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Purpose <p>Life cycle assessment (LCA) studies that attempt to anticipate the future impact of product systems are increasingly widespread. However, a lack of clarity remains on what is the accurate terminology to be used in studies with strong focus on the temporal dimension; what are the specific definitions, and what are the underlying computational approaches. This work sets specifically to clarify the differences between “prospective” and “dynamic” elements in LCA studies and focus on representing them with a matrix-based computational structure, attempting to move beyond the ambiguity of existing definitions.</p> Methods <p>Existing definitions of prospective and dynamic LCA are briefly mapped with special focus on the inventory and impact assessment phases. The classic matrix notation is then expanded to cover all temporal aspects that can be considered in LCA and to propose a classification. Finally, the relation between existing tools that allow prospective and dynamic LCA and the proposed classification is discussed.</p> Results and discussion <p>A classification of prospective and dynamic LCAs is proposed starting from existing practice and using conventional matrix-based computational structures. Its potential and limitations are discussed in the context of existing approaches and tools, and the associated decision-making processes. The study highlights that trade-offs can arise between comprehensiveness, computational effort, and data requirements. Practitioners are advised to carefully consider the extent to which the inclusion of a temporal dimension in the LCA model can better inform decision-making.</p> Conclusion <p>The study pragmatically categorizes LCA studies based on computational structures rather than on definitions. The focus is on classifying a model based on its structure rather than based on its name. The classification should facilitate the choice of modelling approach for a knowledge transfer tailored to match the data availability and the needs of decision-makers. Although the matrix-based framework offers clarity, there is nevertheless the research need to move beyond it to cover non-linearity effects, despite computational challenges.</p>

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A joint conceptual and computational structure for dynamic and prospective LCA

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Fabio Sporchia,
  • Thomas Schaubroeck,
  • Massimo Pizzol

摘要

Purpose

Life cycle assessment (LCA) studies that attempt to anticipate the future impact of product systems are increasingly widespread. However, a lack of clarity remains on what is the accurate terminology to be used in studies with strong focus on the temporal dimension; what are the specific definitions, and what are the underlying computational approaches. This work sets specifically to clarify the differences between “prospective” and “dynamic” elements in LCA studies and focus on representing them with a matrix-based computational structure, attempting to move beyond the ambiguity of existing definitions.

Methods

Existing definitions of prospective and dynamic LCA are briefly mapped with special focus on the inventory and impact assessment phases. The classic matrix notation is then expanded to cover all temporal aspects that can be considered in LCA and to propose a classification. Finally, the relation between existing tools that allow prospective and dynamic LCA and the proposed classification is discussed.

Results and discussion

A classification of prospective and dynamic LCAs is proposed starting from existing practice and using conventional matrix-based computational structures. Its potential and limitations are discussed in the context of existing approaches and tools, and the associated decision-making processes. The study highlights that trade-offs can arise between comprehensiveness, computational effort, and data requirements. Practitioners are advised to carefully consider the extent to which the inclusion of a temporal dimension in the LCA model can better inform decision-making.

Conclusion

The study pragmatically categorizes LCA studies based on computational structures rather than on definitions. The focus is on classifying a model based on its structure rather than based on its name. The classification should facilitate the choice of modelling approach for a knowledge transfer tailored to match the data availability and the needs of decision-makers. Although the matrix-based framework offers clarity, there is nevertheless the research need to move beyond it to cover non-linearity effects, despite computational challenges.