existing in the scientific literature. This paper aims to propose an approach able to
support sustainable decision-making where the considered indicators are defined as
LCSA performances.
2 Methodology
The assessment of the performance of products in each pillar of sustainability is
uncertain and can vary widely due to the parameters of the impact assessment
model and input data. Different sources of uncertainty are present in environmental,
social and economic studies, considering life cycle analysis. It is therefore necessary to take these uncertainties into account when deciding.
As such, our methodology includes three phases: (i) assessing uncertain LCSA
performances for the three pillars of sustainability (ii) extending LCSA performances uncertainty to MCDA methods and (iii) interpreting stochastic rankings
provided by the MCDA methods implemented, followed by conclusions.
The achievement of uncertain performance followed the ReCiPe method [16] for
the environmental component and Social Hotspot database results for the social pillar.
For the economic pillar, life cycle cost was considered. We applied “environmental
life cycle costing (LCC)” according to Hunkeler et al. [17] to calculate the sum of
private costs supported by all stakeholders involved throughout entire product life
cycle, i.e. beyond the costs of the producer. Please note that in environmental LCC, no
externalities are monetized (e.g. health care costs due to air pollution from trucks).
Because of the strong inflation in Brazil in recent years, costs from year 2012 were
have been adjusted with national inflation rates. No discounting was considered
because of the relatively short duration of the tire life cycle.
The sources of uncertainty for environmental, social and economic dimensions
are related to reference flows (number of tires required—considering the lifetime
and the fuel consumption during use). For environmental dimension, we also
considered the uncertainty related to ecoinvent, the end-of-life benefits, tire wear,
transport distances, land use change and yield for hevea and soybean agriculture
and emission factors. On the other hand, we did not consider the uncertainty
associated to prices for social and economic dimensions and the ones associated to
environmental and social characterization factors.
To represent these sources of uncertainty in LCSA performances, we performed
a Monte Carlo simulation for all indicators comprised in the three pillars of
sustainability.
In order to propagate the uncertainty on LCSA performance scores to
decision-making problem, we applied three MCDA models (Weighted sum, Topsis
and Prométhée II). These models were chosen according to the type of results
provided (multiple criteria methods able to provide full rankings), the ease of being
implemented without specific software package (ease of use in generic software, for
example Microsoft Excel) and the type of parameters requested from decision
Propagating Uncertainty in Life …
319
support sustainable decision-making where the considered indicators are defined as
LCSA performances.
2 Methodology
The assessment of the performance of products in each pillar of sustainability is
uncertain and can vary widely due to the parameters of the impact assessment
model and input data. Different sources of uncertainty are present in environmental,
social and economic studies, considering life cycle analysis. It is therefore necessary to take these uncertainties into account when deciding.
As such, our methodology includes three phases: (i) assessing uncertain LCSA
performances for the three pillars of sustainability (ii) extending LCSA performances uncertainty to MCDA methods and (iii) interpreting stochastic rankings
provided by the MCDA methods implemented, followed by conclusions.
The achievement of uncertain performance followed the ReCiPe method [16] for
the environmental component and Social Hotspot database results for the social pillar.
For the economic pillar, life cycle cost was considered. We applied “environmental
life cycle costing (LCC)” according to Hunkeler et al. [17] to calculate the sum of
private costs supported by all stakeholders involved throughout entire product life
cycle, i.e. beyond the costs of the producer. Please note that in environmental LCC, no
externalities are monetized (e.g. health care costs due to air pollution from trucks).
Because of the strong inflation in Brazil in recent years, costs from year 2012 were
have been adjusted with national inflation rates. No discounting was considered
because of the relatively short duration of the tire life cycle.
The sources of uncertainty for environmental, social and economic dimensions
are related to reference flows (number of tires required—considering the lifetime
and the fuel consumption during use). For environmental dimension, we also
considered the uncertainty related to ecoinvent, the end-of-life benefits, tire wear,
transport distances, land use change and yield for hevea and soybean agriculture
and emission factors. On the other hand, we did not consider the uncertainty
associated to prices for social and economic dimensions and the ones associated to
environmental and social characterization factors.
To represent these sources of uncertainty in LCSA performances, we performed
a Monte Carlo simulation for all indicators comprised in the three pillars of
sustainability.
In order to propagate the uncertainty on LCSA performance scores to
decision-making problem, we applied three MCDA models (Weighted sum, Topsis
and Prométhée II). These models were chosen according to the type of results
provided (multiple criteria methods able to provide full rankings), the ease of being
implemented without specific software package (ease of use in generic software, for
example Microsoft Excel) and the type of parameters requested from decision
Propagating Uncertainty in Life …
319