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carbon foot print is the emerging area in all sectors. carbon foot print in textile sector in order to know the average energy consumption in this sector is the hot topic.
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This article was downloaded by: [INASP - Pakistan (PERI)]On: 26 August 2015, At: 00:33Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: 5 Howick Place,London, SW1P 1WG
International Journal of Sustainable EngineeringPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tsue20
A carbon footprint analysis in the textile supply chainM. Bevilacqua
a , F.E. Ciarapica
a , G. Giacchetta
a & B. Marchetti
a
a Dipartimento di Energetica , Università Politecnica delle Marche , 60131, Ancona, Italy
Published online: 28 Jul 2010.
To cite this article: M. Bevilacqua , F.E. Ciarapica , G. Giacchetta & B. Marchetti (2011) A carbon footprint analysis in thetextile supply chain, International Journal of Sustainable Engineering, 4:01, 24-36, DOI: 10.1080/19397038.2010.502582
To link to this article: http://dx.doi.org/10.1080/19397038.2010.502582
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7/18/2019 Carbon Footprint Analysis in Textile Suppy Chain
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A carbon footprint analysis in the textile supply chain
M. Bevilacqua1, F.E. Ciarapica*, G. Giacchetta2 and B. Marchetti3
Dipartimento di Energetica, Universita Politecnica delle Marche, 60131 Ancona, Italy
( Received 12 November 2009; final version received 16 June 2010)
This research work focuses on the application of life-cycle assessment methodology to determine the carbon footprint of different players involved in a supply chain of the textile sector. A case study of a product by a textile leader company wascarried out. This study demonstrates that, in the textile chain, the main contribution to the greenhouse effect is provided bythe electrical and thermal energy used and by the transportation (since different production phases are delocalised in a widerange that goes from South Africa, Italy, Romania and all around the world, from the distribution centre to the stores).The Monte Carlo analysis has been used in order to obtain, for each calculated impact, not only the average value but also thedistribution curve of the results characterised by uncertainty parameters. Moreover, a sensitivity analysis was carried out toevaluate the impact of management choices such as:† a change in the transportation modality, from aeroplane to boat;† a combination of road and rail transportation; and† a selection among suppliers that allows the firm to cut environmental impacts.
Keywords: life-cycle assessment; carbon footprint; textile sector; environmental supply chain; sensitivity analysis
1. Introduction
The ‘sustainable development’ philosophy has been
gaining a growing interest from public institution,
customers and companies that, to consolidate and increase
their position in the market, have to necessarily deal with
environmental issues. Through techniques and instruments
of eco-efficiency such as design for environment,
environmental management systems or life-cycle assess-
ment (LCA), the economy and the ecology have to become
synergic and not in contradiction as presented in many
cases (see Halldorsson et al. 2009). Seuring and Muller
(2008) proposed a literature review on sustainable supply
chain management taking into account 191 papers,
published from 1994 to 2007. They found out that
frequently external triggers, which are placed on focal
companies by governing agencies, customers and stake-
holders, are proposed. Such pressure as well as incentives
might lead to action by those companies.
This study was proposed by a leader company itself
compelled by the increasing attention of the market to
environmental issues. The main aim of the project was to
evaluate the application of the LCA methodology in order
to obtain a tool to support the company environmental
policy. The first step was to define the object of theanalysis: one of the basic items produced by the company
was chosen as a case study. A simple wool sweater without
buttons, laces, zippers or other accessories, with standard
production processes for the yarn, weaving and finishing
treatments, was taken into account. The result of this study
was the evaluation of the carbon footprint of such
product to assess its impact on the greenhouse effect.
The audiences to which the results were addressed were
the textile leader company that promoted the research and
its suppliers. Moreover, other than for external communi-
cation, the purposes of the management in applying the
LCA methodology are related to the product improvement,
and to the support for strategic choices and benchmarking.
Seuring and Muller (2008) identified two strategies
followed by leader companies in order to create a
sustainability supply chain management: ‘supplier man-agement for risks and performance’ and ‘supply chain
management for sustainable products’. In the first
approach, one of the main concerns of the companies is
a loss of reputation if related problems are raised, while the
second approach requests the definition of life-cycle-based
standards for the environmental and social performance
of products that are then implemented throughout the
supply chain.
The following sections present the results of this
research work: in Section 2, the material and methods
utilised to define the input and the output of the system and
to perform the uncertainty analysis are described.
In Section 3, the case study, the definition of the system
boundaries, life-cycle inventory and impact analysis are
presented. Sections 4 and 5 present the results of the
sensitivity analysis and conclusions.
