Final Report
A Carbon Footprint for UK
Clothing and Opportunities for
Savings
July 2012
1
WRAP’s vision is a world without waste,
where resources are used sustainably.
We work with businesses, individuals and
communities to help them reap the
benefits of reducing waste, developing
sustainable products and using resources
in an efficient way.
Find out more at www.wrap.org.uk
Written by: Bernie Thomas, Matt Fishwick, James Joyce and Anton van Santen
Environmental Resources Management Limited (ERM)
Front cover photography: [Add description or title of image.]
While we have tried to make sure this report is accurate, we cannot accept responsibility or be held legally responsible for any
loss or damage arising out of or in connection with this information being inaccurate, incomplete or misleading. This material is
copyrighted. You can copy it free of charge as long as the material is accurate and not used in a misleading context. You must
identify the source of the material and acknowledge our copyright. You must not use material to endorse or suggest we have
endorsed a commercial product or service. For more details please see our terms and conditions on our website at
www.wrap.org.uk
Document reference: [eg WRAP, 2006, Report Name (WRAP Project TYR009-19. Report prepared by…..Banbury, WRAP]
2
0.0
Executive summary
Environmental Resources Management Limited (ERM) was commissioned by WRAP to conduct a life
cycle carbon footprint study for UK clothing. The objective of the research was to provide WRAP with an
overview of the carbon impacts of UK clothing through the clothing life cycle, identifying the most
significant contributions to the carbon footprint (ie the ‘hotspots’), and quantifying opportunities for
carbon footprint reduction.
Estimated Current Carbon Footprint for UK Clothing
A strategic-level carbon footprint study was undertaken based on published data and information about
UK clothing. UK Clothing is defined as all clothing, both new and existing, in use in the UK over the
period of one year. The analysis covers both clothing manufactured and used in the UK and clothing
manufactured abroad and used in the UK. The datum is 2009, as the year for which the most recent
data are available.
The study’s results present the annual climate change impact associated with UK clothing, in terms of its
carbon footprint. This includes the impacts associated with the quantity of clothes that are produced for
the UK and consumed and disposed of each year, as well as the impacts associated with clothing that is
actively worn and cleaned each year (approximately 1.1 million tonnes of new clothing is consumed in
the UK each year, ~2.5 million tonnes is in active use. Note that this is greater than the annual
consumption of clothing, because clothes last for more than one year).
Figure 1 presents the baseline (current) carbon footprint estimate for all clothing in use in the UK in
2009. The results are broken down by both life cycle stage and fibre type to show their relative
contributions to the total footprint. The following conclusions can be drawn from the results.

The total annual carbon footprint of all garments, both new and existing, in use in the UK in
2009 (i.e. the volume consumed, and the actively worn quantity) is approximately 38 million
tonnes of CO2e (~0.6 tonnes per person per year). Because the majority of clothing is
manufactured outside the UK, it is estimated that ~32% occurs within the UK (contributing to
the UK’s direct carbon footprint) and 68% occurs abroad. Based on this estimate, the direct
impact of clothing in the UK can be estimated to be ~12 million tonnes of CO2e. Note that this
baseline analysis does not examine the effect of uncertainties, which are considered further in
the sensitivity analysis section of the report (Section 4.6).

To put this carbon footprint of UK clothing into context, the total direct GHG emissions in the
UK in 2009 were reported as 566 million tonnes of CO2e (DECC, 2011). It should be noted that
this total for the UK does not include GHG emissions associated with imported goods or
services or international travel. Therefore, the direct carbon footprint of clothing contributes
approximately 2% to the UK’s total direct carbon footprint.

The carbon footprint of new garments ONLY, in use in the UK in 2009, can also be calculated
by dividing the carbon footprint of both new and existing clothing by its anticipated lifetime.
This figure is approximately 17 million tonnes of CO2e.

The most dominant life cycle stage is fabric production (comprising weaving/knitting etc. and
treatment of fabric), representing 33% of total life cycle GHG impacts.

The carbon footprint of a tonne of garments, both new and existing, in use in the UK in 2009
ranges from around 15 to 46 tonnes CO2e, depending on the fibre type of the garment.

The carbon footprint of each garment, both new and existing, in use in the UK in 2009 ranges
from around 1 to 17 kg CO2e.

The per person carbon footprint of all garments, both new and existing, in use in the UK in
2009 is around 0.6 tonnes of CO2e.
3
Figure 1: Carbon footprint all clothing in use in the UK in 2009, whether manufactured in or imported to the UK, represented as a total for the UK, broken down by life cycle
stage and fibre type
4
Savings Achieved in the Central scenario
The study also quantifies the potential effect of a number of example impact reduction measures relative
to the estimated baseline footprint. Reduction measures are presented for a realistic ‘Central’ future
reduction scenario, and also an aspirational ‘What If?’ reduction scenario. Options for reduction are
considered across the life cycle (eg eco-efficiency in the manufacture, retail and distribution of clothing,
washing at lower temperatures, increasing load size, more reuse etc.).
Table 1 and Figure 2 below present the estimated carbon saving from the baseline footprint for 2009.
Baseline
(t CO2e)
Reduction
(t CO2e)
Reduction %
Eco-efficiency across supply chain
(production, distribution and retail) Central scenario - 5% reduction for all
fibres across supply chain
38,175,293
1,563,219
-4.1%
Design for Durability (and product lifetime
optimisation) - Central scenario - 10%
longer lifetime of clothing
38,175,293
2,941,203
-7.7%
Shift in market to higher proportion of
synthetic fibres - Central scenario replace 10% of cotton with 50:50 polycotton. [Data exclude in-use savings]
38,175,293
164,150
-0.4%
Clean clothing less - Central scenario washes per year reduced by 10%
38,175,293
989,905
-2.6%
Wash at lower temperature - Central
scenario - weighted average wash
temperature of 39.3C
38,175,293
549,604
-1.4%
Increase size of washing and drying loads
- Central scenario - load increases to
3.7kg
38,175,293
531,538
-1.4%
Use the tumble dryer less - Central
scenario - 30% reduction in tumble dryer
use in summer
38,175,293
430,367
-1.1%
Dispose less - reuse more - Central
scenario – 15.4% of clothing ultimately
reused in UK
38,175,293
272,063
-0.7%
Start closed loop recycling of synthetic
fibres - Central scenario - 5% of all
clothing is recycled (closed loop)
38,175,293
352,144
-0.9%
Dispose less - recycle more (open loop) Central scenario - 38% of all clothing is
recycled open loop
38,175,293
195,729
-0.5%
7,989,921
-20.9%
Reduction measure
Cumulative reduction
Table 1: Savings achieved by each reduction measure of the Central scenario
5
Figure 2: Savings achieved by each reduction measure of the Central scenario
From the estimates presented in Table 1 and Figure 2, the following points are evident.

A potential 21% reduction in the carbon footprint of UK clothing would occur if all reduction
measures considered for the Central scenario were achieved.

The largest carbon footprint reductions are achieved by extending product lifetime (8%), ecoefficiency across the supply chain (4% reduction) and washing clothing less (3% reduction).

As calculated, reduction measures resulting in minimal reductions in carbon footprint include
increasing open loop recycling, increasing reuse and a shift in the market to a larger proportion
of synthetic fibres. [Note: the term ‘synthetics’ is used here to include man-made fibres such as
viscose.]
Table 2 presents all the reduction measures considered in order of effectiveness for the Central scenario.
Rank
1
2
3
4
5
6
7
8
9
10
Reduction Measure
Stakeholder
Design for Durability (and Product lifetime optimisation) - central scenario - 10%
longer lifetime of clothing
Eco efficiency across supply chain (production, distribution and retail) - central
scenario - 5% reduction for all fibres across supply chain
Manufacturer/
consumer
Clean clothing less - central scenario - Washes per year reduced by 10%
Wash at lower temperature - central scenario - weighted average wash temperature
of 39.3C
Consumer
Increase size of washing and drying loads - central scenario - load increases to 3.7kg
Use the tumble dryer less - central scenario - 30% reduction in tumble dryer use in
summer
Start closed loop recycling of synthetic fibres - central scenario - 5% of all clothing is
recycled (closed loop)
Consumer
Dispose less - reuse more - central scenario - 15.4% of clothing reused in the UK
Dispose less - recycle more (open loop) - central scenario - 19.5% of all clothing is
recycled open loop
Shift in market to higher proportion of synthetic fibres - central scenario - Replace
10% of cotton with 50:50 poly-cotton
Consumer
Manufacturer
Consumer
Consumer
Consumer
Consumer
Manufacturer/
consumer
Table 2: Reduction measures of the Central scenario in order of effectiveness
6
Savings Achieved in the What If? Scenario
Figure 3 presents the potential estimated carbon saving from the baseline generated by each reduction
measure in a more ambitious ‘What If?’ scenario.
Figure 3: Savings achieved by each reduction measure of the ‘What If?’ scenario
The estimated reductions presented in Figure 3 indicate the following.

A 71% reduction in the carbon footprint of UK clothing will occur if all reduction measures
considered by the ‘What If?’ scenario are achieved.

The largest carbon footprint reductions are achieved by extending product lifetime (27%
reduction), eco-efficiencies across the supply chain (24% reduction) and washing less (4%
reduction).

