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Bibliometric Analysis of ESG Disclosure

ESG開示の計量書誌学的分析 (AI 翻訳)

Loso Judijanto

Sustainable Development Insights📚 査読済 / ジャーナル2026-07-30#ESG
DOI: 10.58812/sdi.v2i02.3020
原典: https://doi.org/10.58812/sdi.v2i02.3020
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🤖 gxceed AI 要約

日本語

本研究はScopusデータベースを用いてESG開示に関する文献の計量書誌学的分析を行い、研究の成長、知的構造、トレンドを明らかにした。VOSviewerによるキーワード共起、引用、共著、機関・国別の協力分析を実施し、ESG開示が企業価値向上や透明性向上に寄与することを示した。また、従来のサステナビリティ報告からAI・機械学習、炭素開示、ESGパフォーマンス測定、持続可能な投資への研究シフトを確認した。

English

This study conducts a bibliometric analysis of ESG disclosure literature using Scopus data and VOSviewer, revealing growth, intellectual structure, and trends. It finds that ESG disclosure enhances corporate value and transparency, and identifies a shift from conventional sustainability reporting toward AI, machine learning, carbon disclosure, and sustainable investment. The analysis highlights contributions from China, US, UK, India, and Italy, and maps future research opportunities in digital ESG assessment and regulatory harmonization.

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開示基準の適用が迫る中、ESG開示研究の全体像を把握する上で有用。AI・MLを活用したESG評価や開示のデジタル化は、今後の日本の開示実務や投資家対応に示唆を与える。

In the global GX context

This bibliometric overview helps global researchers and practitioners understand the evolution of ESG disclosure research, highlighting the growing role of AI and digital tools. It supports the ongoing shift toward ISSB-aligned reporting and the integration of technology in sustainability disclosure, offering a roadmap for future research and practice.

👥 読者別の含意

🔬研究者:Provides a comprehensive map of ESG disclosure research, identifying key themes and future directions including AI integration.

🏢実務担当者:Offers insights into the evolving landscape of ESG disclosure, helping corporate teams anticipate emerging trends and regulatory expectations.

🏛政策担当者:Highlights the global research landscape and the need for regulatory harmonization in ESG disclosure standards.

📄 Abstract（原文）

Environmental, Social, and Governance (ESG) disclosure has become an increasingly important research area due to growing demands for corporate transparency, sustainable business practices, and responsible investment decisions. This study aims to examine the development, intellectual structure, and emerging research trends in ESG disclosure literature using a bibliometric analysis approach. Data were collected from the Scopus database by identifying relevant publications related to ESG disclosure, sustainability reporting, and corporate sustainability. The collected documents were analyzed using VOSviewer to perform keyword co-occurrence analysis, citation analysis, co-authorship analysis, institutional collaboration analysis, and country collaboration mapping. The findings reveal that ESG disclosure research has experienced substantial growth and is primarily focused on themes related to ESG practices, sustainability reporting, corporate social responsibility, financial performance, stakeholder theory, and corporate governance. Influential studies indicate that ESG disclosure plays an important role in enhancing corporate value, improving transparency, reducing information asymmetry, and strengthening stakeholder relationships. The thematic evolution analysis further demonstrates a transition from conventional sustainability reporting toward emerging research areas involving artificial intelligence, machine learning, carbon disclosure, ESG performance measurement, and sustainable investment. The collaboration analysis highlights the dominant contributions of countries such as China, the United States, the United Kingdom, India, and Italy, reflecting the global and interdisciplinary nature of ESG disclosure research. This study contributes to the existing literature by mapping the knowledge structure of ESG disclosure and identifying future research opportunities related to digital ESG assessment, regulatory harmonization, and sustainable corporate value creation.

🔗 Provenance — このレコードを発見したソース

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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。