多言語・リアルタイム供給網における低遅延ニューラル言語処理による製品カーボンフットプリントの自動推計
Automated Product Carbon Footprint Estimation via Low-Latency Neural Language Processing in Multilingual, Real-Time Supply Chains (原題)
Shixiong Guo, Xinzhi Wang
🤖 gxceed AI 要約
日本語
本論文はAuto-LCAを提案する。多言語NLPで非構造化文書からライフサイクル属性を抽出し、機械学習で欠損インベントリを補完、「カーボンヘッダー」で製品別排出量をサプライチェーン階層間で伝播させる。鉄鋼・セメント・自動車の事例で従来LCA比±4%以内の精度を示し、デジタル製品パスポートや開示要件にも対応する。
English
This paper proposes Auto-LCA, a system architecture for continuous product-level carbon estimation combining automated supply-chain data acquisition, low-latency multilingual NLP for extracting life-cycle attributes from unstructured documents, and ML-based completion of missing inventory parameters. A 'carbon header' propagates version-controlled embodied and operational emissions across supply-chain tiers. Case studies in steel, cement, and automotive show estimates within ±4% of conventional LCA benchmarks, supporting digital product passports and disclosure needs.
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Scope 3開示、サプライチェーン排出量算定の実務負担軽減に直結する。ERP/PLM連携やデジタル製品パスポート対応は、日本企業の一次データ収集・取引先エンゲージメントの高度化に示唆を与える。
In the global GX context
Directly relevant to global disclosure regimes (ISSB/CSRD/CBAM) that demand verifiable, granular Scope 3 and product-level carbon data. The API-based, machine-readable carbon metadata approach advances the digital product passport and interoperable PCF exchange agenda central to EU and international supply-chain decarbonization policy.
👥 読者別の含意
🔬研究者:AI×LCA統合のシステム設計と、NLP・MLによる欠損補完の精度検証手法が参考になる。
🏢実務担当者:ERP/PLM連携でScope 3・PCF算定を自動化し、取引先データ収集コストを削減する実装指針となる。
🏛政策担当者:デジタル製品パスポートや開示制度における機械可読な炭素データ標準の設計に示唆を与える。
📄 Abstract（原文）
Real-time product carbon footprint (PCF) reporting is increasingly required in global supply chains, yet conventional life cycle assessment (LCA) remains difficult to scale due to fragmented multitier data, heterogeneous formats, and substantial manual effort. This article proposes Auto-LCA, a system-level architecture for continuous, product-level carbon estimation that integrates (i) automated supply-chain data acquisition, (ii) low-latency multilingual natural language processing for extracting life-cycle-relevant attributes from unstructured documents, (iii) a structured product carbon data model, and (iv) machine-learning-based completion of missing life cycle inventory parameters with provenance and confidence tags. A central mechanism is the "carbon header," which packages accumulated embodied and operational emissions as transferable, version-controlled metadata associated with product identifiers and propagated incrementally across supply-chain tiers. Auto-LCA is designed as an open Application Programming Interface (API) layer to interface with enterprise systems (e.g., ERP/PLM/MES) and supports update-driven recomputation that recalculates only affected nodes in the supply-chain graph, improving scalability for large product portfolios and deep supplier networks. Case studies in the steel, cement, and automotive sectors demonstrate that Auto-LCA yields estimates close to representative conventional LCA benchmarks (typically within ±4%), while enabling broader coverage and more frequent updates under realistic data availability constraints. The framework also aligns with emerging digital product passport and sustainability reporting needs by enabling traceable, machine-readable carbon metadata exchange across organizations.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1177/2167647x261481461first seen 2026-09-11 05:03:03
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