Sustainable Product Development and Production with AI and Knowledge Graphs
AIとナレッジグラフによる持続可能な製品開発と生産 (AI 翻訳)
Svenja Hauck, Lucas Greif
🤖 gxceed AI 要約
日本語
本稿は、持続可能性評価におけるナレッジグラフとAIの役割をレビューし、製品開発での環境影響比較を可能にする枠組みを紹介する。カーボンフットプリント分析のケーススタディも提示。データ相互運用性の向上と意思決定支援に貢献する。
English
This article reviews the role of knowledge graphs and AI in sustainability assessment, enabling comparison of environmental impacts during product development. A case study demonstrates carbon footprint analysis using these technologies, highlighting improved data interoperability and decision support.
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
日本ではSSBJ開示やScope 3算定に向け、製品レベルでのカーボンフットプリント把握が課題。本稿のAIとナレッジグラフによる評価手法は、データ連携と自動化の面で実務に示唆を与える。
In the global GX context
Globally, the paper addresses the need for efficient, interoperable sustainability assessment tools in product design, supporting frameworks like CSRD and the Product Environmental Footprint (PEF). It illustrates how AI and knowledge graphs can accelerate carbon footprint analysis and enhance decision-making.
👥 読者別の含意
🔬研究者:GX researchers can gain insights into applying knowledge graphs for sustainability metrics and identify research gaps in AI-driven lifecycle assessment.
🏢実務担当者:Corporate sustainability and product development teams can leverage knowledge graph-based approaches to streamline carbon footprint assessment and improve data interoperability.
🏛政策担当者:Policymakers may note the potential of AI-assisted sustainability assessment to support regulatory reporting, though standardization and data quality remain key challenges.
📄 Abstract（原文）
Abstract Knowledge graphs and AI enable rapid sustainability assessment in product development. Knowledge graphs structure interconnected semantic information, enhancing data interoperability and managing complex relationships. AI leverages this to automate analysis and aid decision-making. By embedding sustainability metrics, the environmental impacts of choices in product development can be compared. This article reviews knowledge graphs in sustainability assessment and AI’s role in enhancing these capabilities. It also presents a case study on carbon footprint analysis through knowledge graphs and AI.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1515/zwf-2024-0119first seen 2026-08-02 17:17:13
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。