ELECTRONIC SUPPLEMENTARY MATERIAL
1 Data sources
The background processes are from Ecoinvent (Weidema and Hischier 2006) and Gabi (PE
International 2012) database. The detailed information of background processes is shown in Table S1.
Table S1 The detailed information of background processes
Name
Source
CN: cotton fibres, ginned, at farm
Ecoinvent database
CN: electricity, production mix CN [production mix]
Ecoinvent database
CN: hard coal supply mix [Appropriation]
Ecoinvent database
RER: acetic acid, 98 % in H2O, at plant [organics]
Ecoinvent database
RER: hydrogen peroxide, 50 % in H2O, at plant [inorganics]
Ecoinvent database
RER: iron sulphate, at plant [Benefication]
Ecoinvent database
RER: paper, woodfree, coated, at integrated mill [graphic paper]
Ecoinvent database
RER: polyethylene, HDPE, granulate, at plant [polymers]
Ecoinvent database
RER: soap, at plant [Surfactants (tensides)]
Ecoinvent database
RER: soda, powder, at plant [inorganics]
Ecoinvent database
RER: sodium sulphate, powder, production mix, at plant [inorganics]
Ecoinvent database
RER: steam, for chemical processes, at plant [Auxiliary material]
Ecoinvent database
RER: tap water, at user [Appropriation]
Ecoinvent database
RER: transport, lorry 7.5-16t, EURO3
Ecoinvent database
RER: process water
Gabi database
RER: Sodium hydroxide (100 %; caustic soda)
Gabi database
1.1 Cotton production phase
The LCI (life cycle inventory) data of cotton fiber production including cotton cultivation and
ginning are based on Ecoinvent database v2.2: Cotton, “Cotton fibers, ginned, at farm/CN” (CN=China).
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1.2 Transportation phase
Cotton materials are all assumed to be transported from Urumchi in Xinjiang Province to Changshu
city in Jiangsu Province. Xinjiang Province as the largest cotton production base in China accounts for
half of gross cotton production (National Bureau of Statistics of China 2013). Besides, according to the
on-site investigation information, 75 % cotton as the main raw material is from Xinjiang Province, China
mainly because of the Throw Reserve policy, that cotton import quotas are bundled with the State
Reserve cotton in the proportion of 1:3 to protect the benefits of domestic cotton farmers. However, it
should be noted that there are still a quarter of cotton materials from India, America, Australia, Brazil,
Uzbekistan, etc., having various cotton cultivation situations that leads to different water, energy,
pesticide and chemical fertilizer consumption. In addition, we only take into account the transportation
of cotton materials cultivated in the west Xinjiang province to downstream textile industry mainly
concentrated in the east coastal areas, especially Jiangsu province, Zhejiang Province, Shandong
Province, and Guangdong province, because most other materials in the production processes are
purchased locally. Based on the assumption, the distance of cotton transportation is 3800 kilometers.
The LCI data of cotton transportation are based on Ecoinvent database v2.2: Transport, “transport, lorry
7.5-16t, EURO3” (RER=Region Europe).
1.3 Production phase
The data of consumption of energy, material and water as well as emissions to air, water and soil
during textile production are mainly based on-field investigation of representative mills. Besides,
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because of the lack of the emissions of carbon dioxide and nitrogen oxide that brought by burning coal in
the emission inventory data, the calculation is based on correlation coefficient (Chen et al. 2008),
emission factors (Wu 2012), and the consumption of coal. These mills were selected based on a
consideration of their production capacities and production technologies. Almost all the primary data are
provided with only annual totals or for an entire segment of the production process. It is mainly because
most textile SMEs operate based on orders and production modes are usually changed according to the
changes of orders. In these cases, the annual production data are only calculated for the whole 100%
cotton product categories without further distinction of colors. Besides, equipment manufacturers’ data
are used to check the accuracy of energy consumption data. The final primary production data of multiple
mills are aggregated by taking a weighted average of the production capacities of related mills.
Background LCI data of materials, energy and water as well as transportation are taken from the existing
commercial LCI databases whenever possible. If not available, as most complex organic dyes and
auxiliaries, new LCIs are established using data from technical handbooks, patent reports and literature
sources. It should be noted that LCIs of dyes and auxiliaries are only conservative estimation because of
the difficulties to get data, the limitation of the database and little consideration of energy consumption.
1.4 Distribution phase
The distribution of exporting to abroad don’t be considered. The transportation lines of T-shirts
start from Changshu city where the investigated mills located. The administrative center cities are
assumed as the center of each province. The distance from Changshu city to the administrative center
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city is assumed as the transportation distance to the province. The distribution of T-shirt is based on a
simplified gravity model (Linnemann 1996). The distribution quantity to each province is directly
proportional to the population of the province and is inversely proportional to the distance. Based on
our calculation, the distribution distance of one piece of T-shirt is 686 kilometers. The LCI data of
distribution are based on Ecoinvent database v2.2: Transport, “transport, lorry 7.5-16t, EURO3”
(RER=Region Europe).
