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Guidance on Data Quality Assessment for Life Cycle Inventory Data
Edelen, A. AND W. Ingwersen. Guidance on Data Quality Assessment for Life Cycle Inventory Data. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-16/096, 2016.
Life cycle assessment is increasingly being used as a tool to identify areas of potential environmental impact of materials, technologies and policies. EPA NRMRL scientists are working with others in the LCA community to improve the data, methods, and tools available to the LCA community. Understanding and communicating data quality is absolutely essential to the scientific integrity of LCA as it is to other fields. This report makes an important contribution to improving and standardizing the way in which data quality is described for life cycle data.
Data quality within Life Cycle Assessment (LCA) is a significant issue for the future support and development of LCA as a decision support tool and its wider adoption within industry. In response to current data quality standards such as the ISO 14000 series, various entities within the LCA community have developed different methodologies to address and communicate the data quality of Life Cycle Inventory (LCI) data. Despite advances in this field, the LCA community is still plagued by the lack of reproducible data quality results and documentation. To address these issues, US EPA has created this guidance in order to further support reproducible life cycle inventory data quality results and to inform users of the proper application of the US EPA supported data quality system. The work for this report was begun in December 2014 and completed as of April 2016.The updated data quality system includes a novel approach to the pedigree matrix by addressing data quality at the flow and the process level. Flow level indicators address source reliability, temporal correlation, geographic correlation, technological correlation and data sampling methods. The process level indicators address the level of review the unit process has undergone and its completeness. This guidance is designed to be updatable as part of the LCA Research Center’s continuing commitment to data quality advancements.