Data Spaces for Sustainable Product Development - A Structured Analysis of Technical and Semantic Infrastructures

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a structured taxonomy of data-space approaches and gaps for future research on data-driven LCA and digital engineering.

🏢実務担当者:Helps sustainability/disclosure teams select interoperable data-space infrastructures to meet EU and supply-chain data requests.

🏛政策担当者:Clarifies how different data-space standards and semantic models align, supporting decisions on harmonized interoperability requirements.

Sustainable product development relies on the early and systematic availability of environmental information, such as material origin, energy consumption, and emissions. This results in new requirements for data integration and processing to provide valuable sustainability metrics. In recent years, data space initiatives, standards, and regulations have emerged to enable interoperable, and cross-organizational exchange of such information. Examples include the Digital Product Passport, Catena-X, the Partnership for Carbon Transparency, and the Asset Administration Shell. While these infrastructures vary in terms of architecture, semantic modeling, and maturity, there is a lack of structured analysis highlighting their potential relevance for sustainability-oriented use cases. The purpose of this paper is to investigate the research proposition that federated data space architectures support acquisition, exchange, sharing, and processing and use of data required for sustainable product development. The methodology encompasses a structured literature review of use cases and a systematic comparison of existing data space concepts. The analysis evaluates supported data types, semantic models, interoperability features, and the potential contribution of these systems to support sustainability assessments, especially in early design stages. The main findings are (1) there are existing approaches for data spaces in which sustainability typically is a priority. (2) The utilization of data spaces for sustainable product development lacks published use cases in science. (3) Existing architectures focus on the interoperability and sharing of data but lack systematic support of LCA specific context information. Beside these findings, the paper provides a structured overview of data space approaches relevant for sustainable product development. This lays a foundation for future research on data-driven life cycle assessment and the integration of environmental information into digital engineering processes.

• openalex https://doi.org/10.24406/publica-9252first seen 2026-08-02 19:23:57