构建人机协作框架，提升企业可持续性评级的可信度与可比性。
Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction
- 提出人机协同框架STRIDE，用大模型生成标准化评级数据集。
- 通过SR-Delta分析差异，发现评级不一致的根源并提出优化方向。
- 为评估可持续性评级方法提供可扩展、可比较的基准数据支持。
可持续性或ESG评级机构利用企业披露信息和外部数据，对企业的环境、社会与治理表现进行评分。然而，不同机构对同一公司的评级差异巨大，影响其可比性、可信度及决策参考价值。为实现评级结果的统一，本文提出采用通用的人机协作框架，构建可信的基准数据集以评估可持续性评级方法。该框架包含两部分：STRIDE（可持续性可信评级与完整性数据方程）提供原则性标准与评分体系，指导基于大语言模型（LLMs）的企业级基准数据集构建；SR-Delta则是一种差异分析流程框架，可揭示潜在调整建议。该框架实现了可持续性评级方法的可扩展、可比较评估。本文呼吁更广泛的AI研究社区采用智能化方法，推动可持续性评级方法的强化与演进，以支持紧迫的可持续发展目标。
原文摘要 · Abstract (English)
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.
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