Randomized trial evaluates data quality in Life Cycle Assessment for engineering, suggesting improved decision-making.
Environmental impacts are determined during engineering. Reliability of engineering decisions depends significantly on quality of available data, fed into Life Cycle Assessment (LCA). Achievable data quality varies along engineering iterations, life cycle phases and partners in value creation networks. This requires representation of data quality criteria and metrics through metadata-based assessment. Metadata characterizes the data and information in existing systems and software tools. Currently, this metadata is not incorporated into data quality assessments, but it has the potential to greatly improve the quality of both processes and results. A six-step approach is used: collection of data quality assessment approaches through literature review (1), analyzing recurring elements (2), deriving an approach (3), implementation in a knowledge graph (4), application to automotive supplier parts like trunk covers (5) and validation of the developed approach (6). The approach supports engineers with systematic interpretation of data quality behind LCA results, leading to well informed decision-making.
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Graessler et al. (2026) studied this question.
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