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サブプロジェクト報告1：製品開発におけるサステナビリティ要因評価のための手法とツール

Subproject Report 1: Methods and Tools for Assessing Sustainability Factors in Product Development (原題)

Tizian Latzka

Publikationsserver (Universitat Marburg)ジャーナル2026-10-20#AI×ESG経営インパクト: 調達リスク対象セクター: manufacturing
DOI: 10.60948/opus-1442
原典: https://doi.org/10.60948/opus-1442

🤖 gxceed AI 要約

日本語

製品の環境影響の最大80%は開発初期段階で確定するが、詳細なBOMを要する従来LCAは設計者にとって使いにくいという「エコデザインのパラドックス」がある。本報告はPRISMA準拠のAI支援文献レビューと8種のツール実証評価（ステンレス水筒を参照製品）を通じ、専門LCAソフトのユーザビリティ欠陥（SUS≤42.5、180分以上）と手動LCI代理マッピングのボトルネックを特定した。これを14の要件に落とし込み、LLM対話・マルチエージェント・RAG（オープンLCIリポジトリ連携）と決定論的計算エンジンを組み合わせた三層アーキテクチャを提案する。

English

Up to 80% of a product's lifecycle impacts are locked in early design, yet traditional LCA demands full BOMs and expert knowledge—the 'Ecodesign Paradox.' Using an AI-assisted PRISMA review and empirical testing of eight tool archetypes on a stainless-steel bottle reference product, the authors find expert LCA software suffers usability deficits (SUS ≤ 42.5), long evaluation times (≥180 min), and manual LCI proxy mapping as the key bottleneck. They derive 14 requirements and propose a three-tier architecture combining an LLM chat interface, multi-agent orchestration, and a RAG pipeline over open LCI repositories, with deterministic calculation offloaded to an external engine under human-in-the-loop verification.

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・Scope3開示が進む日本企業にとって、設計段階での製品単位環境影響の自動算定は一次データ収集負荷の軽減に直結する。LCA専門人材が限られる中堅製造業の開示対応力強化に資する。

In the global GX context

Aligns with global moves toward product-level and Scope 3 disclosure (CSRD/ESRS E1, ISSB, digital product passports), where AI-assisted, non-expert-accessible LCA can lower the data-collection barrier for upstream value-chain emissions.

👥 読者別の含意

🔬研究者:AI×LCA統合の要件定義とアーキテクチャ設計の参照点として、RAG＋決定論的計算の分離設計が研究上有用。

🏢実務担当者:設計初期に非専門家でも製品環境影響を概算できるツール選定・導入の判断材料になる。

🏛政策担当者:製品単位の環境データ整備やデジタル製品パスポート政策における評価ツール標準化の示唆。

📄 Abstract（原文）

Up to 80% of a product's lifecycle environmental impacts are locked in during the early phases of product development. However, designers face the "Ecodesign Paradox": early conceptual stages offer the highest design freedom but lack detailed product specifications, whereas traditional Life Cycle Assessment (LCA) tools require complete Bills of Materials (BOMs) and specialized domain expertise. This creates a severe data availability and usability gap in the iterative design workflow. To resolve this gap, this subproject report establishes a structured requirements profile and proposes a conceptual framework for an AI-assisted sustainability assessment tool tailored to non-LCA experts. Methodologically, an AI-supported systematic literature review following PRISMA guidelines was conducted using an academic Retrieval-Augmented Generation (RAG) knowledge base. Subsequently, eight representative assessment tool archetypes were empirically evaluated using a standardized multi-material stainless steel water bottle reference product within a two-pillar framework assessing Technical Interoperability (Pillar 1) alongside Usability, Time-to-Result, and Domain Knowledge Barriers (Pillar 2). The empirical evaluation revealed that current expert LCA software suffers from critical usability deficits (SUS ≤ 42.5), extended evaluation durations (≥ 180 min), and steep domain barriers, with manual Life Cycle Inventory (LCI) proxy mapping identified as the primary operational bottleneck. Through a TOWS strategic alignment linking internal tool weaknesses with external AI opportunities, these findings were translated into 14 explicit functional, technical, and user interface requirements (FR1–FR5, TR1–TR5, UIR1–UIR4). Based on these requirements, a three-tier, decoupled conceptual architecture is proposed. The framework combines a natural language chat interface (LLM) for conversational interaction and multi-agent workflow orchestration with a RAG pipeline linked to vector databases of open LCI repositories (e.g., ProBas, BAFU, ELCD) for automated semantic proxy matching. To eliminate generative hallucinations, numerical impact calculations are offloaded to an external, deterministic calculation engine (e.g., via openLCA API). The architecture operates under a Human-in-the-Loop paradigm with explicit engineering verification gates. This report defines the theoretical and architectural baseline (Subproject 1) for the subsequent programmatic implementation and prototype validation in Subproject 2.

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