ISSN 1939-7038 print/ISSN 1939-7046 online
q 2011 Taylor & Francis
DOI: 10.1080/19397038.2010.502582
http://www.informaworld.com
*Corresponding author. Email: [email protected]
International Journal of Sustainable Engineering
Vol. 4, No. 1, March 2011, 24–36
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2. Material and methods
The application of the LCA methodology was carried out
following the international standards and using the
Simapro software and the ecoinvent database (Pre
Consultants 2006; Swiss Centre for Life Cycle Inventory
2009) applied to the production phases of the chosen item
(wool sweater). In accordance with ISO 14041, the stage
of life-cycle inventory assessment (LCIA) involves the
collection of the data concerning the processes and various
calculation procedures. The relationship between the item
produced and the environment is defined.
The focus of this project was the calculation of carbon
footprint measured in CO2 equivalent. For this purpose,
the International Panel of Climate Change (IPCC 2007)
method was selected. This method developed by the IPCC
allows to quantify the greenhouse effect by measuring the
equivalent CO2 and lists the climate change factors of
IPCC with a time frame of 20, 100 and 500 years. In this
work, a time frame of 100 years was considered.
Normalisation and weighting are not a part of this method.The English guidelines (PAS 2050 2008)4 were taken
into account for the following aspects of the boundary
definition:
. the modelling of the transportation phase to the
stores was considered and an average transportation
model was used;. the exclusion of the capital goods from the analysis.
2.1 Related research works
Many companies focused their attention on environmental
impact of their products. Levi Strauss & Co. (2009a,2009b) commissioned a LCA of two of their core
products. By taking a product life-cycle approach to their
work, they were able to develop a set of strategies to
address the greatest impacts of their business on the
environment.
Wiedmann and Minx (2007) calculated that textiles
and clothing are responsible for around 4% of the
secondary carbon footprint of an individual in the
developed world. The problem has been largely addressed
at the European level, the EU COST Action 628
(Nieminena et al. 2007) was established to produce
first-hand industrial environmental data of textiles in
Europe, as well as to suggest tools for comparisons of present technologies and practices with cleaner appli-
cations, including the economic effects. LCA was used to
set up criteria for an environmental product declaration for
textile products. Unique, first-hand industrial data were
collected from five European textile industries.
McCurry (2009) described a study about future trend in
textile industries carried out from a Freedonia Group,
Ohio-based consultancy firm. The study stated that the
industries will pay attention to a host of environmental
issues that aim to reduce their carbon and environmental
footprints.
An ecological footprintstudyin thetextile field hasbeen
proposed by Herva et al. (2008). They analysed a textile
tailoring plant with the overall purpose of developing a tool
for evaluating the environmental impact evolution due to
the performance of the plant, as well as for comparing the
environmental behaviour of different tailoring processes.Therefore, the selected data were those from the
manufacturing work. Data were divided into three main
categories: energy, resources and waste. The principal
contribution to the final environmental factor (expressed in
hectares of land) was the resources category, mainly due to
the high value associated with the cloth. The consumed
energywas thesecondcontributor, while thewastecategory
remained in the third place. The final outcomes were
divided by the production rates to obtain a comparable
relative index, easy to be interpreted by different
stakeholders.
Regarding woollen products, Barber and Pellow
(2006) presented a research that produced a detailed
inventory of resource inputs for New Zealand Merino wool
and assessed its total energy use profile relative also to
other textiles. Results of this study show that New Zealand
Merino fibre production and early-stage processing uses
significantly less energy than synthetic fibres.
Brent (2004) proposed for a wool product a modified
LCIA procedure, which is based on the protection of
resource groups. A distance-to-target approach is used for
the normalisation of midpoint categories, which focuses
on the ambient quality and quantity objectives for four
groups: air, water, land and mined abiotic resources.
The case study establishes the importance of regionspecificity for life-cycle inventory (LCIs) and LCIAs.
In comparison to other works in the textile sector, this
research carried out a carbon footprint analysis of woollen
products involving all the supply chain stakeholders and
evaluating the impact of new management choices, such as
the transportation modality or the criteria for supplier
selection.
2.2 Uncertainty analysis
Uncertainty is a measure of the ‘goodness’ of results.
Since the LCA is a model, there can always be errors
that cause a certain level of uncertainty in the results.The uncertainty may depend on several factors: poor
correspondence between the software models and the
reality and between geographic and temporal aspect,
non-representative measurement samples, non-complete
information, poor reliability of data, etc. (ISO 14040
2006). Traditionally, the LCA (inventory and impact
assessment) is a deterministic model used for estimating
the potential impacts associated with a product. However,
the LCA’s primary weakness lies in its improper treatment
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of the uncertainty resulting from the sparse and imprecise
nature of available information and the simplified model
assumptions (Lo et al. 2005). This fact is demonstrated by
Jimenez-Gonzalez and Overcash (2000) who, comparing
the LCI results for refinery products among several
available databases, have shown that the variability of
estimated emissions to the atmosphere, water-borne and
solid waste are approximately 50– 150, 1000 and 30%,respectively.