Reduction measures resulting in the smallest reductions in carbon footprint include increasing
closed loop recycling, increasing open loop recycling and a shift to a higher proportion of
synthetics.
In addition to the reduction measures presented in the above scenarios, a series of consumer
interventions were analysed in the study to examine their influence on carbon footprint results. The
impact of ten post-sale in-use interventions was examined through a change in the behaviour of 10% of
the UK population under each measure). The purpose of this exercise was to compare the effectiveness
of a variety of measures to change consumer behaviour during the use phase once the clothing has
been purchased. Consistent with the findings of the main scenarios, the in-use interventions resulting in
the greatest savings are a shift towards behaviours that lead to an increase in clothing lifetime by one
year and cleaning clothing less, followed by less reliance on tumble drying.
The report also presents a series of sensitivity analyses to investigate the study’s key uncertainties.
These examine the sensitivity of the results and conclusions to a change in a particular assumption or
data point. The sensitivity analyses undertaken were: the influence of a future decarbonised electricity
grid on the impact of the use phase; the influence of fibre type on washing and drying impacts; the
influence of product lifetime on results; the influence of washing frequency on results; and the influence
of UK fibre mix on results. The findings of these analyses indicate the following.

Future ‘decarbonisation’ of UK electricity will decrease the direct carbon footprint associated
with the cleaning of clothing. The significance of the use phase (primarily washing and drying)
7
impacts, relative to upstream life cycle stages (raw materials, manufacture and distribution,
retail) and end of life impacts will reduce.

Where the energy use impacts of tumble drying are allocated to clothing based on the relative
drying time of fibre types (rather than by its mass only as they are in the main analysis), the
carbon footprint increases from the baseline for natural fibres (by ~2-5%) and decreases for
synthetic fibres (by ~3-5%). The total remains the same.

Where loads are mixed and drying energy is based on the slowest drying item of clothing (ie
natural fibres), the carbon footprint for each fibre type increases from the baseline. This
reflects an increase in the drying time of all fibre types caused by a natural fibre type being
present in each load. This is in comparison to a baseline average energy usage where some
loads are mixed and some are separated.

When the difference in washing temperature is also considered, the reduction achieved from
the shift towards synthetics in both the central and ‘What If?’ scenarios is around a third larger.

The longer the lifetime of clothing (eg from clothing simply being retained in use by the
consumer for longer, design for durability, reuse, or from leasing or resale), the lower the
carbon footprint (reduced supply chain impacts, primarily) and the shorter the lifetime of
clothing that is used, the higher the carbon footprint.

Where it is assumed in the analysis clothing is washed 5 times per kilogram per year, the total
carbon footprint is 13% less than that of the main analysis (where it is assumed clothing is
washed 9.9 times). The carbon reductions achieved through use phase improvement actions
are less and those of non-use phase improvement action are greater. Where it is assumed
clothing is washed 15 times per kilogram per year, the total carbon footprint is 13% greater
than that of the main analysis carbon footprint. The carbon reductions achieved through use
phase improvement actions are greater and those of non-use phase improvement action are
less.

The baseline carbon footprint total with an alternative Carbon Trust fibre mix data set for UK
clothing consumption is 12% less than the baseline total where the Biointelligence fibre mix
data is used. For the ‘What if?’ scenario, the reduction achieved where Carbon Trust fibre mix
data is used is 11% less than the reduction achieved where Biointelligence fibre mix data is
used. Although absolute reduction values change, the order of improvement actions changes
less with the new fibre mix, with the top three and bottom three improvement actions
remaining the same with both fibre data. (The Metrics group of the Sustainable Clothing Action
Plan is currently (July 2012) preparing to collate actual UK retailer data on fibre mix and sales
volumes, which could allow the footprint analysis to be updated at a later date.)
Conclusions
Overall, the total carbon footprint associated with clothing produced for, and in use in, the UK in 2009 is
estimated at approximately 38 million tonnes of CO2e (~0.6 tonnes per person per year). Because the
majority of UK clothing is manufactured outside the UK, it must be noted that ~32% occurs within the
UK and ~68% occurs overseas as a consequence of the garments manufactured for UK consumers. Per
tonne of clothing, the footprint ranges from around 15 to 46 tonnes CO2e per year, depending on the
fibre type of the garment.
To put this carbon footprint of UK clothing into context, the total direct GHG emissions in the UK in 2009
were reported as 566 million tonnes of CO2e (DECC, 2011). It should be noted that this total for the UK
does not include GHG emissions associated with imported goods or services or international travel.
Therefore, the direct carbon footprint of clothing is approximately 2% of the UK’s total direct carbon
footprint.
8
Ten potential options for carbon footprint reduction are presented. According to the study, measures
aimed at reducing the impacts associated with the production of clothing (in design and eco-efficiency
measures in the supply chain and reuse), and also the use phase (less and better washing and drying by
the consumer), show the greatest potential. This is not unexpected, since these life cycle phases
currently contribute the greatest impacts.
For the reduction measures examined in the Central scenario, the combined effect of the ten measures
across the entire life cycle is estimated to be 21%. In the aspirational What If? Scenario, this is
increased to an estimated carbon reduction of 71%. However, it should be noted that the study does
not examine the practicability of implementing each option, or consider other non-carbon sustainability
impacts for these options. It should also be noted that these reductions from the baseline do not
include the potential decarbonisation of energy (electricity) production, which will also reduce the carbon
footprint of clothing in future.
The findings from the study sensitivity analysis indicate that, amongst other factors, the fibre mix of UK
clothing affects the magnitude of the footprint and the overall savings achievable, but has less influence
on the order of the reduction measures.
Overall, the analysis confirms the rationale for encouraging reduction measures at each and every stage
of the life cycle, including nudging consumer behaviour towards favourable outcomes. If UK electricity is
decarbonised, the sensitivity analysis undertaken for the study indicates sustainable production and
consumption measures aimed at reducing the production impacts of clothing will further increase in
importance over time, relative to use phase interventions. The study provides an initial analysis into the
potential indirect effects on the washing and drying footprint if the market is shifted towards one type of
fibre over another. There are uncertainties associated with the findings of this analysis, but it indicates
that fibre choice affects the magnitude of impact in the use phase.
9
Contents
0.0
1.0
2.0
3.0
4.0
Executive summary ..................................................................................................................... 3
Estimated Current Carbon Footprint for UK Clothing ........................................................................... 3
Savings Achieved in the Central scenario ........................................................................................... 5
Savings Achieved in the What If? Scenario ......................................................................................... 7
Conclusions ..................................................................................................................................... 8
Introduction ................................................................................................................................. 1
1.1
About this study .................................................................................................................. 1
1.2
Goal of this Study ................................................................................................................ 1
Project Approach ......................................................................................................................... 1
2.1
Project Scope...................................................................................................................... 1
2.2
System Boundary ................................................................................................................ 2
2.3
Functional Unit .................................................................................................................... 3
2.4
Literature Search................................................................................................................. 5
2.5
Carbon Footprint Calculation ................................................................................................ 5
2.6
Reduction Measures ............................................................................................................ 6
2.7
Baseline and Future Scenarios .............................................................................................. 7
2.8
Further In-use Interventions ................................................................................................ 7
2.9
Sensitivity Analyses ............................................................................................................. 7
2.10
Excel Model ........................................................................................................................ 7
Life Cycle Inventory ................................................................................................................... 10
3.1
Life cycle Description ......................................................................................................... 10
3.1.1
Production of Fibre ................................................................................................ 10
3.1.2
Production of Yarn ................................................................................................ 10
3.1.3
Production of Fabric .............................................................................................. 10
3.1.4
Treatment of Fabric .............................................................................................. 11
3.1.5
Production of Garments ......................................................................................... 11
3.1.6
Distribution and Retail ........................................................................................... 11
3.1.7
Use...................................................................................................................... 11
3.1.8
End of Life ........................................................................................................... 11
3.2
Key Data Sources .............................................................................................................. 13
3.3
Key Data – All Life cycle Stages .......................................................................................... 18
3.4
Key Data - Production of Fibre, Yarn, Fabric and Garments ................................................... 19
3.5
Key Data - Distribution and Retail ....................................................................................... 21
3.6
Key Data – Use ................................................................................................................. 22
3.6.1
Washing............................................................................................................... 22
3.6.2
Drying ................................................................................................................. 23
3.6.3
Ironing................................................................................................................. 23
3.7
Key Data - End of Life........................................................................................................ 24
3.8
Reduction Measures .......................................................................................................... 26
3.9
Baseline and Future Scenarios ............................................................................................ 26
3.10
Data Quality ..................................................................................................................... 29
Impact Assessment ................................................................................................................... 31
4.1
Baseline Scenario .............................................................................................................. 31
4.1.1
Carbon Footprint of all Clothing in Use in the UK in 2009, whether manufactured in or
imported to the UK – UK Total ............................................................................................ 31
4.1.2
Carbon Footprint of all Clothing in Use in the UK in 2009, whether manufactured in or
Imported to the UK – per person ........................................................................................ 34
4.1.3
Carbon Footprint of all Clothing in Use in the UK in 2009, whether manufactured in or
Imported to the UK – per tonne.......................................................................................... 36
4.1.4
Carbon Footprint of all Clothing in Use in the UK in 2009, whether manufactured in or
Imported to the UK – per garment ...................................................................................... 38
4.2
Savings Achieved in the Central Scenario ............................................................................. 40
4.3
Savings Achieved in the ‘What If?’ Scenario ......................................................................... 43
4.4
Benchmarking Against Other Studies ................................................................................... 45
4.5
Further Analysis ................................................................................................................ 46
10
4.5.1
Further In-use Interventions .................................................................................. 46
Sensitivity Analyses ........................................................................................................... 48
4.6.1
Decarbonisation of Grid Electricity (Sensitivity 1)...................................................... 48
4.6.2
Influence of Fibre Type on Drying (Sensitivity 2a and 2b) ......................................... 53
4.6.3
Influence of Fibre Type on Washing (Sensitivity 3) ................................................... 56
4.6.4
Longer Product Lifetimes (Sensitivity 4a and 4b) ...................................................... 58
4.6.5
Washing Frequency (Sensitivity 5) .......................................................................... 62
4.6.6
UK Fibre Mix (Sensitivity 6) .................................................................................... 62
4.7
Conclusions of Sensitivity Analyses ..................................................................................... 63
4.7.1
Decarbonisation of Grid Electricity (Sensitivity 1)...................................................... 63
4.7.2
Influence of Fibre Type on Drying (Sensitivity 2a and 2b) ......................................... 64
4.7.3
Influence of Fibre Type on Washing (Sensitivity 3) ................................................... 65
4.7.4
Longer Product Lifetimes (Sensitivity 4a and 4b) ...................................................... 65
4.7.5
Washing Frequency (Sensitivity 5) .......................................................................... 66
4.7.6
UK Fibre Mix (Sensitivity 6) .................................................................................... 66
Conclusions ................................................................................................................................ 66
5.1
Summary of this Study ...................................................................................................... 66
5.2
Summary of Baseline Results.............................................................................................. 67
5.3
Summary of Reduction Scenarios ........................................................................................ 67
5.4
Further Analysis Findings ................................................................................................... 68
5.5
Findings from Sensitivity Analyses ....................................................................................... 69
5.6
Concluding Remarks .......................................................................................................... 69
5.7
Suggested Next Steps ........................................................................................................ 70
References ................................................................................................................................. 71
4.6
5.0
6.0
11
1.0
Introduction
1.1
About this study
WRAP (Waste & Resources Action Programme) works in England, Scotland, Wales and Northern Ireland
to help businesses and individuals reap the benefits of reducing waste, develop sustainable products and
use resources in an efficient way.
Environmental Resources Management Limited (ERM) was commissioned by WRAP to conduct a life
cycle carbon footprint study for UK clothing and indicate the scope for footprint reduction.
Many previous studies have assessed the carbon impacts of various clothing types and modelled
reduction initiatives. However, none has focused on measuring the carbon footprint of UK clothing as a
whole and modelled the total potential for reduction. To this end, WRAP commissioned ERM to
undertake research on the life cycle carbon impact of clothing in the UK. This study required a strategiclevel carbon footprint for UK clothing, based on published data and information. The footprint was
expressed in a number of ways to show the contribution and scope for reduction. Furthermore, a
scenario assessment was made for a number of different options for footprint reduction.
1.2
Goal of this Study
The stated objective of this research was to provide WRAP with an overview of the impacts of UK
clothing on carbon emissions through the clothing life cycle, identifying the most significant contributions
to the carbon footprint (ie the hotspots), and quantifying opportunities for savings.
The study follows on from a study undertaken for WRAP by URS on the water footprint of UK clothing
entitled ‘Review of Data on Embodied Water in Clothing and Opportunities for Savings’ (URS, 2011).
2.0
Project Approach
This section describes the scope considered in the project and summarises the approach used.
2.1
Project Scope
The scope of the project was to undertake a strategic-level carbon footprint of UK clothing over the
entire life cycle using secondary data available in the literature. UK clothing has been defined in this
study as all clothing, both new and existing, in use in the UK over the period of one year. The analysis
covers both clothing manufactured and used in the UK and clothing manufactured abroad and used in
the UK. The comparatively small amount of clothing manufactured in the UK and exported abroad was
not considered in the analysis. The datum for this analysis is 2009, as the year for which the most
recent data are available.
The project assesses total quantities of all major fibre types purchased (and in use) within the UK during
2009. The fibre types assessed comprise:









