Abstract
PurposeMuch progress has recently been made in modelling future background systems for LCA by including future scenario data, e.g. from Integrated Assessment Models (IAMs), into life cycle inventory (LCI) databases. A key problem is, however, that this yields potentially dozens of scenario LCI databases (i.e. LCI databases that represent different scenarios and reference years), instead of a single background database, which is very impractical for LCA modelling purposes. This paper proposes an approach to overcome this problem.MethodsThe approach consists of transforming all scenario LCI databases into a single superstructure database and an associated scenario difference file. The superstructure database is also a regular LCI database, but is constructed to contain all unique exchanges (elementary and intermediate flows) and processes that exist across all scenario LCI databases. The scenario difference file stores the differences between all scenarios and can be used to turn the superstructure into a specific scenario LCI database. This is very fast as it can be done in memory during LCA calculations.Results and discussionA key advantage of the superstructure approach is that a single LCI database can be used to represent different background systems. Therefore, the practitioner does not need to re-link a foreground system to multiple LCI databases, which is work-intensive and invites modelling errors. LCA results for all scenarios and reference years can be calculated automatically. We also illustrate how the superstructure approach has been implemented in the Activity Browser open source LCA software. Although this paper introduces the superstructure approach for background scenarios, it can also be used to model foreground scenarios, and even, as implemented in the Activity Browser, combinations of background and foreground scenarios. Finally, we briefly discuss further challenges that need to be addressed for a more widespread use of background scenarios in LCA.ConclusionsThe superstructure approach presents a practical solution for making the use of future background scenarios more wide-spread and, therefore, to overcome the problem of performing prospective LCA with temporally inconsistent foreground and background systems. The implementation in the Activity Browser makes the approach available for anyone and may serve as inspiration for other LCA software to implement the superstructure approach or a similar concept. While this may be an important technical milestone, additional coordination between data providers, scenario generators, LCA practitioners, and software developers will be required to further facilitate the use of background scenarios in prospective LCA studies.
Highlights
Cleaner technology is required to meet future climate (IPCC 2014) and other environmental targets (Rockström et al 2009)
Life cycle inventory databases that represent future scenarios based on a combination of data from existing life cycle inventory (LCI) databases and various scenario sources such as integrated assessment models have recently been developed
It is impossible to make precise predictions of the future, these scenario databases fill an important gap for prospective Life cycle assessment (LCA) by providing temporally consistent background data when assessing technologies at a future point in time
Summary
Cleaner technology is required to meet future climate (IPCC 2014) and other environmental targets (Rockström et al 2009). This work is currently being continued in the context of the PREMISE, a python package that aims at streamlining the approach to produce scenario databases for prospective LCA (Sacchi et al submitted), and which has already been applied in a number of studies (Pizzol et al 2021; Sacchi et al 2021) Despite this progress, there are still important challenges to be met for enabling a more widespread use of future background scenarios in LCA (see our discussion). The second issue relates to the quantity of data stored: when only selected parts of the future background databases differ across scenarios (e.g. electricity generation technology and market shares, as described by Mendoza Beltran et al (2018)), parts of the future background databases are identical in all scenarios This means that potentially large amounts of duplicate data are stored across the individual LCI databases, which negatively affects the required hard disk space and the speed of LCA calculations as the same data is loaded several times. We discuss its limitations, requirements for LCA software, and further challenges for making the use of future background scenarios in LCA more widespread
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