Variability and parameter uncertainty of unit process
inputs and outputs, e.g. measurement uncertainties,
process-specific variations, temporal variations, etc., are
expressed in quantitative terms on the level of individual
inputs and outputs of unit processes. This type of
uncertainty has been treated consistently and in a
quantified way within the ecoinvent project (Frischknecht
and Rebitzer 2005). In this work, a lognormal distribution
has been assumed for all unit processes of ecoinvent data.
In fact, several reports in the field of risk assessment and
impact pathway analysis have shown that the lognormal
distribution seems to be a more realistic approximation for
the variability in fate and effect factors than the normal
distribution (Hofstetter 1998).
The Monte Carlo analysis has been used in order to
obtain, for each calculated impact, not only the average
value but also the distribution curve of the results
characterised by uncertainty parameters.
The statistical principle of the Monte Carlo method
consists in repeating calculation many times. Each time a
random value is chosen for each flow, for example an
emission or raw material input. In this work, the
uncertainty analysis was based on 1000 calculations.
According to Langevin et al. (2010), this number of iterations was a compromise solution between simulation
time and precision of results. Uncertainty information can
be derived with basic statistical methods from the
distribution of the calculation results.
The values chosen in the Monte Carlo analysis are
within a specified distribution. In this study, the
uncertainty estimation for each emission was calculated
using the following Equation (1) developed by Weidema
and Wesnaes (1996). The pedigree matrix (see Table A1 in
Appendix) was used to calculate the U i terms ði ¼
1; . . . ; 6Þ shown in the following standard deviation (SD)
equation:
where U 1 is the uncertainty factor of reliability; U 2, the
uncertainty factor of completeness; U 3, the uncertainty
factor of temporal correlation; U 4, the uncertainty factor of
geographic correlation, U 5, the uncertainty factor of other
technological correlation, U 6, the uncertainty factor of
sample size; and U b, the basic uncertainty factor (see Table
A2 in Appendix).
In order to reduce uncertainty as much as possible, the
maximum level of detail in the input data has been reached
by obtaining the main data directly from the different
parties involved in the process.
3. Case study
The LCA methodology has been used to define the carbon
footprint of a wool sweater made by a leader company in
the textile sector. The entire production chain has been
examined and all the single contributions to the
environmental impact have been evaluated. Hypothesis
for improving the resources use and management has been
proposed, again using the LCA approach.
The idea with which many decisions were made was to
maintain a quite generic point of view since the main goal
was to assess if the LCA methodology could be appliedcontinuously and systematically to the entire garment
collection.
3.1 System boundaries
It was decided to define the boundaries of the system
starting from sheep breeding at the farms in South
Africa, from which the greasy wool is obtained, then
considering the other production phases, the distribution
to the final stores all around the world and, finally, the
user phase (washing and final disposal) is shown in
Figure 1.Those boundaries comprehend all the phases related to
the sweater production, distribution and use: wool
scouring, dyeing, spinning, knitting, transportation to the
distribution centres, and then to the selling stores, washing
and final disposal.
All the packaging, especially the ones used for sending
the sweater from the production site to the store, were
considered.
Regarding the environmental boundaries, it was
decided to consider the agricultural systems as part of
the environment and this means, for example, that
pesticides are viewed as emissions.
3.2 Functional unit
In order to maintain a general point of view and to consider
an average sweater, all the different characteristics were
included in the functional unit.
SD95 ¼ s 2g ¼ exp
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi½lnðU 1Þ2 þ ½lnðU 2Þ2 þ ½lnðU 3Þ2 þ ½lnðU 4Þ2 þ ½lnðU 5Þ2 þ ½lnðU 6Þ2 þ ½lnðU bÞ2
q ; ð1Þ
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The sweater chosen as the case study had the following
features:
. 100% Merino wool;
. four colours; and
. 2009 winter collection.
The medium weight method was used to calculate
the average sweater. The parameters included in the
average sweater, representative of the functional unit,
were:
. Distribution size – each size assumes a different
weight as shown in Table 1.. Colour – each colour has a slightly different dyeing
process. Based on the colour distribution for the
examined sweater in the entire winter 2009collection, it was possible to calculate the
contribution of each colour in the medium sweater
in terms of weight (Table 2)..
Net weight – 264.85 g (without accessories).. Colour and accessories.
3.3 Life-cycle inventory analysis (LCIA)
The collected data have been divided into two different
groups: primary and secondary data are shown in Table 3.
The primary data were collected directly from the
companies involved in the different phases of the
production process by a questionnaire. Secondary data
were extracted from:
. The software models (yarn production bast fibres/IN
U 2007)..
‘BREF’ (best available technology referencedocument) of the European Community (2003).
Merino sheepbreeding
(South Africa)
Scouring
(Italy, Biella)
Yarn industry warehouse
(Prato, IT)
Throwing
(Biella, IT)
Knitwearfactory
warehouse(Carpi, IT)
Knitwear
factory(Romania)
Dyeworks
(Prato, Italy)
Spinning
(Varese, Italy)
Yarn industry
warehouse
(Prato, IT)
Knitwearfactory
warehouse(Carpi, IT)
Polybag, safety
pins, Ritzacord,hang tag, origin
label, flag label,
care label(Carpi)
Silk paper,carton,
barcode, care label, polybag.
