Identifying Environmental Risk Hot Spots in the Apparel Supply Chain FINAL

Identifying Environmental Risk Hot Spots in the Apparel Supply Chain:
A Case Study Using a Cradle-to-Gate SimaPro Model analyzed with
IMAPCT2002+
Stefan Kuzmanovski
Master of Environmental Management Candidate 2017
prepared for F&ES 889 Environmental Risk Assessment
Yale University School of Forestry and Environmental Studies
Course Instructor: Prof. Yehia F. Khalil, Ph.D., Sc.D.
Fall 2015
© ACSK Clothing DOOEL 2015, Cut, Make and Trim, Tier 1 Supplier in Macedonia

2
Contents
1 EXECUTIVE SUMMARY........................................................................................................................ 3
2 INTRODUCTION ..................................................................................................................................5
2.1 Introduction to the Apparel and Fashion Industry..........................................................................5
2.2 Study Goal and Scope ................................................................................................................... 7
2.3 System Boundaries and Functional Unit ........................................................................................ 7
2.5 Data Sources .................................................................................................................................8
3 LIFE CYCLE INVENTORY.................................................................................................................... 10
3.1 Cotton Cultivating........................................................................................................................ 11
3.2 Yarn Production........................................................................................................................... 14
3.3 Fabric Production......................................................................................................................... 15
3.4 Fabric Finishing............................................................................................................................ 16
3.4 Stitching (Cut, Make and Trim –CMT).......................................................................................... 19
4 Life Cycle Impact Assessment (LCIA) ................................................................................................. 21
4.1 Assessment with IMPACT 2002+ ................................................................................................. 21
4.2 Hotspots and Recommendations ................................................................................................22
4.2.1 Normalized results................................................................................................................ 23
4.2.1 Climate Change ....................................................................................................................26
4.2.2 Human health....................................................................................................................... 30
4.2.3 Ecosystem Quality................................................................................................................ 32
4.2.4 Resources............................................................................................................................. 34
4.2.5 Unmapped Hotspots/Limitations.......................................................................................... 35
5 Conclusion.......................................................................................................................................... 37
6 Bibliography.......................................................................................................................................39

3
1 EXECUTIVE SUMMARY
ACSK Clothing is a textile management agency located in Bremen, Germany with sourcing offices in
Macedonia and Pakistan. ACSK Clothing caters to the German wholesale B2B market by sourcing
special production of a wide variety of clothing items – corporate-wear, sports-wear and knit-wear in
Macedonia and Pakistan. The German B2B customer is increasingly aware of environmental and social
issues in the textile industry and the need for a better understanding of the environmental risk in the
supply chain of ACSK Clothing’s products has arisen.
We find that while some common environmental risks can be identified along the supply chain in the
textile industry there is a vast difference in supply chains and the need to use fabric, location and facility
specific data makes it very difficult to make general recommendations about environmental hotspots in
the apparel supply chain. Every supply chain is unique and is in a different country where different
pesticides are used to grow the cotton and the electricity mix leads to a different carbon intensity of the
manufacturing process, for example. Instead, it is the recommendation of the author, to use this study
and similar publications as a basis for “buyers” in the textile industry to start employing more widely the
LCIA method for identifying environmental risk hotspots in their unique supply chains.
We establish some of the main environmental hotspots in the supply chain for a 100% cotton t-shirts
manufactured in Macedonia by a commonly used supplier of ACSK Clothing in the country. The supplier
has a vertically integrated production facility which uses cotton grown in India but spins their own yarn,
knits and dyes their own fabric and cuts and makes their own garments as well – delivering a final
packaged product to ACSK Clothing. This study looked at the cradle-to-gate LCIA of a 100% cotton t-
shirt and identified two main environmental risk hotspots while highlighting the limitations of our
model which yield many potential “hidden” environmental risk hotspots.

4
The first main finding is that electricity consumption during the knitting stage (38.2% of the total
GHGs emission related to manufacturing of 1 packaged t-shirt) is the biggest contributor to climate
change, closely followed by the yarn spinning process (34.3%), then dying the greige fabric (13.8%) and
finally the cut, make and trim stage (2.4%). Electricity consumption was also found to be the main
contributing process toward decreased human health and resource depletion. The main
recommendation is to increase education about energy efficiency potentials in the textile industry as
well as look into combined on-site heat and power generation from natural gas in countries with fossil
fuel-heavy grid electricity mix. Photovoltaic on-site energy generation could also be used to power
some parts of the operations during the supply chain but not the heavy-weight machines used for
spinning and knitting.
The second main finding is the intense degradation of ecosystem quality as manifested by the need to
convert natural “un-touched” land into cotton plantations to meet rising demand for cotton, causing
land degradation worldwide. The main recommendation on the production side is to promote using
recycled cotton and fabric scraps to reduce the cultivation of cotton in the first place and look into less
resource and land intensive fibers which can be used to make apparel.

5
2 INTRODUCTION
2.1 Introduction to the Apparel and Fashion Industry
The fashion and apparel industry’s environmental and social impact can be gauged from its sheer
volume and size. Clothing and textiles combined accounted for 4.3% (WTO ITS, 2005, page 71) of all
world-wide exports in 2014 with China, India, Pakistan, Thailand and Indonesia accounting for 47 %
(WTO ITS, 2005, page 116) of all textile exports worldwide and China, Bangladesh, Vietnam and
Indonesia accounting for 49.3% of all clothing exports worldwide (WTO ITS, 2005, page 120). The main
destinations for textiles and clothing, not surprisingly, are the EU and United States with 37.4% of
worldwide clothing imports and 19.7% of all textile imports (most textiles are actually imported into
China, Mexico and Vietnam for CMT – Cut, Make and Trim and then re-exported to developed
countries) (WTO ITS, 2005). The global fashion and apparel industry also accounts for 9% of world’s
employees (Caniato et al., 2012)
In recent years environmental and social sustainability has been brought to the forefront of challenges
being faced by businesses which are both (1) strongly consumer facing and have (2) extensive supply
chains spanning the globe. The fashion and apparel industry meets both of these criteria due to strong
sense of identification of a consumer with their particular fashion brand of choice and the vast network
and infrastructure going into the production of apparel (Caniato et al., 2012). High-profile media
coverage of the fashion industry has increased pressure in the fashion and apparel industry to address
issues in their supply chain has increased after high-profile incidents like the Primark “forced labor”
note stitched inside a dress by a worker at one of Primark’s suppliers which was sold to a customer in
Norther Ireland1
(CNN, 2014) , the collapse of a roof of garment factory in Bangladesh killing 1,129

