Abstract
In life cycle assessment (LCA), temporal considerations are usually lost during the life cycle inventory calculation, resulting in an aggregated “snapshot” of potential impacts. Disregarding such temporal considerations has previously been underlined as an important source of uncertainty, but a growing number of approaches have been developed to tackle this issue. Nevertheless, their adoption by LCA practitioners is still uncommon, which raises concerns about the representativeness of current LCA results. Furthermore, a lack of consistency can be observed in the used terms for discussions on temporal considerations. The purpose of this review is thus to search for common ground and to identify the current implementation challenges while also proposing development pathways.This paper introduces a glossary of the most frequently used terms related to temporal considerations in LCA to build a common understanding of key concepts and to facilitate discussions. A review is also performed on current solutions for temporal considerations in different LCA phases (goal and scope definition, life cycle inventory analysis and life cycle impact assessment), analysing each temporal consideration for its relevant conceptual developments in LCA and its level of operationalisation.We then present a potential stepwise approach and development pathways to address the current challenges of implementation for dynamic LCA (DLCA). Three key focal areas for integrating temporal considerations within the LCA framework are discussed: i) define the temporal scope over which temporal distributions of emissions are occurring, ii) use calendar-specific information to model systems and associated impacts, and iii) select the appropriate level of temporal resolution to describe the variations of flows and characterisation factors.Addressing more temporal considerations within a DLCA framework is expected to reduce uncertainties and increase the representativeness of results, but possible trade-offs between additional data collection efforts and the increased value of results from DLCAs should be kept in mind.
Highlights
Background elementary concentration in ecosphereStrategies for prospective modellingSimulation approaches Historical trends ScenariosConsidering the dynamic of systems installations from the 1990s would probably be relevant for LCA of solar energy before 2000
We present a potential stepwise approach and development pathways to address the current challenges of implementation for dynamic LCA (DLCA)
A more complete analysis of ecoinvent v2.2 showed the important variations of GWP when a DLCA was conducted for processes related to wood, biofuels, infrastructure and electricity (Pinsonnault et al, 2014). These examples show that potential technological improvements and increased lifetimes should be investigated in many DLCA studies, but it is not yet possible to provide a full overview of relevant temporal parameters in models
Summary
These terms are used throughout this review to ensure a consistent and non-ambiguous discussion for future developments. It is the authors' hope that this glossary might bring some uniformity in future discussions. Concepts behind the most recently proposed definitions for types of dynamism and four subtypes of DLCA (Sohn et al, 2020) can be found in this table with a somewhat different perspective
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