(Como)
Distribution Centre : Stores :
Distribution Centre :
Japan
Germany
Canada Canadian
USA USA
Worldwide
Australia
Japan
Australia
Phases under the control of the knitwear factory
Phases under the control of the leader textile company
Phases under the control of yarn industry
WashingFinal disposal
Phases under the control of the final consumer
Figure 1. System boundaries.
Table 1. Percentage distribution for each size.
S M L XL XXL XXXL Total
Distribution piece-size (%) 4.71 20.59 25.29 24.71 17.65 7.06 100Pieces for size (n) 141.18 617.65 758.82 741.18 529.41 211.76 3000Weight total for sizes (g) 32654.12 150798.53 195017.65 201911.29 151025.29 63131.29 794538.18
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This document provides information about the best
techniques for the textile sector.. ‘Analysis of the production cycle in the textile and
wool sector’ of the ARPA of Piemonte Region
(Foddanu et al. 2005).
For the final phases of the life cycle, use and final
disposal, data were obtained from the literature, market
analyses, company data and ENEA database.
3.3.1 The production process
According to Byoungho (2004), the textile industry
represents one of the most various and complicated
productive processes of the entire manufacture system.
Generally speaking, it is possible to distinguish two main
different processes: mechanical (spinning and weaving)
and chemical (washing, dyeing and finishing; see Gold-
bach et al. 2003).The complete processing cycle of the analysed item
involves several steps. First comes shearing, followed by
sorting and grading, making yarn, dyeing, finishing,
making fabrics, making up the sweater and distributing.
In synthesis, seven companies are involved in the
production of the analysed item with 17 transportation
phases for a total of more than 10,000 km, 5 KW h of
electrical energy, 18 MJ of thermal energy, 60 g chemicals
and detergents, 200 g of packaging and 350 g of waste.
Regarding the use of the sweater, data were obtained
from company data and from ENEA (2003) guidelines.
A medium life of 5 years with 15 washes per year was
considered, 308C of washing temperature, 10 litres of
water for each cycle, 130 ml of chemicals5 (soap and
conditioner).For the final life phase, the final disposal, from the
literature data, it was established that 49% was disposed
of, 49% was burned and the last 2% was reused.
The transportation scheme for the distribution centres
and the relative percentage are shown in Figure 2.
3.4 Impact calculated by IPCC (2007)
By using IPCC (2007), it was possible to assess the
contribution of each phase to the carbon footprint
measured as CO2 equivalent. Figure 3 shows that the
total amount of CO2 produced is 1.947 kg for the single
item for the phases that go from breeding to final disposal.
It is also evident that four phases give the main
contribution to the CO2 production:
(1) The transportation from the DC to the stores
(0.470 kg CO2).
(2) The sweater realisation (0.384 kg CO2), since the
material reaches the knitwear factory in Romania by
truck (Euro 3), and it goes back to Carpi in the same
way.
Table 2. Average sweater characteristics.
Accessories
Number Description Weight
1 Main label (regular fit) 0.31 g1 Flag label 0.11 g1 Origin label 0.11 g
1 Hang tag 1.83 g1 Safety pins (hang tag) 0.2 g1 Cord Ritza (hang tag) 0.3 g1 Care label 0.3 g2 Barcode 0.1 g1 Silk paper 4.25 g1 Polybag 15.65 g
Colour 001 032 038 521 522 Total (kg)
Weight distribution (%) 111.81 3.22 82.25 32.00 35.58 264.85
Table 3. Data sources.
Secondary data
Supplying company Production processes Primary data Software model BREF ARPA
A Sheep breeding X XB Scouring X XC Dyeing X X XD Spinning X X XE Trowing and vaporising X XF Knitwear factory X X
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(3) The breeding phase (0.376 kg CO2).
(4) The washing phase (0.280 kg CO2).
3.4.1 Uncertainty assessment (IPCC 2007)
The Monte Carlo analysis was used to determine the
uncertainty in the calculation of the CO2 equivalent by
setting the boundaries from breeding to final disposal.
It calculated an average value of 1.947 kg with a SD of
0.179. The range goes from 1.614 to 2.331 kg with a 95%
confidence interval (Figure 4).
3.5 Life-cycle interpretation
In the research reported here, the LCI results were
analysed and processed by means of a contribution
analysis and an uncertainty analysis based on Monte Carlo
simulations. The accuracy obtained using such method is
acceptable since the range of the confidence interval is
about 20% of the steady-state mean.
The research demonstrated that the CO2 production
related to the chosen sweater depends mostly on the
complexity of the supply chain and on the distribution
system.