6
people (the Guardian, 2015) in 2013 and the factory fire in Karachi, Pakistan killing 300 people in 2012
(New York Times, 2012).
However, how does a fashion or apparel company embark on a journey of increasing environmental
sustainability in their supply chains? Today, fashion and apparel brands are faced with a plethora of
options for environmental and social certification standards as well as many opportunities to address
sustainability in their supply chains which are unique to the products they make or their unique supply
chain circumstances. The main question is at what stage in the supply chain a “sustainability”
intervention is warranted the most and would make the biggest impact on the environment. In order to
answer this question, we will identify the main environmental risks in the apparel supply chain and
make recommendations for remedying these risks.
We establish some of the main environmental hotspots in the supply chain for a 100% cotton t-shirts
manufactured in Macedonia by a commonly used supplier of ACSK Clothing in the country. The supplier
has a vertically integrated production facility which uses cotton grown in India but spins their own yarn,
knits and dyes their own fabric and cuts and makes their own garments as well – delivering a final
packaged product to ACSK Clothing. This study looked at the cradle-to-gate LCIA of a 100% cotton t-
shirt and identified two main environmental risk hotspots while highlighting the limitations of our
model which yield many potential “hidden” environmental risk hotspots.
Previous research from Caniato et al. (2012), Zhang et al. (2015), Brito et al. (2008) have identified hot-
sports in the apparel supply chain almost at all stages, however, their relative impacts are rarely
summarized and assessed in the literature. In Table 1 we can see the main anticipated environmental
risk hot spots in the apparel supply chain based on their research.

7
Table 1 Anticipated environmental risk hotspots based on research from Caniato et al. (2012), Zhang et al. (2015), Brito et al.
(2008)
2.2 Study Goal and Scope
The goal of this study is to perform a cradle-to-gate life cycle assessment using the SimaPro software
for one packaged t-shirt in order to identify major environmental risk hotspots using the IMPACT 2002+
methodology which analyses the impact on human health, resources, ecosystem quality and climate
change. Based on these hotspots recommendations based on a literature survey will be outlined.
2.3 System Boundaries and Functional Unit
Functional Unit: This study looks at a packaged 100% combed cotton t-shirt, 22/1 knitted fabric, with
fabric density of 155 g/m2
, with assumed average fabric consumption of 1.4 m2
(240 grams of fabric) for
the modeled t-shirt (Size M). Thread Count is 22 Tex and the mass of the t-shirt is (227 grams).
Supply Chain Stage Main Input Useful Output Anticipated Environmental
Hotspot
Fiber cultivation Land, water, nutrients Cotton Fibers Water consumption and
chemical consumption/waste
Petroleum Man-made fibers Resource depletion and Energy
consumption
Spinning Fibers and Energy Yarn Energy consumption
Knitting/Weaving Yarn and energy Greige fabric Energy consumption
Dying of fabric Greige fabric, chemical
dyes, water and
energy
Dyed fabric Chemical use, water use and
pollution, and energy
consumption
CMT Dyed fabric Apparel Scrap fabric and energy
consumption
Distribution Apparel and CO2
emissions
Delivered apparel
product
CO2 emissions of different
modes of transportation
Use stage Apparel product Washed apparel
product
Water and energy use due to
washing
End-of-life Recycling Down-cycled
fibers
Landfill Decomposed
apparel product
Incineration Energy recovered
with incineration
Emissions to air

8
Figure 1 System boundary, cotton grown in India and all other processes in Macedonia
The system boundary of the life cycle assessment is shown in Figure 1 and shows the major processes
modelled: (1) cultivation of cotton, (2) spinning of the cotton fiber, (3) knitting of the fabric, (4) batch
dyeing of the fabric, (5) CMT (cut-make-and-trim) and packaging.
The model accounts for waste/scrap rates of 15% during cotton spinning and 15 % during yarn knitting
(Wiegmann, 2002), 4% during fabric dyeing (ACSK Clothing; Wiegmann, 2002), and 10% during CMT
(ACSK Clothing). The model only looks at treatment of the wastewater during the dying phase and
does not model any other waste treatments for the purpose of this study. Human labor, construction of
capital equipment, and maintenance and operation of support equipment are excluded from the
system boundary as they are not relevant for estimating the environmental impact.
2.5 Data Sources
The developed model is based on the Ecoinvent report No. 21, Life Cycle Inventories of Renewable
fibers (Althaus, H. J. et al, 2007). The report contains details for a reference model of a global mix of
cotton production in China and the USA, cotton processing in China and the USA, yarn spinning in
China and the USA, fabric knitting in China and the Czech Republic, and fabric finishing in China and
Italy. CMT is not included in the model but data to build your own processes is available in the original
source files from Wiegmann (2002).The existing Ecoinvent model is a complex supply chain with

9
different stages of the supply chain taking place in different countries and is meant to be a
representative of the most common supply chain of a t-shirt sold in the EU. Of course, while our model
has the same processes our production takes place in different countries and we are also manufacturing
a slightly different t-shirt. Therefore, the reference model was adapted to meet the requirements and
realities of this project to the best extent that new data and region specific data for our manufacturing
countries was available in the literature or the Ecoinvent database. The model from Ecoinvent which is
based on data from Wiegmann (2002) can be seen in the graph bellow, translated from German into
English by the author as only a German version was available online.
Figure 2 Adapted from Wegmann (2002), self-translation, the Ecoinvent-report 21 process flow, percentages are the contributions
in that process from the different countries

10
If we compare the Ecoinvent model based on data from Wiegmann (2002) as it can be seen in Figure 2
and the model for our reference t-shirt we can see that our model employs a vertically integrated
supplier in Macedonia where all stages from spinning to packaging of the t-shirt take place compared to
the mix of USA/Chinese and Check/Italy averages used in the Ecoinvent model. Therefore, the
Ecoinvent model was altered to fit our unique situation of a vertically integrated supplier. Any
differences from the Ecoinvent model will be noted in the LCI (Life Cycle Inventory) section of this
report.
3 LIFE CYCLE INVENTORY
It is important to note that while all of the values for the individual inputs/outputs for the various
process/stages in the t-shirt supply chain can be obtained from the Ecoinvent documentation, there is
only one Ecoinvent process for knitted fabric (Textile, knit cotton {GLO}| textile production, knit cotton,
batch dyed | Alloc Def, S) which can be used in SimaPro. This process is an aggregate process using all
of the values from the Ecoinvent documentation but the processes doesn’t break down the supply chain
to individual stages like we have done in our model. Therefore, we had to manually create each
stage/process in the supply chain using the Ecoinvent values ourselves instead of using ready-made
processes from Ecoinvent. Breaking down the processes/stages allowed us to look at the environmental
impact of each separate stage/process in the supply chain. If we were to analyze Textile, knit cotton
{GLO}| textile production, knit cotton, batch dyed | Alloc Def, S process from Ecoinvent in SimaPro we
would only get aggregate results from the environmental impact of the knitted fabric, and not the
individual contributing processes.
The SimaPro cradle-to-gate model we constructed using the data sources outlined above is composed
of the following processes:

11
1) Cotton cultivation in India (including transport of cotton fibers to Macedonia)
2) Spinning of the cotton fibers into yarn in Macedonia
3) Knitting the yarn into fabric
4) Finishing of the fabric (dyeing)
5) Cut, Make and Trim (CMT) and packaging of the t-shirt in a plastic bag ready for distribution
In the following sections we will look at the LCI for each of the processes.
3.1 Cotton Cultivating
All of the data for this initial process in the model was taken from the Ecoinvent documentation which
is a mixture of hand-picking input/output values for China and the USA, this giving us a good
representation of an average cotton cultivation process globally. However, we can note that in India
cotton cultivation could be much less industrial, meaning less pesticides and much less machinery is
used which could lead to less GHGs emissions as well as fertilizer and pesticide effluent to groundwater
and surface water bodies. However, lacking specific data on Indian cotton cultivation we believe the
China/USA Ecoinvent values are sufficient and well-representative of the global cotton cultivation
inputs/outputs to the environment.
The two main outputs of the cotton cultivation stage are the cotton fiber (1 kg) and the cotton seed (1.7
kg), the cotton seed is a product which is outside the scope of this study so we only look at the cotton
fiber which is used in the next stage in our supply chain.
Transportation of the cotton fiber from India to Macedonia was modelled using freight train, sea freight
and truck transportation data from Ecoinvent using and distance data from the website SeaRates
(www.searates.com)

12
Figure 3 Life cycle inventory (LCI) for the Cotton Production stage and transportation to Macedonia
Products Amount Unit Allocation Notes
_Cotton fiber 1.00E+00 kg 85
_Cotton seed 1.76E+00 kg 15
Inputs
Resources
Occupation, arable land, unspecified use 9.09E-04 ha a
Water, lake, IN 8.00E+00 m3
Materials/fuels
Organophosphorus-compound, unspecified {RER}|
production | Alloc Def, S 5.58E-04 kg Commonly used pesticide
Pesticide, unspecified {RER}| production | Alloc Def, S 2.73E-03 kg Commonly used herbicide
Glyphosate {RER}| production | Alloc Def, S 2.73E-03 kg Commonly used herbicide
Chemicals organic 2.27E-05 kg MSMA for defoliation
Urea, as N {RER}| production | Alloc Def, S 5.91E-02 kg Commonly used fertilizer
Ammonia, liquid {RER}| market for | Alloc Def, S 1.18E-01 kg Commonly used fertilizer
Ammonium nitrate, as N {RER}| ammonium nitrate
production | Alloc Def, S 5.91E-02 kg Commonly used fertilizer
Phosphate fertiliser, as P2O5 {RER}| triple
superphosphate production | Alloc Def, S 9.09E-02 kg Commonly used fertilizer
Potassium chloride, as K2O {RER}| potassium chloride
production | Alloc Def, S 1.45E-01 kg Commonly used fertilizer
Truck 16t 2.00E+02 kgkm
Delivery of fertilizer/pesticides etc.
author approximation.
Transport, freight train {RoW}| market for | Alloc Def, S 1.07E+03 kgkm Transport from Chandigrah, IN to Ahmedabad, IN

13
Transport, freight, sea, transoceanic ship {GLO}| market
for | Alloc Def, S 7.70E+03 kgkm Transport from Ahmedabad, IN to Bar, MN
Truck 40t 4.11E+02 kgkm Transport from Bar, MN to Shtip, MK
Electricity/heat
Electricity, low voltage {IN}| market for | Alloc Def, S 6.39E-01 kWh Energy used for cotton ginning
Outputs
Emissions to air
Heat, waste 2.30E+00 MJ
Dinitrogen monoxide 6.03E-03 kg
Ammonia 2.44E-02 kg
Nitrogen oxides 1.27E-03 kg
Emissions to water
Phosphate 4.55E-04 kg
Phosphate 1.46E-04 kg
Nitrate 1.04E-01 kg
Phosphorus 4.61E-04 kg
Emissions to soil
Cadmium 1.59E-06 kg
Chromium 1.09E-04 kg
Copper -5.25E-08 kg
Mercury -7.33E-33 kg
Nickel 3.62E-06 kg
Lead 3.52E-06 kg
Zinc 3.39E-06 kg
Monocrotophos 9.76E-05 kg
Cyfluthrin 9.76E-05 kg
Dicofol 9.76E-05 kg
Trichlorfon 9.76E-05 kg
Imidacloprid 9.76E-05 kg
Piperonyl butoxide 9.76E-05 kg
Prometryn 2.70E-04 kg
Glyphosate 2.70E-04 kg
Alachlor 2.70E-04 kg
Fluometuron 2.70E-04 kg

14
3.2 Yarn Production
The main input in this process is the cotton fibers from India which are transported to the ACSK Clothing supplier in Macedonia which spins the
cotton fiber into yarn. Wiegmann (2002) assume a material loss during of 10-15% and we take 15% as a more conservative value due to old
machinery used at our supplier’s facility in Macedonia. The main input at this stage and many of the following stages is the on-site energy used.
However, the ACSK Clothing supplier’s facility in Macedonia has only one electricity meter for the whole facility, which means they couldn’t
provide separate data on how much energy is used during each of the stages in their production process. Therefore, we had to rely on literature
values for energy consumption and Wiegmann (2002) estimate this to be around 30.6 MJ per kilogram of cotton yarn for the spinning process.
We employed an Ecoinvent process for the electricity mix in Macedonia (Electricity, medium voltage {MK}| market for | Alloc Def, S ).
_Cotton yarn 1.00E+00 kg 100
Inputs
Materials/fuels
_Cotton fiber 1.15E+00 kg Scrap rate 15%
Electricity/heat
Electricity, medium voltage {MK}| market for | Alloc Def, S 3.06E+01 MJ Macedonia electricity mix
Outputs
Emissions to air
Heat, waste 3.06E+01 MJ
Figure 4 LCI for the Yarn Production Stage

