AI支援型製品カーボンフットプリント算定システムの評価基準：マッピングとサプライチェーン・モデリングの事例
Evaluation criteria for AI-assisted product carbon footprinting systems: the cases of mapping and supply chain modeling (原題)
Shaena Ulissi, Andrew Dumit, P. James Joyce, Jacob Feintzeig, Krishna Rao, Steven Watson, Sangwon Suh
🤖 gxceed AI 要約
日本語
LLMを用いた製品カーボンフットプリント（PCF）自動化システムの品質・文書化・不確実性表示・検証可能性を評価する基準を、標準レビューと実務家・検証機関への聞き取り、試行を通じて開発した。基準は透明な論理、専門家判断とのベンチマーク、不確実性の特性化を軸に構成される。2つの実運用システムに適用した結果、明確な入力のマッピング受容率は91.2%に対し曖昧な入力は59.7%、クレードル・トゥ・ゲートでは269件のEPDに対し中央値相対誤差33%であった。
English
The authors develop evaluation criteria for AI-assisted product carbon footprinting (PCF) systems, covering quality, documentation, uncertainty indication, and reviewability, built from standards review, stakeholder elicitation, and pilot testing. Criteria center on transparent logic, benchmarking against expert judgment, and uncertainty characterization. Applied to two deployed systems, well-specified inputs reached 91.2% expert-accepted mapping versus 59.7% for vague inputs, and cradle-to-gate modeling showed a 33% median relative error against 269 EPDs.
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
Scope 3算定の自動化・効率化は日本企業のSSBJ対応や有報でのサプライチェーン排出開示の実務課題に直結する。AI-CFシステムの品質評価基準は、第三者検証や統合報告書での信頼性確保の観点から日本企業がAIツールを導入する際の目利き基準として有用。
In the global GX context
As ISSB/CSRD and Scope 3 disclosure obligations expand, AI-assisted PCF tools are entering corporate reporting workflows without agreed quality benchmarks. This paper offers a rare operational evaluation framework—transparent logic, expert benchmarking, uncertainty characterization—that regulators, verifiers, and assurance providers can adapt for AI-generated emissions data.
👥 読者別の含意
🔬研究者:AI×カーボン会計の評価手法として、専門家ベンチマークと不確実性特性化の設計を参照できる。
🏢実務担当者:AI-PCFツール導入時の品質・検証可能性の目利き基準として、入力の明確化とコンポーネント別検証の重要性を示す。
🏛政策担当者:AI生成の排出データに対する検証・保証の枠組み設計の参考になる。
📄 Abstract（原文）
Abstract Large language models (LLMs) are increasingly used to automate components of product carbon footprinting (PCF). However, the carbon footprinting literature lacks criteria for evaluating the quality, documentation, uncertainty indication, and reviewability of AI-assisted systems for specific PCF use cases. We developed a set of evaluation criteria for AI-assisted carbon footprinting (AI-CF) through standards review, structured stakeholder elicitation with corporate practitioners and verification specialists, and iterative pilot testing. The resulting criteria are organized around transparent logic, benchmarking against expert judgment, and uncertainty characterization. The criteria were then applied to two use cases: automated life-cycle-inventory proxy mapping and cradle-to-gate PCF generation. Applying the criteria to two deployed proprietary AI-CF systems demonstrates that the framework is operational and can identify where human review or better data are most needed. For automated mapping, well-specified (“non-vague”) inputs achieved a 91.2% expert-accepted mapping rate, whereas inputs with ambiguous or incomplete descriptions (“vague”; e.g., internal product codes or generic descriptors) achieved 59.7%; repeat-run entropy increased with input ambiguity. For cradle-to-gate modeling, end-to-end benchmarking against 269 Environmental Product Declarations yielded a 33% median relative error, while component-level benchmark checks, though unvalidated, showed relatively strong pass rates for proxy mappings, hotspots, and direct emissions and weaker performance for key input rates. The framework provides a practical reference for AI-CF developers, users, and reviewers to assess AI-CF system and output quality for these use cases.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s44498-026-00196-9first seen 2026-11-07 05:04:35
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