Foreword the Fabricant Is Dedicated to Provoking the Physical Fashion Industry to Revolutionise Its Thinking and Its Current S

Foreword

The Fabricant is dedicated to provoking the physical fashion industry to revolutionise its thinking and its current systems that were founded on toxic principles. As leaders in our field, we are committed to establishing fresh perspectives that are equally beneficial to all and sharing knowledge and practices that will make the industry more innovative, creative and sustainable. This year our quest led us to team up with The Imperial College of London. We were supported by Dr Onesmus Mwabonje and Dr Eva Sevigne Itoiz and the talented Yihan Xiong, who led the detailed life cycle assessment comparative research, illustrating the environmental effects of physical versus digital fashion.

IMPERIAL COLLEGE LONDON Faculty of Natural Sciences

Centre for Environmental Policy

The comparative LCA of digital fashion and existing fashion system: is digital fashion a better fashion system for reducing environmental impacts?

By Yihan Xiong

A report submitted in partial fulfilment of the requirements for the MSc and/or the DIC

9 th September 2020

DECLARATION OF OWN WORK

I declare that this thesis:

The comparative LCA of digital fashion and existing fashion system: is digital fashion a better fashion system for reducing environmental impacts? is entirely my own work and that where any material could be construed as the work of others, it is fully cited and referenced, and/or with appropriate acknowledgement given.

Signature:

Name of student: Yihan Xiong

Name of supervisor: Dr Onesmus Mwabonje and Dr Eva Sevigne Itoiz

AUTHORISATION TO HOLD ELECTRONIC COPY OF MSc THESIS

Thesis title:

The comparative LCA of digital fashion and existing fashion system: is digital fashion a better fashion system for reducing environmental impacts?

Author: Yihan Xiong

I hereby assign to Imperial College London, Centre of Environmental Policy the right to hold an electronic copy of the thesis identified above and any supplemental tables, illustrations, appendices or other information submitted therewith (the “thesis”) in all forms and media, effective when and if the thesis is accepted by the College. This authorisation includes the right to adapt the presentation of the thesis abstract for use in conjunction with computer systems and programs, including reproduction or publication in machine-readable form and incorporation in electronic retrieval systems. Access to the thesis will be limited to ET MSc teaching staff and students and this can be extended to other College staff and students by permission of the ET MSc Course Directors/Examiners Board.

Signed: ______Name printed: Yihan Xiong

Date: 09/09/2020

Abstract This thesis investigates the environmental impacts of the life cycles of cotton T-shirts produced in three different scenarios: 1. Conventional fashion system 2. Fashion system aided by virtual design technology 3. Virtual digitized fashion system via 'Cradle-to-grave' life cycle assessment (LCA). The aim is first to assess the environmental impacts and identify the hotspots within the life cycles of three scenarios stated above, therefore conclude whether virtual design assisted system and digitized system generate less environmental impacts. The impact categories considered in this thesis are Global Warming Potential (GWP), Terrestrial Ecotoxicity (TET), Human Non-carcinogenic Toxicity (HNT), land use and water consumption. The main contributors to the environmental impact of scenario 1 and 2 are generally found to be the production and in-use phases. Also, virtual design generates less environmental impacts than conventional design due to no physical samples produced. These findings revealed that comprehensive enhancement is required to reduce the environmental impacts in the fashion industry. Except for virtual design, organic cotton, cultivated without chemicals and recycled cotton can replace conventional cotton to decrease environmental implications. Also, green consumption and use patterns such as using clean energy, extending garments' life-span, purchase necessary garments only and green laundry can reduce the environmental impacts of the fashion system. Besides, it is encouraging that digitized fashion system generates the lowest environmental impacts. The environmental impacts are likely to decrease in future as the greater efficiencies are achieved through improved virtual technology and more clean energy source. However, as virtual fashion cannot protect the human body from external damage, the combination of enhanced physical fashion system and digital fashion system is the future target. Furthermore, potential challenges contain: Firstly, the standards of acceptable environmental impacts level is necessary to regular stakeholders' behaviour and push the whole system to become more sustainable. Secondly, how to avoid 'information overloading' and consumers’ adaption are the main issues in digital fashion system.

Acknowledgements I would like to express my sincere gratitude toward the following people who have not only supported me on this project, but my entire journey at Imperial College London.

Firstly, special thanks to my supervisor Dr Onesmus Mwabonje and Dr Eva Sevigne Itoiz from the Centre for Environmental Policy for their help and guidance throughout the project.

Secondly, many thanks to Ms Adriana Hoppenbrouwer-Pereira, Commercial Director/ Partner of THE FABRICANT; Mr Kerry Murphy, founder of THE FABRICANT; and Ms Amber Slooten and Mr Bram Siebers from THE FABRICANT design team for providing important and relevant information that made this research possible.

Finally, I am grateful to all the staff and students at the Centre for Environmental Policy and Library for making my Master’s year at Imperial an enjoyable and fruitful one.

Content 1. Introduction 13

1.1 Background：Sustainable fashion and digitalisation 13

1.2 Problem statement and objectives 14

1.3 Research Questions 14

2. Literature Review 16 2.1 Current fashion industry and environment 16

2.1.1 The drivers of unsustainability in fashion industry 16

2.1.2 Environmental impacts in the current fashion model 19

2.2 Previous LCAs of environmental impacts in fashion industry 20

2.3 Sustainability and Digitalisation in fashion industry 21 2.3.1 Sustainable fashion 21

2.3.2 Digitalisation in fashion sector 22

2.3.3 Digital design and digital sample 23

2.3.4 The advantages of 3D virtual design 25

2. 4 Virtual fashion garments in future 26 3. Methodology 27

3.1 Life Assessment Analysis (LCA) 27

3.2 Research Design 28

3.2.1 Goal and Scope 28

3.2.2 Functional Unit 29 3.2.3 System Boundaries 29

3.3 Data requirements 30

3.4 Inventory analysis 30

3.4.1 Scenario 1 and 2 31

3.4.2 Scenario 3 36 4 Results 37

4.1 Global Warming Potential (GWP) Results 38

4.1.1 Global Warming Potential Process Breakdowns 38

4.1.2 Global Warming Potential Comparisons 40

4.2 Toxicity 41

4.2.2 Human Non-carcinogenic Toxicity (HNT) 43 4.3 Other relevant impact categories in three scenarios 46

4.3.1 Comparison 46

4.3.2 Water consumption breakdown for scenario 1 and 2 47 4.4 Comparison of conventional design (Scenario 1) and virtual design (Scenario

2) 48 4.5 Comparison with previous LCA studies 49

5 Discussion 50

5.1 life-cycle thinking to enhance current fashion supply chain 50

5.1.1 Virtual design technology help reduce the environmental impacts 50

5.1.2 Other approaches along the life cycle 51 5.2 Brand-new fashion system-Digital Fashion 53

5.3 The combination of enhanced fashion system and digital fashion 54

5.4 Future challenges and adoption 54

5.4.1 Sustainable physical fashion supply chain 54

5.3.2 Digital fashion supply chain 55 5.4 Limitations of Study 56

6. Conclusion 57

6.1 Conclusion 57

6.2 Recommendations 58

7. Reference 60

8. Appendix 71 Appendix A: Significance Heuristic 71 Appendix B: Environmental Impact Tool 71 Appendix C: Detailed inventory analysis of Scenario 1 and 2 72 Appendix D: Data Quality Assessment 79 Appendix E. The result of LCA 84

List of Figures Figure 1. The detailed garment sample development process (modified from Papahristou, 2016)…………………………………………………..……..17 Figure 2. The location of main producers/manufactories and environmental impacts of different processes in garment production phases (Niinimäki et

al., 2020) 18 Figure 3. The Sustainable Consumption and Production (SCP) cycle (Thorisdottir and Johannsdottir, 2019) 22 Figure 4. Examples of digital 3D samples with different texture of material created

with Clo3D (Clo3D, 2020) 24

Figure 5. Clo3D Modelist (Clo3D, 2015) 24

Figure 6. The example of online fitting room (Li et al., 2009) 25 Figure 7. The website (Leela) of digital garment shop, fitting room and showroom

of THE FACBRICANT (http://digital.fashion/collections/fluid/explore) 27 Figure 8. The process of conducting Life Cycle Assessment (LCA) (ISO14040,

1997) 28

Figure 9. The system boundaries of three scenarios made in this project 29 Figure 10. The detailed cotton T-shirt production stage 30 Figure 11. The power consumption ratio of data centres, network and end-user

devices (CustomMade, 2015) 36

Figure 12. Percentage of GWP within the cotton T-shirt production phase 39

Figure 13. The comparison of three scenarios GWP processes breakdown 40 Figure 14. Percentage of TET by processes within the production phase of a cotton

T-shirt 42

Figure 15. The comparison of Terrestrial Ecotoxicity in three scenarios 43 Figure 16. The comparison of Human Non-carcinogenic Toxicity in three scenarios

Figure 17. Other impact categories comparison of three scenarios 47 Figure 18. The overall impact categories comparison of two design methods 49

Figure 19. The list of databases 71

Figure 20. The screenshot of SimaPro page 72 Figure 21. The screenshot of SimaPro to present how to use it to model the life

cycle of a T-shirt 72 Figure 22. The treatment of discarded garments in UK (Modified from Gracey et al.,

2012) 78

Figure 24. The overall comparison of three scenarios 84

Figure 25. The breakdowns of environmental impacts of scenario 1 84

Figure 26. The breakdowns of environmental impacts of scenario 2 85

Figure 27. The breakdowns of environmental impacts of scenario 3 85

List of Tables Table 1. The summary of LCA of environmental impacts in fashion industry

reviewed in this chapter 20 Table 2. The comparison of traditional sample and virtual sample production

process (Jacqui Haker & Sandra Kuijpers, 2019) 26

Table 3. The summary of assumptions applied to LCA assessment 31

Table 4. The LCI of Scenario 1 per FU 35 Table 5. The LCI of virtual design phase in Scenario 2 per FU (All other life cycle stages are the same as Scenario 1) 35

Table 6. The LCI of scenario 3 per FU 37

Table 7. The breakdowns of global warming potential of three scenarios 39

Table 8. The breakdowns of TET of three scenarios 42 Table 9. The breakdowns of Human Non-carcinogenic Toxicity of three scenarios

Table 10. The comparison of three scenarios 46 Table 11. Water consumption breakdown for scenario 1 and 2 47

Table 12. The comparison of conventional design and virtual design 49 Table13. Electricity consumption of desktop and laptop in four different modes.