15
3.3 Fabric Production
In the next step, at the same facility, the cotton yarn is placed on large circular knitting machines which knit the yarn into our greige fabric for
the t-shirt. This process also only requires energy and no other inputs. There is some heat released to the atmosphere due to electricity
consumption.
_Cotton fabric, greige 1 kg 100
Inputs
Materials/fuels
_Cotton yarn 1.05 kg 5% loss Scrap rate 15%
Electricity/heat
Electricity, medium voltage {MK}| market for | Alloc Def,
S 36.4 MJ
Macedonia elec mix, source: Wiegmann K,
2002)
Outputs
Emissions to air
Heat, waste 25 MJ
Figure 5 LCI for the Fabric Production Stage

16
3.4 Fabric Finishing
After the cotton cultivation stage the stage of dyeing the greige fabric is the one with the biggest
uncertainty. There is many different ways of dying the fabric and many different types of dyes which
can be used. There is two main stages at which the dye can be applied: the yarn itself can be dyed or the
knitted fabric can be dyed. In our model we assume, as it can already be seen above, that the yarn is not
dyed itself and that we are batch dying the greige fabric instead (Hasanbeigi, A. and Price, L., 2012).
This is actually more common in the industry and what the supplier in Macedonia actually does as well.
Therefore, the fabric finishing process is basically the dying process of the greige fabric. All of the
values for the dyeing and finishing agents used at this stage were taken from the Ecoinvent
documentation and are based on a Check/Italian facilities surveyed.
There is other additional finishing which can be done to the fabric in addition to the dyeing. For
example, many manufactures in order to increase the softness and the polished feel of the fabric
actually employ silicone and/or enzyme washing of the fabric which is excluded from the scope of this
study due to insufficient data of actual quantities used as they are considered trade secrets (Tyndall, R.
M. , 1992)
Finally, for this stage our model assumes treatment of the waste water on-site by a small waste water
treatment facility as industrial waste water from dyeing processes is not allowed into the local
municipal water treatment facilities (ACSK Clothing interview with supplier).

17
Products Amount Unit Notes
_Cotton fabric, finished 1 kg 100 not defined
Inputs
Materials/fuels
_Cotton fabric, greige 1.04 kg 4% waste
Water, deionised, from tap water, at user {GLO}| market for | Alloc Def, S 138 kg
Sodium chloride, powder {GLO}| market for | Alloc Def, S 0.547 kg salts used for dying
Chemicals organic 0.13 kg organic compunds for dying
Fatty alcohol {GLO}| market for | Alloc Def, S 0.01 kg washing agent
Sodium perborate, tetrahydrate, powder {GLO}| market for | Alloc Def, S 0.01 kg bleaching agent
Alkylbenzene sulfonate, linear, petrochemical {GLO}| market for | Alloc Def,
S 0.01 kg finishing agent
Carboxymethyl cellulose, powder {GLO}| market for | Alloc Def, S 0.01 kg finishing agent
Electricity/heat
Electricity, medium voltage {MK}| market for | Alloc Def, S 3.993 MJ
10% of energy needed acc to
Wiegmann, K. is used for
electricity
Heat, district or industrial, natural gas {Europe without Switzerland}| heat
production, natural gas, at boiler condensing modulating >100kW | Alloc Def,
S 35.937 MJ
90% of energy needed acc to
Wiegmann, K. is used for
heating
Outputs
Emissions to air
Heat, waste 3.99 MJ
Waste to treatment
Wastewater, average {RoW}| treatment of, capacity 1.1E10l/year | Alloc Rec,
S 0.14 m3
Figure 6 LCI for the Fabric Finishing Process

19
3.4 Stitching (Cut, Make and Trim –CMT)
For the final stage of our model there was no Ecoinvent data available. Therefore, the final stage of our
model was modeled based on data reported in the original study of Wiegmann (2002) where the
Ecoinvent documentation draws data from. The main input at this process is the finished fabric which is
cut and sown together into a t-shirt and then packaged into a polybag. Our model assumes that 10% of
the fabric is wasted during the cutting stage based on information from the supplier in Macedonia.
Schmidt (1997) estimate 10-20% so our values is within that range. This model excludes disposal of the
fabric waste, as it’s outside the scope of this study.
The main input at this stage is again the electricity used for running the CMT line at the facility.
Literature values from Altenfelder, K. (1996) suggest an approximate energy use of 1.75 MJ/kg needed
for the CMT phase based on data from one facility in Turkey and one from Poland. For light-confection
(t-shirts) Schmidt, K. (1999) refer to a value of 1.8-2.7 MJ/kg. We use an average value from these two
publications of 2.1 MJ/kg which scales to 0.525 MJ of energy for our 240 gram t-shirt. This comes out to
0.15 kWh per t-shirt in our study while Zhang et al. (2015) for their t-shirt made in China report a value
of 1.5 kWh. However, we doubt the accuracy of this value as it’s very different from other values in the
literature as it can be seen above.
We also added nylon for the sewing thread, polyester resin for the printed non-woven labels, paper for
the printed labels, and polyethylene plastic bags for the packaging. For each of these we used as proxy
existing Ecoinvent processes and the quantities are the authors estimations based on weighing the
individual components on a digital weighing scale.

20
_T-shirt finished 1 p 90
_Fabric scrap 23 g 10
Inputs
Materials/fuels
_Cotton fabric, finished 240 g
10% scrap rate of
fabric
Nylon 6-6 {GLO}| market for | Alloc Def, S 0.1 g sewing thread
Polyester resin, unsaturated {RoW}| production | Alloc Def, S 0.0001 kg
printed non-woven
label
Packaging film, low density polyethylene {RER}| production | Alloc Def,
S 1 g plastic bag
Kraft paper, bleached {GLO}| market for | Alloc Def, S 5 g printed labels
Electricity/heat
Electricity, medium voltage {MK}| market for | Alloc Def, S 0.525 MJ
Figure 7 LCI for the Stitching and Packaging process