acrylic;
cotton;
flax / linen;
polyamide (nylon);
polyester;
polypropylene;
silk;
viscose; and
wool.
1
These are the fibres selected by the Metrics group of the Sustainable Clothing Action Plan as the most
important fibres within their sales mix. There are other fibres in use, but rather less significant in terms
of quantity sold.
The scope of the project also includes consideration of a number of example reduction measures (eg
washing at lower temperatures, increasing load size etc.), whereby potential savings in relation to the
2009 ‘baseline’ are quantified for a ‘Central’ reduction scenario and a ‘What If?’ reduction scenario.
In addition to carbon footprint results for each of these three defined scenarios, the scope includes the
provision of an Excel model for use in this project that allows the modeller to examine new scenarios,
where values for each reduction measure can be changed.
The study provides a carbon footprint assessment. Therefore, it does not consider other potential social,
economic and environmental impacts such as toxicity or labour standards.
2.2
System Boundary
The entire life cycle of UK clothing is considered. Therefore, this study can be considered a full cradle-tograve or business-to-consumer carbon footprint. Exclusions to the assessment have been made following
the general specifications given in PAS 2050 (1). In addition, other exclusions have been made based on
their ‘materiality’, ie any process anticipated to contribute <1% of total life cycle GHG emissions has been
excluded.
The following life cycle stages have been included in the carbon footprint assessment:
 extraction of raw materials required for the production of fibres;
 processing of materials (e.g. production of synthetic polymer resin);
 production of fibres (either at farm or factory);
 production of yarn;
 production of fabric;
 treatment of fabric (eg bleaching, dyeing etc.);
 production of garments;
 packaging of garments;
 transportation of materials and goods to and from production locations;
 waste at all stages of production;
 transportation of garments to the UK;
 storage at regional distribution centre (RDC) in the UK;
 transportation from RDC to retail outlets;
 storage at retail outlets in the UK;
 use of clothing (eg washing (energy, water and detergent use), tumble drying, ironing); and
 end of life of clothing (eg reuse, recycling, landfill and incineration)
The following life cycle stages/burdens have been excluded from the carbon footprint assessment:
 transportation of consumers to and from the point of retail purchase;
 packaging of packaging used at all life cycle stages;
 fabric softeners, colour catches etc. or other material inputs used during washing;
 water use for ironing;
 preparation for reuse burdens (2); and
 stain removers used during the use phase.
In addition, the following aspects have been excluded, which cover more than one life cycle stage:
 capital goods (eg the manufacture of weaving looms, washing machines, irons etc.);
(1) PAS 2050:2008 - Specification for the assessment of the life cycle greenhouse gas emissions of goods and services
http://www.bsigroup.com/Standards-and-Publications/How-we-can-help-you/Professional-Standards-Service/PAS-2050
(2) Preparation for Reuse burdens results from the checking, cleaning or repairing recovery operations, by which products or components of
products that have become waste are prepared so that they can be re-used without any other pre-processing. The impacts associated with them
are typically trivial relative to those at other end of life impacts.
2


