A Service-Based Approach for Predicting the Carbon Footprint in the Supply Chain of Plastic Injection-Moulded Parts During Product Development
製品開発中の射出成形プラスチック部品のサプライチェーン炭素フットプリント予測のためのサービスベースアプローチ (AI 翻訳)
Lukas Nagel, Rainer Gerstbauer, Levon Harutyunyan, Thomas Trautner, Friedrich Bleicher, Matthias Weigold
🤖 gxceed AI 要約
日本語
本論文は、製品開発の戦略計画段階でサプライチェーン全体の炭素フットプリントを予測するデータ提供手法を提案する。EUの気候中立目標に対応し、Gaia-Xデータ基盤を活用したセキュアなサービス提供とデータ共有を実現する。ウェブアプリケーションとして実装され、射出成形カップのサプライチェーンを事例に、生産パラメータが炭素排出に与える影響を評価する。これにより、炭素最適化された製品開発と後期生産段階での炭素回避を可能にする。
English
This paper proposes a data-provision method to predict carbon footprints across supply chains during the strategic planning stage of product development, aligning with EU climate neutrality goals. Leveraging the Gaia-X data infrastructure, it enables secure service offerings and data sharing. Implemented as a web application, it is applied to an injection-moulded cup supply chain, demonstrating how production parameters influence carbon footprint and supporting carbon-optimized product development.
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開示やサプライチェーン排出量算定の重要性が高まる中、製品開発初期段階での炭素予測は、企業のScope 3対応や環境配慮設計に貢献する。Gaia-Xのようなデータ基盤の活用は、日本におけるデータ連携基盤（ウラノス・エコシステム等）の参考となる。
In the global GX context
This paper aligns with global trends in supply chain decarbonization and digital infrastructure, such as Gaia-X, supporting TCFD/ISSB disclosure requirements. It offers a practical approach for early-stage carbon prediction, which is crucial for meeting CSRD and other regulatory demands. The method's focus on data sharing and service-based architecture is relevant for global supply chain transparency.
👥 読者別の含意
🔬研究者:Provides a novel method for early-stage carbon footprint prediction in supply chains, integrating data infrastructure like Gaia-X.
🏢実務担当者:Offers a practical tool for product developers to assess carbon impacts during design, aiding in Scope 3 management and eco-design.
🏛政策担当者:Highlights the role of data infrastructure in enabling supply chain decarbonization, relevant for policy on data sharing and sustainability.
📄 Abstract（原文）
Abstract Driven by the European Union’s commitment to achieving climate neutrality by 2050 and reducing EU emissions by 55% relative to 1990 levels by 2030, a critical assessment of manufacturing processes for their environmental impacts is necessary. During the strategic planning stage of product development and manufacturing, limited data is available to predict the environmental impacts across supply chains. However, decisions made at this phase significantly influence these impacts. This paper presents a data-provision method that aims to support decision-making in product development with quantifiable metrics of ecological sustainability. This is achieved through predicting carbon footprints, while leveraging the European data infrastructure Gaia-X, which enables secure service offerings and data sharing across the supply chain. The prediction service is implemented as a web application that facilitates the input and variation of hypothetical production scenarios. In a specific use-case, the method is applied to the supply chain of an injection-moulded cup. This concept offers a vision for carbon-optimized product development, enabling significant carbon avoidance in later production stages and showcasing the influence of different production parameters on the supply-chain carbon footprint.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/978-3-031-93891-7_22first seen 2026-08-02 17:45:46
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