Figure 2. Scheme of the transportation from the warehouse to the DC.
0.376
0.074
0.003
0.142
0.384
0.218
0.470
0.280
0.0000.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
0.500
Breeding ScouredWool
Dyed FlockWool
FinishedYarn
FinishedGarment
TransportDC
DistributionShops
Washing FinalDisposal
Life-cycle phases
k g C O 2
Figure 3. Percentage of CO2 in the different phases.
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The transportation is the main CO2 generator in the
sweater production process; in fact, the CO2 equivalent
produced in each phase depends mainly on the goods
transportation modality and the travel length. Figure 5
shows the contribution of the transports (goods and
packaging) in all the garment life cycle.
Figure 5 also shows that the packaging gives animportant contribution of nearly 40% to the CO2 emission,
especially in the final phases of the life cycle.
For this reason, a model for designing a new packaging
system has been created. New boxes of different sizes
(small/large-sized) and less weight have been tested in
order to insert more sweaters into a single box. However,
those attempts were often refused from sales managers
mostly for aesthetic reasons, but also because the solution
proposed did not optimise the truck saturation. In fact, the
results demonstrate that the packaging was already been
conceived for minimising the volumes transported and that
a new design provided irrelevant improvements (,1%).
Moreover, the production and disposal of the packaging do
not contribute to the CO2 production.
The model of the transportation to the distribution
centres represents a particularly interesting field for theleader company for two main reasons: it is under its direct
control and it is a phase that involves all the goods
produced by the company. This means that the model
represents big volumes of items and, as a consequence, an
improvement in this phase can affect widely the entire
production chain.
The carbon footprint for each destination was
calculated considering not only the impact related to the
transportation and its modality but also the volume of the
Figure 4. Uncertainty analysis kg CO2 eq 100 (IPCC 2007).
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
0.500
k g C O 2
Transp good 0.344 0.053 0.003 0.074 0.250 0.130 0.360 0.280 0.000
Transp pack 0.000 0.000 0.000 0.035 0.113 0.087 0.108 0.000 0.000
Other 0.032 0.021 0.000 0.033 0.021 0.001 0.002 0.000 0.000
Breeding Scoured
Wool
Dyed Flock
Wool
Finished
Yarn
Finished
Garment
Transport
DC
Distribution
Shops Washing
Final
Disposal
Figure 5. Contribution of transportation for each phase.
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goods. The results attributed to the main impact, in terms
of CO2 equivalent produced, to the Carpi–USA destina-
tion are shown in Figure 6. The following graph clearly
shows that a variation for the USA transportation modality
can have the greatest impact in terms of CO2 saving.Even if Australia and Japan have a similar volume, the
goods travel on a freight ship for Australia, and aeroplane
for Japan, and this justifies the greater impact of the last
route.
4. Sensitivity analysis
A sensitivity analysis was carried out in order to evaluate
the impact of management choices such as:
. a change in the transportation modality, from
aeroplane to boat;. a combination of road and rail transportation;
. a suppliers selection that allows the firm to cut down
the environmental impacts; and. a change in consumer behaviour.
4.1 Transport
It has already been demonstrated that the main impact in
terms of CO2 production is given by transportation. In thisparagraph, the sensitivity of this parameter to the
distance, volumes of goods and transportation means are
presented.
The calculated impact of 1.947 kg CO2 is a global
average value, the real value for the single sweater
depends strongly on the selling-point localisation. Figure 7
shows how the CO2 equivalent produced depends on the
transportation to the selling points but it does not give
indications about the priorities to address in order to
reduce it, since, for this purpose, an analysis of volumes is
required.
The carbon footprint produced during the transpor-
tation phase to the stores is about 34%; by changingthe transportation modality (from road to rail and from
plane to boat), it is possible to cut the CO2 production by
20–30%.
The transportation of the goods to the distribution
centres contributes for the 0.22 kg to the total carbon
footprint. The contribution of each DC weighted over the
volumes is shown in Figure 8.
In order to reduce the CO2 equivalent, two different
approaches are required. First, it is necessary to look upon
the destinations that give the higher contribution
considering distances, volumes and modality. Second,
the impact of a change in the transportation modality has
Distribution Center Contribute
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
Carpi– Wendlingen
Carpi–USA Carpi–Canada Wendlingen– Australia
Wendlingen– Japan
Route
K g
C O 2
e q
Tare
Net weight
Figure 6. CO2 equivalent for the different destinations.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
M e l b o u r n e
G r e a t e r L o n d o n
( V i a S E A )
G r e a t e r L o n d o n
( v i a A I R )
N o r t h R h i n e
W e s t f a l i a
P a r i s
N e w Y o r k
S h a n g a i
T o k y o
K g
C O 2
Carpi–Wendlingen DC - Stores
Production Transport DC Shop Distribution
Wendlingen–Melbourne DCbyboat
Carpi–USA DCby aeroplane
Wendlingen DC - Storeby aeroplane
Carpi–Japan DCby aeroplane
Figure 7. Carbon footprint related to the selling-point location.
