ESG報告における連合学習の活用：新興国文脈における重要導入課題の優先順位付け
Unlocking Federated Learning for ESG Reporting: Prioritizing Critical Adoption Challenges in an Emerging Economy Context (原題)
Naveen Virmani, Srikant Gupta, Koppiahraj Karuppiah, Jose Arturo Garza‐Reyes
🤖 gxceed AI 要約
日本語
本論文は、ESG報告の拡張性と信頼性を高める技術として連合学習（フェデレーテッドラーニング）に着目し、その導入を阻む課題を特定・評価した。文献調査と専門家パネルにより18の課題を抽出し、Pythagorean Delphi法とPythagorean fuzzy AHP法で優先順位を分析した。結果、ESGデータの標準化不足とデータ品質が最大の障壁であることが示された。実務者向けに導入に向けた実践的示唆を提供する。
English
This paper examines federated learning as a means to enhance the scalability and credibility of ESG reporting, identifying and prioritizing adoption challenges. Through literature review and expert panels, 18 validated challenges were analyzed using Pythagorean Delphi and Pythagorean fuzzy AHP methods. Findings show that lack of ESG data standardization and data quality are the top barriers. The study offers actionable insights for stakeholders adopting federated learning in ESG reporting.
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
SSBJ基準や有報でのESG開示が進む日本では、データ標準化と品質確保が共通課題であり、連合学習によるプライバシー保護とデータ連携の両立は、企業の開示インフラ構築に示唆を与える。特にサプライチェーン全体のScope 3データ収集における課題解決に寄与する可能性がある。
In the global GX context
As global disclosure frameworks (ISSB, CSRD, SEC climate) demand more granular and verifiable ESG data, federated learning offers a privacy-preserving approach to data sharing across organizations. This study highlights standardization and data quality as critical barriers, resonating with global efforts to improve ESG data infrastructure and assurance.
👥 読者別の含意
🔬研究者:連合学習とESG報告の交差点における課題を体系的に整理し、AI×ESG研究の新たな方向性を示す。
🏢実務担当者:ESGデータ標準化と品質管理の重要性を再認識し、連合学習導入の優先課題を把握できる。
🏛政策担当者:ESGデータ標準化の政策立案において、技術的障壁と優先順位を考慮する必要性を示唆する。
📄 Abstract（原文）
ABSTRACT The growing focus on environmental, social, and governance (ESG) issues has prompted organizations to explore innovative ways to manage their businesses. It has become imperative for organizations to adopt ESG reporting. The applications of federated learning help to enhance the scalability and credibility of ESG reporting in contemporary settings. However, adopting federated learning for ESG reporting is not straightforward; it involves several complexities. This paper identifies and assesses anticipated challenges. Scholarly research databases, including Scopus and Web of Science, were employed to identify relevant research papers. In the proposed study, an initial pool of 15 challenges was drawn from the literature; four additional challenges were introduced by the expert panel, and one was rejected during the Delphi consensus, yielding a final set of 18 validated challenges. Moreover, these 18 challenges were confirmed using the Pythagorean Delphi technique. Then, the Pythagorean fuzzy AHP method was applied to prioritize anticipated challenges. The results suggest that the lack of ESG data standardization and data quality are the top challenges hindering the adoption of federated learning in ESG reporting. Theoretically, the research contributes to the literature by investigating the role of federated learning in effective ESG reporting. From a practical standpoint, the proposed study provides several actionable insights for stakeholders.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/bse.71517first seen 2026-09-11 05:00:59
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