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

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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

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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.

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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.

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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

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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.

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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

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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

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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

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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:

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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)

http://www.searates.com/

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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

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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

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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

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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

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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).

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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


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

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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.

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_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


Figure 7 LCI for the Stitching and Packaging process

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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.

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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.

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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

Pro

cess

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

Pro

cess

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]

Pro

cess

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]

Pro

cess

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]

Pro

cess

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

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