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AIMER: A reproducible AI-assisted protocol for converting sustainability reports into ESG evidence and decision models

AIMER: サステナビリティ報告書をESGエビデンスと意思決定モデルに変換する再現可能なAI支援プロトコル (AI 翻訳)

Nophea Sasaki

Mendeley Dataデータセット2026-05-26#AI×ESGOrigin: Global
DOI: 10.17632/vmyfg2zd2y
原典: https://doi.org/10.17632/vmyfg2zd2y

🤖 gxceed AI 要約

日本語

AIMERは、企業のサステナビリティ報告書から構造化されたESGデータを抽出するための再現可能なAI支援プロトコルを提案する。文書取得、前処理、AI抽出、コーディング、検証、エビデンス統合のワークフローを公開し、透明性と再現性を重視する。このプロトコルは、研究者や実務者がESG情報を分析し、意思決定に活用するための基盤を提供する。

English

AIMER proposes a reproducible AI-assisted protocol to extract structured ESG data from corporate sustainability reports. It details the workflow for document acquisition, preprocessing, AI extraction, coding, validation, and evidence synthesis, emphasizing transparency and reproducibility. The protocol provides a foundation for researchers and practitioners to analyze ESG information and support decision-making.

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基準に基づく開示が進む中、AIMERのようなAI支援プロトコルは、有価証券報告書や統合報告書からの一貫性あるデータ抽出と分析に有用。特にScope3算定やTCFD開示の実務において、再現性あるエビデンス構築に貢献できる。

In the global GX context

In the global context of ISSB, CSRD, and SEC climate disclosure rules, AIMER offers a standardized, reproducible method to convert diverse sustainability reports into structured analytics. This addresses the need for reliable, comparable ESG data for investors and regulators, facilitating evidence-based policy and transition finance.

👥 読者別の含意

🔬研究者:Provides a validated, open-source protocol for reproducible ESG data extraction, enabling comparative studies and meta-analyses in sustainability accounting.

🏢実務担当者:Offers a systematic approach to automate ESG data collection from public reports, improving efficiency and consistency in corporate disclosure management.

🏛政策担当者:Illustrates how AI can enhance transparency and comparability in ESG disclosures, informing regulatory design for digital reporting standards.

📄 Abstract（原文）

AIMER (AI-assisted Method for ESG Report Interpretation and Reproducible analysis) is a reproducible research protocol for converting publicly accessible sustainability reports and corporate ESG disclosures into structured ESG evidence, coded datasets, and decision-oriented analytical outputs. The dataset supports the accompanying MethodsX manuscript by documenting the methodological workflow used for document acquisition, preprocessing, AI-assisted extraction, coding, validation, and evidence synthesis across corporate sustainability disclosures. The study uses publicly available sustainability reports, ESG reports, annual reports, and related corporate disclosures published online by reporting organizations. The dataset does not redistribute the original corporate reports because copyright remains with the respective publishers. Instead, the repository focuses on reproducible research outputs, including coded variables, extraction protocols, methodological documentation, source inventories, metadata fields, and validation structures necessary to support transparency and replication. Where available, the dataset records company names, report years, report titles, public URLs, and access dates to facilitate verification and reproducibility. AI-assisted tools, Python-based processing workflows, and human-reviewed coding procedures were used to support document interpretation and structured evidence extraction. Final analytical decisions, validation, and manuscript preparation were conducted under human supervision. This repository is intended for academic research, methodological transparency, ESG evidence synthesis, and reproducible sustainability analytics.

🔗 Provenance — このレコードを発見したソース

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