Overview of Gaps in LCA Data Quality and Future Perspectives

Life Cycle Assessment (LCA) is a powerful methodology for evaluating the environmental impacts of products and processes across their entire life cycle. However, the quality of the data used in LCA is critical to ensuring accurate, reliable, and meaningful results. This paper provides an overview of how data quality is assessed in LCA studies, focusing on three key frameworks: the Product Environmental Footprint (PEF), the International Reference Life Cycle Data System (ILCD), and the Pedigree Matrix. These approaches evaluate data quality through different lenses, considering several key factors. While these frameworks offer valuable guidance, they are still plagued by some limitations, particularly in their ability to fully quantify uncertainty and provide actionable insights for data improvement. This paper highlights these shortcomings and discusses the limitations of the existing methods, particularly their reliance on qualitative assessments and the lack of integration with emerging digital technologies. In addressing these gaps, the paper proposes future interventions, including the development of a hybrid quality metric, and the adoption of digital technologies to enable robust data sharing. These advancements aim to enhance data transparency, reliability, and relevance, ultimately improving the credibility of LCA-based studies as a decision-making tool for sustainable development.