Artificial Intelligence in ESG Reporting: A Scopus-Based Bibliometric Analysis and Conceptual Model for Data-Driven Decision Support
ESG報告における人工知能：Scopusベースの文献計量分析とデータ駆動意思決定支援のための概念モデル (AI 翻訳)
Joanna Rosak-Szyrocka
🤖 gxceed AI 要約
日本語
Scopusデータベースから765件の文献を収集し、ビブリオメトリクス分析によりESG報告におけるAIの役割を調査。キーワード共起分析、Ishikawa図、Pareto-Lorenz分析を用いて主要テーマと集中度を明らかにし、機械学習と自然言語処理が非構造化データ分析に重要であることを示した。規制圧力とステークホルダー期待がAI導入の主な推進力であり、AIを中核とするESG報告の概念モデルを提案する。
English
This paper reviews the role of AI in ESG reporting through a bibliometric analysis of 765 publications from Scopus (2004-2026). Using keyword co-occurrence, Ishikawa diagram, and Pareto-Lorenz analysis, it identifies thematic clusters and key drivers such as regulatory pressure and stakeholder expectations. The study highlights the growing importance of machine learning and NLP in analyzing unstructured ESG data and proposes a conceptual model for AI-driven ESG reporting that integrates data processing, decision-making, and enforcement.
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
本論文は、AIがESG報告を変革する可能性を示しており、日本でもSSBJに基づく開示義務化が進む中、AIを活用したデータ分析・意思決定支援の重要性が高まっている。特に非構造化データの処理は日本企業の統合報告書作成においても課題であり、本モデルは参考になる。
In the global GX context
The paper contributes to the global discourse on AI in ESG reporting, aligning with trends under ISSB and CSRD that require sophisticated data analysis. It provides a bibliometric foundation and a conceptual model that can inform both academic research and practical implementation of AI-driven disclosure systems.
👥 読者別の含意
🔬研究者:Researchers interested in the intersection of AI and ESG reporting will find a comprehensive bibliometric overview and a conceptual framework.
🏢実務担当者:Corporate sustainability teams can use the conceptual model to understand how AI can enhance their ESG data processing and decision-making.
🏛政策担当者:Policymakers can draw on the identified drivers (regulatory pressure, stakeholder expectations) to design effective AI-supportive disclosure regimes.
📄 Abstract（原文）
Abstract The purpose of this manuscript is to review the role of AI in evolving ESG reporting from a historical, compliance-driven approach toward an integrated analysis and decision-support system. The analysis is based on a bibliometric analysis of manuscripts gathered from the Scopus database, with 765 publications from 2004–2026. It was performed keyword co-occurrence analysis using VOSviewer to reveal the main thematic clusters and structure of the research field. An analysis was also performed by using the Ishikawa diagram and a Pareto–Lorenz analysis to determine whether this degree of concentration was indicative of the relative importance among key concepts. The results reflect an increased role for technologies like machine learning and natural language processing in the analysis of ESG data, notably unstructured information. Among the most common were key drivers of AI adoption, such as regulatory pressure, stakeholder expectations and the desire to improve data quality. In this context, the manuscript seeks to propose a conceptual model for AI–driven transformation of ESG reporting, whereby AI serves as an integrating factor connecting data processing and analytics, decision-making and due diligence and enforcement in practice.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.30657/pea.2026.32.35first seen 2026-06-25 04:44:12
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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。