Carbon Information Disclosure Quality in China’s Petroleum and Petrochemical Enterprises: An LLM Approach
中国石油化学企業における炭素情報開示品質：LLMアプローチ (AI 翻訳)
Meng Yuan, Ma Zhong
🤖 gxceed AI 要約
日本語
本研究は、中国のA株上場石油化学企業15社の炭素情報開示品質を、DeepSeek-V3.2大規模言語モデルを用いて評価した。7つの一次指標、15の二次指標、33の三次指標からなる枠組みを構築し、開示文書を自動スコアリング。手動評価とのICCは0.974と高い信頼性を示した。平均スコアは34.02点（理論最大66点）で全体的に低水準だが、2022年から2024年にかけて29.34%上昇した。企業間格差も顕著で、Sinopecが最高52.67点、Yunnan Yunweiが最低10.67点だった。高排出産業における構造的な開示品質評価手法として貢献する。
English
This study develops an industry-specific carbon information disclosure quality (CIDQ) framework for Chinese listed petroleum and petrochemical firms, using LLM (DeepSeek-V3.2) to score disclosure texts. The framework comprises 7 primary, 15 secondary, and 33 tertiary indicators. Reliability against manual scoring is high (ICC=0.974). The mean score (34.02/66) indicates low overall quality, but it improved 29.34% from 2022 to 2024. Large inter-firm differences exist. The study provides a methodological reference for structured CIDQ evaluation in high-emission industries.
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
中国の石油化学企業の炭素情報開示にAIを活用した評価手法。日本企業が中国サプライチェーンを持つ場合に参考になるが、直接的な日本政策（SSBJ等）との連動は薄い。ただし、LLMによる開示評価の信頼性検証は日本の有報・統合報告書分析にも応用可能。
In the global GX context
This paper applies LLM to assess carbon disclosure quality in China's high-emission sector, relevant for global supply chain transparency. It demonstrates high reliability of AI-based scoring, which could inform TCFD/ISSB-aligned disclosure assessments. However, it is China-specific and does not directly address global frameworks.
👥 読者別の含意
🔬研究者:Methodology for using LLMs to automatically evaluate carbon disclosure quality with high reliability; applicable to other sectors and regions.
🏢実務担当者:Insight into Chinese petroleum firms' disclosure practices; useful for benchmarking if operating in or sourcing from China.
🏛政策担当者:Demonstrates potential of AI for monitoring disclosure compliance; relevant for regulators considering automated oversight.
📄 Abstract（原文）
Global climate governance and corporate low-carbon transition have made carbon information disclosure important for assessing firms’ environmental governance and climate-risk responses. This study develops an industry-specific carbon information disclosure quality (CIDQ) framework for Chinese A-share listed petroleum and petrochemical firms, using 45 firm-year observations from 15 firms during 2022–2024. The framework includes 7 primary, 15 secondary, and 33 tertiary indicators. Disclosure texts were scored by the DeepSeek-V3.2 large language model (LLM) under predefined rule-based criteria, with temperature set to 0. Reliability was assessed against manual scoring of 15 reports, yielding an intraclass correlation coefficient (ICC) of 0.974. The full-sample mean score is 34.02, accounting for only 51.55% of the theoretical maximum of 66, indicating that the overall disclosure level remains relatively low. The annual mean score increased from 29.07 in 2022 to 37.60 in 2024, representing a cumulative rise of 8.53 points, or 29.34%. Substantial inter-firm differences are also observed: Sinopec recorded the highest three-year average score of 52.67, whereas Yunnan Yunwei recorded the lowest at 10.67. This study may provide a methodological reference for structured CIDQ evaluation and disclosure improvement in high-emission industries.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18105089first seen 2026-06-29 08:15:18
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