21
4 Life Cycle Impact Assessment (LCIA)
4.1 Assessment with IMPACT 2002+
Life cycle impact assessment (LCIA) methods aim to connect, as far as possible, each life cycle
inventory results with their corresponding environmental impacts. According to the ISO 14040
standard, LCI results are classified into impact categories, each with a category indicator. The category
indicator can be located at any point between the LCI results and the category endpoints where the
environmental impact occurs. Within this framework, there are two main methods for analyzing LCI
results:
a) The classical impact assessment methods which restrict quantitative modeling to relatively
early stages of the model and group LCI results into mid-point categories according to themes
like human toxicity and global warming. The obtained midpoint values have relatively low
uncertainties.
b) However, recently damage orientated methods have been developed to try to model the
cause-effect chain up to the endpoint, or damage, and sometimes with great uncertainty.
Multiple midpoint categories can contribute toward a particular endpoint damage category.
The difference between methods employing the midpoint or endpoint thinking can be best seen in
Figure, based on Joliiet et al. (2003).
One of the damage orientated methods is the IMPACT 2002+ which is now widely used in Europe for
LCIA studies. IMPACT 2002+ proposes a feasible implementation of the aforementioned combined
midpoint/damage-oriented approach. The methodology looks at 14 mid-point categories and 4 main
damage categories and provides impact results at both the mid-point and end-point levels both
categorical and normalized values. This study will not look at the weighted values although that is an

22
option for future research in this area. For a detailed review of the IMPACT 2002+ methodology please
see Joliiet et al., (2003)
Figure 8 Overall scheme of the IMPACT 2002+ framework, linking LCI results via the midpoint categories to damage categories
4.2 Hotspots and Recommendations
In the following sections we will look at the results from analyzing our model with the IMPACT 2002+
methodology in SimaPro and the results obtained for the 4 damage end-point categories. The main
benefit and use which can be derived from an LCIA study is not the exact values which are obtained but
an opportunity to identify trends and environmental hot spots which can be later looked at and
analyzed in greater detail.

23
4.2.1 Normalized results
We are going to start our results discussion of the LCIA data analysis by looking at the normalized
results for the main contributing process to the 4 damage categories. The four damage categories are
all reported in different units: human health (DALY), Ecosystem quality (PDF*m2*yr), Climate change
(kg CO2 eq) and Resources (MJ primary). This presents a challenge if we want to assess the relative
impacts of the damage categories to each other. Which is higher - the human health or climate change
impact? This question can only be answered by looking at the normalized values generated by SimaPro
which gives us the opportunity to compare the 4 damage categories relative to each other so we can
determine which process has the largest relative impact. If we look at Figure 9 we can see that the
electricity used at the supplier facility in Macedonia has by far the biggest contribution relative to the
other processes. We will need to look at this process in detail to determine at which stage the biggest
human health impact is observed but we estimate that it will be proportional to the stage which is most
energy intensive. Moreover, human health appears to be the most significant damage category and is
two degrees of magnitude higher than climate and resources for the electricity process specifically.
If we want to look at the other processes we have to remove the Electricity process so we can see the
other values in a rescaled graph, the results of this can be seen in Figure 10. From Figure 10 we can
conclude that the cotton fiber cultivation process, the electricity used in India for cotton cultivation and
the heat energy used in the dyeing process also have significant contributions to the damage
categories, however, noting that the electricity used in Macedonia has an impact two magnitudes
higher compared to these process. This needs to be kept in mind as we make recommendations about
reducing the environmental impact during the various stages in the supply chain. We will refer to the
normalized values often when we inter-compare damage categories or processes.

24
Figure 9 Normalized results for the 4 main damage categories - Resources, Human Health, Ecosystem Quality and Climate Change
0.00E+002.00E-034.00E-036.00E-038.00E-031.00E-021.20E-021.40E-021.60E-02
_Cotton fiber2
Electricity, medium voltage {MK}| market for | Alloc Def, S
Waste paper, sorted {GLO}| market for | Alloc Def, S
Wastewater, average {RoW}| treatment of, capacity 1.1E10l/year |…
Electricity, low voltage {IN}| market for | Alloc Def, S
Ammonium nitrate, as N {RER}| ammonium nitrate production | Alloc…
Ammonia, liquid {RER}| market for | Alloc Def, S
Chemicals organic
Urea, as N {RER}| production | Alloc Def, S
Fatty alcohol {GLO}| market for | Alloc Def, S
Truck 40t
Water, deionised, from tap water, at user {GLO}| market for | Alloc…
Truck 16t
Potassium chloride, as K2O {RER}| potassium chloride production |…
Heat, district or industrial, natural gas {Europe without Switzerland}|…
Phosphate fertiliser, as P2O5 {RER}| triple superphosphate production…
Sodium chloride, powder {GLO}| market for | Alloc Def, S
Transport, freight, sea, transoceanic ship {GLO}| market for | Alloc…
Transport, freight train {RoW}| market for | Alloc Def, S
Carboxymethyl cellulose, powder {GLO}| market for | Alloc Def, S
Glyphosate {RER}| production | Alloc Def, S
Normalized values
Process Normalized results for the damage categories
Resources
Human Health
Ecosystem Quality
Climate Change

25
Figure 10 Normalized results for the 4 main damage categories - Resources, Human Health, Ecosystem Quality and Climate Change with the electricity process excluded
0.00E+00 2.00E-04 4.00E-04 6.00E-04 8.00E-04 1.00E-03 1.20E-03
_Cotton fiber2
Wastewater, average {RoW}| treatment of, capacity 1.1E10l/year |…
Ammonium nitrate, as N {RER}| ammonium nitrate production |…
Chemicals organic
Fatty alcohol {GLO}| market for | Alloc Def, S
Truck 40t
Water, deionised, from tap water, at user {GLO}| market for |…
Truck 16t
Potassium chloride, as K2O {RER}| potassium chloride production…
Heat, district or industrial, natural gas {Europe without…
Phosphate fertiliser, as P2O5 {RER}| triple superphosphate…
Transport, freight, sea, transoceanic ship {GLO}| market for | Alloc…
Transport, freight train {RoW}| market for | Alloc Def, S
Carboxymethyl cellulose, powder {GLO}| market for | Alloc Def, S
Glyphosate {RER}| production | Alloc Def, S
Normalized values
Process Normalized results for the damage categories, excluding the electricity process
Resources
Human Health
Ecosystem Quality
Climate Change

26
4.2.1 Climate Change
In the previous section we put in perspective the relative impacts of the various damage categories to
each other and the various contributing processes to each other. In this section we will look at the
characterization values for each damage category starting with Climate Change. We can clearly see in
Figure 11 that the biggest contributor to climate change impact is the GHGs emitted from the
electricity used at the supplier in Macedonia. It’s important to note that is cumulative electricity used
during all stages as all the stages are done at one supplier (spinning, knitting, dying and cutting and
packaging). If we want a more process-based understanding of the electricity consumption we can look
at the contribution flow diagram which can be seen in Figure 12. We can observe that almost 78% of all
GHGs emissions come from this indirect source, i.e the production of electricity, the facility basically
has no direct GHGs emission to air.
Figure 11 Characterization values of the processes contribution towards the Climate Change impact category
0 1 2 3 4 5 6
Electricity, medium voltage {MK}| market for | Alloc…
Heat, district or industrial, natural gas {Europe…
_Cotton fiber2
Ammonium nitrate, as N {RER}| ammonium nitrate…
Chemicals organic
Water, deionised, from tap water, at user {GLO}|…
Phosphate fertiliser, as P2O5 {RER}| triple…
Climate Change [kg CO2 eq]
Process
Climate Change

