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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

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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



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

International Journal of Sustainable Engineering 25



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

M. Bevilacqua et al.26



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




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




(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.




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.




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.




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.




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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0.000

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Figure 10. CO2 production changing the washing temperature.




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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


.

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


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


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


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 ) .




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


carbon footprint analysis in textile suppy chain

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