Life Cycle Assessment (LCA) is recognized to be one of the most reliable tools
for environmental analysis. LCA is ruled by the ISO 14040 [4] and ISO 14044 [5]
standards. LCA methods are described in more detail in the ILCD Handbook [6].
However, none of these references specifically describe how to model the use
phase. In fact, the relationship between the usage of product and its environmental
performance is rarely considered in LCA. Usually an average usage scenario is used
which does not take into account the effects of the usage context on environmental
performance, which can be positive or negative. The effect of usage context and its
modelling has been recognized as a priority by LCA researchers and practitioners.
Telenko and Seepersad [7] proposed to model usage context by using Bayesian
network models. Among the usage context factors considered were human (who?
skills or habits?), situational (where? when? for what task?) and product (design and
specification influencing the use of the product) ones. Ma and Kim [8] proposed a
time usage model in which the lifespan of the product was proven to have a strong
impact. Egede et al. [9] analyzed the influence of internal and external factors such
as vehicle characteristics, location of use and user influence.
In this paper, we consider the case of a LCA of truck refrigeration units (TRUs).
It has been shown that TRUs’ environmental footprint is mainly due to use of
refrigerant and the energy needed during their lifetime [10–12]. However, the use
phase it depends on several factors which are influencing the energy consumption.
The latter include: trailer specification, size and packaging of product loaded,
outside climate (temperature, hygrometry), operating mode (continuous run vs.
start/stop), start/stop parameters, type of product (fresh or frozen), type of transport
(urban distribution vs. long haul), speed of engine, coefficient of performance
(COP) of the unit and refrigerant efficiency [13–18].
In the ecoinvent database V3.2 [19], the fuel consumption of truck refrigeration
systems is modelled as an average scenario, assuming a 20% increase as compared
to conventional truck transport without refrigeration [20]. In this scenario, none of
the aforementioned parameters that can influence the energy consumption have
been taken into consideration.
This paper shows:
• The potential benefits of modelling the usage phase in detail as compared to an
average scenario and how the results can be used to provide specific recommendations to end-users that may significantly improve the environmental
impact of TRUs.
• The difficulties of collecting reliable and real-time data to perform a LCA of the
use phase.
• The kind of data that can be used and how to collect it.
• The potential influence of the use phase life cycle inventory, using an example
of a TRU with an average scenario of energy consumption versus different
specific scenarios.
• The potential benefits of using specific energy consumption data to improve
product design in order to promote sustainable behaviour when using
energy-using products.
232
C. Heslouin et al.
for environmental analysis. LCA is ruled by the ISO 14040 [4] and ISO 14044 [5]
standards. LCA methods are described in more detail in the ILCD Handbook [6].
However, none of these references specifically describe how to model the use
phase. In fact, the relationship between the usage of product and its environmental
performance is rarely considered in LCA. Usually an average usage scenario is used
which does not take into account the effects of the usage context on environmental
performance, which can be positive or negative. The effect of usage context and its
modelling has been recognized as a priority by LCA researchers and practitioners.
Telenko and Seepersad [7] proposed to model usage context by using Bayesian
network models. Among the usage context factors considered were human (who?
skills or habits?), situational (where? when? for what task?) and product (design and
specification influencing the use of the product) ones. Ma and Kim [8] proposed a
time usage model in which the lifespan of the product was proven to have a strong
impact. Egede et al. [9] analyzed the influence of internal and external factors such
as vehicle characteristics, location of use and user influence.
In this paper, we consider the case of a LCA of truck refrigeration units (TRUs).
It has been shown that TRUs’ environmental footprint is mainly due to use of
refrigerant and the energy needed during their lifetime [10–12]. However, the use
phase it depends on several factors which are influencing the energy consumption.
The latter include: trailer specification, size and packaging of product loaded,
outside climate (temperature, hygrometry), operating mode (continuous run vs.
start/stop), start/stop parameters, type of product (fresh or frozen), type of transport
(urban distribution vs. long haul), speed of engine, coefficient of performance
(COP) of the unit and refrigerant efficiency [13–18].
In the ecoinvent database V3.2 [19], the fuel consumption of truck refrigeration
systems is modelled as an average scenario, assuming a 20% increase as compared
to conventional truck transport without refrigeration [20]. In this scenario, none of
the aforementioned parameters that can influence the energy consumption have
been taken into consideration.
This paper shows:
• The potential benefits of modelling the usage phase in detail as compared to an
average scenario and how the results can be used to provide specific recommendations to end-users that may significantly improve the environmental
impact of TRUs.
• The difficulties of collecting reliable and real-time data to perform a LCA of the
use phase.
• The kind of data that can be used and how to collect it.
• The potential influence of the use phase life cycle inventory, using an example
of a TRU with an average scenario of energy consumption versus different
specific scenarios.
• The potential benefits of using specific energy consumption data to improve
product design in order to promote sustainable behaviour when using
energy-using products.
232
C. Heslouin et al.