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Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

Addressing temporal considerations in life cycle assessment.

Didier Beloin-Saint-Pierre¹, Ariane Albers², Arnaud Hélias³

¹Empa Materials Science and Technology, Lerchenfeldstrasse 5, CH-9014 St. Gallen, Switzerland.

The Science of the Total Environment

|August 8, 2020

Summary

This summary is machine-generated.

Life cycle assessment (LCA) often misses temporal details, creating uncertain results. This review proposes a dynamic LCA (DLCA) framework to improve accuracy by incorporating time-based data and definitions.

Keywords:

Dynamic LCA Implementation challenges Recommendations Review Temporal considerations

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Area of Science:

Environmental Science
Sustainability Studies
Industrial Ecology

Background:

Life cycle assessment (LCA) typically aggregates impacts, losing temporal dynamics.
This temporal aggregation is a significant source of uncertainty in current LCA results.
Existing methods to address temporal aspects are not widely adopted by practitioners.

Purpose of the Study:

To establish common ground and terminology for temporal considerations in LCA.
To identify challenges in implementing dynamic LCA (DLCA).
To propose development pathways for integrating temporal dynamics into LCA.

Main Methods:

Review of current literature and approaches for temporal considerations in LCA.
Development of a glossary for temporal LCA terms.
Analysis of temporal integration in different LCA phases (goal and scope, LCI, LCIA).

Main Results:

Identified a lack of consistency in terminology and adoption of temporal methods.
Proposed three key areas for temporal integration: temporal scope, calendar-specific data, and temporal resolution.
Outlined a stepwise approach and development pathways for DLCA.

Conclusions:

Integrating temporal considerations into LCA, via DLCA, can reduce uncertainty and enhance result representativeness.
Careful consideration of trade-offs between data effort and result value is necessary.
Standardizing terminology and methods is crucial for advancing DLCA.