2.3
human energy inputs to processing; and
animals providing transport services.
Functional Unit
In Life Cycle Assessment and carbon footprinting methods, environmental impacts are represented in
terms of a metric known as the functional unit. The functional unit allows a quantified environmental
impact to be expressed as a function of the desired purpose of the product or service and ideally allows
for a straightforward comparison between similar products or services.
The carbon footprint results of this assessment are represented in terms of the following functional unit:
The entire life cycle of all garments, both new and existing, in use in the UK in 2009.
The results provided in the study relate to the annual impacts associated with UK clothing. They include
the impacts associated with the quantity of clothes that are produced for the UK and consumed and
disposed of each year, but they also include the impacts associated with clothing that is actively worn
and cleaned each year (approximately 1.1 million tonnes of new clothing is consumed in the UK each
year, ~2.5 million tonnes is in active use - note that this is greater than the annual consumed clothing
because clothes last for more than one year).
The chosen functional unit is the total carbon footprint of clothing (both new and old) in a given year (ie
in 2009). As such, it uses the anticipated lifetime of each garment type to consider the proportion of
clothing manufactured and disposed of in 2009. Use phase impacts are for one year for all clothing in
active use (both new and old) in 2009.
The rationale behind including both new and existing clothing within the functional unit is that it follows
an inclusive approach where the annual impact of all clothing is considered. An alternative approach,
that would yield identical results (assuming sales are static), is to look at new clothing only throughout
its life cycle, whereby life cycle impacts are considered throughout all years of use (ie 2009, 2010 and a
portion of 2011). This is the approach used in a water footprinting study recently carried out by URS for
WRAP. It was decided not to use this approach as, with the ultimate aim of the SCAP in mind,
measuring total impacts of all clothing on an annual basis shows in full the opportunities for reduction
and any progress towards targets that can be fully measured year on year.
The method that was used to calculate the quantity of clothing in use in a given year (both new and old,
by using the annual quantity of clothing purchased and the anticipated lifetime of that clothing) has
three main assumptions. Firstly, it is assumed that purchasing behaviour has remained static insofar as
the quantity of clothing purchased in 2009 was the same in previous years and will be the same in future
years1. In other words, new clothing will eventually replace existing clothing on a one for one basis.
Secondly, as the quantity of clothing purchased was used to calculate the quantity of clothing in use,
there is an assumption that all clothing purchased is used, rather than being purchased and never used.
Thirdly, the ‘wardrobe stockpile’ is treated separately and is not considered within the functional unit of
this study. The ‘wardrobe stockpile’ includes clothing that is retained within the home but not in active
use (eg stored away in wardrobes, boxes, the loft, garage etc.) and therefore was thought not to
constitute clothing in use.
The rationale for including both clothing manufactured and used in the UK and clothing manufactured
abroad and used in the UK is that it places the emphasis of ‘burden ownership’ on the user; the ultimate
reason for the product. In this approach, the GHG emissions associated with clothing manufactured in
China and exported to the UK for use, for example, are covered under the UK’s clothing carbon footprint.
However, those GHG emissions associated with the comparatively small amount of clothing
manufactured in the UK and exported to Italy for use in Italy, for example, are not considered under the
UK’s clothing carbon footprint (i.e. they ‘belong’ to Italy). The chosen functional unit reflects a
consumption-based approach to GHG reporting.
1
This assumption is noted as a simplification and a limitation. It is likely that consumption has grown and may continue to
grow in line with gross domestic product (GDP) or retail price index (RPI). However, it was thought that accounting for
economic growth adds further complexity and is unnecessary for the purposes of this study.
3
4
As alternative expressions of this functional unit, carbon footprint results are also presented in this study
in terms of: one tonne of garments in use in the UK in 2009; garments used by one UK resident in 2009;
and one garment used in the UK in 2009.
Carbon footprint results are broken down per life cycle stage and per fabric or garment type, and are
presented in terms of the impact of those garments manufactured in the UK, those garments imported
to the UK and a sum of the two.
2.4
Literature Search
Numerous studies have been published that examine life cycle impacts of clothing. These studies vary
widely in scope. For example, some focus on particular garment or fibre types, some are qualitative or
semi-quantitative, they may consider different impact categories, and some focus on individual life cycle
stages (e.g. the use phase, in particular) rather than the entire life cycle. Alongside the information on
the environmental impacts of clothing, much of the available research also lists potential opportunities
for reduction. Therefore, at the start of the project, it was felt that the available literature would
provide data and information sufficient for a strategic-level carbon footprint of UK clothing.
The literature search initially focused on assessing previous ERM clothing studies, publications
recommended by WRAP, studies undertaken as part of the Sustainable Clothing Roadmap programme
and references cited by each of these publications and a general literature search of government,
industry and academic publications. References are provided in Section 6 of the report.
Relevant data were extracted from the literature sources, collated and reviewed for quality. Relevant
data included:









life cycle inventory (LCI) data of input and outputs to a particular process;
individual data points, such as energy used for a particular process;
GHG emissions factors for a particular process (eg for GHG emissions associated with 1 kWh of
electricity or 1 litre of tap water);
production information, such as location of raw material and finished garments by fibre type;
consumption information, such as total quantity of each fibre and garment used in UK;
information on production processes of fibre, yarn, fabric, textiles and clothing;
consumer behaviour information, such as ironing times, washing temperatures etc.;
information on clothing attributes, such as typical mass, lifetimes etc.; and
suggested carbon footprint reduction measures for estimating potential savings in the future.
A brief search of the academic literature concerning the different physical properties of clothing fibre
was undertaken for the study. No robust data were identified from this search to establish a relative
index of water retention or other properties for fibres which might affect the size of use phase burdens.
The uncertainty associated with the relative cleaning and drying impacts of different fibre types was
subsequently examined in further analysis (Section 4.6).
2.5
Carbon Footprint Calculation
Product carbon footprinting is a technique used to assess the global warming potential of a product or
service. Carbon footprinting usually takes a systematic view of the supply chain from raw material
extraction through to the final disposal (ie cradle to grave). As with any carbon footprint assessment,
this study therefore began by defining the scope of assessment, i.e. the system boundary. The inputs
and outputs of each process within the system boundary were quantified in a process of inventory
analysis. The life cycle inventory (LCI) was built entirely from relevant data collected from the literature.
An impact assessment followed, which first assessed the inventory for greenhouse gas (GHG) emissions
and quantified these over the entire life cycle. GHGs considered include carbon dioxide, methane,
nitrous oxide, hydrofluorocarbons, perfluorocarbons and sulphur hexafluoride. The most significant of
these in terms of global contribution to global warming are carbon dioxide (CO2), methane (CH4) and
nitrous oxide (N2O).
5
The total emissions of individual greenhouse gases were subsequently normalised to CO2 using global
warming potentials which consider the ability of each gas to absorb infra-red radiation and its lifetime in
the atmosphere over a certain period of time (usually 100 years). The resulting metric is a quantity of
carbon dioxide equivalents (CO2e). The impact method applied in this study uses the 100 year global
warming potentials from the Intergovernmental Panel on Climate Change (IPCC) in BSI (2008).
This study follows the ‘attributional’ Life Cycle Assessment (LCA) approach, whereby environmental
burdens are attributable to a life cycle as described, as opposed to the consequential approach, where
possible consequences (indirect effects) of a life cycle on the wider world are considered. One
advantage of the attributional approach is that it allows the relative contribution of each process of the
life cycle to be assessed and ‘hotspots’ to be identified. However, as with many attributional carbon
footprints, it was necessary in this study to use consequential thinking for certain aspects of the life cycle
(eg using a system expansion approach to consider avoided products as a result of reuse and recycling).
The study did not consider the indirect consequential effects of the reduction options on consumption
patterns, for example of clothing in countries to which UK second hand clothing is sent. Another
consequential effect not considered was that, for reduced clothing consumption scenarios, the effect of
purchasing less clothing may indirectly reduce the demand for land to produce natural fibre clothing,
hence reducing land use change and the implications to the carbon cycle of land use change. Wearing
more layers or different types of clothing might result in less household heating required during the
winter.
Neither did the study consider potential ‘rebound effects’. These are changes in consumption patterns
as a consequence of an action or behaviour. For example, the outcome of an initiative to reduce
clothing consumption might be a reduction in consumer spending on clothing. In theory, this could lead
to an outcome where households spend more of their disposable income on environmentally damaging
activities.
2.6
Reduction Measures
Many options for environmental impact reduction of clothing have been suggested in previous research
literature, some more effective and practicable than others. The approach taken in this study was to use
the initial results of the carbon footprint assessment to identify ‘hotspots’ in the life cycle, where carbon
impacts are largest. This hotspot analysis helped to focus attention on those areas of the life cycle
where the greatest savings could be achieved, which in turn dictated which reduction measures should
be considered.
The number of identified reduction measures was narrowed down by ERM to 17. For each of these, the
potential stakeholders involved in each reduction measure were identified and a simple communication
message underpinning each option formed. WRAP and selected stakeholders of the Sustainable Clothing
Action Plan (SCAP) were consulted and the number of reduction options considered for analysis was
subsequently reduced to 10. Hence, the final options examined are based on expert understanding of
the sector and measures currently being/likely to be considered, rather than quantitative cost benefit
analysis.
Three scenarios (or three ‘versions’ of the carbon footprint model) were developed for each reduction
measure, which are listed below and discussed in the next section:

A baseline scenario: the current (2009) situation in the UK;

A Central scenario: a realistic future situation in the UK where modest reductions have
occurred for each measure; and

