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Testing life-cycle assessment data quality with Benford’s law reveals geographic variationBogdan Šinik,
Aleksandar Tošić, 2025, original scientific article
Abstract: Life-Cycle Assessment (LCA) is a methodology that is used extensively for evaluating the environmental impacts of products and processes throughout their lifetime. The method is highly dependent on the quality and accuracy of the underlying data. Moreover, the data acquisition process can be subjective, raising concerns about potential inconsistencies. In this study, we perform Benford’s law conformity tests (first digit) on all numerical data in ecoinvent, focusing on individual compartments (air, water, soil, and natural resources) and environmental elementary flows (carbon, toxic substances, greenhouse gases, and heavy metals), and discrepancies across continents are examined. Life Cycle Inventory data met the requirements of Benford’s law and generally exhibited high conformity. Substantial differences in conformity were observed between Africa and Europe. Individual processes and measurements were inspected to further isolate potential sources of the non-conformity. The statistical significance of the results was increased using open-source databases available on OpenLCA Nexus, including WorldSteel, OzLCI2019, ELCD, NEEDS, BioenergieDat, and Exiobase. Finally, the Environmental Performance Index (EPI) was used, and a strong correlation between continental Benford conformity results and corresponding EPI scores was observed. The findings suggest that discrepancies in conformity across continents reflect differences in data transparency and reporting practices. European datasets generally show higher conformity, likely owing to the use of more standardized methodologies. In contrast, data from regions with limited infrastructure or less established LCA practices tend to show lower conformity. Benford’s Law offers a simple and computationally efficient alternative to conventional data quality assessments without requiring additional metadata or probabilistic modeling. Its application can support the detection of systemic biases and improve the reliability of LCA-based indicators such as environmental product declarations.
Keywords: anomaly detection, Benford’s law, data integrity
Published in RUP: 12.06.2025; Views: 104; Downloads: 9
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