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

X 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.