(Pothitou, Hanna and Chalvatzis, 2017) 73

Table 14. The ratio of cotton yield in different countries (Ecoinvent 3 database) 74 Table 15. The assembly and data resource of transportation phase 76 Table 16. The composition of 1kg powder detergent (Modified from Yamaguchi et

al., 2011) 77

Table 17. The assembly sheet and data resource of disposal phase 79

Table 18. Data quality assessment sheet 83

Executive Summary: The comparative LCA of digital fashion and existing fashion system: Is digital fashion a better fashion system for reducing environmental impacts?

YIHAN XIONG Centre for Environmental Policy, Imperial College London Academic year: 2019-2020 Supervisor: Dr Onesmus Mwabonje and Dr Eva Sevigne Itoiz Objectives This thesis aims to investigate the environmental impacts of three fashion scenarios: 1. Current fashion system; 2. Virtual design aided fashion system; 3. Digitized fashion system via life cycle assessment (LCA) and determine whether virtual design together with digitized fashion can contribute to reducing environmental impacts. Introduction Due to its substantial environmental impacts of current fashion system, sustainable becomes the key word for fashion sector development. The environmental impacts of the fashion industry mainly refers to carbon emission, chemical use (such as pesticide, defoliate agent and dye material) and water consumption (such as irrigation and wetting processes). Meanwhile, due to the digitalization in fashion industry, the virtual

10 design technology that can create 3D photorealistic products to replace physical samples and a brand new digitized fashion system that refers to the replacement of production and consumption of physical garment with digital garments are come up to reduce the environmental impacts. However, there is little quantitative research to prove this effect. Methodology The ‘Cradle to grave’ life cycle assessment (LCA) which is an environmental management technique was conducted to model the three scenarios and evaluate the input, output and environmental impacts. The functional unit is a cotton T-shirt for scenario 1 and 2 stated above and a digital cotton T-shirt for scenario 3. The life cycle of scenario 1 and 2 contains design phase, production phase (Start from cotton cultivation), in-use phase (laundry only) and disposal phase, while the life cycle of scenario 3 contains design phase, 3D stitching and uploading phase and in-use phase. The data included in this scenario 1 and 2 are mainly from peer-reviewed literatures and Ecoinvent 3 database on SimaPro, while the data related to digital fashion was provided by THE FABRICANT, a Netherlands digital fashion company. Also, the impact categories considered in this project are Global Warming Potential, Terrestrial Ecotoxicity, Human Non-carcinogenic Toxicity, water consumption and land use. Results Firstly, virtual design generates less environmental impacts compared to conventional design, which reveals that virtual design can reduce environmental impacts through creating 3D photorealistic garment to replace physical samples. But the main contributors to the environmental impact of physical fashion system are the production and in-use phases. Within production phase, cotton cultivation requires a large amount of water, chemicals (including fertilizers, pesticide, defoliant and etc.) and electricity consumed by agricultural machinery, while the manufactory processes are electricity-intensive. Also, the wetting processes, bleaching and dyeing processes consume water and chemicals. In terms of in-use phase (laundry), fresh water, electricity and detergents are required, while the amount of environmental impacts depends on the use pattern. Reducing washing times per garment, using environmentally friendly detergent and green wash model can decrease environmental implications. For digitized fashion system, the environmental impacts which is the minimum

11 compared with other two systems originated from the manufacture and electricity consumption of digital device (computer/smartphone). Discussion These results revealed that a comprehensive enhancement of each phase within the life cycle is required to reduce the environmental impacts in the physical fashion system. Firstly, organic cotton cultivation without chemicals use and recycled cotton can partly replace conventional cotton to reducing environmental impacts and saving resource. Also, green awareness is crucial, as consumers have an absolute effect on in-use and disposal pattern. A green use pattern and reselling/recycling unwanted garments leads to a more sustainable fashion system. Also, the environmental impacts of digitized fashion system is lowest. It is encouraging for stakeholders as the environmental impacts of digitized fashion system is likely to continually decrease in future as the greater efficiencies are achieved through improved virtual technology and available clean energy source. However, it cannot satisfy the physical function of clothes to protect the human body from external damage. Hence, the combination of virtual design aided fashion system and digital fashion system is the future target, as only basic garments are produced to protect the human body and digital garments can satisfy consumers' psychological need. Besides, there are still some potential challenges. On the one hand, the standards of acceptable environmental impacts level is necessary to regular stakeholders' behaviour and drive the whole system to become more sustainable. On the other hand, how to avoid 'information overload' situation and achieving customer expectation of virtual fashion to improve consumers’ adaption are the main issues in digital fashion system. Limitations Initially, using only a cotton T-shirt to represent the environmental impacts of the fashion industry limits the study, as the environmental impact varied from the FU selection. Secondly, both primary data from THE FABRICANT and secondary data from literatures and database are used in this thesis, resulting in discrepancies generated from varying data collection and allocation methods. Thirdly, the lack of data in certain processes like the manufactory of router in digital fashion system reduce the accuracy of this thesis, as well. Fourthly, as certain assumptions are applied to help calculation, the scenarios in this paper cannot fully represent the real situation. Finally, the dataset used are not up-to-date and data in the past 15 years are used

Future recommendation LCA studies with higher data standards and multiple Functional Units should be conducted for detailed and more accurate evaluation. The detailed LCA of digitized fashion system that assesses the life cycle of one specific digital product could be conducted to get more accurate result. Moreover, researches about consumers’ expectation and attitude to understand market potential, interest and feasibility.

1. Introduction

1.1 Background ：Sustainable fashion and digitalisation

“Sustainable fashion”, "eco-fashion", “ethical consumerism” and “clothing with a conscience” has become the latest buzzwords in the fashion industry due to the growing awareness of environmental impacts generated from current fashion supply chains (Store, 2014 and Hackett, 2015). To reduce the environmental impacts and comprehensively enhance the fashion sector, abundant new fashion theories and technologies have emerged in the last two decades. However, behind concept and terms like "slow fashion", "circular economy" and "digital fashion" that attempt to reshape the fashion industry and consumers' behaviour, their actual effectiveness in reducing their environment footprint remains unproven. Among all these new concepts, digital fashion attracts public attention in this digital age, to not only achieve sustainability of fashion but also keep fashion as we know it. With the innovation of virtual 3D technologies like body scanning and virtual 3D design tools, digital fashion can provide a sustainable way that we can design, develop, and wear fashion (Papahristou, 2016). Currently, big fashion names like Tommy Hilfiger and Carling have already been trying to utilize 3D virtual design software to simplify their supply chain and reduce the cost of their design and marketing phase (Eng, 2017). As the current fashion supply chain is design and market-driven, large amount of resources is going into unsellable sample garments made for design refinement, market studies and marketing purposes (Bertola and Teunissen, 2018). By replacing the traditional physical samples with virtual digital samples produced by 3D design software, the sample waste generated in design, sourcing and go-market phases can be minimized (Eng, 2017).

With the normalization of design digitization and social media, fashion pioneers have been exploring the separation of the physical and psychological need of style by creating and selling purely digital forms of fashion only used virtually to manifest personalities and share trends on social media and other online platforms. (THE FABRICANT, 2019). Consumers of these digital fashion get their body shapes digitized and “wear” their garment in the form of video or as augmented reality layers. This is a vast and untapped creative terrain, where the previously physically impossible becomes possible (THE FABRICANT, 2019). In this scenario, it is assumed that the environmental impacts of the fashion industry can be largely reduced, as the production and consumption of physical garments is minimized (Bertola and Teunissen, 2018).

1.2 Problem statement and objectives

Although is easy to assume that virtual design technology aided fashion production together with growing popularity of digitized fashion can contribute to reducing the environmental impacts generated by the fashion industry, there is little quantitative studies to prove the claim. Hence, this paper aims to compare the environmental implications of the current fashion system, 3D virtual technology aided fashion system and digitized fashion system through a life cycle assessment approach. The comparative ‘cradle to grave’ life cycle assessment (LCA) study uses ReCiPe 2016 (H) method to assess key environmental impact categories, including global warming potential, toxicity (terrestrial toxicity and human non-carcinogenic toxicity), water consumption and land use of three fashion scenarios. These three scenarios are: 1. Scenario 1: The conventional fashion system (Act as baseline) 2. Scenario 2: Virtual 3D design aided fashion system 3. Scenario 3: The replacement of production and consumption of physical garments with digital-only garments in social lives, defined as digitized fashion system.

1.3 Research Questions

This study answers the following questions: 1. How much environmental impacts generated in three scenarios? 2. Which life cycle stages are the main contributor to total environmental impacts in three fashion scenarios?

3. How effective does the virtual design technology on reducing the environmental impacts? 4. Out of the three scenarios, does digitized fashion system generate the minimum environmental impacts?

2. Literature Review

2.1 Current fashion industry and environment

Due to its widespread and substantial environmental impacts, current fashion system

15 has come under plentiful animadversions (Niinimäki et al., 2018). The fashion industry is an extensive industry, including clothing, footwear, accessories and etc., and is dominated by the fast fashion model (Thorisdottir and Johannsdottir, 2019). Fast fashion is a business model that emphasizes massive production, low price, fast turnover of designs and fierce marketing strategies, which is highly unsustainable (Hines and Bruce, 2007). The main drivers of unsustainability and relevant environmental impacts on fashion industry are summarized in this section.

2.1.1 The drivers of unsustainability in fashion industry

2.1.1.1 Overproduction and overconsumption

In the modern society, the prevalence of social media like Facebook, Twitter, Instagram and YouTube tightly linked the physical world and virtual digital world (Binet et al., 2019). A growing number of people are gaining thousands of followers by sharing their lifestyle, especially outfits on social media and become celebrity in the virtual world (Felsted and Kuchler, 2015). Hence, people tends to renew their wardrobe frequently with cheap and low quality products to continuously share new fashion items online (Gwozdz et al., 2017). Research of Barnardo in 2015 with 1500 women reported that 33% of respondents considered clothes old and discarded after three wears. The physical function of clothes is diminished, most clothes are purchased for their psychological functions and will be discarded after being shown-off in the virtual world (Tan, 2017). Existing fashion-consumption practice can be characterised as excessive production, impulse buying recurring consumption and short-lived garment use (Bick, Halsey and Ekenga, 2018).

