does not predict granularity, and AI effects are strongest among poor ESG performers — consistent with strategic impression management rather than genuine accountability. The study introduced the concept of 'algorithmic greenwashing': the production of granular, machine-readable disclosures optimised for AI detection without substantive improvement to underlying ESG practices. Regulators should mandate granularity standards with independent verification mechanisms, and must not treat algorithmically optimised disclosure as a proxy for genuine sustainability progress. Investors and bank boards must ensure detailed reporting reflects substantive rather than reputational compliance.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.51583/ijltemas.2026.150300139first seen 2026-05-20 05:29:39
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