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AI and Data Analytics in Sustainable Financial Reporting and ESG Disclosure: A Systematic Literature Review

Percy Antonio Vílchez Olivares, Barbara Braga Cruz

Sustainability📚 査読済 / ジャーナル2026-05-27#AI×ESGOrigin: Global
DOI: 10.3390/su18115393
原典: https://doi.org/10.3390/su18115393

🤖 gxceed AI 要約

日本語

本論文は、CSRDやISSBの拡大するESG開示義務に対応するため、AIとデータ分析の活用に関する文献をPRISMAに従い系統的にレビューした。45件の査読論文を分析し、NLP、機械学習、AI保証、規制枠組みの4つのテーマを特定。年率91.9%で成長する分野を示し、AIがESG開示をデータ駆動型で検証可能な透明システムに変革しつつあると結論付ける。

English

This PRISMA-compliant systematic review of 45 peer-reviewed articles (2020-2025) maps AI and data analytics in ESG disclosure amid expanding CSRD and ISSB mandates. Four themes emerge: NLP/text mining, ML for ESG scoring, AI-enabled assurance, and regulatory digital transformation. The field grows at 91.9% CAGR, shifting ESG from narrative to data-driven verifiable transparency.

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基準や有報でのESG情報開示が進む中、AI活用による開示の効率化・検証可能性向上は実務上の関心が高い。本レビューは最新研究の俯瞰を提供し、日本の開示実務や監査への示唆に富む。

In the global GX context

With ISSB and CSRD driving global mandatory ESG disclosure, this review synthesizes the rapidly growing AI-in-ESG literature, offering a structured map of how NLP, ML, and assurance technologies are reshaping disclosure reliability and comparability. It serves as a foundational reference for researchers, practitioners, and standard-setters.

👥 読者別の含意

🔬研究者:Provides a comprehensive taxonomy of AI applications in ESG disclosure, identifying research gaps and the explosive growth trajectory (91.9% CAGR) for future work.

🏢実務担当者:Highlights how AI tools can automate ESG data collection, scoring, and verification, supporting corporate reporting teams in meeting evolving disclosure standards.

🏛政策担当者:Documents the transition toward data-driven, verifiable ESG disclosures, informing regulatory design for AI-enabled assurance and digital reporting infrastructure.

📄 Abstract（原文）

Expanding ESG disclosure mandates under the Corporate Sustainability Reporting Directive (CSRD) and the International Sustainability Standards Board (ISSB) have driven rising demand for artificial intelligence (AI) and data analytics capable of supporting sustainability reporting and verification at scale. Nevertheless, the scholarly literature remains dispersed across discrete disciplinary fields—natural language processing, machine learning, auditing, and regulatory compliance—with limited integrative synthesis. To address this gap, the present study conducts a PRISMA 2020-compliant systematic review of 45 peer-reviewed articles indexed in Scopus and published between 2020 and 2025. The methodology combines bibliometric mapping through VOSviewer with qualitative thematic content analysis. Findings document a rapidly expanding field exhibiting a compound annual growth rate of 91.9%. Four principal thematic dimensions emerge: (i) NLP and text mining for ESG disclosure analysis; (ii) machine learning 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 evidence indicates that AI is progressively reshaping ESG disclosure from a largely narrative and self-reported practice into a data-driven, independently verifiable transparency system. These developments carry substantive implications for regulators, corporate practitioners, assurance providers, and investors seeking to strengthen the reliability and comparability of sustainability disclosures.

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