A ‘What If?’ scenario; an optimistic future situation in the UK where significant reductions have
occurred for each measure.
6
2.7
Baseline and Future Scenarios
To consider the effectiveness of a reduction measure, a baseline needs to be established against which
potential savings can be reported. The baseline scenario for this assessment is the current situation in
the UK (based on 2009 data), which assumes that none of the reduction scenarios considered is in place.
This was created through the collation and review of data, and used to build up a carbon footprint model
of the entire life cycle of UK clothing (described in Section 2.4 and Section 2.5).
Two different future scenarios were created in order to assess the mid-range (Central scenario) and
upper aspirational (‘What If?’ scenario) potentials for reduction. Each reduction measure considered can
be selected individually or in combination to assess the potential savings in carbon footprint that can be
made. When a reduction measure is selected, only data associated with that measure are changed in
the model; all other data will remain fixed, as per the baseline.
The Central scenario can be considered a credible future situation in the UK where modest reductions
occur for each measure. A review of data in the literature and other sources provided insight into likely
values for reduction for each measure (eg based on commitments by manufacturers or retailers). Where
possible, the values for potential reductions were aligned to the URS water footprint report for
consistency (see Section 3.8).
The ‘What If?’ scenario can be considered to be an optimistic future situation in the UK, where significant
reductions have occurred for each measure. In the same approach as above, sources were used to
create values for an optimistic reduction for each measure. Again, the values were aligned with the URS
water footprint report where possible.
WRAP and selected industry stakeholders were consulted with regard to the magnitude of each
reduction to be represented in the modelling.
2.8
Further In-use Interventions
The SCAP ‘In-use’ group identified the use phase as an area warranting further analysis. Therefore, in
addition to the reduction measures presented in the future scenarios, a series of consumer interventions
were tested for their influence on carbon footprint results. In a similar approach taken to identifying the
overall reduction measures, ERM presented a number of in-use interventions to WRAP and selected
stakeholders from the SCAP group, who agreed on which interventions should be modelled.
2.9
Sensitivity Analyses
In product carbon footprinting, it is inevitable that surrogate data and assumptions will be required for
certain aspects of the life cycle, which will lead to uncertainty in the results. For key uncertainties,
sensitivity analyses can be performed to examine the sensitivity of results and conclusions to a change in
a particular assumption or data point. By performing sensitivity analyses, the significance of a particular
assumption or use of a particular data point can be tested. A number of sensitivity analyses were
performed in this study.
2.10
Excel Model
Figure 1 provides a summary of the main information flows in the project. The modelling began with
the development of carbon footprint models in the LCA software tool SimaPro for fibre production,
manufacturing, distribution and retail by fibre type. Results for each fibre type, by life cycle stage, were
transferred to an ERM-developed Excel model. This model enables results for each of the three defined
scenarios (ie baseline, central and ‘What If?’) to be calculated and broken down by fabric type and life
cycle stage. A set of results is presented for garments manufactured in the UK, garments manufactured
outside of the UK and a sum of the two. Each of these results can be represented in terms of each of
the functional unit and the three alternative expressions of the functional unit. These results can be
considered fixed, or static, as they reflect the three scenarios that ERM has defined.
7
Figure 4 below summarises the stages involved in this project.
Figure 4: Summary of project
As well as the fixed outputs generated by the model, its dynamic aspect allows the modeller to develop
additional reduction scenarios (see Figures 5 and 6). For each reduction measure, the modeller is able
to change parameters to observe the effect on the carbon footprint. For example, the modeller can
investigate the impact on carbon footprint of increasing the average size of washing loads by 20%,
and/or if more of the population washed at 15oC.
The results of this exercise are presented in terms of the carbon footprint of the scenario created versus
the baseline, where savings are given for each reduction measure and cumulatively for all reduction
measures selected (see Section 4.2).
Figure 5 and Figure 6 below show some screen shots of the Excel model for illustrative purposes.
8
Figure 5: Screen shot of the use phase and end of life calculator of the Excel model
Figure 6: Screen shot of the transformation section of the Excel model
9
3.0
Life Cycle Inventory
This section provides a description of the life cycle under investigation and the key data used in the
study to build up the life cycle inventory of clothing in use in the UK.
3.1
Life cycle Description
Figure 7 shows a generic process map of the life cycle of clothing both manufactured in and imported to
the UK. The process map represents all fibres of this study (ie acrylic, cotton, linen, polyamide,
polyester, polypropylene, silk, viscose and wool). Inputs and outputs are displayed for each process
relevant to this carbon footprint assessment. For each input of materials and energy to a process, there
are associated GHG emissions occurring upstream from this process. Similarly, for each waste output
from a process, there are associated GHG emissions occurring downstream from this process. Where
more than one product arises from a process (i.e. co-products such as wool and meat from livestock
rearing), GHG emissions of that process are allocated on an economic or mass basis.
3.1.1 Production of Fibre
The production of natural fibre involves various farming activities; broadly, either the cultivation of crops;
or the rearing of livestock.
Cotton and linen fibre is produced through the cultivation of crops, where fertilisers, seeds, water,
pesticides (crop protection) and fuel are among the many inputs required. Outputs include the fibre, coproducts (eg seed, oils, and straw), waste and direct GHG emissions, which are released through the
breakdown of nitrate fertilisers, combustion of fuels and breakdown of crop residues. Some further
processing is required to produce fibres from crops. For example, cotton needs to be ‘ginned’, which is a
process of separating fibre from seeds.
Wool and silk are produced from livestock, where inputs include feed and water. Outputs include the
fibre, co-products (eg meat, bone and skin), waste and direct GHG emissions from enteric fermentation,
the breakdown of manure and combustion of fuels.
The production of synthetic fibre usually involves the production of a base material, in the form of a
resin or granulates, then conversion of this base into a fibre. Polyamide, polyester, polypropylene,
acrylic and viscose are all made by a process of polymerisation, which involves inputs of chemicals,
energy and water. The resulting polymer output is processed further to produce a synthetic fibre, which
in turn requires more inputs of materials and energy and produces more waste.
3.1.2 Production of Yarn
Spinning is the approach that is generally used to manufacture yarn from both natural and synthetic
fibres. This method involves twisting fibres to create a continuous length of yarn. Before spinning can
take place, other processes are sometimes required to prepare the fibre (eg roving). Inputs to this
process comprise fibre – either virgin, waste fibre from industry or from post-consumer waste – and
energy. Outputs comprise yarn, direct GHG emissions from combustion and waste fibre/yarn.
3.1.3 Production of Fabric
Yarn is then used to produce fabric using a variety of methods, including weaving, knitting, crocheting,
braiding, lacing and felting. Again, virgin material, industrial waste or post-consumer waste can be used
as the yarn feedstock and, of course energy is required. Outputs comprise the fabric itself, direct GHG
emissions from combustion and waste fabric/yarn.
10
3.1.4 Treatment of Fabric
Fabric then undergoes various treatment processes to enhance its properties, depending on its
application. These processes may include dyeing, bleaching, printing and adding substances to
preventing creasing, to reduce water retention etc. Inputs of fabric (virgin or recycled), chemicals,
water, energy and fuels are required and outputs comprise the finished fabric and waste fabric.
3.1.5 Production of Garments
Finished fabric is then used to produce garments through a process of measuring, cutting, gluing,
sewing and packaging. Other input material in the form of fibre is required for the sewing process in
addition to energy. Outputs comprise the finished and packaged garments, direct GHG emissions from
combustion and waste fabric/garments.
3.1.6 Distribution and Retail
This stage involves transportation of finished garments by road, air and sea from the manufacturer to
RDC in the UK and transportation by road from RDCs to retail outlets. Inputs of fuel and outputs of GHG
emissions from combustion are associated with the process of transportation.
This stage also involves the storage of garments in RDC and retail outlets, with associated inputs of
energy required to heat, cool and light buildings and outputs of GHG emissions from combustion.
3.1.7 Use
Activities of the use phase comprise washing, drying and ironing. Washing requires material inputs of
water, detergent and potentially fabric conditioner. Drying generally requires no inputs of materials.
Water use in ironing was not included, but is likely to be insignificant
All activities of the use phase require inputs of energy. Each of these activities is assumed to use
