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Identifying Environmental Risk Hot Spots in the Apparel Supply Chain:
A Case Study Using a Cradle-to-Gate SimaPro Model an...
2
Contents
1 EXECUTIVE SUMMARY...............................................................................................
3
1 EXECUTIVE SUMMARY
ACSK Clothing is a textile management agency located in Bremen, Germany with sourcing offices in
Mac...
4
The first main finding is that electricity consumption during the knitting stage (38.2% of the total
GHGs emission relat...
5
2 INTRODUCTION
2.1 Introduction to the Apparel and Fashion Industry
The fashion and apparel industry’s environmental and...
6
people (the Guardian, 2015) in 2013 and the factory fire in Karachi, Pakistan killing 300 people in 2012
(New York Times...
7
Table 1 Anticipated environmental risk hotspots based on research from Caniato et al. (2012), Zhang et al. (2015), Brito...
8
Figure 1 System boundary, cotton grown in India and all other processes in Macedonia
The system boundary of the life cyc...
9
different stages of the supply chain taking place in different countries and is meant to be a
representative of the most...
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 ou...
11
1) Cotton cultivation in India (including transport of cotton fibers to Macedonia)
2) Spinning of the cotton fibers int...
12
Figure 3 Life cycle inventory (LCI) for the Cotton Production stage and transportation to Macedonia
Products Amount Uni...
13
Transport, freight, sea, transoceanic ship {GLO}| market
for | Alloc Def, S 7.70E+03 kgkm Transport from Ahmedabad, IN ...
14
3.2 Yarn Production
The main input in this process is the cotton fibers from India which are transported to the ACSK Cl...
15
3.3 Fabric Production
In the next step, at the same facility, the cotton yarn is placed on large circular knitting mach...
16
3.4 Fabric Finishing
After the cotton cultivation stage the stage of dyeing the greige fabric is the one with the bigge...
17
Products Amount Unit Notes
_Cotton fabric, finished 1 kg 100 not defined
Inputs
Materials/fuels
_Cotton fabric, greige ...
18
19
3.4 Stitching (Cut, Make and Trim –CMT)
For the final stage of our model there was no Ecoinvent data available. Therefo...
20
Products Amount Unit Allocation Notes
_T-shirt finished 1 p 90
_Fabric scrap 23 g 10
Inputs
Materials/fuels
_Cotton fab...
21
4 Life Cycle Impact Assessment (LCIA)
4.1 Assessment with IMPACT 2002+
Life cycle impact assessment (LCIA) methods aim ...
22
option for future research in this area. For a detailed review of the IMPACT 2002+ methodology please
see Joliiet et al...
23
4.2.1 Normalized results
We are going to start our results discussion of the LCIA data analysis by looking at the norma...
24
Figure 9 Normalized results for the 4 main damage categories - Resources, Human Health, Ecosystem Quality and Climate C...
25
Figure 10 Normalized results for the 4 main damage categories - Resources, Human Health, Ecosystem Quality and Climate ...
26
4.2.1 Climate Change
In the previous section we put in perspective the relative impacts of the various damage categorie...
27
Figure 12 Network diagram for the Climate Change impact. The percentages are the relative contributions of the processe...
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 ...
29
shirt are directly correlated to the Macedonian electricity mix. Macedonia has a mixture of
around 60-70% fossil fuels ...
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
n...
31
Figure 13 Characterization values of the processes contribution towards the Human Health impact category
Figure 14 Midp...
32
4.2.3 Ecosystem Quality
The next impact category we are going to look at is Ecosystem Quality. The Ecosystem Quality
‘d...
33
recycled cotton for the production of their garments. While this is not possible in all cases, some brands
have started...
34
4.2.4 Resources
We are now at the final damage category – Resource depletion. The damage category “Resources” is
the su...
35
4.2.5 Unmapped Hotspots/Limitations
Water consumption during cultivation: The IMPACT 2002+ version 2.1 which was used i...
36
Pesticides and fertilizers used in cotton cultivation: While both our model and the Ecoinvent one
have included in thei...
37
5 Conclusion
The main goal of this study has been to establish the main hotspots of the apparel supply chain.
However, ...
38
recommendation on the production side is to promote using recycled cotton and fabric scraps to reduce
the cultivation o...
39
6 Bibliography
Althaus, H. J., et al. (2007). "Life cycle inventories of renewable materials." Final report
ecoinvent d...
40
Jaramillo, P., Griffin, W. M., & Matthews, H. S. (2007). Comparative life-cycle air
emissions of coal, domestic natural...
41
World Trade Organization. International trade statistics. World Trade Organization, 2005.
Wiegmann, K. (2002). Anbau un...
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Identifying Environmental Risk Hot Spots in the Apparel Supply Chain FINAL

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Identifying Environmental Risk Hot Spots in the Apparel Supply Chain FINAL

  1. 1. 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. 2. 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. 3. 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. 4. 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. 5. 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. 6. 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. 7. 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. 8. 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. 9. 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. 10. 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. 11. 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. 12. 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. 13. 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. 14. 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 ). Products Amount Unit Allocation Notes _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. 15. 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. Products Amount Unit Allocation Notes _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. 16. 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. 17. 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
  18. 18. 18
  19. 19. 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. 20. 20 Products Amount Unit Allocation Notes _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. 21. 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. 22. 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. 23. 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. 24. 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. 25. 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 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 |… 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 |… 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… 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, excluding the electricity process Resources Human Health Ecosystem Quality Climate Change
  26. 26. 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 Electricity, low voltage {IN}| market for | Alloc Def, S Ammonium nitrate, as N {RER}| ammonium nitrate… Ammonia, liquid {RER}| market for | Alloc Def, S Urea, as N {RER}| production | Alloc Def, S 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. 27. 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. 28. 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. 29. 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. 30. 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. 31. 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] Electricity, medium voltage {MK}| market for | Alloc Def, S _Cotton fiber2 Electricity, low voltage {IN}| market for | Alloc Def, S Heat, district or industrial, natural gas Other Respiratory inorganics 99% Other 1% Midpoints contributing towards Human Health
  32. 32. 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. 33. 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 Electricity, medium voltage {MK}| market for | Alloc Def, S Waste paper, sorted {GLO}| market for | Alloc Def, S Wastewater, average {RoW}| treatment of, capacity… Electricity, low voltage {IN}| market for | Alloc Def, S Phosphate fertiliser, as P2O5 {RER}| triple superphosphate… Sodium chloride, powder {GLO}| market for | Alloc Def, S Other Heat, district or industrial, natural gas {Europe without… Ammonium nitrate, as N {RER}| ammonium nitrate… Ecosystem quality [PDF*m2*yr] Process Ecosystem Quality
  34. 34. 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… Heat, district or industrial, natural gas {Europe without… Electricity, low voltage {IN}| market for | Alloc Def, S Ammonia, liquid {RER}| market for | Alloc Def, S Urea, as N {RER}| production | Alloc Def, S Chemicals organic Ammonium nitrate, as N {RER}| ammonium nitrate… Phosphate fertiliser, as P2O5 {RER}| triple… Water, deionised, from tap water, at user {GLO}|… Waste paper, sorted {GLO}| market for | Alloc Def, S Resources [MJ primary] Process Resources
  35. 35. 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. 36. 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. 37. 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. 38. 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.
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