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Purpose <p>Integrated Assessment Models (IAMs) are increasingly used to generate prospective Life Cycle Inventory (pLCI) databases for prospective Life Cycle Assessment (pLCA). This offers advantages, such as reducing temporal mismatches and representing sector-wide mitigation. However, studies often show limited awareness of associated limitations. We aim to raise awareness of these and provide practice-oriented recommendations for the informed and responsible use of IAM-based pLCI databases in pLCA.</p> Methods <p>Drawing on literature from both the IAM and LCA communities and recent conceptual discussions on the limitations of IAMs in the context of pLCA, we identify key characteristics, and limitations of IAMs relevant for pLCA practice. Based on the authors’ experience and literature, we derive practice-oriented recommendations for the informed and responsible use of IAM-based pLCI databases.</p> Results and discussion <p>We identify nine key IAM characteristics that pLCA users need to be aware of, including sectoral perspective, focus on climate change, weak representation of material cycles, techno-optimism, and narrow economic paradigms. We offer four main recommendations: using IAM-based pLCI databases selectively and purposefully; focussing on a limited set of diverse scenarios; interpreting results critically; and ensuring transparency and reproducibility. We emphasise treating IAM-based pLCI databases as exploratory reasoning tools rather than predictive models by acknowledging the limitations, using systematic approaches to scrutinise results, and exercising caution when interpreting.</p> Conclusions and Recommendations <p>IAM-based pLCI databases are valuable for exploring future environmental impacts but require careful, informed application. Their limitations must be explicitly acknowledged, and results interpreted as conditional insights rather than predictions. When used responsibly and transparently, these tools support sustainable decision-making. Future research should evaluate which IAMs provide sufficient technical detail for pLCI integration and expand the diversity of economic paradigms represented. Furthermore, efforts should focus on developing formal decision rules linking IAM characteristics to concrete modelling choices, ensuring a more systematic application of IAM data in pLCA.</p>

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Recommendations for an informed and responsible use of Integrated Assessment Models in prospective LCA

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Aaron Paris,
  • Marc van der Meide,
  • Amelie Müller,
  • Bernhard Steubing,
  • Jeroen Guinée,
  • Stefano Cucurachi,
  • Nils Thonemann

摘要

Purpose

Integrated Assessment Models (IAMs) are increasingly used to generate prospective Life Cycle Inventory (pLCI) databases for prospective Life Cycle Assessment (pLCA). This offers advantages, such as reducing temporal mismatches and representing sector-wide mitigation. However, studies often show limited awareness of associated limitations. We aim to raise awareness of these and provide practice-oriented recommendations for the informed and responsible use of IAM-based pLCI databases in pLCA.

Methods

Drawing on literature from both the IAM and LCA communities and recent conceptual discussions on the limitations of IAMs in the context of pLCA, we identify key characteristics, and limitations of IAMs relevant for pLCA practice. Based on the authors’ experience and literature, we derive practice-oriented recommendations for the informed and responsible use of IAM-based pLCI databases.

Results and discussion

We identify nine key IAM characteristics that pLCA users need to be aware of, including sectoral perspective, focus on climate change, weak representation of material cycles, techno-optimism, and narrow economic paradigms. We offer four main recommendations: using IAM-based pLCI databases selectively and purposefully; focussing on a limited set of diverse scenarios; interpreting results critically; and ensuring transparency and reproducibility. We emphasise treating IAM-based pLCI databases as exploratory reasoning tools rather than predictive models by acknowledging the limitations, using systematic approaches to scrutinise results, and exercising caution when interpreting.

Conclusions and Recommendations

IAM-based pLCI databases are valuable for exploring future environmental impacts but require careful, informed application. Their limitations must be explicitly acknowledged, and results interpreted as conditional insights rather than predictions. When used responsibly and transparently, these tools support sustainable decision-making. Future research should evaluate which IAMs provide sufficient technical detail for pLCI integration and expand the diversity of economic paradigms represented. Furthermore, efforts should focus on developing formal decision rules linking IAM characteristics to concrete modelling choices, ensuring a more systematic application of IAM data in pLCA.