2.1.1.2 High volume of pre-consumption waste in supply chain

The pre-consumption waste consists of sample waste and production waste in manufacture process (Niinimäki et al., 2020). The waste rate in garment manufacture ranged from 10~30% in different researches (Cooklin, 1997; Abernathy et al., 1999 and Runnel et al., 2017). Otherwise, the sample waste refers to the new, unsellable garments that are only used for design adjustment and marketing (Niinimäki et al.,

2020). Mathews reported in 2016 that around 30% of garments were unsold in UK, which consisted of samples, overproduced garments and garments returned to sellers. In detail, the traditional design process starts from the extraction of 2D patterns from designers’ concept, followed by the idea to realisation stage through producing a physical sample as close as the original design before reaching the production stage (Court, 2015). Detailed design and sample creation process is listed in figure 1 below.

Figure 1. The detailed garment sample development process (modified from Papahristou, 2016)

It is often observed that in the design process, designers' disengagement from production and inefficient miscommunication with their manufacturer inevitably result in broken communication chains, generating a high volume of sample waste. (Elias J, 2014). For instance, Tesco’s clothing brand F&F required 2-8 samples for each design (Parisi S, 2015). Niinimaki (2018) has pointed out that in order to reduce pre-consumption waste, the fashion industry has to either slow down production rate to compromise the waste produced during design stage, or to find a more efficient communication method between the designer, manufacturer and consumers.

2.1.1.3 Globalisation of fashion supply chain

The carbon trust (2011) has summarised the major countries involved in various stages of garment production across the globe (Figure2.) and highlighted that this has

17 and will continue to result in uneven distribution of environmental consequences brought about by the fashion industry in the respective countries. Perry et al (2015) also reinforced that due to the low manufacture and labour cost; production of fibre, textile and most physical aspects of the fashion supply chain happen in developing countries, bearing the burden of developed countries who design and consumes fashion items in large quantities. Besides, the increasingly fragmented and globalised fashion supply process caused unnecessary pre-consumption waste and transportation cost (Karaosman et al., 2018).

Figure 2. The location of main producers/manufactories and environmental impacts of different processes in garment production phases (Niinimäki et al., 2020)

2.1.2 Environmental impacts in the current fashion model

Environmental footprints are inevitably left behind by every stage of fashion supply chain; from design, textile and garment production, to marketing and global transportation. (Bertola and Teunissen, 2018). These footprints mainly refer to three broad categories: water consumption, carbon emission and chemical usage. Firstly, the fashion industry is the third-largest industrial water user who consumed up to 20% of total industrial water consumption and 2% of freshwater extracted annually in global (Common Objectives, 2018).Averagely, 200 tonnes water is required to produce a tonne of textile (Anguelov, 2015). Most water usage is in production process such as cotton cultivation which has the highest water footprint among all

18 fashion fibre (WRAP,2017) and the wet processes of textile production (bleaching, dyeing, printing and finishing) (Common Objectives, 2018). Secondly, United Nations Climate Change, 2018 claimed that the textile and fashion sector is responsible for 10% of global greenhouse gas emission, while Quantis reported that the fashion industry emitted approximately 4.0 gig tonnes (Gt) of CO2 equivalent in 2016, which is higher than the total carbon emissions from international airlines and maritime transport combined (Ellen MacArthur Foundation, 2017). The biggest contributors to carbon emission are production processes, such as yarn spinning, printing, dyeing and in-use processes including washing, drying and bleaching (Environmental Audit Committee, 2019). Thirdly, over 15,000 different chemicals, mostly toxic, are used in the textile manufacturing process (Roos et al., 2019), acting as accelerators, bleaches, softeners, dyestuffs and etc. (Rovira, Nadal, Schuhmacher and Domingo, 2015). Around two-thirds of garment-producing firms often incorrectly disposed of or washed out of fabrics produced with the toxic chemicals (Bechi et al., 2010). As a result, they end up in lakes, rivers and oceans, where they are turned into nonylphenols (NPs) which are even more hazardous (Greenpeace, 2012). Due to their hormone-disrupting characteristics, they have devastating effects on the environment and human health. For example, they decrease fertility in many organisms.

2.2 Previous LCAs of environmental impacts in fashion industry

Currently available LCAs of environmental impacts in fashion industry are summarised in table 1 below.

Table 1. The summary of LCA of environmental impacts in fashion industry reviewed in this chapter

Functional Units selection. FUs are the units that the results of an LCA are normalised to, which should be carefully chosen to reflect as closely as possible the function of the processes under consideration (Piontek and Müller, 2018). Although a wide range of functional units (FUs) were used by previous LCA analysis of environmental impacts in fashion industry, the 100% cotton T-shirt was selected in most of LCA researches, as it is an indispensable item widely owned by consumers (Hackett, 2015). Dependency of results on data resources. Also, the data resources for previous LCAs are the combination of primary factorial data, relevant literature review and online database, like EDIPTEX and Ecoinvent. It can be observed that there are significant differences between the results in general, depending on the data used, the assumption made, LCA method selected and the impact categories considered. However, the data collection and assumption are a common problem when conducting LCA, so some authors appealed to the standardization of the methods applied in the field (Piontek

20 and Müller, 2018). Lack of available data. In addition to the standardization, a lack of available data on certain processes, like detergents in washing processes should be noticed. For instance, among the papers reviewed in this project, only two included the in-use phase of garments, while only water and electricity consumption of washing machine were calculated (Hansen and Larsen, 2007 and Baydar, Ciliz and Mammadov, 2015). However, the environmental impacts generated from detergents used in washing processes were ignored, because of no available data. Besides, all the LCAs doesn’t include the design phase of garments. As mentioned before, the sample waste and electricity consumption of computer in design process result in sever environmental impacts, which would be better be included, as well. Importance of the in-use phase. Multiple studies highlight the emissions and electricity inputs during laundry. Among the reviewed papers, Hansen and Larsen, 2007 and Baydar, Ciliz and Mammadov, 2015 stated that laundry is the second biggest carbon producer, following the production phase.

2.3 Sustainability and Digitalisation in fashion industry

2.3.1 Sustainable fashion

Sustainable fashion, defined as reducing the environmental and social cost of the entire life cycle of garments (Fletcher, 2008), became a heat topic. Starting from the very first stage of raw material production to the manufactory techniques and marketing strategies, researchers systematically reviewed the lifecycle of a fashion apparel to achieve sustainability in all aspects (Gwilt, 2014). Figure 3 shows the potential research directions throughout the entire life cycle of fashion garments, leading to a more sustainable fashion system. Base on this framework about how to improve the sustainability in fashion industry, the digitalisation processing and innovation of virtual design/fitting technologies provide a feasible approach to achieve the sustainability.

Figure 3. The Sustainable Consumption and Production (SCP) cycle (Thorisdottir and Johannsdottir, 2019)

2.3.2 Digitalisation in fashion sector

‘Digitalisation’ allows firms in all sectors to more efficiently gather, analyse, manage and transform information than ever before (Hagberg et al., 2015) via integrated interactive digital technologies (Nylén, 2015), which blur the line between physical and digital world (Schwab, 2015). Specifically in fashion industry, the emergency of 3D reality technologies (3D visualization, 3D Body Scanning and virtual try-on technology) guided the fashion innovation (Karl and Larsson, 2018). On the one hand, with the help of digital samples, the design effect can be presented immediately and intuitively on 3D avatar without physical samples (Chunyan and Yue, 2014). On the other hand, the virtual garments can tried on and shared online to satisfy modern people’s psychological demand and alleviate the overconsumption behaviour (Chunyan and Yue, 2014). With the advancing of digitalisation and continuous innovation of virtual technology, fashion design and even the whole fashion industry can be more close to people’s virtual life style in future (Bertola and Teunissen, 2018).

2.3.3 Digital design and digital sample

According to McKinsey Group’s survey in 2019, 83 percent of their respondents believed that physical samples would be used less often and gradually be replaced by digital samples by 2025. It revealed that digital design and digital sample attracted high interest in fashion industry today (Berg et al., 2017).

2.3.3.1 3D virtual technology in design

Virtual design technology creates a 3D photorealistic product that enables real-time editing of the pattern, 3D image rendering and preview of errors and possibilities of fit and silhouette (Li & Fung, 2018). Also, the virtual garment creation can simulate the effect of different fabrics, colour and pattern (Showed in Figure 4 and 5), resulting in the quick decision from designers and minimal sample waste (Adeddoff L, 2014). Besides, on the base of 3D body scanning and virtual try-on technology, 3D fitting room (See figure 6) furtherly contributes to reducing the garment waste that generated from poor fitting effect, unwanted products and unsuitable size (Papahristou, 2016).

Figure 4. Examples of digital 3D samples with different texture of material created with Clo3D (Clo3D, 2020)

Figure 5. Clo3D Modelist (Clo3D, 2015)

Figure 6. The example of online fitting room (Li et al., 2009)

2.3.4 The advantages of 3D virtual design

From Table 2- the comparison of the traditional design process and 3D virtual design process, the advantages of 3D Virtual fashion stands out. Initially, all adjustment requirement like fabric choice, colour and size of print can be identified on the computer without physical samples, resulting in less samples waste, zero transportation cost of samples and faster process from design to production

(Papahristou, 2016). Secondly, it provides a more flexible, all-round, multi-Angle, dynamic presentation of design to manufactories, sales team and retail team, while reducing the misinterpretation (Papahristou, 2016). The shrink of pre-consumption waste means less energy for transportation, less chemical used, less water consumption in fibre preparation and less carbon emission in the whole process (Rudolph, 2016). Overall, digital samples replacing physical garments during design and development phases dramatically reduce the brand’s carbon footprint up to 30% and help to achieve sustainability goals (Iqbal, 2012).

Table 2. The comparison of traditional sample and virtual sample production process (Jacqui Haker & Sandra Kuijpers, 2019)

2. 4 Virtual fashion garments in future

Network virtual garments is a complete brand-new concept that is only about imagination and data. It relies on 3D virtual and electronic computer technology and animation techniques to provide consumers the immersive and a full range of perfect experience on the virtual garments (Chunyan and Yue, 2014). Also, the experience of digital garments is recordable and sharable to adapt to people’s virtual social life. In addition, the virtual garments can combine the fashion design itself with certain external factors, such as colour, light, area, location, flat, three-dimensional, visual and spatial features (Chunyan and Yue, 2014). Apart from this, virtual clothes can manage to achieve the harmony of garments, people and environmental (display background)

25 via taking people’s emotional, psychological, aesthetic perception, and many other factors into account during creation process (Chunyan and Yue, 2014). For instance, THE FABRICANT, a Netherlands Company devoted to creating a 3D virtual world with pure digital garments (Figure 7 shows one of their digital garment products). Those digital-only garment can be purchased, dressed, photographed to share with others or post on social media in the virtual world (THE FABRCANT, 2019). Also, effects that could not be achieved in the physical world and non-existent or rare fabric materials can be created in the virtual fashion world. More important, virtual fashion system is deemed to a potential way to minimize the environmental impacts in fashion industry due to its nature, which need to be proved through professional researches.

