56
Many proposals have been developed in this area (see Jørgensen et al. 2008 ), but the
range of methods proposed and developed differs widely. They also often face
implementation problems. The bottom line is that there is not at present ‘anything
resembling an agreed approach or methodology’ (Clift 2014 ). Most efforts so far
have focused on fi nding and developing ways to include social impacts using impact
categories and indicators, similar to environmental LCA. Considering the challenges identifi ed in Annex 1 and the period over which discussions on SLCA’s challenges have continued, one may wonder ‘whether it is really appropriate to model
social LCA on environmental LCA’ and whether or not ‘Social LCA is more likely
to develop as a useful tool if it is not forced into the mould of environmental LCA’
(Clift 2014 ). This is not a new discussion since Udo de Haes (see Klöpffer 2008 )
already argued in 2008 that ‘social indicators do not fi t in the structure of LCA’
because developing ‘a quantitative relationship of the indicator to the functional
unit’ or properly handling the high spatial dependency of the indicator is problematic when trying to squeeze such impacts into environmental LCA. To prevent progress on SLCA coming to a dead end, fundamental re-examination of SLCA’s
paradigm seems necessary eventually leading to increased applicability and a more
comprehensive coverage of social benefi ts and impacts of life cycles. Since a platform for this discussion seems to be lacking, the ISIE-LCSA section could offer
this.
Life cycle-based approaches have an important role to play in assessing scenarios on how to feed, fuel and fi bre about nine billion people – all longing for the
‘good’ life – in a sustainable way in 2050 (cf. Frosch and Gallopoulos 1989 ). We
need to develop approaches and tools within the LCSA framework for evaluating
the sustainability of scenarios for such a future. One of the sub-challenges is to
make sensible and proper use of the different modes of LCA and LCSA available.
The key challenge is to effectively combine backcasting LCSA
5 (BLCSA; Heijungs
et al. 2014 ) with forecasting LCSA (FLCSA) approaches (e.g. Hertwich et al. 2014 ;
Koning et al. 2015 ) and eventually also product LCA (CLCA as well as ALCA) in
such a way that policies and transitions towards a more sustainable future can be
properly supported and monitored.
6
All our life cycle tools should be accompanied with proper ways of dealing with
uncertainties of data, methodological choices, assumptions and scenarios and pref5 Heijungs et al. ( 2014 ) defi ned backcasting LCSA as exploring ways, in a life cycle perspective, to
stay within normatively defi ned sustainability levels (e.g. planetary boundaries) through adapted
affl uence, population growth and/or technologies.
6 Note that we make a distinction between supporting policy development and monitoring developed policies. It’s our belief that we need different tools for supporting policy development (e.g.
CLCA; see also Chap. 2 of this book) and for monitoring accepted policy (e.g. ALCA for monitoring bioenergy performance-based regulation through carbon footprint studies). For policy development, we need to analyse all possible direct and indirect consequences of potential policy options
using life cycle-based scenario analysis for which CLCA, BLCSA, FLCSA and other scenariobased life cycle approaches (e.g. Spielmann et al. 2005 ; Hertwich et al. 2014 ; Koning et al. 2015 )
are best suited. For monitoring existing, accepted policies, we need clear black and white answers
and no scenario-based ranges of answers; for this, ALCA seems better suited.
J. Guinée
Many proposals have been developed in this area (see Jørgensen et al. 2008 ), but the
range of methods proposed and developed differs widely. They also often face
implementation problems. The bottom line is that there is not at present ‘anything
resembling an agreed approach or methodology’ (Clift 2014 ). Most efforts so far
have focused on fi nding and developing ways to include social impacts using impact
categories and indicators, similar to environmental LCA. Considering the challenges identifi ed in Annex 1 and the period over which discussions on SLCA’s challenges have continued, one may wonder ‘whether it is really appropriate to model
social LCA on environmental LCA’ and whether or not ‘Social LCA is more likely
to develop as a useful tool if it is not forced into the mould of environmental LCA’
(Clift 2014 ). This is not a new discussion since Udo de Haes (see Klöpffer 2008 )
already argued in 2008 that ‘social indicators do not fi t in the structure of LCA’
because developing ‘a quantitative relationship of the indicator to the functional
unit’ or properly handling the high spatial dependency of the indicator is problematic when trying to squeeze such impacts into environmental LCA. To prevent progress on SLCA coming to a dead end, fundamental re-examination of SLCA’s
paradigm seems necessary eventually leading to increased applicability and a more
comprehensive coverage of social benefi ts and impacts of life cycles. Since a platform for this discussion seems to be lacking, the ISIE-LCSA section could offer
this.
Life cycle-based approaches have an important role to play in assessing scenarios on how to feed, fuel and fi bre about nine billion people – all longing for the
‘good’ life – in a sustainable way in 2050 (cf. Frosch and Gallopoulos 1989 ). We
need to develop approaches and tools within the LCSA framework for evaluating
the sustainability of scenarios for such a future. One of the sub-challenges is to
make sensible and proper use of the different modes of LCA and LCSA available.
The key challenge is to effectively combine backcasting LCSA
5 (BLCSA; Heijungs
et al. 2014 ) with forecasting LCSA (FLCSA) approaches (e.g. Hertwich et al. 2014 ;
Koning et al. 2015 ) and eventually also product LCA (CLCA as well as ALCA) in
such a way that policies and transitions towards a more sustainable future can be
properly supported and monitored.
6
All our life cycle tools should be accompanied with proper ways of dealing with
uncertainties of data, methodological choices, assumptions and scenarios and pref5 Heijungs et al. ( 2014 ) defi ned backcasting LCSA as exploring ways, in a life cycle perspective, to
stay within normatively defi ned sustainability levels (e.g. planetary boundaries) through adapted
affl uence, population growth and/or technologies.
6 Note that we make a distinction between supporting policy development and monitoring developed policies. It’s our belief that we need different tools for supporting policy development (e.g.
CLCA; see also Chap. 2 of this book) and for monitoring accepted policy (e.g. ALCA for monitoring bioenergy performance-based regulation through carbon footprint studies). For policy development, we need to analyse all possible direct and indirect consequences of potential policy options
using life cycle-based scenario analysis for which CLCA, BLCSA, FLCSA and other scenariobased life cycle approaches (e.g. Spielmann et al. 2005 ; Hertwich et al. 2014 ; Koning et al. 2015 )
are best suited. For monitoring existing, accepted policies, we need clear black and white answers
and no scenario-based ranges of answers; for this, ALCA seems better suited.
J. Guinée