Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction
持続可能性評価手法の信頼性評価に向けて：ベンチマークデータセット構築のための人間-AI協調フレームワーク (AI 翻訳)
Xiao-Xing Cai, Wang Yang, Xiyu Ren, C. Law, Rohit Sharma, Peng Qi
🤖 gxceed AI 要約
日本語
本論文は、サステナビリティ（ESG）格付けの信頼性と比較可能性を高めるため、大規模言語モデル（LLM）を活用した人間-AI協調フレームワークを提案する。フレームワークは、STRIDE（スコアリング基準）とSR-Delta（差異分析手順）の二部構成で、企業レベルのベンチマークデータセットを生成し、格付け手法の評価を可能にする。AIコミュニティに対し、サステナビリティ評価の高度化へのAI活用を呼びかけている。
English
This paper proposes a human-AI collaborative framework to enhance the trustworthiness and comparability of sustainability (ESG) ratings. The framework consists of STRIDE (principled criteria and scoring) and SR-Delta (discrepancy analysis), using large language models to construct firm-level benchmark datasets for evaluating rating methodologies. It calls on the AI community to adopt AI approaches to advance sustainability ratings.
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基準の策定が進み、ESG格付けの信頼性・比較可能性は投資家対応の重要課題である。本フレームワークは、日本企業の格付け間の乖離を分析し、開示改善につなげる実践的なツールとなり得る。
In the global GX context
With TCFD, ISSB, and CSRD demanding credible sustainability disclosures, inconsistent ESG ratings undermine decision-making. This framework offers a scalable, AI-driven method to benchmark and improve rating methodologies, directly supporting global disclosure harmonization and transition finance.
👥 読者別の含意
🔬研究者:Provides a systematic framework combining LLMs and human oversight for evaluating ESG rating methodologies, opening avenues for empirical research on rating convergence.
🏢実務担当者:Offers a practical tool for companies to benchmark their ESG ratings across agencies and identify areas for disclosure improvement.
🏛政策担当者:Highlights the need for standardized benchmark datasets to regulate ESG rating agencies, supporting policy efforts like IOSCO's recommendations.
📄 Abstract（原文）
Sustainability or ESG rating agencies use company disclosures and external data to produce scores or ratings that assess the environmental, social, and governance performance of a company. However, sustainability ratings across agencies for a single company vary widely, limiting their comparability, credibility, and relevance to decision-making. To harmonize the rating results, we propose adopting a universal human-AI collaboration framework to generate trustworthy benchmark datasets for evaluating sustainability rating methodologies. The framework comprises two complementary parts: STRIDE (Sustainability Trust Rating&Integrity Data Equation) provides principled criteria and a scoring system that guide the construction of firm-level benchmark datasets using large language models (LLMs), and SR-Delta, a discrepancy-analysis procedural framework that surfaces insights for potential adjustments. The framework enables scalable and comparable assessment of sustainability rating methodologies. We call on the broader AI community to adopt AI-powered approaches to strengthen and advance sustainability rating methodologies that support and enforce urgent sustainability agendas.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.48550/arxiv.2602.17106first seen 2026-07-18 08:11:17
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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。