Computability Is Not Uniform: Axis-Specific Frictions in Corporate Sustainability Disclosure Infrastructure
計算可能性は一様でない：企業サステナビリティ開示インフラにおける軸別の摩擦 (AI 翻訳)
Kokubu, Hiroyuki
🤖 gxceed AI 要約
日本語
日本のプライム市場89社のGHG開示データを対象に、機械可読性、単位標準化、APIアクセシビリティの3軸で計算可能性支援スコア（CSS）を構築。機械可読性と単位標準化は92.1%と88.8%の合格率だが、APIアクセシビリティは47.2%と低い。手動検証により誤分類を修正し、CSSの下限平均は0.760。開示インフラの非一様な計算可能性を診断する手法を提案。
English
This data note examines the computability of GHG disclosure from 89 Japanese Prime Market firms using a Computability Support Score (CSS) across three axes: machine readability, unit normalization, and API accessibility. Machine readability and unit normalization show high pass rates (92.1% and 88.8%), while API accessibility is a bottleneck (47.2%). Manual verification corrected false zeros, yielding a lower-bound mean CSS of 0.760. The study argues for axis-specific diagnosis rather than blanket conclusions about disclosure computability.
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が開示基準を策定中であり、本稿の計算可能性診断手法は開示インフラ整備に示唆を与える。特にAPIアクセシビリティの課題は電子開示システム改善に直結する。
In the global GX context
Globally, as TCFD, ISSB, and CSRD push for machine-readable disclosure, this study provides a diagnostic framework to identify specific infrastructure gaps. The finding that API accessibility is the primary bottleneck is relevant for regulators designing digital disclosure systems.
👥 読者別の含意
🔬研究者:Offers a methodology (CSS) to diagnose computability at axis level, useful for future studies on disclosure infrastructure.
🏢実務担当者:Manual verification of PDF disclosures can correct false negatives; API accessibility is a critical weakness for data users.
🏛政策担当者:Provides evidence that API accessibility is a key bottleneck in Japan's disclosure infrastructure, guiding policy for mandatory digital filing.
📄 Abstract（原文）
Data Note 003 — A Computational Substrate Audit of GHG Disclosure across 89 Japanese Prime Market Firms This data note examines whether corporate sustainability disclosure provides a computable substrate for downstream analysis. Using 89 Japanese Prime Market firms, the study constructs a Computability Support Score (CSS) over three verified primary axes: machine readability, unit normalization, and API accessibility. The central finding is diagnostic rather than dismissive. Corporate disclosure is not uniformly non-computable; instead, computability support is unevenly distributed across infrastructure axes. Machine readability and unit normalization are substantially stronger than an initial machine-only classification suggested, with pass rates of 92.1% and 88.8%, respectively. By contrast, API accessibility emerges as the primary bottleneck, with a pass rate of 47.2%. Manual verification of voluntary PDF disclosures corrected 35 false-zero classifications in the unit-normalization axis, demonstrating that a structured-filing-only audit can overstate substrate failure if it ignores evidence available in voluntary reporting artifacts. The resulting three-axis Primary CSS has a conservative lower-bound mean of 0.760 and a median of 0.667, indicating that the median firm passes two of three verified axes while failing on API accessibility. Audit trail is retained only as a secondary lower-confidence diagnostic axis, and time-series retrievability is treated as exploratory rather than quantitative due to mixed evidence classes. The note argues that CSS should be read as a diagnostic instrument that localizes where computational substrate integrity breaks, not as a blanket verdict that disclosure is unusable.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20837995first seen 2026-07-18 00:30:23
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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。