Appropriateness and reliability of life cycle assessment results in relation to data quality: avoiding result discrepancy while improving decision certainty via use of adequate inventory data
This paper explores the critical relationship between data quality and the appropriateness and reliability of life cycle assessment (LCA) results. By analysing the user categories involved in LCA—namely academia, industry, consultancy and policy—it highlights the differing responsibilities and inter...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research: Infrastructure and Sustainability |
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| Online Access: | https://doi.org/10.1088/2634-4505/adec1a |
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| author | Martin Baitz Magnus Piotrowski |
| author_facet | Martin Baitz Magnus Piotrowski |
| author_sort | Martin Baitz |
| collection | DOAJ |
| description | This paper explores the critical relationship between data quality and the appropriateness and reliability of life cycle assessment (LCA) results. By analysing the user categories involved in LCA—namely academia, industry, consultancy and policy—it highlights the differing responsibilities and interpretations of data suitability and quality across these domains. The paper emphasizes that the selection of data is related to availability, suitability and quality, and that discrepancies in LCA results often stem from inconsistent documentation, improper engineering information, varied methodological approaches and misunderstandings surrounding data types. It advocates for the establishment of rigorous quality assurance processes and clear definitions of data types to enhance the reliability of LCA outcomes. The paper also discusses the implications of emerging data concepts and the potential of artificial intelligence to improve data quality while stressing the importance of appropriate methodological application. Ultimately, it underscores the necessity for stakeholders to take responsibility for their data usage to ensure that LCA serves its intended purpose effectively, thereby influencing critical decisions in sustainability and regulatory compliance. |
| format | Article |
| id | doaj-art-f0fa7d609b2b49a689a73df181f34d55 |
| institution | DOAJ |
| issn | 2634-4505 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research: Infrastructure and Sustainability |
| spelling | doaj-art-f0fa7d609b2b49a689a73df181f34d552025-08-20T02:39:42ZengIOP PublishingEnvironmental Research: Infrastructure and Sustainability2634-45052025-01-015303300110.1088/2634-4505/adec1aAppropriateness and reliability of life cycle assessment results in relation to data quality: avoiding result discrepancy while improving decision certainty via use of adequate inventory dataMartin Baitz0https://orcid.org/0000-0003-2492-0234Magnus Piotrowski1Sphera Solutions GmbH , Stuttgart, GermanySphera Solutions GmbH , Stuttgart, GermanyThis paper explores the critical relationship between data quality and the appropriateness and reliability of life cycle assessment (LCA) results. By analysing the user categories involved in LCA—namely academia, industry, consultancy and policy—it highlights the differing responsibilities and interpretations of data suitability and quality across these domains. The paper emphasizes that the selection of data is related to availability, suitability and quality, and that discrepancies in LCA results often stem from inconsistent documentation, improper engineering information, varied methodological approaches and misunderstandings surrounding data types. It advocates for the establishment of rigorous quality assurance processes and clear definitions of data types to enhance the reliability of LCA outcomes. The paper also discusses the implications of emerging data concepts and the potential of artificial intelligence to improve data quality while stressing the importance of appropriate methodological application. Ultimately, it underscores the necessity for stakeholders to take responsibility for their data usage to ensure that LCA serves its intended purpose effectively, thereby influencing critical decisions in sustainability and regulatory compliance.https://doi.org/10.1088/2634-4505/adec1alife cycle assessmentlife cycle inventorydata qualityartificial intelligenceuser responsibilityresult quality |
| spellingShingle | Martin Baitz Magnus Piotrowski Appropriateness and reliability of life cycle assessment results in relation to data quality: avoiding result discrepancy while improving decision certainty via use of adequate inventory data Environmental Research: Infrastructure and Sustainability life cycle assessment life cycle inventory data quality artificial intelligence user responsibility result quality |
| title | Appropriateness and reliability of life cycle assessment results in relation to data quality: avoiding result discrepancy while improving decision certainty via use of adequate inventory data |
| title_full | Appropriateness and reliability of life cycle assessment results in relation to data quality: avoiding result discrepancy while improving decision certainty via use of adequate inventory data |
| title_fullStr | Appropriateness and reliability of life cycle assessment results in relation to data quality: avoiding result discrepancy while improving decision certainty via use of adequate inventory data |
| title_full_unstemmed | Appropriateness and reliability of life cycle assessment results in relation to data quality: avoiding result discrepancy while improving decision certainty via use of adequate inventory data |
| title_short | Appropriateness and reliability of life cycle assessment results in relation to data quality: avoiding result discrepancy while improving decision certainty via use of adequate inventory data |
| title_sort | appropriateness and reliability of life cycle assessment results in relation to data quality avoiding result discrepancy while improving decision certainty via use of adequate inventory data |
| topic | life cycle assessment life cycle inventory data quality artificial intelligence user responsibility result quality |
| url | https://doi.org/10.1088/2634-4505/adec1a |
| work_keys_str_mv | AT martinbaitz appropriatenessandreliabilityoflifecycleassessmentresultsinrelationtodataqualityavoidingresultdiscrepancywhileimprovingdecisioncertaintyviauseofadequateinventorydata AT magnuspiotrowski appropriatenessandreliabilityoflifecycleassessmentresultsinrelationtodataqualityavoidingresultdiscrepancywhileimprovingdecisioncertaintyviauseofadequateinventorydata |