Abstract
This chapter explores data quality in life cycle inventory (LCI) datasets and calculation results, introduces the history, explains the relevance of data quality for life cycle assessment (LCA), and the difficulty to deal with the application-dependency of data quality. Recent data quality systems, introduced by the United States Environmental Protection Agency (US EPA) and in the course of the European “Product Environmental Footprint” (PEF) project, are elaborated in more detail. The application-dependency of data quality has led to a more refined view on data quality in a recent United Nations GLAD (Global Life Cycle Access to Data) project. GLAD distinguishes between data quality when a dataset is created and when it is used. In addition, data quality is broadened by including modeling details that are typically set differently in different application contexts. Outcomes of the GLAD project are therefore introduced in this chapter as well and it is expected that these might lead to a more comprehensive, better management of data quality for differing application contexts, as well as for creating inventory datasets.
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Notes
- 1.
Quality management systems – Fundamentals and vocabulary (ISO 9000: 2015); German and English version EN ISO 9000:201
- 2.
Global Network of Interoperable LCA Databases – Global LCA Data Access, Brasília meetings. 13–16 March 2016, Summary of decisions by Steering Committee from Brasília meeting, UNEP 2016
- 3.
“Our high-quality LCI datasets are based on industrial data […],” https://www.ecoinvent.org/database/buy-a-licence/why-ecoinvent/why-ecoinvent.html
- 4.
“European Commission: GaBi energy data has the best quality,” http://www.gabi-software.com/news/news-detail/article/european-commission-gabi-energy-data-has-the-best-quality/
- 5.
See Chap. 2 in this book “Principles of Life Cycle Inventory Modeling: The Basic Model, Extensions and Conventions” by Andreas Ciroth, Francesca Recanati, and Rickard Arvidsson for more details about foreground and background systems in LCA.
- 6.
It is not entirely clear, but “uncertainty,” as mentioned in ISO, may include both random uncertainty and variability; it seems intentionally vague and broad (“… the information (e.g., data…)”)
- 7.
Environmental labels and declarations – Type III environmental declarations – Principles and procedures
- 8.
In the EF hotspot analysis, “most relevant” is defined: Processes ordered by their contribution are most relevant up to the 80% percentile, i.e., if the sum of the impacts is equal or higher than 80% of the total impact (i.e., sum of all the impacts after normalization and weighting), see http://ec.europa.eu/environment/eussd/smgp/pdf/PEFCR _guidance_v6.3.pdf (page 50)
- 9.
In the development of various PEFCRs, some could not be finished since an agreement on the definition of the product category could not be reached; for example, for coffee, it is a question about the size of the cup (Americano vs. espresso) and whether milk should be included or not.
- 10.
- 11.
Who continues: “When the determination of the [items] included in a sample involves personal judgement, one cannot have an objective measure of the reliability of the sample results, because the various [items] may have differing and unknown chances of being drawn.” (Hansen et al. 1953, Vol I p. 9.)
- 12.
The Technical Secretariat is responsible for steering the whole process of PEFCR development for a specific product category.
- 13.
The Handbook Emission Factors for Road Transport, http://www.hbefa.net/e/index.html
- 14.
- 15.
Source: Foreign Agricultural Service, Official USDA Estimates, https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery
- 16.
The United Nations Standard Products and Services Code, https://www.unspsc.org/
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Ciroth, A. (2021). Data Quality in Life Cycle Inventories. In: Ciroth, A., Arvidsson, R. (eds) Life Cycle Inventory Analysis . LCA Compendium – The Complete World of Life Cycle Assessment. Springer, Cham. https://doi.org/10.1007/978-3-030-62270-1_5
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