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to be modelled: from aeroplane to boat and from road to
rail.
By changing these parameters, a total reduction of
0.18 kg that corresponds to 84% in this phase has been
calculated.
4.2 The combination of road– rail transportation
In the research project, the transportation distance between
C ar pi a nd We nd li ng en w as a na ly se d w it h a n
‘accompanied combined transportation’ model. From
Carpi, the trucks travel by road to the hub in Como,
from which they reach Chiasso where they can be loaded
directly on the trains and arrive in Basilea. From there, the
trucks go back to the road until they arrive at the finaldestination in Wendlingen.
The difference, in terms of grams of CO2 equivalent
produced for each sweater, considered for the different
transportation modalities is shown in Figure 9.
The adoption of the combined transportation methods
would allow the company to reduce about 37% of the
carbon footprint along this road. The total reduction
(considering the entire process from sheep breeding to the
stores) would be only 0.4%. This is due to the fact that the
Como– Wendlingen road provides a small contribution to
the global impact. Even if the benefit on the single sweater
seems to be low, nevertheless, the improvement obtained
with this operation on the total flow of goods transported
by the leader company along this road is significant.
Considering that the average gross weight of the sweater is
476 g and the annual flow is 500.000 kg, the benefit, in
terms of CO2 saving, is of 5.3 tonnes per year.
4.3 Suppliers selection
The selection of suppliers that guarantee good standards in
terms of environmental impact with reference to the
European Commission Standard (BREF) is a key factor in
terms of CO2 equivalent reduction. For the sweaterproduction processes, an amount of 0.066 kg CO2 per
piece has been accounted for. Referring to the BREF, this
is a fair low contribution and it is due to the company
environmental awareness in the supplier selection.
From the study, it has been assessed that a high
percentage of CO2 derives from the transportation
between suppliers. By hypothesising that all the suppliers
(the dyeing mills, the spinning and the knitwear factory)
are located in a nearest area, it is possible to save more
than 0.4 kg of CO2 for garment (28%). In Table 4, it is
shown how the reduction of distances between suppliers
could decrease the CO2 produced.
Moreover, the use of Euro 3 trucks to travel back and
forth from the warehouse to the knitwear factory in
Romania increases the effect. If Euro 4 trucks were used,
the benefit would be 2.15% for pieces (about 3 tonnes for
the annual production).
4.4 A change in consumer behaviour
The washing phase provides a great climate change impact
(0.28 kg CO2). A change in consumer behaviour could
0.00
0.02
0.04
0.06
–25% –91% –89% –98%
0.08
Volume = 1.13%Aeroplane
Volume = 0.85%
Boat
Volume = 1.29%Aeroplane
Volume = 5.54%
Aeroplane
Volume = 93.17%
Truck
0.10
0.12
0.14
GermanyCarpi–Germany
USACarpi–USA
CanadaCarpi–Canada
AustraliaGermany–Australia
JapanGermany–Japan
K g C O 2
e q
Figure 8. Contribution of transportation modality related to the volumes of goods.
15.6
9.69.8
0
2
4
6
8
10
12
1416
18
Road Train Rain container
Means of transport
g C O 2 e q / p i e c e
–5.8 g/piece
Figure 9. CO2 production along the road Como–Wendlingen.
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considerably decrease this impact. As an example, it
should be possible to cut down the number of washing per
year to less than the 15 times established in the ENEA
study and used in this work to assess the impact of the final
life phase. Moreover, the consumer could modify the
washing temperature (assumed as 308C in this research)
changing considerably the climate change impact
(Figure 10).
5. Discussion and conclusions
In the last few years, life-cycle thinking has been the focal
point in the environmental policy development of the
European Community standardised by the IntegratedProduct Policy. Many other standards are utilised in other
countries.
In this scenario, LCA provides the scientific references
for all the activities related to the Corporate Sustainability
Report, which is a tool being used by more than 500 of the
major companies all around the world to communicate
their environmental policies to the market. The use of the
LCA methodology allows the companies to evaluate and
communicate the environmental impact of their processes
and products.
With the introduction of more restrictive environmen-
tal standards, such as PAS 2050, the carbon footprint
analysis could be one of the criteria to evaluate thesuppliers from an environmental point of view and to
improve the entire supply chain and to support the
realisation of green products by the eco-design.
The aim of this paper was to assess the carbon footprint
associated with a particular product of a leader textile
company. The selected item has been utilised as a case
study: average characteristics were chosen in order to
define the input for the LCA model. This decision was
made with the objective of performing a pilot project to
assess the feasibility of the LCA methodology to be
applied to the entire company production.
