Results demonstrate that even when the business of the company strongly
influences the scenario of use (e.g. it is not possible to compare fresh products with
frozen products), end-users can affect the energy consumption e.g. by setting the
temperature (comparison of scenarios 6 and 11). A variation of 36.7% of energy
consumption for a delta of 5 °C in the setting of the temperature is shown.
When comparing scenario 6 and 7 (influence of operating mode), the energy
consumption varies by 11.5% for 1500 h of use for frozen products.
When comparing scenario 8 and 9 (TRU turns on or off when doors open)
energy consumption varies by 6.2%.
The influence of the use of curtains when doors open (scenario 9 and 10)
involves a change of energy consumption of approximately 27.5%.
As a result, the environmental footprint of the use phase can be significantly
influenced by the end-user behaviours (setting of the temperature, choice of operating mode, the operation of the TRU when doors are opened and use of accessories
such as curtains).
Results also show the importance of taking into consideration the business
activity of the customers. The business activity mainly concerned the type of
transported products and the type of delivery (long haul transportation or urban
delivery with multiple door openings). The business activity will also influence
other parameters linked with end-user behaviours. So, the global energy consumption for the whole lifespan of the product and the specific recommendation to
end-users will not be the same.
3 Discussion
These results highlight how a specific usage scenario can change the lifecycle
inventory when considering energy consumption (from +9.6 to −82%). One difficulty in the use of specific energy modelling is to find a good compromise between
the need for reliable data and the resources (human, financial, time) that must be
allocated to the collection. The difference between the average usage scenario data
and the specific usage data is significant.
The study of experimental data took almost two months to be performed due to
the size of the system (trailer refrigeration box) and the number of parameters to
test. Also, refrigeration measure experimentation had to be conducted over a longer
period of time in order to ensure the thermal stabilization of the system. This
allowed to check the influence of different parameters on energy consumption in
order to provide specific recommendations to the end-user to the aim to guarantee
the continuity of the cold chain while reducing energy consumption. Experimental
measures can provide as well interesting feedback to engineers for the ecodesign of
the units and recommendations to the end-user to enforce sustainable behaviours. It
can show the potential benefit of having an innovative design helping to reduce
unsustainable end-user behaviours. However, it has to be checked that the
238
C. Heslouin et al.
influences the scenario of use (e.g. it is not possible to compare fresh products with
frozen products), end-users can affect the energy consumption e.g. by setting the
temperature (comparison of scenarios 6 and 11). A variation of 36.7% of energy
consumption for a delta of 5 °C in the setting of the temperature is shown.
When comparing scenario 6 and 7 (influence of operating mode), the energy
consumption varies by 11.5% for 1500 h of use for frozen products.
When comparing scenario 8 and 9 (TRU turns on or off when doors open)
energy consumption varies by 6.2%.
The influence of the use of curtains when doors open (scenario 9 and 10)
involves a change of energy consumption of approximately 27.5%.
As a result, the environmental footprint of the use phase can be significantly
influenced by the end-user behaviours (setting of the temperature, choice of operating mode, the operation of the TRU when doors are opened and use of accessories
such as curtains).
Results also show the importance of taking into consideration the business
activity of the customers. The business activity mainly concerned the type of
transported products and the type of delivery (long haul transportation or urban
delivery with multiple door openings). The business activity will also influence
other parameters linked with end-user behaviours. So, the global energy consumption for the whole lifespan of the product and the specific recommendation to
end-users will not be the same.
3 Discussion
These results highlight how a specific usage scenario can change the lifecycle
inventory when considering energy consumption (from +9.6 to −82%). One difficulty in the use of specific energy modelling is to find a good compromise between
the need for reliable data and the resources (human, financial, time) that must be
allocated to the collection. The difference between the average usage scenario data
and the specific usage data is significant.
The study of experimental data took almost two months to be performed due to
the size of the system (trailer refrigeration box) and the number of parameters to
test. Also, refrigeration measure experimentation had to be conducted over a longer
period of time in order to ensure the thermal stabilization of the system. This
allowed to check the influence of different parameters on energy consumption in
order to provide specific recommendations to the end-user to the aim to guarantee
the continuity of the cold chain while reducing energy consumption. Experimental
measures can provide as well interesting feedback to engineers for the ecodesign of
the units and recommendations to the end-user to enforce sustainable behaviours. It
can show the potential benefit of having an innovative design helping to reduce
unsustainable end-user behaviours. However, it has to be checked that the
238
C. Heslouin et al.