Sustainability Reporting and Artificial Intelligence: A Systematic Literature Review
サステナビリティ報告と人工知能：系統的文献レビュー (AI 翻訳)
Mareta Putri, I. Mutia, S. F. Kartasari
🤖 gxceed AI 要約
日本語
本論文は、人工知能（AI）がサステナビリティ報告に与える役割を系統的文献レビューにより評価した。1,087件の初期文献からPRISMAに従い30件を選定し、AIが効率性、透明性、ビッグデータ管理、意思決定支援にもたらす利点と、アルゴリズムバイアス、データ保護、コスト、標準化欠如といった課題を明らかにした。規制と基準の重要性を強調している。
English
This paper conducts a systematic literature review on the role of AI in sustainability reporting, analyzing 30 relevant studies from 1,087 initial articles. It finds benefits in efficiency, accuracy, transparency, and big data management, while noting challenges like algorithm bias, data protection, cost, and lack of global standards. The study highlights the need for regulation and standardization.
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
日本のGX実務では、サステナビリティ報告（有報・統合報告書）へのAI活用が進みつつある。本レビューはAI導入の利点と課題を整理しており、SSBJ基準対応や投資家向け開示の高度化を目指す企業にとって参考となる。ただし、日本特有の文脈（例：国内規制や文化）は扱われていない。
In the global GX context
This review provides a global snapshot of AI's potential and pitfalls in sustainability reporting, relevant to evolving frameworks like ISSB, CSRD, and SEC climate rules. It underscores the need for standardized AI governance, a key concern as regulators worldwide push for reliable ESG data. The findings are broadly applicable but lack region-specific insights.
👥 読者別の含意
🔬研究者:For GX researchers, this review maps the current state of AI in sustainability reporting, identifying research gaps such as developing-country contexts and algorithmic bias.
🏢実務担当者:Corporate sustainability teams can use this to understand AI tools for improving reporting efficiency and data quality, while being aware of challenges in bias and cost.
🏛政策担当者:Policymakers can note the call for global standards and regulations to ensure AI-driven sustainability reports are reliable and comparable.
📄 Abstract（原文）
Purpose: This study aims to evaluate the role of Artificial Intelligence (AI) in sustainability reporting. The main focus is on how AI can improve quality, efficiency, and transparency, as well as the challenges that arise in the application of AI in sustainability reporting. Methodology/approach: The method used was a Systematic Literature Review (SLR) with three stages: planning, implementation, and reporting over the past five years (2020-2025) from 1,087 initial articles. After the PRISMA process, 30 relevant articles were selected and analyzed. Results/Findings: The study found that the benefits of AI include improved efficiency and accuracy, management of big data, enhancement of transparency and accountability, and aiding in sustainable decision-making. The main challenges of this research are algorithm bias, personal data protection, cost and technology constraints, and the lack of global standards in AI-based reporting. Conclusions: AI has the potential to improve quality and transparency through automation, predictive analysis, and efficient data management. However, its implementation requires regulations, guidelines, and ESG standardization. Limitations: Most studies originate from developed countries, while developing countries contribute relatively little. Contributions: This research highlights the importance of regulation and standardization in the implementation of AI in sustainable financial reporting. It describes the current state of affairs and provides a strong foundation for further research and policy formulation at the global level.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.35912/jakman.v7i2.5427first seen 2026-05-15 18:35:38 · last seen 2026-06-15 05:27:34
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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。