Abstract

Every decision-oriented life cycle assessment (LCAs) entails, at least to some extent, a future-oriented feature. However, apart from the ex-ante LCAs, the majority of LCA studies are retrospective in nature and do not explicitly account for possible future effects. In this review a generic theoretical framework is proposed as a guideline for ex-ante LCA. This framework includes the entire technology life cycle, from the early design phase up to continuous improvements of mature technologies, including their market penetration. The compatibility with commonly applied system models yields an additional aspect of the framework. Practical methods and procedures are categorised, based on how they incorporate future-oriented features in LCA. The results indicate that most of the ex-ante LCAs focus on emerging technologies that have already gone through some research cycles within narrowly defined system boundaries. There is a lack of attention given to technologies that are at a very early development stage, when all options are still open and can be explored at a low cost. It is also acknowledged that technological learning impacts the financial and environmental performance of mature production systems. Once technologies are entering the market, shifts in market composition can lead to substantial changes in environmental performance.

Highlights

  • The purpose of life cycle assessment (LCA) can differ across studies, but in most cases the target is to provide information for decision support, aimed at the achievement of a more sustainable society [1,2]

  • A similar reasoning applies to dynamic LCA, a mode of LCA that is often associated with future states as well

  • Before zooming in on the specific characteristics of the individual case studies and on how they fit into the framework, some important observations on ex-ante LCA in general are presented first

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Summary

Introduction

The purpose of life cycle assessment (LCA) can differ across studies, but in most cases the target is to provide information for decision support, aimed at the achievement of a more sustainable society [1,2]. Once a decision is made, its implementation requires time and the outcomes will only be evident in the future. All decision-oriented LCAs entail, at least to some extent, a future-oriented feature. A key assumption in this case is that historical trends can be considered as representative for forthcoming situations. Incorporating a more nuanced analysis of expected future developments in LCA can be essential. Such future-oriented LCAs, referred to as ex-ante LCAs in this review, have received little attention so far

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