1.5 Use phase
The use phase of textile products involves four main human activities: wearing, washing, drying
and ironing. Different consumer behaviors can result in quite different environmental consequences
mainly in terms of water and energy consumption and wastewater discharge. The current situations of
Chinese consumer behaviors of T-shirt are the combination of eight consumer scenarios which grouped
the choices Chinese consumers make in washing (by washing machine or by hand), drying (by dryer or
by air) and ironing (by ironing machine or not). Information about consumer behaviors of T-shirt,
including life time, use frequency and habit of washing such as the use of detergents, drying and
ironing, is collected from 924 valid questionnaires which were delivered through the Internet and
face-to-face interview. The online questionnaire receivers are throughout the country and the onsite
questionnaire distribution was completed in Nanjing city. Water temperature is not been taken into
account because wheeled washing machine is more popular in Chinese family and wheeled generally
cannot adjust temperature. Besides, T-shirts are mainly worn in summer and most people choose
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normal temperature washing.
Because the use frequency of different use phase is different, we assumed that use phase can be
divided into two stages: earlier stage and later stage. The following equation demonstrates the calculation
of washings per life as calculated from consumer behavior data. Based on our calculation of the
questionnaire results, one piece of T-shirt can be washed 45 times throughout the whole life cycle.
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(U1 +U2 )× ×M×Y=T
2
Description:
U1: Use frequency (wearing events) per month in earlier stage
U2: Use frequency (wearing events) per month in later stage
M: Months per year the T-shirt is worn
Y: Years per T-shirt lifetime
T: Total times of washes per T-shirt lifetime
1.6 End-of-life phase
A disposal phase which contains standard landfill of textiles, papers and plastics are used here with
no consideration of recycling due to the fact that the landfill rate of urban household solid waste has
reached 70% in China (Jiang 2013). However, it can be seen from previous research that certain
numbers of used textiles are sold again or donated which certainly lead to environmental benefits
(Farrant et al. 2010; Woolridge et al. 2006).The LCI data of disposal phase are based on PE database:
Plastic, “landfill of plastic waste, PE, EU-27”; Paper, “landfill of paper waste, PE, EU-27” Textiles,
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“landfill of textiles, PE, EU-27”.
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Table S2 Relative contributions to each impact category for T-shirt
Category
Cotton cultivation
Transportation
Spinning
Knitting
Dyeing
Making-up
Distribution
Consumption
Disposal
ADP-e
ADP-f
AP
EP
GWP
POCP
WU
ECP
HTPC
HTPNC
28.66 %
9.97 %
16.71 %
28.40 %
6.04 %
6.88 %
78.87 %
85.71 %
7.93 %
27.89 %
4.48 %
4.81 %
1.63 %
1.43 %
3.00 %
4.14 %
0.01 %
0.64 %
2.77 %
5.29 %
0.11 %
4.50 %
5.70 %
1.34 %
4.95 %
4.62 %
0.04 %
0.65 %
2.68 %
4.61 %
0.02 %
0.93 %
1.17 %
0.28 %
1.02 %
0.95 %
0.01 %
0.13 %
0.55 %
0.95 %
58.04 %
31.09 %
28.53 %
17.87 %
34.79 %
31.99 %
6.49 %
5.99 %
58.85 %
18.05 %
0.79 %
35.29 %
33.79 %
8.06 %
31.96 %
31.07 %
0.27 %
3.89 %
16.72 %
26.94 %
0.83 %
0.89 %
0.30 %
0.27 %
0.56 %
0.77 %
0.00 %
0.12 %
0.51 %
0.98 %
7.05 %
12.13 %
12.03 %
40.35 %
10.08 %
17.54 %
13.84 %
2.85 %
9.95 %
14.65 %
0.03 %
0.39 %
0.13 %
2.01 %
7.61 %
2.04 %
0.45 %
0.01 %
0.02 %
0.64 %
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References
Chen YF, Jiang Y, Chen WM, Qin CX (2008) The research of the distribution of nitrogen content in the
Chinese coal. Clean Coal Technol:71-74 (in Chinese with English abstract)
Farrant L, Olsen SI, Wangel A (2010) Environmental benefits from reusing clothes. Int J Life Cycle
Assess 15:726-736
Jiang HF (2013) The current situation of urban household garbage disposal and analysis of existing
problems in China. Resources Economization and Environment Protection:167-168 (in
Chinese)
Linnemann H (1996) An econometric study of international trade flows. North-Holland Publishing
Company Amsterdam, Amsterdam
National Bureau of Statistics of China (2013) China Statistical Yearbook 2012. China Statistics Press,
Beijing
PE International (2012) GaBi 6 Software and Databases. Leinfelden-Echterdingen, Germany
Weidema B, Hischier R (2006) Ecoinvent data v2. 2. St. Gallen, Switzerland
Woolridge AC, Ward GD, Phillips PS, Collins M, Gandy S (2006) Life cycle assessment for
reuse/recycling of donated waste textiles compared to use of virgin material: an UK energy
saving perspective. Resources, Conservation and Recycling 46:94-103
Wu GH (2012) Calculation and analysis of fossil energy consumption carbon emissions —taking Jinan
as an example. Theory J:61-65 (in Chinese)
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