electricity as an energy source and therefore no direct GHG emissions are released (ie emissions from
combustion occur upstream at power stations). Although clothes are normally washed and dried as
mixed loads, each garment is actually likely to require a different quantity of electricity to be washed or
dried, depending on its weight and the composition of fibres and the physical properties of these fibres
(e.g. drying kinetics). However, there is considerable uncertainty in quantifying these differences.
Therefore, in common with previous studies, such as Biointelligence (2009), electricity used for washing,
drying and ironing was allocated to clothing on a mass basis, rather than differentiated by fibre type.
Subsequently, a sensitivity analysis was performed to consider the impact of fibre type on drying
(Section 4.6.2).
In terms of materials outputs in the use phase, only wastewater from washing processes is considered.
3.1.8 End of Life
Five potential routes are modelled for clothing considered by consumers to be at the end of its useful
life, as follows.
1.
Reuse – The garment is directly reused in the UK or outside of the UK. The clothing may be
reused in the UK through family/friendship networks; internet-based exchanges; car boot
sales/jumble sales; charity shops etc, or collected through charities; bring banks; or kerbside
collection and prepared for reuse, including the segregation of clothing unfit for reuse. Where
the garment is reused, there is said to be an output of an avoided product. In other words, by
reusing the garment, the need to manufacture a new garment is displaced.
11
2.
Closed loop recycling – The garment is collected from the consumer for recycling and, being
of good enough quality, fibres can be reprocessed and reused by the clothing industry to make
another garment.
3.
Open loop recycling – The garment is collected from the consumer for recycling but, being of
low quality (torn, worn or stained) it is converted into wiping cloths or processed back into
fibres to be used in equally low grade products. Uses for reclaimed fibres include filling
materials for mattresses, car insulation, roofing felts or furniture padding.
4.
Disposal – The garment is disposed of by the consumer as domestic ‘black bin’ waste and
either sent to landfill or incineration. Both processes can recover energy, so there is an avoided
product of grid electricity (and possibly heat) through the combustion of clothing or landfill gas.
5.
Storage – The garment is no longer used by the consumer and stored (eg in the loft or
wardrobe).
12
Figure 7: Generic process map for clothing (both synthetic and natural) in the UK
3.2
Key Data Sources
Key sources of data used in this project are provided in Table 3 and Table 4 below. Table 3 provides the
ultimate data source per fibre type for each production stage and Table 4 provides the data sources for
the remaining life cycle stages (which are the same regardless of fibre type). A full list of references
used in this study is provided at the end of this report.
13
Fibre production
Yarn Production
Fabric Production
Wet Treatment – all fibres
treated the same
Garment Production (Making
up) – all fibres treated the
same
Acrylic
Ecoinvent, 2010 for all stages –
‘Polyacrylonitrile fibres (PAN),
from acrylonitrile and
methacrylate, prod. Mix, PAN’.
EDIPTEX, 2007 for waste and total
energy. Ecoinvent 2010 for
breakdown of energy per fuel
type. ERM assumption for
transportation of incoming
materials.
EDIPTEX, 2007 for waste;
Danish EPA, 1993 for
production energy; ERM
assumption for transportation
of incoming materials.
Kazakevičiūtė et al, 2004 for
materials, waste and
production energy; ERM
assumption for transportation
of incoming materials.
EDIPTEX, 2007 for waste; Danish
EPA, 1995 for production energy;
ERM assumption for
transportation of incoming
materials.
Cotton
Ecoinvent, 2010 for all stages –
‘Cotton fibres, ginned, at
farm/CN U’.
Ecoinvent, 2010 for production
energy and transportation of
incoming materials – ‘yarn
production, cotton fibres/GLO U’.
Roberts, 1980 for waste.
Ecoinvent, 2010 for production
energy and transportation of
incoming materials – ‘weaving,
cotton fibres/GLO U’. Danish
EPA, 1993 for waste.
Kazakevičiūtė et al, 2004 for
materials, waste and
production energy; ERM
assumption for transportation
of incoming materials.
EDIPTEX, 2007 for waste; Danish
EPA, 1995 for production energy;
ERM assumption for
transportation of incoming
materials.
Linen (flax)
INRA, 2006 for all stages. LCI
data refers to flax production in
France/Belgium.
Ecoinvent, 2010 for production
energy and transportation of
incoming materials – ‘Yarn
production, bast fibres/IN U’.
Roberts, 1980 for waste.
Ecoinvent, 2010 for all aspects
of production – ‘weaving, bast
fibres/IN U’. Danish EPA, 1993
for waste.
Kazakevičiūtė et al, 2004 for
materials, waste and
production energy; ERM
assumption for transportation
of incoming materials.
EDIPTEX, 2007 for waste; Danish
EPA, 1995 for production energy;
ERM assumption for
transportation of incoming
materials.
Polyamide
Australasian, 2004 for all
stages – ‘Polyamides (Nylon)
PA 6’.
EDIPTEX, 2007 for waste. ERM
M&S study for total processing
energy. Ecoinvent 2010 for
breakdown of energy per fuel
type. ERM assumption for
transportation of incoming
materials.
EDIPTEX, 2007 for waste;
Danish EPA, 1993 for
production energy; ERM
assumption for transportation
of incoming materials.
Kazakevičiūtė et al, 2004 for
materials, waste and
production energy; ERM
assumption for transportation
of incoming materials.
EDIPTEX, 2007 for waste; Danish
EPA, 1995 for production energy;
ERM assumption for
transportation of incoming
materials.
Polyester
Ecoinvent, 2010 for all stages
of resin – ‘Polyethylene
terephthalate, granulate,
amorphous, at plant’, used as a
proxy for polyester granulate.
ERM M&S study for production
of fibre.
EDIPTEX, 2007 for waste. ERM
M&S study for total processing
energy. Ecoinvent 2010 for
breakdown of energy per fuel
type. ERM assumption for
transportation of incoming
materials.
EDIPTEX, 2007 for waste; ERM
M&S study, 2002 for
production energy; ERM
assumption for transportation
of incoming materials.
Kazakevičiūtė et al, 2004 for
materials, waste and
production energy; ERM
assumption for transportation
of incoming materials.
EDIPTEX, 2007 for waste; Danish
EPA, 1995 for production energy;
ERM assumption for
transportation of incoming
materials.
Fibre Type
14
Polypropylene
Ecoinvent, 2010 for all stages –
‘Polypropylene fibres (PP),
crude oil based, production
mix, at plant’, crude oil based,
production mix, at plant’.
EDIPTEX, 2007 for waste. ERM
M&S study for total processing
energy. Ecoinvent 2010 for
breakdown of energy per fuel
type. ERM assumption for
transportation of incoming
materials.
EDIPTEX, 2007 for waste;
Danish EPA, 1993 for
production energy; ERM
assumption for transportation
of incoming materials.
Kazakevičiūtė et al, 2004 for
materials, waste and
production energy; ERM
assumption for transportation
of incoming materials.
EDIPTEX, 2007 for waste; Danish
EPA, 1995 for production energy;
ERM assumption for
transportation of incoming
materials.
Silk
ERM data on input output
analysis from FAO public data
for silk fibre production.
Ecoinvent, 2010 for production
energy and transportation of
incoming materials – ‘yarn
production, cotton fibres/GLO U’.
Roberts, 1980 for waste.
Ecoinvent, 2010 for production
energy and transportation of
incoming materials – ‘weaving,
cotton fibres/GLO U’. Danish
EPA, 1993 for waste.
Kazakevičiūtė et al, 2004 for
materials, waste and
production energy; ERM
assumption for transportation
of incoming materials.
EDIPTEX, 2007 for waste; Danish
EPA, 1995 for production energy;
ERM assumption for
transportation of incoming
materials.
Viscose
Ecoinvent, 2010 for all stages –
‘Viscose fibres, at plant/GLO’.
EDIPTEX, 2007 for waste and total
energy. Ecoinvent 2010 for
breakdown of energy per fuel
type. ERM assumption for
transportation of incoming
materials.
EDIPTEX, 2007 for waste;
Danish EPA, 1993 for
production energy; ERM
assumption for transportation
of incoming materials.
Kazakevičiūtė et al, 2004 for
materials, waste and
production energy; ERM
assumption for transportation
of incoming materials.
EDIPTEX, 2007 for waste; Danish
EPA, 1995 for production energy;
ERM assumption for
transportation of incoming
materials.
Wool
Biswal et al. (2010) for
Australian wool used for all
stages
Ecoinvent, 2010 for production
energy and transportation of
incoming materials – ‘yarn
production, cotton fibres/GLO U’.
Roberts, 1980 for waste.
Ecoinvent, 2010 for production
energy and transportation of
incoming materials – ‘weaving,
cotton fibres/GLO U’. Danish
EPA, 1993 for waste.
Kazakevičiūtė et al, 2004 for
materials, waste and
production energy; ERM
assumption for transportation
of incoming materials.
EDIPTEX, 2007 for waste; Danish
EPA, 1995 for production energy;
ERM assumption for
transportation of incoming
materials.
Table 3: Key data sources for all production stages, per fabric type
15
Fibre Type
All Fibres
Packaging of
Garments
ERM assumptions
based on previous
study.
Distribution to
the UK
ERM assumption for
departure ports;
Portworld, 2011 for
distances;
ecoinvent, 2010 for
vehicle GHG
emissions factors.
Storage at RDC
and Retail Outlet
Based on previous
ERM study.
Washing
URS, 2011 for
washing frequency
and average load
per wash; ERM
previous study for
washing machine
energy.
Drying
Defra, for drying
behaviour;
manufacturer
websites for drying
energy.
Ironing
End of Life
Biointelligence, 2009
for ironing behaviour
and energy
consumption.
Calculations informed by
Oakdene Hollins, 2009, ERM,
2006 and WRATE, 2010 for
GHG emissions per tonne of
waste via each disposal
pathway; Defra, 2010 for fate
of waste in the UK; WRAP,
2011 Benefits of Reuse;
Oakdene Hollins/Defra, 2009
for fate of separated clothing.
Table 4: Key data sources for all other life cycle stages
16
Figure 8: Composition of each garment type based on data from Biointelligence (2009)
17
3.3
Key Data – All Life cycle Stages
Information on typical clothing attributes was necessary to model a number of life cycle stages. Of
these, clothing mass and anticipated lifetime are considered to be the key data, since these have