Figure 7. The website (Leela) of digital garment shop, fitting room and showroom of THE FACBRICANT (http://digital.fashion/collections/fluid/explore)

3. Methodology

Following a brief introduction of LCA, this chapter will outline the LCA methodology used, including justification of consumption and data used. It is organized according to the structure of LCA methodology (ISO, 2006). SimaPro software 8.0 version was used for modelling the product systems, while the ReCiPe method 2016 (H) is selected to analyze the result.

3.1 Life Assessment Analysis (LCA)

Life Cycle Assessment (LCA), one of the environmental management technique, is used to assess the environmental impacts generated from the entire life cycle of a certain product or service (ISO14040, 1997). In this project, LCA is the compilation and evaluation of input, output and environmental impacts associated with current fashion system and digital fashion system for further comparison and concluding which one is better. According to Figure 8, there are four steps to conduct LCA properly: the goal and scope definition phase, the inventory analysis phase (LCI), the impact assessment phase (LCIA) and the interpretation phase (ISO14040, 1997).

Figure 8. The process of conducting Life Cycle Assessment (LCA) (ISO14040, 1997)

3.2 Research Design

3.2.1 Goal and Scope

This study aims to determine and compare the environmental impacts in the three fashion scenarios to analyse whether the virtual technologies contribute to reducing the total environmental impacts. Global warming potential (GWP), Toxicity (Terrestrial Ecotoxicity and Human non-carcinogenic toxicity), land use and water use will be considered. Also, the LCA will highlight hotspots within the three life cycles separately which represent opportunities of decreasing relevant environmental impacts. The potential audience of this research are those related stakeholders in fashion sector or people interested in the sustainability of fashion industry and the potential

27 environmental effects digitalisation in fashion may have. This study is especially relevant to THE FABRICANT, a Netherlands digital fashion company, as composition and production data for their digital virtual garments were used to carry out the LCA.

3.2.2 Functional Unit

Cotton T-shirt is selected to assess the environmental impacts generated in the fashion sector, as the T-shirt is a staple piece for both genders (Hackett, 2015). The functional unit for Scenario 1 and 2 is a cotton T-shirt, while that of scenario 3 is a digital cotton T-shirt. To clarify, the T-shirt is made of pure cotton without multi-coloured patterns or prints.

3.2.3 System Boundaries

Detailed system boundaries of three scenarios are listed in figure 9 below, while the concise cotton T-shirt production stage is presented in Figure 10. Packaging of the product and further steps such as marketing and retailing processes are considered to be outside the system boundaries of this LCA. This LCA still have a considerable expansion on the system boundaries by including the whole design and sample phase, compared with previous LCAs. Besides, except the manufactory of desktop/laptop, infrastructure was largely not included in the system boundaries of this study to keep the consistency of LCA method, as it was not included in most literature LCAs considered (ISO, 2006). The Inventory Analysis in following section provides more information about the data used.

Figure 9. The system boundaries of three scenarios made in this project

Figure 10. The detailed cotton T-shirt production stage

3.3 Data requirements

As this project is one of the first LCA includes design phase and the first to look at the environmental impacts of digitized fashion system, low requirements on data quality will be accepted. It is hoped that this study will encourage further studies with higher data standards. Only well-respected secondary data resource, like peer reviewed literatures and online LCA database available on SimaPro can be used to maintain an accepted level of data quality. In this project, only the LCA database ‘Ecoinvent 3 - allocation at point of substitution – unit’ (Weidema et al., 2003) from SimaPro was used for the consistency of data resource. Also, as there is no considerable changes in the production process of garments happen in the past 20 years, supplementary data (except database from SimaPro) found from peer-reviewed literature within this period was considered acceptable. However, for the data related to digital fashion, the most recent primary data from THE FABRICANT, 2020 is used to reveal the most up-to date production process of virtual digital garments.

3.4 Inventory analysis

Table 3 below shows the assumptions to clarify FUs that are applied to the modelling and assessment of three scenarios. Scenario 1: a 100% Scenario 2: a 100% cotton Scenario 3: a cotton T-shirt T-shirt digital cotton

T-shirts Design Desktop/ Laptop 2 hours 1 hour 1 hour Sample 2 physical samples Digital sample Digital sample Production 100% cotton 1 hour on Reactive dye desktop/laptop Transformation rate: 400g cotton fibre-275g textile-a cotton T-shirt (250g) In-use 60 ℃ wash in automatic washing machine 10 min on (capacity: 6kg) smart-phone/lap No need for drying top/desktop Disposal Landfill Delete Incineration Resell inside UK Resell to the third countries Table 3. The summary of assumptions applied to LCA assessment

3.4.1 Scenario 1 and 2

By comparing the system boundaries of scenario 1 and 2, it can be found that the only difference is the design phase, while the scenario 1 uses the conventional design and scenario 2 applies the virtual design technology. Therefore, the assumptions and data related to other life cycle stages are the same. Table 4 and 5 listed all assumptions, data and other related information in Scenario 1 and 2. Detailed justification of each assumption and data description in Scenario 1 and 2 can be found in Appendix C.

Assembly Unit Reference Dataset used 1. Design and sample creation phase (2 hours) Desktop/ 0.00026P D.R. Williams, 2013 Computer, laptop {GLO}| Laptop APOS, U Electricity 0.169KWh Pothitou, Hanna and Electricity, low voltage Chalvatzis, 2017 {GLO}| APOS, U

2. Production phase Cotton 400g Lu S., 2018 and Cotton fibre {GLO}| cotton cultivation Ecoinvent 3 production | APOS, U Textile 275g Ecoinvent 3 Textile, knit cotton manufacture {GLO}|APOS, U CMT (Cut-Make-Trim) Electricity 0.67KWh Electricity, high voltage {GLO}| APOS, U 4. Transportation Cotton 40 kgkm by Hansen and Larsen, Transport, freight, lorry lorry 2007 3.5-7.5 metric ton, euro4 {RoW}| APOS, U Yarn 33.3 kgkm by Ecoinvent 3 Transport, freight, inland Inland waterways, barge {GLO} waterways APOS, U 167 kgkm by Ecoinvent 3 Transport, freight, lorry, Lorry unspecified {GLO} | APOS, U 513 kgkm by Ecoinvent 3 Transport, freight, sea, Transoceanic transoceanic ship {GLO} | ship APOS, U 53.4 kgkm by Ecoinvent 3 Transport, freight train Train {GLO}| APOS, U Textile 51.1 kgkm by Ecoinvent 3 Transport, freight, aircraft Aircraft {GLO}| APOS, U 24.6 kgkm by Ecoinvent 3 Transport, freight, light Light commercial vehicle {GLO}| commercial APOS, U vehicle 385.7 kgkm Ecoinvent 3 Transport, freight, lorry, by Lorry unspecified {GLO}| APOS, U

143.9 kgkm Ecoinvent 3 Transport, freight, sea, by transoceanic ship {GLO}| Transoceanic APOS, U ship T-shirt 50 kgkm by Hansen and Larsen, Transport, freight, light Lorry 2007 commercial vehicle {GLO}| APOS, U 6. In-use phase tap water 49L Baydar, G., Ciliz, N. Tap water {GLO}|APOS, U consumption and Mammadov, A., 2015 electricity 1.14KWh Baydar, G., Ciliz, N. Electricity, low voltage consumption and Mammadov, A., {GLO}| APOS, U 2015 powder 0.033kg Baydar, G., Ciliz, N. See below detergent and Mammadov, A., 2015 To produce 1kg detergent: Linear 0.15kg Yamaguchi, Y. et al., Alkylbenzene sulfonate, alkylbenzene 2011 linear, petrochemical sulphonate {GLO}|APOS, U (LAS-Pc) Polyoxyethylen 0.1kg Yamaguchi, Y. et al., Alkylbenzene sulfonate, e alkylether 2011 linear, petrochemical (AE-CNO) {GLO}|APOS, U Zeolite 0.28kg Yamaguchi, Y. et al., Zeolite, powder {GLO}| (Aluminosilicat 2011 market for | APOS, U e) Soda ash 0.24kg Yamaguchi, Y. et al., Soda ash, dense {GLO}| 2011 modified Solvay process, Hou's process | APOS, U Sodium 0.128kg Yamaguchi, Y. et al., Layered sodium silicate,

Silicate 2011 SKS-6, powder {GLO}| APOS, U Sodium Sulfate 0.06kg Yamaguchi, Y. et al., Sodium metasilicate 2011 pentahydrate, 58% active substance, powder {RER}| APOS, U Fluorescent 0.02kg Yamaguchi, Y. et al., Fluorescent agent, DAS1, dye 2011 triazinylaminostilben type {GLO}| market for | APOS, U Water 0.04kg Yamaguchi, Y. et al., Tap water {GLO}| market 2011 group for | APOS, U

6. Disposal 14% Reuse in UK (35 g) Transportation Light (Castellani, Sala and Transport, freight, light commercial Mirabella, 2015) commercial vehicle vechicle:0.7k {Europe without gkm Switzerland}| APOS, U 34% Reuse in the third countries (85 g) Transportation Ship:680kgk (Castellani, Sala and Transport, freight, sea, m Mirabella, 2015) transoceanic ship {GLO}| APOS, U Landfill 31% for Peters et al., 2019 Treatment of waste textile, landfill: 77.5 and Ecoinvent 3 soiled, municipal g incineration with fly ash extraction | APOS, U Incineration 21% for Peters et al., 2019 Treatment of waste textile, incineration: and Ecoinvent 3 soiled, municipal 52.5 g incineration with fly ash extraction | APOS, U Table 4. The LCI of Scenario 1 per FU

Assembly Unit Reference Dataset used 1. Virtual design and sample creation phase (1 hour/ Bram, 2020) Desktop/ Laptop 0.00013P D.R. Williams, 2013 Computer, laptop {GLO}| APOS, U Electricity 0.0843KW Pothitou, Hanna and Electricity, low voltage h Chalvatzis, 2017 {GLO}| APOS, U Table 5. The LCI of virtual design phase in Scenario 2 per FU (All other life cycle stages are the same as Scenario 1)

3.4.2 Scenario 3

Scenario 3 contains three main phases: design phase, 3D T-shirt stitching and uploading and in-use phase. According to the design group from THE FABRICANT (Bram, 2020), it is assumed that 1 hour work with computer is required to finish the design of a T-shirt patterns. Meanwhile, another 1 hour is required on stitching the patterns together and uploading virtual T-shirt to the official website that works as an online shop with fitting room and showroom. Both phases are finished on computer and consume electricity, while extra electricity consumption to support internet access is required in the stitching and uploading phase. Hence, the electricity consumption of network and data centre to provide internet access should be included, as well. Based on the Figure 11 from CustomMade, 2015, the electricity consumed by networks and data centres can be calculated. Besides, according to the customer research from THE FABRICANT, 2019, the average in-use time length of their digital product is 10 minutes per item. All in all, Table 6 below provides the complete inventory sheet of scenario 3 at the end of this section.

