アパレル向けAI駆動型グラニュラーLCA：持続可能な設計と循環性のための製品別GHG排出量の定量化
AI-driven granular LCA for apparel: Quantifying product-specific GHG emissions for sustainable design and circularity (原題)
Dami Moon, Sumiaki Kusahata, Yoshihiro Kawahara
🤖 gxceed AI 要約
日本語
本研究は、C2Cプラットフォーム上の非構造化UGCからVision-Language Modelを用いて素材組成やサイズなどの製品属性を抽出し、製品別GHG排出量を自動算出する手法を提案・検証した。3,500件のアパレル出品に適用し、サイズ抽出92.3%、素材組成81.6%の精度を達成、平均排出量は11.9 kg-CO2eq/点と算出された。素材選択の影響を可視化し、EUのデジタル製品パスポート（DPP）等の政策に対応する情報透明性向上に資する。
English
This study proposes and validates an automated method using a Vision-Language Model to extract product-specific attributes (e.g., fabric composition) from unstructured user-generated content on C2C platforms and calculate product-specific GHG emissions. Applied to 3,500 apparel listings, the VLM achieved 92.3% accuracy for size and 81.6% for fabric composition, yielding an average of 11.9 kg-CO2eq per item. The granularization of material composition highlights the impact of material selection and supports sustainable design, circularity, and alignment with the EU Digital Product Passport.
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開示が進む日本企業にとって、UGCを活用した製品別排出量の自動算定は、サプライチェーン上流のデータ空白を補完する実践的手法として参考になる。特にアパレル・繊維業界の統合報告書における製品別環境負荷の開示高度化に寄与し得る。
In the global GX context
As global disclosure frameworks (CSRD, ISSB) move toward product-level and value-chain transparency, this work demonstrates how AI can operationalize granular LCA from unconventional data sources. It directly supports emerging instruments like the EU Digital Product Passport and offers a scalable model for product-specific GHG accounting in consumer goods.
👥 読者別の含意
🔬研究者:VLMとLCAを組み合わせた製品別排出量推定の新規手法と、UGCデータの活用可能性・限界を提示する。
🏢実務担当者:自社製品の素材組成データが不完全な場合でも、AIを用いて製品別GHGを推定し、DPP対応やScope 3開示に活用できる可能性を示す。
🏛政策担当者:DPP等の製品別情報開示政策の実現可能性を裏付けるとともに、非構造化データ活用の課題を提示する。
📄 Abstract（原文）
Product-specific Life Cycle Assessment (LCA) for apparel is hindered by generalized data, failing to quantify unique environmental burdens crucial for sustainable production and consumption. This study addresses this challenge by proposing and validating an automated methodology using a Vision-Language Model (VLM) to extract product-specific details (e.g., fabric composition) from unstructured User-Generated Content (UGC) on consumer-to-consumer (C2C) platforms and subsequently calculate corresponding product-specific greenhouse gas (GHG) emissions. The study seeks to demonstrate the significance of AI-enabled product-specific information as a tool for deriving insights into sustainable production, cleaner manufacturing, and resource efficiency. Applying this framework to 3500 apparel listings from a major C2C marketplace, our VLM demonstrated high accuracy in extracting physical attributes like garment size (92.3%) and fabric composition (81.6%). The subsequent LCA quantified an average emission of 11.9 kg-CO 2 eq/item. More importantly, by analyzing the product-related parameters required for these computations, we attempted to extract and structure data on product attributes and user behavior from unstructured UGC, enabling more effective estimation of product-specific GHG emissions. Although challenges remain in precisely inferring complex user behaviors from unstructured data, the successful granularization of material composition highlights the significant impact of material selection, providing actionable insights for sustainable eco-design and circularity. Ultimately, by significantly enhancing information transparency across the value chain, our work directly aligns with emerging global policies like the EU Digital Product Passport (DPP), fostering data-driven decision-making for cleaner production goals.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.jclepro.2026.149664first seen 2026-10-28 05:03:08 · last seen 2026-11-06 05:03:10
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