greatest influence on the magnitude of the final footprint. Table 5 below shows typical masses of
clothing and anticipated lifetime of clothing from URS (2011), originally from Biointelligence (2009).
Figure 8 displays a breakdown of each garment by fibre type.
Garment Type
Mass (grams)
Lifetime (Years)
Tops
388
2
Underwear, nightwear and hosiery
129
2
Bottoms
568
2
Jackets
821
3
Dresses
1,125
3
Suits and ensembles
921
3
Gloves
52
2
Sportswear
475
3
Swimwear
140
3
Scarves, shawls, ties etc
98
3
Table 5: Key attributes of clothing per garment type
Based on the volumes of each fabric type given in Table 6, the lifetime of each garment type and the
composition of clothing (ie proportion of each fabric type used in garments) given in Table 5, the
‘average’ weighted lifetime of clothing in the UK was calculated to be approximately 2.177 years. Data
on clothing lifetime remains consistent with the URS (2011) report. It should be noted that this lifetime
refers to the length of time clothing is in active use, rather than being retained within the home as
‘wardrobe stock’. In addition, the variability surrounding data on the lifetime of clothing is large and
therefore represents an area of uncertainty in this study.
Total quantities of new clothing in use in the UK in 2009 were extracted from the URS (2011) report on
the water footprint of UK clothing, which was given as 1,143,039 tonnes. As the defined functional unit
in this study considers all clothing in use in the UK in a year, rather than just new clothing, the quantity
provided by URS was uplifted by 2.177 years to 2,488,396 tonnes; then production impacts and end of
life impacts were allocated per annum. Note that this method is compatible with calculations made in
the URS water footprint. This information is provided in Table 6 below. (The proportion of clothing
manufactured in the UK is 10% and is taken to be the same for all fibre types.)
Fabric Type
Cotton
Proportion
of Total
Consumption
Total
Quantity
(tonnes)
Total Quantity
Imported to the
UK (tonnes)
Total Quantity
Manufactured in
the UK (tonnes)
43%
1,070,010
963,009
107,001
Wool
9%
223,956
201,560
22,396
Silk
1%
24,884
22,396
2,488
Flax / linen
2%
49,768
44,791
4,977
Viscose
9%
223,956
201,560
22,396
Polyester
16%
398,143
358,329
39,814
Acrylic
9%
223,956
201,560
22,396
Polyamide
8%
199,072
179,164
19,907
Polyurethane /
polypropylene
3%
74,652
67,187
7,465
2,488,396
2,239,556
248,840
Total
Table 6: Total quantity of clothing in use in UK in 2009
The data on fibre mix shown in Table 6 are taken from Biointelligence (2009): in the absence of a
complete and reliable UK specific dataset regarding the split of UK clothing by fibre type, EU average
18
data from the IMPRO textiles study was used. The original source of this data is the EUROPROM
database and combines information of the production, imports and exports of manufactured textile
products in Europe. (Section 4.6.6 provides a sensitivity analysis for the carbon footprint, calculated
using a different estimate of the fibre split for UK clothing. This reduced the footprint estimate, but had
little impact on the relative importance of the carbon footprint reduction measures modelled in this
study.)
3.4
Key Data - Production of Fibre, Yarn, Fabric and Garments
A large quantity of data was used to model the production of fibre, yarn, fabric and finished garments
for each fibre type. To provide all inventory data for these stages is beyond the scope of this report.
However, all data are referenced in this report. Key data are presented here as an example of the
approach.
The weighted average locations of major producers of fibre and major producers of garments (for
modelling purposes) are summarised in Table 7 (per fibre type)1. (For more information on the
detail of locations, see Appendix 1 of the URS report.) Using this information, production stages
for each fibre type were modelled separately for each geographic location where large scale production
occurs. Where country-specific inventory data were available, they were used in the carbon footprint
model.
Fibre Type
Locations of Major Producers
of Fibres
Location of Major Overseas
Producers of Garments for UK
Acrylic
60% China, 40% India2
100% China
Cotton
47% China, 33% India, 20% USA
18% Bangladesh, 48% China, 18%
India, 16% Turkey
Linen
15% Belgium, 85% France
100% China
Polyamide
60% China, 40% India
100% China
Polyester
60% China, 40% India
100% China
Polypropylene
60% China, 40% India
100% China
Silk
89% China, 11% India
50% China, 18% France, 32% Italy
Viscose
58% China, 24% Indonesia, 18%
Europe
100% China
Wool
81% Australia, 19% New Zealand
71% China, 29% Italy
Table 7: Modelling assumptions – locations of major producers of fibre and finished garments
In cases where country-specific inventory data were not available, inventory data were adjusted to
reflect the situation in the exporting country. The key adjustment made was to the fuel mix for grid
electricity. This was considered to be an important adjustment due to the relative contribution of
electricity to the carbon footprint. Table 8 gives examples of fuel mixes for grid electricity for major
producers of fibre and fabric used in the study (International Energy Agency, 2011).
1
The URS report on the water footprint of clothing provides a more detailed breakdown of locations for fibre raw materials and
garment production by fibre type in Tables A1 and A2.
In the absence of robust data on locations of fibre production, data on locations of fibre exports were used as a proxy in some
cases. The URS report provides more detail on data sources and identifies the fibres for which alternative data were used (as
export data did not provide a reliable basis for modelling).
2
Global man-made fibre production for 2009/10. The split recognises China and Southern Asia as the majority synthetic fibreproducing regions of the world. Indian production was taken as a proxy for all Rest of World countries for the data. This is
considered fair given variability in the carbon intensity of electricity production across the countries. Production country of
origin data was not available for synthetic fibre types individually. (Data taken from Oerlikon (2012), The Fibre Year 2009/10,
A World Survey on Textile and Non Wovens Industry, World Man Made Fibre Volumes 2009)
Alternative data were sought following a peer review of the URS water footprint study, identifying China as the leading
synthetic fibre producing country (for the process steps of polymerisation and resin conversion into fibre).
19
Fuel Type
Quantity per
kWh of
electricity in
China (kWh)
Quantity per
kWh of
electricity in
India (kWh)
Quantity per
kWh of
electricity in
USA (kWh)
Quantity per
kWh of
electricity in
UK (kWh)
Hard coal
0.78979
0.67762
0.48179
0.31541
Oil
0.00676
0.02868
0.01305
0.01519
Natural gas
0.00897
0.12221
0.20572
0.44000
Wood
0.00068
0.00219
0.01134
0.02014
Waste
incineration
n/a
n/a
0.00501
0.00715
Nuclear
0.01976
0.02048
0.18927
0.13066
Hydropower
0.16909
0.11749
0.06389
0.02304
Geothermal
n/a
n/a
0.00384
n/a
Photovoltaic
0.00005
0.00003
0.00055
0.00004
Wind power
0.00378
0.01971
0.01258
0.01767
Imports
0.00112
0.01158
0.01295
0.03071
Table 8: Fuel mixes for grid electricity for major producers of fibre and fabric
Two aspects of the inventory thought to be central to this assessment are the electricity required and
the waste fibre/yarn/fabric created in the production of yarn, fabric and garments. These data are given
in Table 9 and Table 10, respectively.
Fibre Type
Electricity
required for Yarn
Production (kWh
per kg)
Electricity
required for
Fabric
Production
(kWh per kg)
Electricity
required for
Wet Treatment
(kWh per kg)
Electricity
required for
Making Up
(kWh per kg)
Acrylic
7.7
3.5
4.1
0.0806
Cotton
8.5
10.1
4.1
0.0806
Linen
2.7
0.7
4.1
0.0806
Polyamide
3.6
3.5
4.1
0.0806
Polyester1
3.6
2.9
4.1
0.0806
Polypropylene
3.6
3.5
4.1
0.0806
Silk
8.5
10.1
4.1
0.0806
Viscose
7.7
3.5
4.1
0.0806
Wool
8.5
10.1
4.1
0.0806
Table 9: Production energy required for yarn, fabric and garment manufacturing stages
1
There are two common routes to polyester manufacture: spinning; and filament, with the latter reported as representing
around 65% of the market. Data are available for the filament route, but none were found for polyester spinning. Therefore, it
is noted as a limitation that only one of the two major routes to polyester manufacture has been modelled in this study,
although it is not thought significantly to affect the results.
20
Waste created
from Fabric
Production (kg
per kg)
Waste created
from Wet
Treatment (kg
per kg)
Waste created
from Making Up
(kg per kg)
Fibre Type
Waste created
from Yarn
Production (kg
per kg)
Acrylic
0.176
0.015
0
0.143
Cotton
0.176
0.031
0
0.143
Linen
0.176
0.015
0
0.143
Polyamide
0.176
0.015
0
0.143
Polyester
0.176
0.015
0
0.143
Polypropylene
0.176
0.015
0
0.143
Silk
0.176
0.031
0
0.143
Viscose
0.176
0.015
0
0.143
Wool
0.176
0.031
0
0.143
Table 10: Waste fibre, yarn and fabric created at yarn, fabric and garment manufacturing stages
3.5
Key Data - Distribution and Retail
Transportation routes were assumed for all stages of the life cycle, including transportation of raw
materials, fibre to yarn production, yarn to fabric production, garments to UK RDC, garments to stores
and waste to waste treatment facilities.
Table 11 below displays transportation distances used to model the distribution of finished garments to
the UK as the most environmentally significant transport stage. These were calculated based on the
assumed transportation routes for each major producer. Data on the proportion of garments imported
to the UK via sea and air were extracted from the Biointelligence (2009) report and found to be 92%
sea, 8% air. In addition, assumed transportation routes included transportation by road to and from
ports (at either end of the journey).
Country
Distance
transported by sea
(km)
Distance by air (km)
Distance by road
(km) (1)
India
11,047
7,859
650
Pakistan
10,679
6,595
650
Bangladesh
13,408
8,720
300
Sri Lanka
11,882
9,472
300
Turkey
5,199
2,703
450
Western Europe
2,454
2,147
650
USA
5,408
6,453
650
Australia
20,902
18,639