27
Figure 12 Network diagram for the Climate Change impact. The percentages are the relative contributions of the processes
If we look at the process contribution towards the final t-shirt product, the largest contribution to the
GHG emissions budget is knitting the fabric (38.2%) closely followed by spinning the yarn (34.3%), then
34.3%
38.2%
13.8%
2.4%

28
dying the fabric (13.8%) and finally the cut, make and trim stage is only 2.4%. Zhang et al. (2015) made
an LCIA study for a cotton t-shirt made in China and they obtained very different values. It should be
noted that they analyzed their model with CML 2001 method and not with IMPACT 2002+ like in this
study. They report that the majority of GHGs emissions come from the steam used to burn coal to
make on-site steam (34.79%). In our case the dying process adds only 13.8 %, however, in our case coal
is not used to make steam but steam is obtained using industrial heat. Also they report a very large
contribution of the cut, make and trip stage of 31.96% which is much higher than our results or the
values used in Ecoinvent from Wiegmann (2002) and Shmidt (1992). Therefore, the different supply
chains for a same 100% cotton t-shirt have apparently a very different carbon footprint. However, they
also identify that most of the GHGs contribution is from indirect Co2 emissions because of on-site
electricity consumption which agrees with our findings.
In addition to the reasons above we can identify additional reasons for the difference in results:
1) Most textile production is not done at vertically integrated facilities like the one in our
study. Instead, different stages are done at different facilities sometimes on different
continents. This is the case with the fabric model available in Ecoinvent 3.0. These specialized
facilities work with larger economies and volumes of scale so they would have less energy
intensive production then a vertically integrated facility like the one in Macedonia.
2) Differences in technology and age of machinery. A study by Hasanbeigi, A., & Price, L. (2012)
found that variation in machines used as well as their energy efficiency can significantly
contribute towards a larger or smaller electricity use values. More importantly, they pin point
areas of potential improvement in terms of energy efficiency in the textile supply chain.
3) Differences in the electricity mix of the country where the factory (factories) are located. In
our study we have one facility and it is located in Macedonia. The factory draws all of its
electricity form the grid which means the GHGs emissions which would be attributed to our t-

29
shirt are directly correlated to the Macedonian electricity mix. Macedonia has a mixture of
around 60-70% fossil fuels (95% coal) and 30% renewable energy (exclusively hydropower)
(ELEM, 2015). So the bulk of the GHGs related to our t-shirt is CO2 emitted from burning coal at
power plants in Macedonia. If production were in a different country the net amount of GHGs
per t-shirt would be of course different. We postulate that the relative contributions of the
supply chain stages to be approximately the same as it can be also see from the Hasanbeigi, A.,
& Price, L. (2012) study.
In conclusion, for the Climate Change impact category we can identify electricity consumption during
the knitting and spinning stages in the supply chain as a potential environmental hotspot. Because
factories and fashion brands usually do not have the power nor resources to make greener the
electricity mix of a particular country we would suggest on-site generation of electricity from greener
energy sources as well as increasing education about energy efficiency improvement potentials in the
textile industry. While renewable energy might not suffice for supporting the operation of spinning or
knitting machines (a base-load energy source is needed) a combined heat and power co-generation
plant based on natural gas could reduce GHG emissions significantly according to studies done (See for
example Jaramillo, P, 2007) . As both heat and power are used during the knitting stage a future study
should look into replacing the electricity processes and the heat generation processes with a combined
heat and gas process in SimaPro and determine if this will reduce GHG emissions and reduce the impact
on the environment from a climate change point of view.

30
4.2.2 Human health
The Human Health which is by far the biggest impact determined with IMAPCT 2002+ if we look at the
normalized data previously reported. This ‘damage category’ is composed of the following ‘midpoint
categories’: ‘human toxicity’, ‘respiratory effects’, ‘ionizing radiation’, ‘ozone layer depletion’ and
‘photochemical oxidation’. The Human health impact is expressed in Disability-Adjusted Life Years
(DALY), which describes the severity of impact on health by accounting for both mortality risk and
disability risk (Hubert et al. 2014).
For our particular study we can see that by far again electricity used in Macedonia dominates Figure 13
with more than 95% contribution towards the human health impact. If we look into the midpoint
categories contributing the most towards human health degradation we can see that by far “respiratory
inorganics” are the biggest contributor (Figure 14).
In conclusion, we can see that “respiratory inorganics” (part of the Human Health damage category) by
far have the highest normalized impact of 2 orders of magnitude higher than the second highest
contributor. Therefore, for both human health and climate change energy consumption during the
different stages of production directly correlate with GHGs emissions which because of the coal-based
electricity in Macedonia correlates with the amount of “respiratory inorganics” and more detrimental
human health impact. That is, the more energy used during a particular stage in the supply chain the
more damaging it is to the environment and human health. Our recommendations made for the
climate change impact category are also valid for human health as both relate to electricity used at the
facility.

31
Figure 13 Characterization values of the processes contribution towards the Human Health impact category
Figure 14 Midpoint characterization categories contribution towards the Human Health impact category. Others are: Non-
carcinogens, Carcinogens, Ionizing radiation, Respiratory organics, Ozone layer depletion
Electricity, medium voltage
{MK}| market for | Alloc Def, S,
2.18777E-05
_Cotton fiber2,
5.14278E-07
Electricity, low voltage
{IN}| market for | Alloc
Def, S, 3.175E-07
Heat, district or
industrial, natural gas,
1.25462E-07Other , 1.16207E-07
Human Health [DALY]
_Cotton fiber2
Heat, district or industrial, natural gas
Other
Respiratory
inorganics
99%
Other
1%
Midpoints contributing towards Human Health

