Using Generative Artificial Intelligence to Evaluate the Quality of Chinese Environmental Information Disclosure in Chemical Firms
生成AIを用いた中国化学企業の環境情報開示品質評価 (AI 翻訳)
Yun Zhu, Qinghan Chen, Ma Zhong
🤖 gxceed AI 要約
日本語
本研究は、化学企業の環境情報開示品質を評価するための枠組みを提案し、生成AI（DeepSeek-V3.2）を用いた自動スコアリングシステムを開発。2020～2024年の中国化学企業38社のESG報告書190件を分析し、開示品質が向上傾向にあること、AIスコアが専門家評価と高い一致を示すことを確認した。
English
This study proposes a framework for evaluating environmental information disclosure quality (EIDQ) of chemical firms, using a generative AI (DeepSeek-V3.2) scoring system. Analyzing 190 ESG reports from 38 Chinese chemical firms (2020-2024), it finds upward EIDQ trends and high alignment with human expert evaluations, demonstrating AI feasibility in disclosure assessment.
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
本論文は中国の事例だが、AIによる開示品質評価手法は、日本の有報や統合報告書の品質チェックにも応用可能。SSBJ基準への適合性評価などへの展開が期待される。
In the global GX context
This paper demonstrates the use of generative AI to evaluate environmental disclosure quality, relevant to global trends in automated assurance and ISSB/CSRD compliance. The method could be adapted for assessing transition plan disclosures and other GX-related reporting.
👥 読者別の含意
🔬研究者:Highlights a novel AI-driven approach for disclosure quality assessment, with cross-model validation and transferability insights.
🏢実務担当者:Provides a scalable AI tool for internal or third-party verification of environmental disclosure quality, useful for sustainability teams.
🏛政策担当者:Offers evidence that AI can support regulatory monitoring of disclosure quality, potentially reducing burden on reviewers.
📄 Abstract（原文）
Environmental information disclosure plays a critical role in corporate sustainability, yet existing evaluation approaches often rely on subjective judgment or limited textual features. This study proposes a structured framework for assessing the environmental information disclosure quality (EIDQ) of chemical enterprises and develops a generative artificial intelligence (GAI)-driven automated scoring system to enhance evaluation consistency. Using 190 Environmental, Social, and Governance (ESG) reports from 38 Chinese chemical firms between 2020 and 2024, we applied a multi-stage process combining indicator construction, DeepSeek-V3.2–based large language model (LLM) scoring, and cross-model validation. The results show that EIDQ exhibited a steady upward trend over the study period, reflecting a shift toward more quantitative and verifiable disclosure practices. The AI-generated scores demonstrated a high degree of alignment with human expert evaluations, and robustness tests confirmed the method’s transferability across different large language models. These findings provide methodological evidence for the feasibility of AI-assisted EIDQ assessment and offer practical implications for corporate sustainability reporting and regulatory oversight.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.3390/su172411348first seen 2026-05-14 22:26:08
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