Figure 11. The power consumption ratio of data centres, network and end-user devices (CustomMade, 2015)

Production Unit Database used Stage 1. Design (1 hour) Desktop/ 0.00013121P Computer, laptop {GLO}| market for | APOS, U Laptop Electricity 0.0843KWh Electricity, low voltage {GLO}| APOS, U * The virtual 3D design software doesn't require internet to launch 2. 3D T-shirt stitching and uploading Computer/ 0.00013121P Computer, laptop {GLO}| market for | APOS, U Laptop Electricity consumption End-user Device 0.0843KWh Electricity, low voltage {GLO}|APOS, U Data centre 0.0366KWh Electricity, low voltage {GLO}|APOS, U Network 0.0382KWh Electricity, low voltage {GLO}| APOS, U 3. In-use phase (10 min) Electricity consumption End-user device 0.0141KWh Electricity, low voltage {GLO}| APOS, U Data centre 0.0061 KWh Electricity, low voltage {GLO}| APOS, U Network 0.0064 KWh Electricity, low voltage {GLO}| APOS, U Table 6. The LCI of scenario 3 per FU

4 Results

This chapter will present the LCA results for the models of three scenarios in this project. The 20% significance heuristic (See Appendix A for the explanation of significance heuristic) will be used throughout when comparing the environmental impacts of products. All values of results are kept to 3 decimal places for consistency

35 and ease of comparison. More detailed tabulation of results can be found in Appendix E.

4.1 Global Warming Potential (GWP) Results

4.1.1 Global Warming Potential Process Breakdowns

Table 7 presents the breakdown of each stage’s contribution to the total global warming potential (GWP) for the three scenarios. Discussion of scenario 3 will be separate due to the difference of physical T-shirt (produced in scenario 1 and 2) and virtual T-shirt (produced in scenario 3). In Scenario 1 and 2, it can be observed that the T-shirt production phase from cotton cultivation to garment manufacture is the biggest contributor of GWP (82%/83%). Figure 12 provides a further breakdown of GWP percentage by each process within the production phase. In the cotton cultivation process, the GWP is mainly present in the form of chemical products usage such as artificial fertilizer, pesticide and herbicide and the burning of fossil fuels. Following which, the yarn manufacture and knitting process contributes to the biggest portion that is roughly 65% of GWP in the production phase, mainly due to the electricity consumption of manufacturing factories. The Textile finishing procedure generates the 2nd largest portion of the production phase GWP. This procedure includes the pre-treatment, dyeing and finishing processes and consumes electricity via the use of machinery. Finally, the machineries used for Cut-Make-Trim process consume electricity that makes up about 2% of GWP. Within the in-use phase of scenario 1 and 2, the electricity consumption by washing machines generate 13% of total GWP, while the actual electricity usage depends on the numbers of uses in the T-shirt’s life cycle. Also, the GWP of design and sample production phase (2% and 1% in scenario 1 and 2 respectively) is due to the electricity consumed by computers used for designing and making of sample T-shirts. Meanwhile, the GWP of the transportation phase (contributing 2%) reflects the use of diesel and petrol. Finally, the disposal phase contributes 1% to total GWP in scenario 1 and 2, generated from the burning of discarded T-shirt and resultant air pollution. In terms of scenario 3, the total GWP is 0.261 kg CO2. Similarly the production phase is still the biggest contributor (contributing 57%) of total GWP. As all processes in scenario 3 are digital and virtual, the GWP includes only electricity consumptions by

36 digital devices: for designing (35%), producing and uploading (57%) and user purchase (8%). As the electricity consumption depends on the time of usage of digital devices of each process, total GWP of scenario 3 can be directly reduced by increasing the work efficiency and shortening the time spent on each process. Scenario 1 (kg CO2) Scenario 2 (kg CO2) Scenario 3 (kg CO2) Design 0.182 (2% of total 0.090 (1% of total 0.090 (35% of total contribution) contribution) contribution) Production 6.440 (82% of total 6.440 (83% of total 0.150 (57% of total contribution) contribution) contribution) Transportation 0.175 (2% of total 0.175 (2% of total None contribution) contribution) In-use 1.010 (13% of total 1.010 (13% of total 0.0211 (8% of total contribution) contribution) contribution) Disposal 0.009 (1% of total 0.009 (1% of total None contribution) contribution) In total 7.820 7.730 0.261 Table 7. The breakdowns of global warming potential of three scenarios

Figure 12. Percentage of GWP within the cotton T-shirt production phase

4.1.2 Global Warming Potential Comparisons

Figure 13 shows that scenario 3’s GWP is the significantly less than that of the Scenario 1 and 2 with the 20% significance heuristic. On the other hand, under the significance heuristic of 20% chosen in this study, there is no significant difference

37 between GWP of scenario 1 and 2. It can be deduced that the small difference in GWP between scenario 1 and 2 is caused by their different design methods used, as all other stages of the life cycle are kept the same. The GWP of design phase in scenario 1 and 2 has two parts: the carbon emission from computer usage and carbon emission from the production of two sample T-shirts, including chemical production, electricity consumption of machineries and burning of fossil fuel. For the virtual design in scenario 3, the GWP only includes the electricity consumption of two hours of the computer working time to design, as no physical samples were produced. If we were to assume that 10,000 T-shirts will be produced per design, the GWP difference generated by the different design methods in three scenarios becomes insignificant to the entire life cycle of each T-shirt.

Figure 13. The comparison of three scenarios GWP processes breakdown

4.2 Toxicity

The toxicity contains five impact categories: Terrestrial Ecotoxicity, Freshwater Ecotoxicity, Marine Ecotoxicity, Human carcinogenic toxicity and Human non-carcinogenic toxicity. Only terrestrial Ecotoxicity and human non-carcinogenic toxicity will be analysed in details in this section. As the contribution rate of Freshwater Ecotoxicity, Marine Ecotoxicity and Human carcinogenic toxicity are similar in all 3 scenarios, it is not necessary to assess them in the case of this study.4.2.1 Terrestrial Ecotoxicity (TET)

4.2.1.1 Terrestrial Eco Toxicity (TET) Breakdowns

According to Table 8- the breakdowns of total Terrestrial Ecotoxicity, it is obvious that the top three biggest contributors of the overall TET in scenario 1 and 2 are their production phase, In-use phase and transportation phase. Figure 14 presents a further breakdown of TET by processes within the production phase of a cotton T-shirt. Approximately 90% of TET originates from the use of pesticides in the cotton cultivation process and defoliation agent in the harvesting process; 4% of TET is generated from the electricity consumption in the knitting process; and 6% of TET is generated from the use of softening agent and reactive dyes in textile dyeing and finishing process. TET of in-use phase is generated from the use of detergents and electricity consumption of the washing machines (depending on the numbers of uses per life cycle of the T-shirt). The other portions of TET are contributed by design phase in scenario 1 and 2; while TET generated from the disposal phase is negligible and can be omitted, as the TET is equal to 0%. In terms of scenario 3, the total TET is at a low of 0.692 kg 1, 4-DCB, which are almost produced from design and production phases (43% and 53% respectively). Only 4% TET are produced in the in-use phase. The TET of scenario 3 originates only from the electricity consumption and the use of chemicals and metals in the production process of digital devices such as computers and smartphones; where by increasing work efficiency, TET of scenario 3 can be reduced further. Scenario 1 (kg Scenario 2 (kg Scenario 3 (kg 1,4-DCB) 1,4-DCB) 1,4-DCB) Design 0.595 (5% of total 0.297 (3% of total 0.297 (43% of total contribution) contribution) contribution) Production 9.150 (74% of total 9.150 (75% of total 0.37 (53% of total contribution) contribution) contribution) Transportation 1.100 (9% of total 1.100 (9% of total Omitted contribution) contribution) In-use 1.500 (12% of total 1.500 (13% of total 0.0259 (4% of total contribution) contribution) contribution) Disposal 0.022 (0% of total 0.022 (0% of total Omitted

contribution) contribution) In total 12.300 12.000 0.692 Table 8. The breakdowns of TET of three scenarios

Figure 14. Percentage of TET by processes within the production phase of a cotton T-shirt

4.2.1.2 Terrestrial Ecotoxicity (TET) Comparisons

Initially, based on the comparison of total TET of three scenarios (See figure 15), there is no significant difference between the TET of scenario 1 and 2 under the 20% significance heuristic, while the TET of scenario 3 is significantly lower by a 94% compared to scenario 1 and 2. Also, it can be deduced by comparing the TET of design phase in scenario 1 and 2 that the tiny drop of TET of scenario 2 is because that physical samples production is avoided with the aid of virtual design technology.