32
4.2.3 Ecosystem Quality
The next impact category we are going to look at is Ecosystem Quality. The Ecosystem Quality
‘damage category’ is expressed in PDF*m2*y and composed of the following ‘midpoint categories’:
‘aquatic ecotoxicity’, ‘terrestrial ecotoxicity’, ‘terrestrial acidification’, ‘land occupation’, ‘aquatic
acidification’, ‘aquatic eutrophication’ and ‘water turbined’ (Hubert et al. 2014). “PDF·m2·y”
(“Potentially Disappeared Fraction of species over a certain amount of m2 during a certain amount of
year”) is the unit to “measure” the impacts on ecosystems. The PDF·m2·y represents fraction of species
disappeared on 1 m2 of earth surface during one year. For example, a product having an ecosystem
quality score of 0.2 PDF·m2·y implies the loss of 20% of species on 1 m2 of earth surface during one
year (Qantis, 2015).
If we look at our results in Figure 15 we can see a value of 3.06 PDF·m2·y which would imply a 306% loss
of species on 1 m2 area which is not possible. The results generated by SimaPro should technically add
up to 100% which is not the case here. If we look at the mid-point categories to see where the 3.06
PDF·m2·y values comes from we can see that land occupation has a value of 2.41 making it the largest
contributor to the 3.06 PDF·m2·y followed by terrestrial ecotoxicity. 0.556 PDF·m2·y. This result makes
sense as unoccupied land has to be cleared to allow for cotton cultivation, which would result in almost
complete loss of species, however, the author of this paper cannot explain the larger than 100% result.
In conclusion, we can clearly identify the cultivation of cotton as an environmental risk hot spot in our
supply chain as it directly reduces ecosystem quality. However, how can this hotspot be addressed?
Land clearance for human-based activity is certainly not unique to the apparel industry. How can the
ecosystem impact of cotton cultivation be minimized especially the biggest contributor of land
degradation? The only real solution is to limit and reduce the production/cultivation of virgin cotton.
On the supplier/fashion brand side this can be done by companies using post-consumer cotton and

33
recycled cotton for the production of their garments. While this is not possible in all cases, some brands
have started manufacturing clothing from fabric scraps and industrially recycled (post-consumer
cotton) (Woolridge, A. C., 2006)
Figure 15 Characterization values of the processes contribution towards the Ecosystem Quality impact category
0 0.5 1 1.5 2 2.5 3 3.5
_Cotton fiber2
Wastewater, average {RoW}| treatment of, capacity…
Phosphate fertiliser, as P2O5 {RER}| triple superphosphate…
Other
Ecosystem quality [PDF*m2*yr]
Process
Ecosystem Quality

34
4.2.4 Resources
We are now at the final damage category – Resource depletion. The damage category “Resources” is
the sum of the midpoint categories “non-renewable energy consumption” and “mineral extraction”.
This damage category is expressed in “MJ” of resources consumed. In Figure 15 we can clearly see again
the result that Electricity consumed at the supplier in Macedonia dominates the impact contribution for
this stage and this is mostly due to the non-renewable energy consumption midpoint category, which is
the depletion of the fossil fuels (mostly coal) used to generate the electricity used on-site at the
supplier. In second place, we can see the heat process which also depletes a fossil fuel raw material.
Figure 16 Figure 14 Characterization values of the processes contribution towards the Resources impact category
0 10 20 30 40 50 60 70
Electricity, medium voltage {MK}| market for | Alloc…
Chemicals organic
Phosphate fertiliser, as P2O5 {RER}| triple…
Water, deionised, from tap water, at user {GLO}|…
Resources [MJ primary]
Process
Resources

35
4.2.5 Unmapped Hotspots/Limitations
Water consumption during cultivation: The IMPACT 2002+ version 2.1 which was used in this study
does not take into account water consumption and water withdrawal. The latest version 2.21 from
Quanties now includes these two midpoint categories but this database is still not available in SimaPro
(Humber et al., 2012). This is a problem for this particular study because the water footprint of cotton
cultivation has been one of the most commonly mentioned environmental risks associated with the
textile industry (Hoekstra, A. Y., & Chapagain, A. K., 2005)
Overestimated net GHGs emissions for the cradle-to-gate t-shirt: The net value of total GHG
emissions in our model is overestimated as we do not credit the CO2 consumed during the growth of
the cotton plant. This was noticed too far into the project and will be added in future work. If we run a
comparative analysis of the Textile, knit cotton {GLO}| textile production, knit cotton, batch dyed | Alloc
Def, S Ecoinvent process and our _Cotton fabric, finished process using the IMPACT 2002+ method and
we look at kg CO2 eq we can see that our model has 31.20 kg CO2 eq emissions compared to the
Ecoinvent process of 19.53 kg CO2 eq for 1 kg of knitted fabric. We believe that a large part of this
difference can be attributed to the lack of crediting CO2 consumption during cotton cultivation. We can
see a comparison of the Climate change damage category as well as the other damage categories in
Table
Table 2 Comparison of analyzing 1 kg of fabric modeled with ready-made Ecoinvent process and our model using IMPACT 2002+
Damage category Unit
ECOINVENT PROCESS
Textile, knit cotton {GLO}|
textile production, knit
cotton, batch dyed | Alloc
Def, S
OUR MODEL
_Cotton fabric,
finished
Human health DALY 2.0E-05 1.1E-04
Ecosystem quality PDF*m2*yr 13.46 17.65
Climate change kg CO2 eq 19.53 31.20
Resources MJ primary 268.68 362.67

36
Pesticides and fertilizers used in cotton cultivation: While both our model and the Ecoinvent one
have included in their models some of the most common fertilizers and pesticides we believe these vary
from country to country and potentially toxic and harmful chemicals could arise as environmental risk in
particular instances. In countries with poor access to proper use of fertilizers, it’s a common
misconception among farmers that more fertilizers means higher crop yields which leads to over
fertilization and actually pollution of the soil with fertilizers. Therefore, our values can be conservative
and this could be a hidden environmental risk especially in developing countries.
Toxicity of pesticide chemicals: SimaPro is not a tool which should be used to model for the toxicity of
particular chemicals which are used as pesticides and insecticides. While our model does contain toxic
chemicals (toxic mostly to wildlife) like cypermethrin (Stephenson, R. R., 1982) and prometryne
(Johnson & Johnson – Lyclear, for toxicity see: Imgrund, H., 2003 ) as emissions to soil from pesticide
use, analyzing the model with IMPACT 2002+ will not flag these chemicals as dangerous due to the
small volume used and SimaPro cannot model for bio magnification and bioaccumulation for example
from persistent use of these chemicals. We would suggest using a different analysis model than
IMPACT 2002+ to analyze our LCI model, a model with an up-to-date database of dangerous and toxic
chemicals (for example USEtox). Another very worrying factor is that even if we do manage to flag the
toxic chemicals, while most dangerous and toxic pesticides have been banned in the USA and Europe
they are still widely used in developing countries for example India, one of the world’s largest cotton
producer (The Hindu, 2011). This has led to the rise of organic cotton certified textile goods (Standard,
G. O. T., 2008).

