AI and Data Analytics in Sustainable Financial Reporting and ESG Disclosure: A Systematic Literature Review
サステナブル財務報告とESG開示におけるAIとデータ分析：系統的文献レビュー (AI 翻訳)
Percy Antonio Vilchez Olivares, Brandelt Jesús Artorga de la Cruz
🤖 gxceed AI 要約
日本語
本系統的文献レビューは、ESG開示におけるAIとデータ分析の活用を、2020〜2025年の45件の査読付き論文から分析した。4つの主要テーマ（NLP・テキストマイニング、MLによるESGスコアリング、AI保証・監査、規制・デジタル変革）を抽出し、AIがESG開示をデータ駆動型の検証可能なシステムに変革しつつあることを示している。
English
This systematic literature review examines the use of AI and data analytics in ESG disclosure, analyzing 45 peer-reviewed articles from 2020-2025. It identifies four major themes: NLP for ESG text mining, machine learning for ESG scoring, AI-enabled assurance and auditing, and regulatory digital transformation. The findings indicate AI is transforming ESG disclosure into a data-driven, verifiable system, with implications for regulators, practitioners, and investors.
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
日本でもSSBJ基準の適用が迫る中、本レビューはAI活用による開示効率化・信頼性向上の知見を提供する。特にCSRDやISSBを踏まえた国際動向を整理しており、日本企業のグローバル対応に示唆を与える。
In the global GX context
Amid escalating ESG disclosure requirements under CSRD and ISSB, this review provides a timely synthesis of how AI and data analytics are being applied to enhance reliability, comparability, and efficiency. It offers a structured overview of emerging tools and regulatory implications, valuable for global practitioners and standard-setters.
👥 読者別の含意
🔬研究者:This review maps the fragmented landscape of AI in ESG disclosure, highlighting research gaps and growth areas for future work.
🏢実務担当者:Corporate sustainability teams can identify AI-driven tools for automating ESG data collection, scoring, and assurance.
🏛政策担当者:Regulators can understand how AI is reshaping disclosure verification and consider its implications for standard-setting.
📄 Abstract（原文）
The intensification of ESG disclosure requirements under the Corporate Sustainability Reporting Directive (CSRD) and the International Sustainability Standards Board (ISSB) has increased the demand for artificial intelligence (AI) and data analytics to support large-scale sustainability reporting and verification. However, the existing academic literature remains fragmented across disciplinary domains, including natural language processing, machine learning, auditing, and regulatory compliance. This study addresses this gap through a PRISMA 2020-compliant systematic literature review of 45 peer-reviewed articles published between 2020 and 2025 and indexed in the Scopus database. The analysis combines bibliometric techniques using VOSviewer with qualitative thematic content analysis. The results reveal a rapidly expanding research field with a compound annual growth rate of 91.9%. Four major thematic dimensions emerge: (i) NLP and text mining for ESG disclosure analysis; (ii) machine learning applications for ESG scoring and corporate performance; (iii) AI-enabled ESG assurance, auditing, and governance; and (iv) regulatory frameworks and the digital transformation of sustainability reporting. The findings indicate that AI technologies are progressively transforming ESG disclosure from a predominantly narrative and self-reported practice into a data-driven and verifiable transparency system. These developments have important implications for regulators, corporate practitioners, assurance providers, and investors seeking to enhance the reliability and comparability of sustainability disclosures.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.20944/preprints202603.1378.v1first seen 2026-05-14 22:01:49
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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。