Figure 15. The comparison of Terrestrial Ecotoxicity in three scenarios

4.2.2 Human Non-carcinogenic Toxicity (HNT)

4.2.2.1 Human non-carcinogenic toxicity (HNT) breakdowns

As it can be seen in Table 9 about the breakdowns of total Human Non-carcinogenic Toxicity (HNT), in the scenario 1/2, the largest portion of HNT is generated from the production phase (72% and 77% respectively), followed by in-use phase which contributes to 12%/13% of total HNT of scenario 1/2. Also, the design phase generates 12% of total HNT in scenario 1 and only 6% in scenario 2. The rest 4% of HNT is from the transportation phase, while the HNT of disposal phase is only 0.004 kg 1, 4-DCB, and equivalent to 0% of total HNT. Similarly to the analysis of TET, the HNT of production phase is because of the use of pesticides in the cultivation phase and the electricity consumption of machinery in other necessary processes. Meanwhile, the HNT of in-use phase is only originated from the electricity consumption of the washing machine, as detergent composition used in this project doesn't have human toxicity. Also, the burning of diesel and fossil fuel for transportation and discarded T-shirt in disposal phase emit harmful gas to air, leading to human toxicity. Besides, almost all HNT of scenario 3 is from design and sample production phase (46%) and production phase (52%), while only 2% of total HNT is from the in-use phase,

41 which shows that the HNT is associated with electricity consumption of each processes. The more electricity consumed, the higher relevant HNT is. To reduce the HNT of scenario 3, increasing work efficiency to reduce electricity used and using clean-source electricity are the only choices, as the scenario 2 is purely digital and only consumed electricity.

Scenario 1 (kg Scenario 2 (kg Scenario 3 (kg 1,4-DCB) 1,4-DCB) 1,4-DCB) Design and 0.654 (12% of total 0.326 (6% of total 0.326 (46% of total sample contribution) contribution) contribution) creation Production 4.100 (72% of total 4.100 (77% of total 0.363 (52% of total contribution) contribution) contribution) Transportation 0.225 (4% of total 0.225 (4% of total Omitted contribution) contribution) In-use 0.669 (12% of total 0.669 (13% of total 0.013 (2% of total contribution) contribution) contribution) Disposal 0.004 (0% of total 0.004 (0% of total Omitted contribution) contribution) In total 5.650 5.320 0.703

Table 9. The breakdowns of Human Non-carcinogenic Toxicity of three scenarios

4.2.2.2 Human non-carcinogenic toxicity (HNT) comparison

Comparing the total HNT of the three scenarios (See Figure 16), there is no significant difference between scenario 1 and 2 under the 20% significance heuristic. However, the total HNT of scenario 3 is significantly less than that of scenario 1 or 2. Also, it can be observed that the HNT difference between scenario 1 and 2 is purely due to different methods used during design and sample production phase, even though the difference is not significant.

Figure 16. The comparison of Human Non-carcinogenic Toxicity in three scenarios

4.3 Other relevant impact categories in three scenarios

4.3.1 Comparison

As observed from table 10 and Figure 17, the difference between total water consumption of scenario 1 and 2 is not significant under the 20% significance heuristic, while there is a significant difference between scenario 3 and scenario 1 or 2. Besides, the land use is all for cultivating 400g cotton fibre for both scenario 1 and 2, which is significantly higher than that of scenario 3. As scenario 2 only consumes electricity, water consumption and land use in scenario 2 is close to 0 and can be omitted. Water consumption/m3 Land use/m2a crop

Scenario 1 0.683 2.620 Scenario 2 0.682 2.620 Scenario 3 0.002 0.004 Table 10. The comparison of three scenarios

Figure 17. Other impact categories comparison of three scenarios

4.3.2 Water consumption breakdown for scenario 1 and 2

From Table 11, 99% of total water consumption of scenario 1/2 is contributed only by two phases：Wetting processes such as washing, bleaching, dying, etc. within the production phase (91% in both two scenarios) and in-use phase (which depends on the number of washing, taken as 8% in both scenarios in this case). Life cycle stage Water consumption/Litre Scenario 1 Scenario 2 Design and sample 1 (0.5% of total 0.700 (1% of total production contribution) contribution) Production 624 (91% of total 624 (91% of total contribution) contribution) Transportation 1 (0.5% of total omitted contribution) In-use 57(8% of total 57(8% of total contribution) contribution)

Disposal 0 0 Total 683 682 Table 11. Water consumption breakdown for scenario 1 and 2

4.4 Comparison of conventional design (Scenario 1) and virtual design (Scenario 2)

In this paper, it is assumed that each design that goes into production phase will result in at least 10,000 items being produced. Hence, the environmental impacts of design and sample production phase only takes up 0.02% or 2/10,000 ratio of the entire lifecycle of a fashion piece (refer to methodology). Meanwhile, from the comparison of three scenarios, the environmental impacts of scenario 2 are slightly less than the scenario 1 due to the differences in methodology during the design phase. To thoroughly understand the differences, the conventional design phase in scenario 1 and virtual design in scenario 2 are compared. According to Table 12 and Figure 18, the environmental impacts of virtual design is significantly lower than the conventional design under the selected significance heuristic. The GWP of the conventional design phase is 13.4 kg CO2, while that of virtual design is only 0.143 kgCO2. Also, the TET and HNT of design phase in scenario 1 are 1.27 and 21.1 kg 1, 4-DCB respectively, while those of virtual design method are 0.297 and 0.713. Also, the water consumption (9.31 m3) and land use (5.21 m2a crop) of the design phase in the scenario 1 are significantly higher than those of virtual design phase in scenario 2. It is because by conducting virtual design, at least two samples and related environmental impacts can be saved.

Global Terrestrial Human Water Land use warming ecotoxicity/ non-carcinogenic consumption /m2a Potential/kg kg 1, 4-DCB toxicity /m3 crop CO2 /kg 1, 4-DCB Design 13.400 1.270 21.100 9.310 5.210 phase (Scenario 1) Design 0.143 0.297 0.713 omitted omitted phase (Scenario 2) Table 12. The comparison of conventional design and virtual design

Figure 18. The overall impact categories comparison of two design methods

4.5 Comparison with previous LCA studies

It would be beneficial to evaluate the result via comparing with previous LCA studies. However, fair comparison can only be achieved when the system boundaries of studies are the same. As this project is one of first LCAs to include the design and sample production phase in the analysis therefore have a different system boundary. Thus the results of this study is incomparable with previous LCA studies. However, although without direct comparison, the conclusion from previous LCA studies can act as the reference to assess the accuracy of this project. Similar to this

46 project, Zamani, Sandin and Peters, 2017 and Baydar, Ciliz and Mammadov, 2015’s researches confirmed that the top three contributors in conventional fashion life cycle are production phase, in-use phase and transportation phase.

5 Discussion

In this chapter, the implications of the results of this study will be discussed and compared with findings from similar researches, along with potential problems related to digital fashion. Also, limitations to the methodology and data collection used in this study will be also discussed.

5.1 life-cycle thinking to enhance current fashion supply chain

5.1.1 Virtual design technology help reduce the environmental impacts

The effect of virtual design on reducing environmental impacts is not significant when the functional unit is a physical cotton T-shirt (final products). However, when only considering the environmental impacts of designing a T-shirt, the comparison of solely the design phases in scenario 1 and 2 shows that virtual design does help reduce environmental impacts rather significantly, as no physical samples are produced and all related environmental impacts and raw material wastage can be reduced. Moreover, as mentioned, the design and sample production phase is iterative, the number of required samples before the finalization to the stage of production depends on the design complexity and communication effectiveness among designer, sales group and manufacturer. The more complex the piece garment is, the longer the design phase, therefore the more effective virtual design technology is able to reduce environmental impacts of the design phase. Currently, virtual design technology is not yet widely applied around the world, but it is a direction for interested stakeholders to invest in creating a more sustainable business system. More researches and technology development will need to be implemented before virtual design process become a prevalent and normalised technique for fashion design and production.

5.1.2 Other approaches along the life cycle

It can be concluded that overall, garment production phase (especially cotton cultivation and wetting processes), laundry and transportation are the top three contributors to all impact categories assessed in this project. Therefore, to reduce the environmental impacts of the life cycle of garments/ fashion pieces, methods to environmentally enhance these three processes should be looked at.

5.1.2.1 Organic cotton and recycled cotton

Cotton growing requires 1. Agricultural machineries that is electricity-consuming; 2. Fertilizer/ growth regulators that are typically combinations of nitrogen (N), phosphorus (P) and potassium (K) (Silvertooth, 2002); 3. Water irrigation and 4. Large amount of toxic chemicals like pesticides and herbicides (Catchot, 2007), which are the causes of significant global warming potential, toxicity and high water consumption. To reduce the environmental impacts, organic cotton cultivation, referring to the cultivation system with zero synthetic chemical use (Ingram, 2002) can be adopted. Using recycled materials to partially replace raw materials can also lessen the pressure fashion production puts on the environment. For organic cotton, as cotton is cultivated without prohibited chemicals for a period of three years, the carbon emission of chemicals production and toxicity of applied chemicals itself can be minimized (Baydar, Ciliz and Mammadov, 2015). However, the yield of organic cotton can be decreased up to 50% in the first few years, which increases the cost of cotton cultivation. To encourage organic cotton cultivation, organic certification is used credit organic producers who meet the standards of organic cultivation, which adds credibility to final products and encourage consumers to pay the extra price for organic products (Senthil et al., 2019). The certificated standards may vary in different regions but are all based on standards provided by the International Federation of Organic Agriculture Movements (IFOAM) (Radhakrishnan S, 2017). In terms of the cotton recycling technology, it avoids the entire cotton growing process and then resulted in reducing consumption of water and agrochemicals. Furthermore, in the dyeing process, dyestuff, wetting agents, softener and other related chemicals can all be avoided if the colour of recycled material is similar to the final products (Khan et

48 al., 2018). All environmental impacts generated from cotton cultivation, yarn manufacture and dyeing will be replaced by the additional cutting and shredding of recycled garments before ginning. Khan et al., 2018 reported that the environmental impacts of recycling prodecures is significantly lower than that of producing new materials.

5.1.2.2 Consumers’ in/after-use behaviour

The second-largest contributor to environmental impacts of the in-use phase are affected by the fashion piece’s washing frequency, water temperature and the choice of detergent used. It is clear that the consumer has an absolute impact and control over the overall environmental profile of the T-shirt and can knowingly save the environment by thinking and selecting the proper washing procedure. For example, less washes can reduce the environmental impacts and extend the lifetime of a T-shirt, which means a decrease in the environmental impacts in the manufacturing and production phase (Giagnorio et al., 2017). Also, it was supported by Hansen and Larsen, 2007 that water temperature and the choice of washing programme (with or without pre-wash) will influence the environmental impacts of laundry, as well. On the scenario that the water temperature and washing programme will not affect the wash quality, low water temperature and cancelling pre-wash can help reduce environmental impacts. Finally, selecting a less toxic detergent also decreases the environmental influences, even though the reduction is less significant (Giagnorio et al., 2017). Users and business owners can refer to Legislation eco-labelling’s list of the substances should not be used from an environmental perspective, which is a guideline for environmental work at enterprises (Yamaguchi et al., 2011). The final phase in the life cycle of a garment is disposal. A large part of the environmental impacts of raw material production and manufacture can be eliminated via recycling clothes. Furthermore, a lower demand for raw material production leads to more environmental friendly harvesting and growing processes (Zamani, Sandin and Peters, 2017). Used or unwanted garments can be sent to local second-hand shops or charity or resell to the third world countries with reduced prices. Studies have shown that even with additional transportation and other recycling-induced costs, recycling garments still can reduce the environmental burden of clothing. The environmental

49 impacts of recycling garments is insignificant in comparison to the environmental impacts due to replacement of raw materials (Woolridge et al., 2006; Farrant et al., 2010).

