ESG Reporting Lifecycle Management with Large Language Models and AI Agents
大規模言語モデルとAIエージェントを用いたESG報告ライフサイクル管理 (AI 翻訳)
Thong Hoang, Mykhailo V. Klymenko, Xiwei Xu, Shidong Pan, Yi Ding, Xushuo Tang, Zhengyi Yang, Jieke Shi, David Lo
🤖 gxceed AI 要約
日本語
本研究は、ESG報告ライフサイクル管理のためのAIエージェントフレームワークを提案する。複数のエージェントが情報抽出、検証、報告書更新を自動化し、静的な報告プロセスを動的で適応的なシステムに変革する。技術要件と品質属性を定義し、単一モデル、単一エージェント、マルチエージェントの3つのアーキテクチャを提示する。
English
This paper introduces an agentic ESG lifecycle framework that integrates multiple AI agents to automate ESG reporting tasks such as information extraction, verification, and report updating. It transforms static ESG reporting into a dynamic, accountable system. The study defines technical requirements and presents three architectural approaches: single-model, single-agent, and multi-agent.
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
本フレームワークは、日本の有価証券報告書や統合報告書におけるESG開示の効率化に貢献し得る。特に、SSBJ基準への対応や投資家向け情報提供の自動化に応用が期待される。
In the global GX context
This paper advances global ESG disclosure scholarship by demonstrating how AI agents can address challenges in data consistency and adaptability, supporting frameworks like ISSB and CSRD. It offers a practical path toward automated, real-time sustainability reporting.
👥 読者別の含意
🔬研究者:The agentic framework provides a novel integration of AI into ESG lifecycle management, offering a foundation for future research on automation and accountability.
🏢実務担当者:Corporate sustainability teams can leverage the multi-agent architecture to streamline report generation, validation, and cross-report comparison.
📄 Abstract（原文）
Environmental, Social, and Governance (ESG) standards have been increasingly adopted by organizations to demonstrate accountability towards ethical, social, and sustainability goals. However, generating ESG reports that align with these standards remains challenging due to unstructured data formats, inconsistent terminology, and complex requirements. Existing ESG lifecycles provide guidance for structuring ESG reports but lack the automation, adaptability, and continuous feedback mechanisms needed to address these challenges. To bridge this gap, we introduce an agentic ESG lifecycle framework that systematically integrates the ESG stages of identification, measurement, reporting, engagement, and improvement. In this framework, multiple AI agents extract ESG information, verify ESG performance, and update ESG reports based on organisational outcomes. By embedding agentic components within the ESG lifecycle, the proposed framework transforms ESG from a static reporting process into a dynamic, accountable, and adaptive system for sustainability governance. We further define the technical requirements and quality attributes needed to support four main ESG tasks, such as report validation, multi-report comparison, report generation, and knowledge-base maintenance, and propose three architectural approaches, namely single-model, single-agent, and multi-agent, for addressing these tasks. The source code and data for the prototype of these approaches are available at https://gitlab.com/for_peer_review-group/esg_assistant.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.semanticscholar.org/paper/78a02dd46073ff6a4f874d76ad42ed1120bbb8bafirst seen 2026-05-05 23:28:59
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。