54
ment. Note that with respect to SLCA, there are many more authors that identifi ed
these challenges (e.g. Jørgensen et al. 2008 ), but their references were excluded due
to the limitations of our bibliometric analysis (see above).
The number of indicators that the various studies adopt for addressing the three
pillars of sustainability in a life cycle perspective varies from a few (e.g. Moriizumi
et al. limit their LCSA of two mangrove management systems in Thailand to just
three indicators, one for each dimension of the ‘triple bottom line’) to several dozen
indicators (e.g. Stamford and Azapagic adopted 43 indicators to address the same
three pillars in their LCSA on electricity options for the UK). The challenges faced
by studies adopting only a few indicators obviously include how to broaden the
number of indicators. The challenges for studies adopting dozens of indicators
include how to communicate their results to decision-makers and/or how to further
weight (evaluate) and aggregate the indicator results, for example, applying (multicriteria) decision analysis.
The topic of ‘deepening’ is addressed less by the studies listed in Annex 1 .
Nevertheless, several references mention (e.g. Cinelli et al. 2013 ; Sala et al. 2013b ;
Zamagni 2012 ; Zamagni et al. 2013 ; Pesonen and Horn 2013 ; Kucukvar and Tatari
2013 ; Kucukvar et al. 2014a , b ) and some even address (Hertwich et al. 2014 ) typical ‘deepening’ topics such as the need for comprehensive uncertainty assessment
and methods for dealing with rebound effects. But, again, these references were
excluded from Table 3.1 and Annex 1 due to the limitations of our bibliometric
analysis (see above). However, we feel that deepening discussions are very important as part of maturing LCSA approaches. We illustrate this by the example of
modelling rebound effects in a life cycle perspective, which has been addressed by
several authors (Hertwich 2005 ; Hofstetter et al. 2006 ; Thiesen et al. 2008 ; Girod
et al. 2011 ; Druckman et al. 2011 ; Font Vivanco and Voet 2014 ).
Hertwich ( 2005 ) defi nes the rebound effect as ‘a behavioural or other systemic
response to a measure taken to reduce environmental impacts that offsets the effect
of the measure. As a result of this secondary effect, the environmental benefi ts of
eco-effi ciency measures are lower than anticipated (rebound) or even negative
(backfi re)’. For example, the positive effect of more effi cient cars has largely been
offset by an overall shift to larger and heavier cars (see Chap. 18 ). Similarly, the
introduction of high-effi cient light bulbs has been combined with an expansion of
the number of light points. Recently, Font Vivanco and Voet ( 2014 ) performed a
review describing the state of the art in incorporating the rebound effect into LCAbased studies and analysed their main strengths and weaknesses. Their literature
review identifi ed a total of 42 relevant scientifi c documents, from which 17 provided quantitative estimates of the rebound effect using LCA-based approaches. It
appeared that ‘the inclusion of the rebound effect into LCA-based studies is still one
of the most relevant unresolved issues in the fi eld; […] only few studies provide
quantitative estimates (mostly for carbon dioxide and global warming […])’. Font
Vivanco and Voet concluded that ‘while a number of LCA-based studies have considered such effects […], no generally applicable guidelines have been developed so
far; […] consequently, a panoply of non-consensual defi nitions and analytical
approaches have arisen within the LCA community, and rebound effects have been
J. Guinée
ment. Note that with respect to SLCA, there are many more authors that identifi ed
these challenges (e.g. Jørgensen et al. 2008 ), but their references were excluded due
to the limitations of our bibliometric analysis (see above).
The number of indicators that the various studies adopt for addressing the three
pillars of sustainability in a life cycle perspective varies from a few (e.g. Moriizumi
et al. limit their LCSA of two mangrove management systems in Thailand to just
three indicators, one for each dimension of the ‘triple bottom line’) to several dozen
indicators (e.g. Stamford and Azapagic adopted 43 indicators to address the same
three pillars in their LCSA on electricity options for the UK). The challenges faced
by studies adopting only a few indicators obviously include how to broaden the
number of indicators. The challenges for studies adopting dozens of indicators
include how to communicate their results to decision-makers and/or how to further
weight (evaluate) and aggregate the indicator results, for example, applying (multicriteria) decision analysis.
The topic of ‘deepening’ is addressed less by the studies listed in Annex 1 .
Nevertheless, several references mention (e.g. Cinelli et al. 2013 ; Sala et al. 2013b ;
Zamagni 2012 ; Zamagni et al. 2013 ; Pesonen and Horn 2013 ; Kucukvar and Tatari
2013 ; Kucukvar et al. 2014a , b ) and some even address (Hertwich et al. 2014 ) typical ‘deepening’ topics such as the need for comprehensive uncertainty assessment
and methods for dealing with rebound effects. But, again, these references were
excluded from Table 3.1 and Annex 1 due to the limitations of our bibliometric
analysis (see above). However, we feel that deepening discussions are very important as part of maturing LCSA approaches. We illustrate this by the example of
modelling rebound effects in a life cycle perspective, which has been addressed by
several authors (Hertwich 2005 ; Hofstetter et al. 2006 ; Thiesen et al. 2008 ; Girod
et al. 2011 ; Druckman et al. 2011 ; Font Vivanco and Voet 2014 ).
Hertwich ( 2005 ) defi nes the rebound effect as ‘a behavioural or other systemic
response to a measure taken to reduce environmental impacts that offsets the effect
of the measure. As a result of this secondary effect, the environmental benefi ts of
eco-effi ciency measures are lower than anticipated (rebound) or even negative
(backfi re)’. For example, the positive effect of more effi cient cars has largely been
offset by an overall shift to larger and heavier cars (see Chap. 18 ). Similarly, the
introduction of high-effi cient light bulbs has been combined with an expansion of
the number of light points. Recently, Font Vivanco and Voet ( 2014 ) performed a
review describing the state of the art in incorporating the rebound effect into LCAbased studies and analysed their main strengths and weaknesses. Their literature
review identifi ed a total of 42 relevant scientifi c documents, from which 17 provided quantitative estimates of the rebound effect using LCA-based approaches. It
appeared that ‘the inclusion of the rebound effect into LCA-based studies is still one
of the most relevant unresolved issues in the fi eld; […] only few studies provide
quantitative estimates (mostly for carbon dioxide and global warming […])’. Font
Vivanco and Voet concluded that ‘while a number of LCA-based studies have considered such effects […], no generally applicable guidelines have been developed so
far; […] consequently, a panoply of non-consensual defi nitions and analytical
approaches have arisen within the LCA community, and rebound effects have been
J. Guinée