5.2 Brand-new fashion system-Digital Fashion

Digital fashion model has the minimum environmental impacts due to its nature. As there is no resource-intensive production of physical items, digital fashion is all about data, computer and electricity. The environmental impacts of digital fashion are originated from electricity consumption and digital devices production. Hence, to furtherly reduce the environmental impacts of digital fashion, improving work efficiency to shorten the working time on digital devices and using electricity from clean resources are recommended. Also, as design is intuitive and inspiration-driven, the design period largely varies and difficult to predict, while the in-use time fully depends on consumers’ behaviour. Current technology mainly supports consumers to try on and share purchased digital items on their virtual avatars. With limited interactions with digital fashion, users’ using time also tends to be limited. With the advancing technology innovation, more interactions between products and consumers as well as the interaction among consumers will likely develop, resulting in potential extension of using time. In this development process, green awareness is always the key to help reduce environmental impacts in digital fashion industry.

5.3 The combination of enhanced fashion system and digital fashion

Virtual fashion cannot completely replace physical garments. Physical protection and social functions of real garments cannot be replaced. Hence, to achieve the sustainability in fashion industry, it would be ideal to combine the virtual design fashion supply chain and digital garment model. Limited basic physical garments to be produced and consumed to meet the physical protection and social needs; while the psychological need of garments- such as staying in fashion, expressing personality or communicating with others can be encouraged to go virtual. Hence, the overproduction and overconsumption phenomenon can be alleviated to reduce the resource loss and pollution production in general rather than from the technical aspect only. Meanwhile, the physical fashion supply chain should be

50 improved with the aid of 3D virtual technology and other approaches along the lifecycle mentioned above to lower the pollution.

5.4 Future challenges and adoption

5.4.1 Sustainable physical fashion supply chain

As discussed in previous sections, systemic adjustment of fashion system should be applied to achieve a sustainable supply chain, which requires enhancement of each phase of the life cycle and the joint effort of all individuals in this industry from designer to consumer (Hillstrom, 2018). It is needed to produce strict standards on the acceptable environmental impacts of products and let consumers be clear about the standards. Consumers need to have environmental awareness and willingness to make purchase decision depending on the environmental impacts of a garment or even willing to pay extra for sustainable products. Otherwise, there is no motivation for enterprises to modify their business model and production chain. The government also plays a vital role, as governments have the opportunity to set standards for the environmental impacts of garments and enforce environmental rules to be met by manufacturers and businesses. (E.g. for products that cannot meet the standards, taxes and tariffs can be applied to offset negative environmental impacts.)

5.3.2 Digital fashion supply chain

There are two main challenges for the virtual digital fashion system: ‘Information Overload’ and customer adaption (Industry, 2016). While the virtual digital garments reduce the environmental impacts, potential problems may arise in the future: 'information overload', which refers to when the amount of data available becomes too vast to distinguish the useful data and waste (Business Sweden, 2015). How to properly sort and store user data in the collection is one of the big challenges to achieve the success of digital fashion. Meanwhile, consumers’ attitude towards this brand-new fashion system is crucial for future development. It is the first challenge to convince consumers that it is worthwhile to pay for virtual digital garments that they cannot touch or feel. The aim of digital fashion system is to replace physical garments and provide customers with a virtual

51 platform to present and communicate their understanding of fashion and personality. However, as the interactions of digital garments are still limited to websites, recorded videos and screenshot/photos on online platforms, the virtual digital fashion system is currently still immature and unable to fully achieve this aim. Researches about the expectations and opinions of consumers towards digital fashion should be conducted to guide future development and marketing strategy. At the same time, continual technology innovation is required to complete the digital fashion supply chain and create more value.

5.4 Limitations of Study

Initially, the functional unit selected in this project is a cotton T-shirt, as it is widely owned by large number of consumers. However, using only a cotton T-shirt to represent the environmental impacts of the fashion industry limits the study, as it is only one possible scenario in fashion industry. The environmental impacts calculated via LCA varied on the selection of the functional unit. When a more complex fashion item is selected as functional unit, the environmental impacts will be different. Secondly, the data resources used in this project are a combination of primary data from THE FABRICANT (such as the electricity consumption of virtual design) and secondary data like Ecoinvent 3 database available on SimaPro and obtained from literature reviews. There could be some discrepancies in data, in the resulted of varying data collection methods. The allocation method used in the literature review could be different from that used in this project, which may also lead to error. Thirdly, as this project is one of the first LCA of virtual design technology and virtual digital garment system, there is little to no previous LCA results that can be used to compare to increase accuracy. Also, it is difficult to find reliable data for certain small processes like the router production in the in-use phase of scenario 2. The percentage of powder detergent components is found in an LCA report about domestic laundry in Japan, as there is no available data of washing detergent produced in EU for domestic use, which also limits this project. Fourthly, there are certain assumptions used to calculate the environmental impacts, resulting in that the scenarios in this project cannot fully represent the reality, which also influence the accuracy of the results. Lastly, the dataset in the past 15 years that is

52 not up-to-date was also a hinder in the collection of data. Detailed data quality assessment can be found in Appendix D.

6. Conclusion

6.1 Conclusion

This project studied the ‘cradle to grave’ LCA analysis of three scenarios about current fashion supply chain and digital fashion supply chain with the functional unit of a cotton T-shirt, across the impact categories of GWP, Toxicity (Terrestrial Ecotoxicity and Human Non-carcinogenic Toxicity), water consumption, land use and ionizing radiation. The three scenarios refer to the conventional production process (referred to as scenario 1), virtual design aided production process (referred to as scenario 2) and virtual digital T-shirt production process (referred to as scenario 3). It was observed that for scenario 1 and 2, the top three contributors to all impact categories assessed in this project are: 1. production phase from the cotton cultivation to garment manufacture, 2. In-use phase (laundry) and 3. Transportation. Also, as the environmental impacts of the scenario 1 is slightly higher than scenario 2, the application of virtual design does help achieve a more sustainable fashion supply chain. Moreover, the study highlighted the importance of specific improving approaches at different life cycle phases of a product lead to the overall improvement of product’ sustainability. Such as organic cotton and recycling cotton materials can partially replace cotton produced in a conventional system with a high amount of toxic chemicals like fertilizers, pesticides and herbicides to reduce the environmental impacts. These also decrease the environmental impacts of wetting, softening and dyeing processes. In terms of the laundry process that is most affected by consumers’ awareness and behaviour, reducing the washing frequency, extending the lifespan of garments, using low-temperature water, cancelling pre-wash model and using high-quality, low toxic detergents can minimize the environmental profile of in-use phase. Also, for the aspect of environmental sustainability, the discarded garments should be transported to local second-hand shops or charity or resold to the third world countries rather than send to incineration/ landfill stamps directly.

The comparison of three scenarios has proven that scenario 3- virtual digital model generates the least environmental impacts, as the only environmental input is electricity consumed by digital devices, network and data centre. However, the digital garments cannot completely replace the physical garment for physical protection and social purposes. Hence, this paper suggests that the ideal fashion system is described to be a smart segregation of purposes of physical and digital garments; where physical garments were to be produced in basic, comfortable and durable styles in less amount, to satisfy basic physical and social needs of people; while encouraging people to turn to digital fashion pieces to satisfy their psychological need of fashion items. Potential future challenges are also predicted in this project. In order to achieve a systemic advancement of physical garment supply chain environmentally, the need of clear environmental standards and the environmental awareness and willingness to co-operate from stakeholders, designers to consumers are the main challenges. On the other hand, for digital fashion, the effectiveness of user data management to avoid ‘information overload’ situations; and the need for continual innovation of virtual technology to complete the digital fashion supply chain are the main challenges of advancement. With the advancing of digitalization, it is not supervised that virtual fashion will become the future trend. Only forward-looking companies with passion and technology to solve above-stated challenges can come out to top.

6.2 Recommendations

Recommendations from this study are: - THE FABRICANT could label/brand their products ‘virtual design products’ as highlights of their products and educate customers about the benefits of virtual design and digital fashion. - THE FABRICANT should consider carrying out an individual LCA of their virtual digital fashion supply chain with the primary data source from industry to better understand the details of environmental impacts of digital fashion system. - THE FABRICANT should conduct researches about the expectations and opinions of target consumers towards digital fashion to understand market potential, interest and feasibility.

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8. Appendix

Appendix A: Significance Heuristic

This study aims to provide conclusive qualitative results about whether the application of virtual design can reduce the environmental impacts of fashion supply chain and whether the digital fashion supple chain generates the minimum environmental impacts. To do this, a predetermined threshold is selected to determine how much difference between the two results would need to be considered significant. According to the standard from Scott Matthews et al., 2014, a difference of at least 20% is required. There is no scientific basis for using 20%, it is simply assumed in this project that if the difference between two values is greater than 20%, the qualitative fact that there is a difference between them is likely to be true, despite the uncertainty of the

63 data used to obtain these values.

Appendix B: Environmental Impact Tool

Figure 19, Figure 20, and Figure 21 are screenshots of the LCA tool- SimaPro 8.0.

