サステナビリティ・ナラティブを超えて：IFRS S1およびIFRS S2に基づくガバナンスと環境データの整合性を検証するフォレンジック・アルゴリズム
Beyond Sustainability Narratives: A Forensic Algorithm for Verifying Governance and Environmental Data Integrity under IFRS S1 and IFRS S2 (原題)
Gikonyo Ndugu
🤖 gxceed AI 要約
日本語
本論文は、IFRS S1/S2に基づく開示の取引レベルの証拠を検証するためのフォレンジック保証アルゴリズムを開発・実証する。ジェンダーパリティ基準を組み込んだ調整ハーフィンダール指数と、環境データパイプラインの成熟度を評価する5段階データ品質スコアを提案し、ケニアの農業企業Kakuzi PLCに適用。ガバナンス集中度が基準を25%超過し、環境データ品質スコアがゼロであることを診断した。
English
This paper develops and demonstrates a forensic assurance algorithm to verify transaction-level evidence for IFRS S1/S2 disclosures. It proposes an adjusted Herfindahl-Hirschman Index with a gender-parity baseline and a five-tier Data Quality Score, applied to Kenyan agricultural firm Kakuzi PLC, revealing governance concentration 25% above benchmark and zero environmental data quality.
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、有報でのサステナビリティ情報の保証が課題となる中、本論文の取引レベルの検証アプローチは、日本の開示実務におけるデータ品質向上と保証の枠組みに示唆を与える。特に、定性的なナラティブから監査可能な証跡への移行は、日本の投資家対応にも有用。
In the global GX context
Globally, as ISSB and CSRD push for auditable sustainability disclosures, this paper offers a concrete algorithmic method to verify governance and environmental data integrity, addressing the gap between reported narratives and transaction-level evidence. It provides a template for emerging markets and beyond, aligning with the shift toward continuous assurance.
👥 読者別の含意
🔬研究者:Provides a novel quantitative method for verifying sustainability disclosures, combining governance concentration and data quality scoring.
🏢実務担当者:Offers a diagnostic tool to assess and improve the auditability of sustainability data pipelines, useful for disclosure teams.
🏛政策担当者:Highlights the need for standardized verification mechanisms in sustainability reporting, relevant for regulators implementing ISSB standards.
📄 Abstract（原文）
The regulatory enforcement of the International Sustainability Standards Board (ISSB) frameworks is forcing a mandatory transition in emerging-market corporate disclosure, moving reporting away from qualitative, public-relations-oriented narratives and toward auditable, transaction-backed evidence. Under IFRS S1, entities must transparently disclose governance body composition and progress against stated targets, while IFRS S2 requires precise, financially material climate-related disclosures covering greenhouse gas emissions. Despite these mandates, no standardised, automated mechanism currently exists for verifying the transaction-level evidence that underlies such disclosures, leaving a persistent gap between what companies report and what can actually be audited in real time. This paper addresses that gap by developing and empirically demonstrating a unified Forensic Assurance Algorithm built from two original quantitative instruments: an adjusted Herfindahl-Hirschman Index, recalibrated from competition economics to a strict fifty-percent gender-parity baseline, used to evaluate governance concentration risk; and a multi-weighted, five-tier Data Quality Score that scores the systemic maturity of environmental data pipelines rather than the completeness of their narrative description. Working from a positivist research paradigm, the algorithm is applied to the public sustainability disclosures of Kakuzi PLC, a market-leading, transparently reporting agricultural enterprise selected specifically as an illustrative diagnostic anchor rather than as a representative of poor practice. The resulting diagnostic identifies a governance concentration index of 0.6250, a twenty-five percent variance above the parity benchmark that also breaches Kenya's own constitutional two-thirds gender rule, and an environmental data quality score of zero, reflecting complete reliance on retrospective, manually compiled spreadsheets. The paper concludes with a proposed automated data-engineering architecture intended to move corporate sustainability reporting toward continuous, audit-ready assurance in emerging markets.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://journals.eanso.org/index.php/eajbe/article/download/5618/5964first seen 2026-08-30 05:00:35 · last seen 2026-09-21 04:52:47
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