ESG格付け品質を評価する二重次元フレームワーク：中国A株企業における地域適応性と起業支援への応用

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:ESG格付けの品質評価とAI活用型評価モデル構築の方法論的枠組みを提供する。

🏢実務担当者:自社のESG評価が地域・規模のバイアスを受けていないか検証し、開示改善に活かせる。

🏛政策担当者:ESG格付けの品質基準や中小企業の開示負担軽減策を設計する際の参考になる。

Amid growing divergence and confusion in Environmental, Social, and Governance (ESG) rating methodologies and results for assessing corporate sustainability, this study proposes a dual-dimensional framework that conceptualizes ESG rating quality through two distinct yet complementary lenses—validity (the rigor, transparency, and reproducibility of rating methodologies) and utility (the practical value and relevance of rating outputs for user-specific objectives). While the framework provides a structured approach for evaluating existing ratings, it also serves as prescriptive guidance for constructing user-oriented ESG assessment models. To demonstrate its operational value, the framework is implemented in the Chinese A-share market with two explicit utility targets—local adaptability (addressing the poor cross-regional transferability of international ESG standards) and entrepreneurial enablement (counteracting the systematic size-based ESG discrimination). The findings demonstrate that the dual-dimensional framework not only provides a coherent basis for assessing ESG rating quality from the bottom (an overall quality score of 71.67 assessed for the implemented model) but also yields meaningful empirical patterns that support the top objectives (corresponding ESG trends following China’s major policy events reflected in both rating distributions and market reactions, and significantly flattened ESG–size correlation from 0.27 to 0.18 and the reduced missing indicator ratio for smaller firms from 81% to 74%). Methodologically, the target alignment is benefited by four streams of data science techniques—event-based and location-based data, machine learning for carbon footprint estimation, generative AI for extracting and summarizing structured ESG information, and a hybrid analytic hierarchy process–entropy-weighting approach. This study contributes a replicable and user-interactive approach to ESG assessment, bridging macro-level policy influence and micro-level data validity, with practical implications for investors, regulators, rating agencies, and small enterprises navigating the “long tail” of sustainable development.

• semanticscholar https://doi.org/10.3390/su18189453first seen 2026-09-19 05:18:56 · last seen 2026-09-21 04:53:22