Figure 19. The list of databases

Figure 20. The screenshot of SimaPro page

Figure 21. The screenshot of SimaPro to present how to use it to model the life cycle of a T-shirt

Appendix C: Detailed inventory analysis of Scenario 1 and 2

C.1 Design Phase

Scenario 1: Designers need to use 2D design software to transfer their design concept in mind into patterns on the computer. According to the data provided by Design group from THE FABRICANT (Bram, 2020), it is assumed that 2 hours is required on computer/laptop to create the patterns for a basic cotton T-shirt. According to Table 4, the average electricity consumption of desktop and laptop in active mode for 1 hour is 0.0842 KWh ((Pothitou, Hanna and Chalvatzis, 2017). Hence, the primary electricity use in this process is 0.1684 KWh. Meanwhile, according to D.R. Williams, 2013’s study, the average lifespan of desktop/laptop is 2.9 years (working 7.2h/day, 365 days/year). Hence, two hours working on computer will consume 0.00026 P desktop/laptop. Environmental impacts generated from it should be taken into calculation, as well. Once the patterns are finished, it will be sent to the stitching shop nearby to create the first physical sample for fitting-in and adjusting. As the plain cotton T-shirt is the simplest design, it is assumed that only 2 physical samples which are not sellable and will be discarded are produced in this phase. Then, once the design is confirmed, at least 10,000 items will be produced at the same process as actual production phase

65 and get into market.

Scenario 2: According to the data from design group from The FABRICANT (Bram, 2020), it is assumed that 1 hour (Hence, 0.0842 KWh electricity and 0.00013P desktop/laptop are consumed) is spent on computer to finish the design of the simplest T-shirt. Once the design is accepted, the virtual sample is sent to manufacture for production directly.

Device Type Electricity consumption (KWh) Active Idle Sleep Off Desktop 0.1121 0.0573 0.005 0.0028 Laptop 0.0564 0.028 0.0037 0.0012 Average 0.0842 0.04265 0.00435 0.002 Table13. Electricity consumption of desktop and laptop in four different modes. (Pothitou, Hanna and Chalvatzis, 2017)

C.2 Production phase (The same for scenario 1 and 2)

Cotton cultivation Cotton is cultivated in many countries under different geographical and climatic conditions, while the top two largest cotton producers are China and the USA (Lu S., 2018). Table 5 tells the production share of different countries when 1 kg cotton fibre produced globally.

Country Amount of cotton produced Database used (From SimaPro) (kg) China 0.25676 Cotton fibre {CN}| cotton production | APOS, U U.S.A 0.23362 Cotton fibre {US}| cotton production | APOS, U Rest of 0.50962 Cotton fibre {RoW}| cotton production | world APOS, U

Table 14. The ratio of cotton yield in different countries (Ecoinvent 3 database)

Manufactory The dataset named as ‘Textile, knit cotton {GLO}| market for | APOS, U’ in Ecoinvent 3 database which contains all processes: yarn manufacturing, knitting, pre-treatment and dyeing to finishing is selected and calculated in SimaPro. Cut-Make-Trim （CMT） As the database from SimaPro only contains the textile production processes and exclude the final garment manufacture process, the data for the CMT process are found from a previous LCA done by Khan et al., 2018. It was reported that to finish this process, 0.67 KWh electricity is consumed.

C.3 Transportation

The transportation included in the life cycle can be furtherly divided into four parts: transportation of cotton, transportation of yarn, transportation of textile and transportation of garment. It is because the locations of manufactories for cotton production, yarn production, textile production and garment production are different, because of the globalized supply chain. In this project, THE FABRICANT is a Netherlands company with design centre, while the manufactories are located in developing countries (China, Indian, Pakistan, etc.) Hence, the transportation are estimated below:

Item Unit Reference Database used Cotton 40 kgkm by lorry Hansen and Larsen, Transport, freight, lorry 3.5-7.5 2007 metric ton, euro4 {RoW}| APOS, U Yarn 33.3 kgkm by Ecoinvent 3 database Transport, freight, inland Inland waterways, barge {GLO} APOS, U waterways 167 kgkm by Ecoinvent 3 database Transport, freight, lorry, Lorry unspecified {GLO} | APOS, U 513 kgkm by Ecoinvent 3 database Transport, freight, sea, Transoceanic transoceanic ship {GLO} | APOS, ship U 53.4 kgkm by Ecoinvent 3 database Transport, freight train {GLO}| Train market group for | APOS, U Textile 51.1 kgkm by Ecoinvent 3 database Transport, freight, aircraft {GLO}| Aircraft market for | APOS, U 24.6 kgkm by Ecoinvent 3 database Transport, freight, light Light commercial vehicle {GLO}| APOS, commercial U vehicle 385.7 kgkm by Ecoinvent 3 database Transport, freight, lorry, Lorry unspecified {GLO}| APOS, U 143.9 kgkm by Ecoinvent 3 database Transport, freight, sea, Transoceanic transoceanic ship {GLO}| APOS, ship U T-shirt 50 kgkm by Hansen and Larsen, Transport, freight, light

Lorry 2007 commercial vehicle {GLO}| APOS, U Table 15. The assembly and data resource of transportation phase

C.4 In-use phase

As mentioned before, it is assumed that the T-shirt will be washed 50 times before it is discarded, if the T-shirt is washed after each use (Hansen and Larsen, 2007). Only the washing process of T-shirts is included in the use phase while drying and ironing are excluded, as clothes are usually dried by hanging up in most Europe countries and Asia (Baydar, Ciliz and Mammadov, 2015). In terms of clothes washing, the environmental impact of detergent due to the accumulation in organisms and resilience to biodegradation is significant, apart from the water and electricity consumption of washer (Giagnorio et al., 2017). Based on the data provided by Yamaguchi et al., 2011 and Baydar et al., 2015, the assumptions of washing made in this project are: 60 ℃ water, no prewash, washing machine with 6kg capacity, 33g powder detergent (the standard use is 20g powder detergent/30L water), 49 L tap water and 1.14 kWh energy consumption. As limited available data of detergent production process inside Europe (considered as the geographical region where the detergent was fabricated), the composition of powder detergent produced in Japan from a LCA of laundry (Yamaguchi et al., 2011) is used and listed below.

Table 16. The composition of 1kg powder detergent (Modified from Yamaguchi et al., 2011)

C.5 Disposal

According to the report of Briga-Sá et al, 2013, there are 5.8 million tons textiles are discarded annually within European Union. Figure 22 illustrates that 48% of discarded

69 textiles will be reused in total, in which 14% of discarded garments will be transported to local charity shop or second-hand shop and 34% of them will be shifted and resold to the third-countries like India and south-eastern Asia. Meanwhile, 52% of them will be send to landfill (31%)/incineration (21%) for energy recovery directly.

Figure 22. The treatment of discarded garments in UK (Modified from Gracey et al., 2012) Assumption and allocation Although the functional unit in this project is a T-shirt that can only be disposed in one way, to get a comprehensive assessment of different scenarios of disposal stage, weight allocation according to the percentage of different treatments listed above is applied. The life cycle inventory dataset of disposal phase is below:

Assumption Amount Reference Database used 14% Reuse in UK (35 g)

Transportation From user Light (Castellani Transport, freight, light to commerc , Sala and commercial vehicle second-han ial Mirabella, {Europe without d shop: vechicle: 2015) Switzerland}| market averagely 0.7kgkm for| APOS, U 20 km 34% Reuse in the third countries (85 g) Transportation From EU to Ship:680 (Castellani Transport, freight, sea, India: kgkm , Sala and transoceanic ship averagely Mirabella, {GLO}| market for | 8000 km) 2015) APOS, U Landfill Inside UK 31% for Peters et Treatment of waste landfill: al., 2019 textile, soiled, municipal 77.5 g and incineration with fly ash Ecoinvent extraction | APOS, U 3 database Incineration Inside UK 21% for Peters et Treatment of waste incinerati al., 2019 textile, soiled, municipal on: 52.5 and incineration with fly ash g Ecoinvent extraction | APOS, U 3 database Table 17. The assembly sheet and data resource of disposal phase

Appendix D: Data Quality Assessment

Data quality assessment was carried out using the ‘pedigree matrix for data quality assessment’ adapted from Weidema and Wesnaes (1996). This allows a more objective assessment of the quality of the data used in a study. The matrix is given below in Figure23. The data quality evaluation based on this matrix is given in Table 16

Figure 23. The ‘pedigree matrix for data quality assessment’ (Weidema and Wesnæs, 1996)

Source Reliability Complet Temporal Geographic Further eness correlation correlation technological correlation Scenario 1 and 2: Design and sample Computer E 1 1 3 2 1 manufacture Electricity T 1 1 4 2 1 consumption Sample E 4 2 2 2 1 production

Or Computer E 1 1 3 2 1 manufacture Electricity T 2 1 4 2 1 consumption Production phase: Cotton E 1 1 3 2 1 cultivation Yarn E 1 1 3 2 1 manufacture Textile E 1 1 3 2 1 knitting CMT L 2 2 2 2 2 (Cut-Make-Tri m) Transportatio L 2 1 3 1 1 n assembly Cotton E 1 1 3 1 1 transportatio n Yarn E 1 1 3 1 1 transportatio n Textile E 1 1 3 1 1 transportatio n Garment E 2 1 3 1 1 transportatio n In-use phase: Tap water L 2 1 3 2 2 consumption

Electricity L 2 1 4 2 2 consumption Powder L 3 2 4 4 3 detergent Disposal: Reuse in UK L 2 1 2 1 2 Reuse in the L 2 1 2 1 2 third countries Landfill L 3 1 2 1 2 Incineration E 3 2 2 1 2 Scenario 3: Design and sample creation Computer E 1 1 3 2 1 manufacture Electricity E 2 1 4 2 1 consumption Garment creation and uploading Computer E 1 1 3 2 1 manufacture Electricity T 2 1 4 2 1 consumption by computer Electricity L 2 1 4 2 1 consumption by network Electricity L 2 1 4 2 1 consumption by data centre In-use phase:

Smartphone E 2 1 3 2 2 manufacture Electricity T 2 1 4 2 1 consumption by phone Electricity L 2 1 4 2 1 consumption by Wi-Fi/3G T = THE FABRICANT, L = Literature value, E = Ecoinvent 3 - allocation at point of substitution - unit Table 18. Data quality assessment sheet

The analysis shows that overall the data quality is medium. Temporal correlation is a particular area of weakness due to the data available from the Ecoinvent 3 - allocation at point of substitution – unit database being fairly old. Geographical correlation is sometimes also not as good as would be wished, again due to limitations of the database used. Reliability, completeness and technological correlation, however, are all good for the data in this study. Turning to the specific data requirements laid out in the methodology, all secondary data were from a peer reviewed journal article or an LCA database.

Appendix E. The result of LCA

Figure 24. The overall comparison of three scenarios

Figure 25. The breakdowns of environmental impacts of scenario 1

Figure 26. The breakdowns of environmental impacts of scenario 2

Figure 27. The breakdowns of environmental impacts of scenario 3