Large Language Models And The Measurement Of Climate Disclosure: Evidence From Tcfd Conformity
大規模言語モデルと気候情報開示の測定：TCFD準拠のエビデンス (AI 翻訳)
Abdullah Albizri, Ahmad Jumah
🤖 gxceed AI 要約
日本語
本研究は、大規模言語モデル（LLM）を用いて企業のサステナビリティ報告書からTCFDフレームワークへの準拠度を測定する手法を開発。米国上場企業を対象に分析した結果、TCFD準拠度が高い企業ほどESG格付けが高く、将来のESG格付けも予測できることが明らかになった。透明性が高く拡張可能な手法を提供する。
English
This study develops a large language model (LLM) approach to measure firms' conformity with the TCFD framework from unstructured sustainability reports. Analyzing a sample of U.S.-listed firms, it finds that higher TCFD conformity is associated with stronger current and future ESG ratings, particularly on environmental dimensions. The method offers a transparent, scalable solution for climate disclosure analysis.
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基準の策定が進む中、TCFDに基づく開示の質を自動測定する手法は、企業の開示改善や投資家対応に有用。本研究のLLMアプローチは、日本の上場企業にも適用可能であり、SSBJや有報での気候関連開示の評価に貢献し得る。
In the global GX context
This paper provides a novel LLM-based method for measuring TCFD conformity, which is directly relevant to the global shift toward mandatory climate disclosure (ISSB, CSRD, SEC). It offers a transparent alternative to proprietary ESG ratings, enabling regulators and investors to assess disclosure quality at scale.
👥 読者別の含意
🔬研究者:Demonstrates the application of LLMs to measure TCFD conformity, offering a reproducible methodology for climate disclosure quality assessment.
🏢実務担当者:Provides a scalable tool for benchmarking own TCFD disclosures against peers and understanding how disclosure quality influences ESG ratings.
🏛政策担当者:Highlights the potential for automated monitoring of TCFD conformity, supporting regulatory oversight of climate-related financial disclosures.
📄 Abstract（原文）
Sustainability reports are increasingly used to communicate firms' environmental commitments, yet measuring the quality of these disclosures remains difficult.Existing approaches often rely on manual coding or proprietary ESG ratings, both of which have important limitations.This study develops a large language model (LLM) approach to measure firms' conformity with the Task Force on Climate-Related Financial Disclosures (TCFD) framework using unstructured sustainability reports.The approach incorporates salience cues related to governance, strategy, risk management, and metrics to capture both the presence and emphasis of climate-related disclosures.Using a sample of U.S.-listed firms, we examine whether TCFD conformity is associated with current and future ESG ratings.Results show that firms with higher TCFD conformity tend to receive stronger ESG ratings, especially on the environmental dimension, and that TCFD conformity also predicts future ESG ratings.The study provides a transparent, scalable method for analysing climate disclosure.
🔗 Provenance — このレコードを発見したソース
- openalex https://aisel.aisnet.org/treos_ecis2026/59first seen 2026-06-10 04:50:12
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