A strong support of a leader company was essential to
overcome the reluctance of some stakeholders in
providing data. According to Seuring and Muller (2004),
inhumane working conditions or contaminations of the
(local) environment could be frequently a problem in order
to obtain information from suppliers.With the use of LCA, it is possible to face the
environmental analysis at different levels. In the proposed
case study, the main aim was the carbon footprint
evaluation but along the study, many other aspects were
considered.
The LCA method could be a powerful tool to address
the eco-efficiency promotion and to provide several
benefits to the company. Particularly, the work developed
in this case study allowed to make the customers aware
that the product is environmentally sound; in fact, the
results obtained were introduced in the marketing
campaign. The study also allowed the company to
improve production performances in terms of efficient
managing and use of resources.
Moreover, the collaboration between several stake-
holders made this work useful to increase the environ-
mental consciousness of the employees and of the supply
chain operator involved along the production process.
Acknowledgements
This research work was made possible due to the collaborationbetween the Knitwear and the IT & Logistic Departments of thecompany that, for privacy reasons, will be called GHB and theDepartment of Energy Studies of Marche Polytechnic University.
Notes
1. Email: [email protected]. Email: [email protected]. Email: [email protected]. Where products are distributed to different points of sale
(i.e. different locations within a country), emissionsassociated with transport will vary from store to store dueto different transport requirements. Where this occurs,organisations should calculate the average release of GHGs associated with transporting the product based inthe average distribution of the product within eachcountry, unless more specific data is available (publiclyavailable specification, PAS 2050 (2008), par. 6.4.6,note 4).
The GHG emissions arising from the production of capital goods used in the life cycle of the product shall beexcluded from the assessment of the GHG emissions of the life cycle of the product (publicly availablespecification, PAS 2050 (2008), par. 6.4.3).
5. Data obtained from ENEA.
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T a b l e A 1 .
P e d i g r e e m a t r i x .
S c o r e
1
2
3
4
5
U 1 ,
R e l i a b i l i t y
V e r i fi e d d a t a b a s e d
o n m e a s u r e m e n t s
V e r i fi e d d a t a p a r t l y b
a s e d o n
a s s u m p t i o n s o r n o n - v
e r i fi e d
d a t a b a s e d o n m e a s u r e m e n t s
N o n - v e r i fi e d d a t a p a
r t l y
b a s e d o n q u a l i fi e d
e s t i m a t e s
Q u a l i fi e d e s t i m a t e
( e . g . b y i n d u s t r i a l e x p e r t ) ;
d a t a d e r i v e d f r o m
t h e o r e t i c a l
i n f o r m a t i o n
( s t o i c h i o m e t r y , e n t h a l p y , e t c . )
N o n - q u a l i fi e d
e s t i
m a t e
1 . 0 0
1 . 0
5
1 . 1 0
1 . 2 0
1 . 5 0
U 2 ,
C o m p l e t e n e s s
R e p r e s e n t a t i v e d a t a f r o m
a l l
s i t e s r e l e v
a n t f o r t h e m a r k e t
c o n s i d e r e d
o v e r a n a d e q u a t e
p e r i o d
t o
e v e n
o u t n o r m a l
fl u c t u a t i o n
s
R e p r e s e n t a t i v e d a t a f r o m
.
5 0 %
o f t h e s i t e s r e l e v a n t
f o r t h e m a r k e t c o n s i d
e r e d M
o v e r a n a d e q u a t e p e r i o d
t o e v e n o u t n o r m a l fl u c t u a t i o n s
R e p r e s e n t a t i v e d a t a
f r o m
o n l y s o m e s i t e s ( p
5 0 % )
r e l e v a n t f o r t h e m
a r k e t
c o n s i d e r e d
o r .
