Martín-Gamboa et al. [1] highlights the need to develop frameworks able to integrate sustainability indicators and decision-makers preferences.
One of the challenges in assessing these potential impacts is the dispersion of
them throughout product life cycle, making complex the assessment process. Life
cycle approach is able to deal with it. As such, Life Cycle Sustainability
Assessment (LCSA) analysis takes into account all stages of a product’s lifecycle,
when assessing performances, being considered a holistic tool used to measure
product sustainability. Therefore, LCSA refers to environmental, social and economic assessments of product systems from a life-cycle perspective in order to
promote product sustainability [2, 3].
However, the use of this type of result in organizational decision-making is not
obvious. Halog and Manik [4] highlight three characteristics that increase the
complexity of decision-making through LCSA results: (i) the indicators are multidimensional (each one is expressed in different units), (ii) the objectives are
contradictory for the majority of decision-making problems (it is impossible to
maximize the performance of a product system in all indicators) and (iii) performance evaluation is uncertain.
In this type of decision-making problem, decision makers must choose according
to different criteria (LCSA indicators), leading to multiple criteria decision problem.
Multiple criteria decision analysis (MCDA) approach aims to recommend an ideal
solution, which is not necessarily optimal in all criteria, but a compromise solution
according to the value judgment of decision-makers. The main advantage of this
approach is its ability to consider a relatively large number of criteria when making
a decision [5]. Laurin et al. [6] remark this approach when comparing product
systems through the analysis of compromise recommendations.
LCSA results could be too disaggregated and, as such, too difficult to understand
and interpret by decision-makers [7]. The use of these indicators in combination to
support sustainable decision-making can be done through MCDA methods, as
proposed by several authors [8–11].
We found few scientific researches discussing the use of the multiple criteria
decision analysis approach into decision-making comprising LCSA results for
sustainable decision-making ([9, 12–15, 7]).
The researches carried out by Vynies et al. [9] and Keller et al. [12] rely on
arbitrary and unjustified aggregation procedures and neglect uncertainty when
selecting a product system between a set of alternatives based on LCSA performances. Myllyvitta et al. [13] used MCDA approach to identify and weight relevant
impact categories when assessing the environmental impacts of biomass production. Traverso et al. [7] provide a tool for comparing LCSA performance-based
product systems. However, it is not clear how they defined the procedure for
establishing the weighting factors and they do not take into account the uncertainty
associated to LCSA performances. Finally, Hanandeh and El-Zein [14] adapted
Electre III method to account for the uncertainty associated with preference data as
weighting factors when choosing between alternatives based on LCA performance.
As such, we did not find many studies carrying out analyses including LCSA
uncertainties, or the implications of choosing among the various MCDA methods
318
B. B. T. do Carmo et al.
One of the challenges in assessing these potential impacts is the dispersion of
them throughout product life cycle, making complex the assessment process. Life
cycle approach is able to deal with it. As such, Life Cycle Sustainability
Assessment (LCSA) analysis takes into account all stages of a product’s lifecycle,
when assessing performances, being considered a holistic tool used to measure
product sustainability. Therefore, LCSA refers to environmental, social and economic assessments of product systems from a life-cycle perspective in order to
promote product sustainability [2, 3].
However, the use of this type of result in organizational decision-making is not
obvious. Halog and Manik [4] highlight three characteristics that increase the
complexity of decision-making through LCSA results: (i) the indicators are multidimensional (each one is expressed in different units), (ii) the objectives are
contradictory for the majority of decision-making problems (it is impossible to
maximize the performance of a product system in all indicators) and (iii) performance evaluation is uncertain.
In this type of decision-making problem, decision makers must choose according
to different criteria (LCSA indicators), leading to multiple criteria decision problem.
Multiple criteria decision analysis (MCDA) approach aims to recommend an ideal
solution, which is not necessarily optimal in all criteria, but a compromise solution
according to the value judgment of decision-makers. The main advantage of this
approach is its ability to consider a relatively large number of criteria when making
a decision [5]. Laurin et al. [6] remark this approach when comparing product
systems through the analysis of compromise recommendations.
LCSA results could be too disaggregated and, as such, too difficult to understand
and interpret by decision-makers [7]. The use of these indicators in combination to
support sustainable decision-making can be done through MCDA methods, as
proposed by several authors [8–11].
We found few scientific researches discussing the use of the multiple criteria
decision analysis approach into decision-making comprising LCSA results for
sustainable decision-making ([9, 12–15, 7]).
The researches carried out by Vynies et al. [9] and Keller et al. [12] rely on
arbitrary and unjustified aggregation procedures and neglect uncertainty when
selecting a product system between a set of alternatives based on LCSA performances. Myllyvitta et al. [13] used MCDA approach to identify and weight relevant
impact categories when assessing the environmental impacts of biomass production. Traverso et al. [7] provide a tool for comparing LCSA performance-based
product systems. However, it is not clear how they defined the procedure for
establishing the weighting factors and they do not take into account the uncertainty
associated to LCSA performances. Finally, Hanandeh and El-Zein [14] adapted
Electre III method to account for the uncertainty associated with preference data as
weighting factors when choosing between alternatives based on LCA performance.
As such, we did not find many studies carrying out analyses including LCSA
uncertainties, or the implications of choosing among the various MCDA methods
318
B. B. T. do Carmo et al.