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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.1
原典: https://doi.org/10.17632/vmyfg2zd2y.1

🤖 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ESGエビデンスを抽出し、構造化データセットと意思決定モデルを生成する再現可能な研究プロトコルである。AIツールと人によるレビューを組み合わせ、透明性と再現性を確保する。本手法はESG情報の標準化と分析に貢献する。

English

AIMER is a reproducible protocol for converting sustainability reports into structured ESG evidence and decision models. It combines AI-assisted extraction with human validation to ensure transparency and replicability. The method supports standardized ESG data analysis for research and practice.

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基準に対応したESG情報開示が進むが、本プロトコルは報告書からのデータ抽出の効率化に寄与し、日本の実務にも応用可能である。

In the global GX context

As global disclosures expand under ISSB, CSRD, and SEC rules, AIMER offers a reproducible method to extract structured data from diverse reports, supporting evidence synthesis and decision-making.

👥 読者別の含意

🔬研究者:Researchers can use AIMER to systematically extract ESG variables from large corpora of reports for empirical studies.

🏢実務担当者:Corporate sustainability teams can adopt the protocol to streamline reporting and ensure data consistency.

🏛政策担当者:Regulators can leverage the approach to monitor disclosure quality and promote comparability.

📄 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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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。