AIとサステナビリティ開示の未来:2028年までの強制非財務報告に向けた公立大学の準備状況に関する系統的デスクレビュー
AI and the Future of Sustainability Disclosures: A Systematic Desk Review Preparedness of Public Universities for Mandatory Non-Financial Reporting by 2028 (原題)
Jeremiah Osida Onunga, Jared Okello
🤖 gxceed AI 要約
日本語
本論文は、ケニアの公立大学が2028年までに迫る強制的な非財務情報開示(IPSASB SRS1等)に備える上で、AIがサステナビリティ指標の測定・検証・報告をどう支援しうるかをPRISMA準拠の系統的デスクレビューで検討する。高等教育向けESG指標とAI活用技術を統合し、ケニアの大学の実情に即したAI活用型開示アーキテクチャを提案。AIデータシステムが開示の正確性・効率性・信頼性を高め、機関統治と説明責任を強化しうる一方、準備ギャップが残ると指摘する。
English
This systematic desk review (PRISMA-guided) examines how AI can support measurement, verification, and reporting of sustainability indicators in Kenyan public universities as mandatory non-financial reporting (e.g., IPSASB SRS1) approaches by 2028. It synthesizes literature on disclosure frameworks, higher-education ESG indicators, and AI sustainability analytics, then proposes an AI-enabled reporting architecture tailored to institutional realities. Findings suggest AI-driven data systems can improve accuracy, efficiency, and credibility of disclosures while strengthening governance, though critical readiness gaps remain.
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基準の確定と有報・統合報告書での開示拡大が進む中、公的セクター・大学等の非財務開示準備は未整備な部分が多い。本論文のAI活用型開示アーキテクチャと準備ギャップの整理は、日本の大学・公的機関がScope・ESGデータ基盤を整える際の参照枠になりうる。
In the global GX context
As ISSB/ISSB-aligned standards (SSBJ in Japan, CSRD in the EU, IPSASB SRS1 for public sector) expand mandatory non-financial reporting, this paper offers a rare public-sector and higher-education perspective from the Global South. It contributes to global disclosure scholarship by linking AI-enabled data systems to institutional readiness and accountability in resource-constrained settings.
👥 読者別の含意
🔬研究者:AI×ESG開示の交点を公的セクター・高等教育に拡張する枠組みと、準備ギャップの整理を提供する。
🏢実務担当者:大学・公的機関の開示担当者が、AIを用いたESGデータ収集・検証・報告体制を設計する際の実務的示唆を得られる。
🏛政策担当者:非財務報告義務化を見据え、公的機関のデータ基盤・AIガバナンス整備を政策課題として位置づける根拠となる。
📄 Abstract(原文)
The global shift toward sustainability reporting has substantially broadened expectations for organizations to disclose non-financial information pertaining to environmental, social, and governance (ESG) performance. In Kenya, the gradual adoption of international sustainability disclosure standards—like the anticipated application of the IPSASB Sustainability Reporting Standard (SRS1) to public sector entities signals a consequential transformation in institutional accountability. Public universities, which operate large campuses and fulfil essential public service roles through teaching, research, and community engagement, will increasingly be required to report sustainability performance in a transparent, structured, and measurable manner. Yet many of these institutions continue to rely on fragmented administrative systems that render sustainability data collection both difficult and inconsistent. This paper examines how Artificial Intelligence (AI) can support the measurement, verification, and reporting of sustainability indicators in Kenyan public universities as the country prepares for mandatory non-financial reporting requirements. Employing a systematic desk review methodology guided by PRISMA principles, the study synthesizes scholarly and policy literature on sustainability reporting frameworks, ESG indicators relevant to higher education, and emerging AI technologies applied to sustainability analytics. The study proposes an AI-enabled sustainability reporting architecture tailored to the institutional and operational realities of Kenyan public universities and identifies critical readiness gaps that must be addressed. The findings indicate that AI-driven data systems can significantly enhance the accuracy, efficiency, and credibility of sustainability disclosures while simultaneously strengthening institutional governance and accountability. This paper contributes to the growing discourse on responsible AI, digital governance, and sustainability reporting within higher education institutions across Africa.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.68050/jams.2026.477first seen 2026-09-26 04:47:24
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