A Disclosure-Based Method for Measuring Non-Financial Evidence Infrastructure: N4/N5 Axes, ΔN, and Multi-Provider LLM Scoring

This preprint documents a disclosure-based research method for measuring the maturity of non-financial evidence infrastructure in corporate sustainability disclosures. The framework introduces two scoring axes — N4 (Business-Environment Integration) and N5 (Governance Integration, 0–5) — alongside ΔN (year-over-year score difference), four regression type classifications (Target Retreat, Governance Retreat, Narrative Retreat, Broad Deterioration), and a multi-provider LLM consensus scoring pipeline using four major AI providers. A six-firm FY2025–FY2026 pilot on Japanese listed companies validates the pipeline and illustrates the source-mismatch failure mode that makes ΔN uninterpretable when the underlying document type changes between years. The purpose of this note is to document the research methodology transparently and to provide a reproducible, citable reference in the domain of AI-assisted sustainability disclosure evaluation. The method is disclosure-based and does not depend on any specific company, product, patent, or proprietary system.