AI駆動型炭素・グリーン会計：アーキテクチャ、応用、そしてAIサステナビリティ・パラドックス
AI-driven carbon and green accounting: architectures, applications, and the AI sustainability paradox (原題)
Qiao-Rong Yang
🤖 gxceed AI 要約
日本語
AIと炭素・グリーン会計の融合領域を扱った15件の研究を批判的にレビュー。データ中心型、オントロジー駆動型、リアルタイムタグ付け型の3つの技術経路を特定した。AIが精緻な炭素測定を可能にする一方、その学習・推論自体が大量排出を伴うという「AIサステナビリティ・パラドックス」を指摘し、純サステナビリティ・インテリジェンス枠組みを提唱する。
English
A critical review of 15 studies at the intersection of AI, carbon accounting, and green accounting. It identifies three technical pathways: data-centric systems, ontology-driven hybrids for SMEs, and real-time tagging platforms. The paper highlights the 'AI sustainability paradox'—AI enables finer carbon measurement while its own training and inference generate emissions—and proposes the Net Sustainability Intelligence framework.
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
SSBJ基準や有報でのScope3開示が進む日本企業にとって、AIを用いた炭素会計の自動化・精緻化は実務的関心が高い。特にSME向けオントロジー駆動型アプローチは、サプライチェーン全体の排出量把握を求める日本企業の課題に直結する。
In the global GX context
As ISSB and CSRD push for granular Scope 3 disclosure, this review maps how AI can automate and refine carbon accounting—directly relevant to global disclosure infrastructure. The AI sustainability paradox also raises questions for TCFD/ISSB reporters about the carbon cost of their own digital tools.
👥 読者別の含意
🔬研究者:AI×炭素会計の研究動向を整理し、未解決の方法論的課題と新たな分析枠組みを提示する。
🏢実務担当者:AIによる炭素会計自動化の技術選択肢と、SMEサプライチェーンへの適用可能性を評価できる。
🏛政策担当者:AI利用に伴う排出量の把握・規制の必要性と、炭素会計デジタル化の政策支援の方向性を示唆する。
📄 Abstract（原文）
Carbon accounting and green accounting have converged with artificial intelligence over the past five years, yet the resulting literature remains split across research communities that rarely cite one another. This study adopts a structured critical review rather than an exhaustive systematic review. A curated corpus of fifteen studies published between 2020 and 2026 is examined to identify recurring technological pathways, methodological patterns, and unresolved accounting issues at the intersection of AI, carbon accounting, and green accounting. Three technical pathways emerge. Data-centric systems fuse enterprise resource planning records, sensor streams, and machine-learning emission prediction. Ontology-driven hybrids target small and medium enterprises through semantic gap-filling under sparse data. Real-time tagging platforms combine blockchain traceability, digital twins, and behavioural personalisation to push product-level carbon information to consumers. Empirical correlations between green accounting practice and economic outcomes are documented across multiple country contexts, alongside self-reported carbon footprint reductions of up to sixty percent that carry very-low certainty. A cross-cutting tension runs through the corpus: AI simultaneously enables finer-grained carbon measurement and draws on emissions-intensive training and inference workloads. The review responds with the Net Sustainability Intelligence framework, an analytical lens that formalises the relationship between the carbon insight produced by an AI-enabled accounting system and the carbon cost of producing that insight, and sketches three architectural extensions whose operational details remain open for future empirical work.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://jaeps.ewapub.com/article/view/37242.pdffirst seen 2026-09-23 05:41:45 · last seen 2026-09-29 05:29:42
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