37
5 Conclusion
The main goal of this study has been to establish the main hotspots of the apparel supply chain.
However, the vast difference in supply chains and the need to use location and facility specific data
makes it very difficult to make general recommendations about environmental hotspots in the apparel
supply chain. Instead, it is the recommendation of the author, to use this study and similar publications
as a basis for fashion brands to start more widely to employ the LCIA method to identify environmental
hotspots unique to their supply chains.
This study looked at the cradle-to-gate LCIA of a 100% cotton t-shirt determined two main findings
applicable to the functional unit and system boundary of choice in this paper. The first main finding is
identifying electricity consumption during the knitting stage (38.2%) closely followed by the spinning
process (34.3%), then dying the fabric (13.8%) and finally the cut, make and trim stage (2.4%) as the
major individual steps in the supply chain contributing towards the GHG emissions related to
manufacturing of the packaged t-shirt. Electricity consumption on-site at the ACSK Clothing suppliers’
facility in Macedonia was also found to be the main contributing process toward decreased human
health and resource depletion due to “respiratory inorganics” emitted during the burning of coal at
coal-fired power plants in Macedonia. The main recommendation is to look into increasing energy
efficiency through education of best practices in the textile industry combined on-site heat and power
from natural gas in countries with fossil fuel-heavy grid electricity mix. Photovoltaic on-site generation
could also be used to power some parts of the operations during the supply chain but not the heavy-
weights of spinning and knitting.
The second main finding is the intense degradation of ecosystem quality as manifested by the need to
convert natural “un-touched” land into cotton plantations causing land degradation. The main

38
recommendation on the production side is to promote using recycled cotton and fabric scraps to reduce
the cultivation of cotton in the first place.
Future work would include addressing adding CO2 credits for the cotton cultivation stage, improving
the data quality and reliability of especially energy consumption values for our model, as well as
analyzing the model using the USEtox impact assessment method to look for any toxic or dangerous
chemicals. Moreover, uncertainty analysis for the energy consumption values at the different stages in
the supply chain are highly recommended to be done in the future.

39
6 Bibliography
Althaus, H. J., et al. (2007). "Life cycle inventories of renewable materials." Final report
ecoinvent data v2. 0 No 21
Altenfelder, K. (1996). Vergleichende Untersuchung von Freizeitkleidung aus Baumwolle
bzw. Hanf mittels der Produktlinienanalyse (Doctoral dissertation, Diplomarbeit: Fachbereich Bio-
Ingenieurwesen. Fachhochschule Hamburg (unveröffentlicht)).
Cable News Network (2014). Accessed on 11/12/2015 from
http://www.cnn.com/2014/06/26/world/asia/primark-chinese-prison-labor-note/
ELEM: Macedonian Power Plants main website (2015). Accessed on 05.12.2015 at
www.elem.com.mk.
Hasanbeigi, A., & Price, L. (2012). A review of energy use and energy efficiency
technologies for the textile industry. Renewable and Sustainable Energy Reviews, 16(6), 3648-3665.
Humbert, S., De Schryver, A., Margni, M., & Jolliet, O. (2012). IMPACT 2002+: user
guide. Draft for version Q, 2.
Hoekstra, A. Y., & Chapagain, A. K. (2005). The water footprint of cotton
consumption. Globalization of Water: Sharing the Planet's Freshwater Resources, 103-130.
Imgrund, H. (2003). Environmental fate of permethrin. Environmental Monitoring Branch
Department of Pesticide Regulation. Sacramento, California. Accessed on January, 4, 2009.
ISO, E. (2006). 14040: 2006. Environmental management-Life cycle assessment-Principles and
framework. European Committee for Standardization.

40
Jaramillo, P., Griffin, W. M., & Matthews, H. S. (2007). Comparative life-cycle air
emissions of coal, domestic natural gas, LNG, and SNG for electricity generation. Environmental Science
& Technology, 41(17), 6290-6296.
Jolliet, O., Margni, M., Charles, R., Humbert, S., Payet, J., Rebitzer, G., & Rosenbaum, R.
(2003). IMPACT 2002+: a new life cycle impact assessment methodology. The International Journal of
Life Cycle Assessment, 8(6), 324-330.
Schmidt, K. (1999). Zur ökologischen Produktbewertung in der Textil-und
Bekleidungsindustrie: theoretische Grundlagen und praktische Umsetzung. Univ. Witten/Herdecke.
Stephenson, R. R. (1982). Aquatic toxicology of cypermethrin. I. Acute toxicity to some
freshwater fish and invertebrates in laboratory tests. Aquatic Toxicology, 2(3), 175-185.
Standard, G. O. T. (2008). Global Organic Textile Standard. Recuperado el,27.
The Hindu, (2001). Accessed on 12/12/2015 from
http://www.thehindu.com/news/national/kerala/more-than-60-pesticides-banned-in-other-countries-
in-use-in-india/article2339998.ece.
The Guardian (2015). Accessed on 12/14/2015 from
http://www.theguardian.com/sustainable-business/2015/apr/24/bangladesh-factories-building-
collapse-garment-dhaka-rana-plaza-brands-hm-gap-workers-construction
The New York Times (2012). Accessed on 12/14/2015 from
http://www.nytimes.com/2012/09/13/world/asia/hundreds-die-in-factory-fires-in-pakistan.html?_r=0
Tyndall, R. M. (1992). Improving the Softness and Surface Appearance of Cotton Fabrics
and Garments by Treatment with Cellulase Enzymes. Textile Chemist & Colorist, 24(6).

41
World Trade Organization. International trade statistics. World Trade Organization, 2005.
Wiegmann, K. (2002). Anbau und Verarbeitung von Baumwolle-Dokumentation der
GEMIS-Daten. Oeko-Institut, Freiburg, Germany, 1-25.
Woolridge, A. C., Ward, G. D., Phillips, P. S., Collins, M., & Gandy, S. (2006). Life cycle
assessment for reuse/recycling of donated waste textiles compared to use of virgin material: An UK
energy saving perspective. Resources, conservation and recycling, 46(1), 94-103.

Identifying Environmental Risk Hot Spots in the Apparel Supply Chain FINAL

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Ähnlich wie Identifying Environmental Risk Hot Spots in the Apparel Supply Chain FINAL

Ähnlich wie Identifying Environmental Risk Hot Spots in the Apparel Supply Chain FINAL (20)

Identifying Environmental Risk Hot Spots in the Apparel Supply Chain FINAL