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Purpose <p>A reproducibility crisis is widely documented across scientific disciplines, and life cycle assessment (LCA) is no exception. Because reproducibility constitutes a core scientific criterion, irreproducible results fail a foundational requirement of the scientific method and should be considered as unsubstantiated claims. An investigation of the drivers of this challenge has to be conducted to propose a viable solution.</p> Methods <p>Three complementary reviews were conducted. First, a critical review of guidance documents examines how reproducibility is treated in current LCA practice, specifically whether it is explicitly addressed and whether reporting procedures are provided. Second, a systematic review of LCA research synthesizes reported reproducibility challenges and recommendations. Third, a systematic review of meta-LCAs identifies challenges encountered when remodeling multiple published LCAs and extracts proposed solutions. Insights from these reviews, combined with practical experience from conducting LCAs and meta-LCAs, underpin the framework and tool, which are then validated through structured workshops involving practitioners with varied backgrounds and experience levels.</p> Results and discussion <p>The findings of these reviews together with experience gained conducting large LCA remodeling projects permitted the development of a reporting framework. This framework enables reproducible LCAs by disclosing all relevant methodological choices, inventory data, and impact assessment results. The framework accounts for data confidentiality by allowing model reproducibility and third-party verification using alternative input data. We evaluated the framework by implementing it in a spreadsheet-based tool and by conducting user testing with novice and experienced LCA practitioners.</p> Conclusions <p>This reproducibility gap risks undermining the scientific credibility of LCA results and may lead to ill-informed decisions. We invite LCA practitioners to report their work using the proposed framework, and we encourage LCA software developers to enable the automatic generation of reports aligned with the framework, with the aim of producing scientifically testable results that support evidence-based sustainability decision making.</p>

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Reproducibility as a missing scientific criterion in life cycle assessment: a reporting framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月7日

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  • A. Kamal Kamali,
  • Anish Koyamparambath,
  • Bertrand Laratte,
  • Guido Sonnemann

摘要

Purpose

A reproducibility crisis is widely documented across scientific disciplines, and life cycle assessment (LCA) is no exception. Because reproducibility constitutes a core scientific criterion, irreproducible results fail a foundational requirement of the scientific method and should be considered as unsubstantiated claims. An investigation of the drivers of this challenge has to be conducted to propose a viable solution.

Methods

Three complementary reviews were conducted. First, a critical review of guidance documents examines how reproducibility is treated in current LCA practice, specifically whether it is explicitly addressed and whether reporting procedures are provided. Second, a systematic review of LCA research synthesizes reported reproducibility challenges and recommendations. Third, a systematic review of meta-LCAs identifies challenges encountered when remodeling multiple published LCAs and extracts proposed solutions. Insights from these reviews, combined with practical experience from conducting LCAs and meta-LCAs, underpin the framework and tool, which are then validated through structured workshops involving practitioners with varied backgrounds and experience levels.

Results and discussion

The findings of these reviews together with experience gained conducting large LCA remodeling projects permitted the development of a reporting framework. This framework enables reproducible LCAs by disclosing all relevant methodological choices, inventory data, and impact assessment results. The framework accounts for data confidentiality by allowing model reproducibility and third-party verification using alternative input data. We evaluated the framework by implementing it in a spreadsheet-based tool and by conducting user testing with novice and experienced LCA practitioners.

Conclusions

This reproducibility gap risks undermining the scientific credibility of LCA results and may lead to ill-informed decisions. We invite LCA practitioners to report their work using the proposed framework, and we encourage LCA software developers to enable the automatic generation of reports aligned with the framework, with the aim of producing scientifically testable results that support evidence-based sustainability decision making.