5 0 %
o f
s i t e s
b u t
f r o m
s h o r t e r
p e r i o d s
R e p r e s e n t a t i v e d a t a f r o m
o n l y o n e s i t e r e l e v a n t f o r
t h e m a r k e t c o n s i d e r e d
o r s o m e s i t e s b u t f r o m
s h o r t e r p e r i o d s
R e p
r e s e n t a t i v e n e s s
u n k
n o w n o r d a t a
f r o m
a s m a l l
n u m
b e r o f s i t e s a n d
f r o m
s h o r t e r p e r i o d s
1 . 0 0
1 . 0 2
1 . 0
5
1 . 1 0
1 . 2 0
U 3 ,
T e m p o r a l
c o r r e l a t i o n
, 3 y e a r s o f d i f f e r e n c e t o o u r
r e f e r e n c e y e a r ( 2 0 0 0 )
,
6 y e a r s o f d i f f e r e n c e
t o o u r r e f e r e n c e y e a r
( 2 0 0 0 )
,
1 0 y e a r s o f d i f f e r e
n c e
t o
o u r
r e f e r e n c e
y e a r
( 2 0 0 0 )
,
1 5 y e a r s o f d i f f e r e n c e
t o o u r r e f e r e n c e y e a r
( 2 0 0 0 )
A g e o f d a t a
u n k
n o w n o r m o r e
t h a n 1 5 y e a r s o f
d i f f
e r e n c e t o o u r
r e f e
r e n c e y e a r
( 2 0 0 0 )
1 . 0 0
1 . 0 3
1 . 1 0
1 . 2 0
1 . 5 0
U 4 ,
G e o g r a p h i c a l
c o r r e l a t i o n
D a t a f r o m
a r e a u n d e r s t u d y
A v e r a g e d a t a f r o m l a
r g e r a r e a
i n w h i c h t h e a r e a u n d
e r s t u d y
i s i n c l u d e d
D a t a
f r o m
s m a l l e r
a r e a
t h a n a r e a u n d e r s t u d y , o r
f r o m s i m i l a r a r e a
D a t a f r o m
u n k n o w n
o r d i s t i n c t l y
d i f f
e r e n t a r e a s
( N o
r t h A m e r i c a
i n s t e a d o f M i d d l e
E a s
t , O E C D - E u r o p e
i n s t e a d o f R u s s i a )
1 . 0 0
1 . 0 1
1 . 0 2
1 . 1 0
U 5 ,
F u r t h e r
t e c h n o l o g i c a l
c o r r e l a t i o n
D a t a f r o m
e n t e r p r i s e s , p r o -
c e s s e s a n d
m a t e r i a l s u n d e r
s t u d y ( i . e .
i d e n t i c a l t e c h n o l -
o g y )
D a t a o n r e l a t e d p r o c e s s e s
o r
m a t e r i a l s
b u t
s a m e
t e c h n o l o g y , o r D a t a
f r o m
p r o c e s s e s
a n d
m a t e r i a l s
u n d e r
s t u d y
b u t
f r o m
d i f f e r e n t t e c h n o l o g i e
s
D a t a o n r e l a t e d p r o c e s s e s
o r m a t e r i a l s b u t
d i f f e r e n t t e c h n o l o g i e s ,
o r d a t a
o n
l a b o r a t o r y - s c a l e
p r o c e s s e s a n d s a m e
t e c h n o l o g i e s
D a t a o n r e l a t e d
p r o c e s s e s o r
m a t e r i a l s b u t o n
l a b o r a t o r y s c a l e
o f d i f f e r e n t
t e c h n o l o g i e s
1 . 0 0
1 . 2 0
1 . 5 0
2 . 0 0
U 6 ,
S a m p l e s i z e
. 1 0 0 , c o
n t i n u o u s m e a s u r e -
m e n t , b a l a n c e o f p u r c h a s e d
p r o d u c t s
.
2 0
.
1 0 , a g g r e g a t e d fi g u r e i n
e n v i r o n m e n t a l r e p o r t
$
3
U n k n o w n
1 . 0 0
1 . 0 2
1 . 0
5
1 . 1 0
1 . 2 0
A p p e n d i x
T a b l e A 1 r e p r e s e n t s t h e p e d i g r e e m a t r i x a n d T a b l e A 2 t h e b a s i c u n c e r t a i n t y f a c t o r U b
v a l u e s a r e d e fi n e d i n P r e C o n s u l t a n t s ( 2 0 0 6 ) .
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Table A2. Basic uncertainty factor.
Input/output group U b Input/output group U b
Demand of Emission to air of Thermal energy 1.05 CO2 1.05Electricity 1.05 SO2 1.05Semi-finished products 1.05 Combustion: NO X , NMVOC total, methane, N2O and NH3 1.50Working materials 1.05 Combustion: CO 5.00
Transport services 2.00 Combustion: individual hydrocarbons, TSM 1.50Waste treatment services 1.05 Combustion: PM10 2.00Infrastructure 3.00 Combustion: PM2.5 3.00
Resources Combustion: polycyclic aromatic hydrocarbons (PAH) 3.00Primary energy carriers 1.05 Combustion: heavy metals 5.00Metals, salts 1.05 Process emissions: individual VOCs 2.00Land use, occupation 1.50 Process emissions: CO2 1.05Land use, transformation 2.00 Process emissions: TSM 1.50
Waste heat Process emissions: PM10 2.00Emission to air, water and soil 1.05 Process emissions: PM2.5 3.00
Emission to water of From agriculture: CH4, NH3 1.20BOD, COD, DOC, TOC 1.50 From agriculture: N2O, NO X 1.40Inorganic compounds (NH4, PO4, NO3, Cl, Na, etc.) 1.50 Radionuclides (e.g. Radon-222) 3.00Individual hydrocarbons, PAH 3.00 Process emissions: other inorganic emissions 1.50Heavy metals 5.00 Emission to soil of
From agriculture: NO3, PO4 1.50 Oil, hydrocarbon total 1.50From agriculture: heavy metals 1.80 Pesticides 1.20From agriculture: pesticides 1.50 Heavy metals 1.50Radionuclides 3.00 Radionuclides 3.00
M. Bevilacqua et al.36