650
New Zealand
20,955
19,947
650
Middle East
11,138
5,363
450
6,052
2,769
650
Russia
Eastern Europe
China
World average
2,163
1,591
450
18,639
10,050
300
9,330
7,097
485
Table 11: Distances to the UK by sea, air and road
Storage at Retail Distribution Centre (RDC) is based on ERM’s experience of carbon footprinting retail
operations. Metrics of electricity and gas use per pallet per day were applied to the assumed volume of
(1) Transport from manufacturer to exporting port and UK transport to RDC. Additionally, transport preceding these stages was also included in
the calculations.
21
clothing for an assumed duration of 30 days. A similar approach was used for storage at retail outlet,
where the assumed duration was 20 days.
3.6
Key Data – Use
3.6.1 Washing
The proportion of UK washing by hand is very small (Biointelligence, 2009). Therefore, 100% machine
washing use was assumed.
An important data point is the frequency of washes. Defra (2009) provide a value of 274 washes per
household per year, which was extracted from a report by the Market Transformation Programme
(2006). The original source of this data point is the research carried out by the Oxford Environmental
Change Institute (published in Lower Carbon Futures for European Households, 2000).
As the carbon footprint is based on the mass of UK clothing, it was necessary to normalise washing
frequency to a metric of ‘number of washes per kilogram of clothing’. This was achieved by multiplying
the number of washes per household by the number of UK households (26,300,000) and the average
washing load size (3.43 kg), which provides the mass of clothing washed in the UK 1. This value was
subsequently divided by the mass of clothing in use in the UK (2.49 million tonnes), to provide a value
of 9.9 washes per kilogram of clothing per year2.
The data can be seen as a ‘top-down’ estimate of the number of times clothing is typically washed in a
UK household. This approach was seen more representative than using ‘bottom-up’ data on the number
of washes per garment, as there are uncertainties surrounding the variation in washing frequency
between individual items of clothing of the same garment type (e.g. not all shirts will be used at the
same frequency; some may be worn once a week, some may not be worn at all, or very infrequently).
Supporting the figure of 274 washes per household per year, a separate study from Danish Energy
Agency (1995) provides a value of 4.6 washes per household per week (~240 washes per household per
year).
In terms of materials consumed during use, water and detergent use during washing were considered.
For water use, data from the Biointelligence (2009) report of 46 l per wash was used. The value used
for detergent use was 78 g per wash, which is an averaged value of manufacturers’ recommended doses
from a selection of commonly used brands (see Table 12 below and
http://www.mysupermarket.co.uk/#/grocery-categories/laundry_detergent_in_tesco.html).
Price
(£)
Mass
(kg)
Number of
washes
Persil 2in1
7.15
2.03
23
Persil Biological Powder
7.15
2.125
25
Persil Biological Powder
12.00
4.25
50
Persil Biological Colour Powder
3.59
0.85
10
Persil Biological Colour Powder
7.15
2.125
25
Tesco 2in1 Biological Gel
2.19
0.63
18
Tesco 2in1 Powder
5.60
3.36
42
Tesco 2in1 Powder
4.00
2
25
Tesco Biological Powder
5.99
3.36
42
Table 12: Manufacturers’ data used to calculate detergent mass and price per wash
(1) Mass of washed clothing per year = washing frequency per household per year (274, from Defra, 2009) x number of households in the UK
(26,300,000, from Office for National Statistics, 2012) x average washing load size (3.43, Biointelligence, 2009) = 24,717,266,000 kg
(2) Washing frequency per kilogram per year = mass of clothing in use in the UK (2,488,395,661 kg) / mass of washed clothing per year
(24,717,266,00 kg) = 9.9 kg
22
Table 13 below displays data on the energy consumption of different washing machines (per load) at
various temperatures, ranging from 15oC to 90oC. Energy consumption values for 20oC and 50oC were
interpolated from this data. This data was calculated in a study by MTP (2009). In addition, energy
consumption of the washing machine in ‘stand by’ mode was also considered, which was taken from the
same study and given as 0.019 kWh per load.
Power Consumption (kWh/load)
Washing
Temperature
A++ Rated
A+ Rated
A Rated
90°C
1.39
1.66
1.77
60°C
0.83
1
1.06
40°C
0.5
0.6
0.64
30°C
0.312
0.379
0.401
15°C
0.0435
0.06
0.061
Table 13: Power consumption of washing machines at different temperatures per load of washing
In terms of washing behaviour, Biointelligence (2009) gave the average washing temperature for Europe
as 46oC (where 6% of households wash at 20oC; 18% wash at 30oC; 37% wash at 40oC; 9% wash at
50oC; 23% wash at 60oC; and 7% wash at 90oC). Biointelligence (2009) also give the average washing
load size as 3.4 kg.
A Defra (2009) report states that A-rated washing machines are the most widely used. Therefore, an
assumption was made that all washing machines used in 2009 were A-rated.
3.6.2 Drying
An important piece of data on drying behaviour is the proportion of the population drying clothes using a
tumble dryer. A report by Defra (2009) gave a figure for the UK of 32%. The remaining 68% is
assumed to be dried on washing lines, balconies, clothes horses, radiators etc., where no additional
energy or material inputs are assumed to be required. The same Defra (2009) report highlights the
possible negative indirect consequences of drying indoors on radiators, in that increased ventilation (eg
opening windows) to remove moisture could lead to loss of heat from the home. No consideration was
given to this aspect in this assessment, nor to additional demand on central heating systems from indoor
drying.
Other key data covered aspects such as the energy required to operate the dryer (per load), the average
size of load and the speed of spin cycle during the washing phase. The Biointelligence (2009) report
provided data for power consumption of a tumble dryer per load and the typical mass of a load, which
were given as 2 kWh and 3.4 kg respectively. The impact of speed of spin cycle is captured within both
the energy consumption of a washing machine and the energy consumption of a dryer.
3.6.3 Ironing
Table 14 below shows typical ironing times for each garment type, weighted by the mass of each
garment and the proportion of washes where the garment is ironed. The information provided in this
table was calculated using data from Biointelligence (2009). To calculate energy consumption, the
weighted ironing time was then applied to the typical power rating of an iron taken from Defra (2009),
which was given as 0.75 kW.
23
Proportion of
washes
where
garment is
Ironed
Ironing
Time
(hours per
garment)
Ironing Time
(hours per kg
of garment)
Tops
0.043
0.017
100%
0.017
Underwear, nightwear and hosiery
0.057
0.007
0%
0.000
Bottoms
0.072
0.041
100%
0.041
Jackets
0.040
0.032
100%
0.032
Dresses
0.075
0.084
100%
0.084
Suits and ensembles
0.050
0.046
0%
0.000
Gloves
0.000
0.000
0%
0.000
Sportswear
0.033
0.016
100%
0.016
Swimwear
0.000
0.000
0%
0.000
Scarves, shawls, ties etc
0.033
0.003
0%
0.000
Garment Type
Weighted
Ironing Time
(hours per kg of
garment)
Table 14: Ironing time per garment
Based on the weight of each garment type given in Table 5 and the weighted ironing time for each
garment type given in Table 14, the average weighted ironing duration of clothing in the UK was
calculated to be 0.022 hours per kg of washed clothing.
3.7
Key Data - End of Life
Table 15 below provides a breakdown of the fate of clothing waste in the UK, which was extracted from
a study carried out by ERM for WRAP entitled the ‘Benefits of reuse, case study: clothing’, and relates to
the quantities of clothes that are ultimately reused, rather than the proportion of waste clothing collected
for reuse/recycling before any rejects and the directly reused fraction which together are greater than
the percentage reuse fraction in this table.
Fate of Waste
Proportion to this Route
Reuse (UK)
13.9%
Reuse (abroad)
33.7%
Recycling (closed loop)
Recycled (open loop)
0.0%
14.5%
Incineration (with energy recovery)
7.2%
Incineration (without energy
recovery)
0.0%
Landfill
30.7%
Table 15: Fate of clothing waste in the UK
For each fate, there are both positive and negative implications on the carbon footprint. GHG emissions
result from activities such as transportation, sorting, recycling, the operation of an incinerator or from
the decomposition of waste in landfill. To an extent, these impacts are offset by activities that displace
the need to produce equivalent items elsewhere in the economy. For this, a benefit is given. For
example, reusing clothing displaces the need to buy new clothing; incinerating clothing generates
electricity, which displaces the need to generate electricity from conventional means; and recycling
clothing displaces the need to produce fibres. In some cases, the benefit of the displaced product
outweighs the burdens associated with the waste management and consequently a net benefit is seen,
reported as a negative carbon footprint.
Table 16 below shows carbon footprint values associated with each of the fates of clothing waste
described in Table 15. These values were calculated using the following approach.
24

Reuse abroad – Clothing that is reused abroad is not considered to displace any UK clothing
and therefore burdens and impact for this fate are zero.

Reuse in UK – The displaced product from reuse was assumed to be a finished garment of
the same fibre type as the reused garment. GHG emissions associated with this benefit were
calculated using cradle-to-gate carbon footprints of finished garments from this study. The
calculation assumes that reused clothing displaces 60% of a new finished garment (Farrant,
2008). GHG emissions associated with the sorting of clothes for reuse were calculated using a
value from WRATE (2010) of 37.8 kWh per tonne for sorting and assumes that clothing is
transported 50 km to a regional sorting facility and sorted clothing is transported 50 km to a
RDC.

Closed Loop Recycling – The displaced product from closed loop recycling was assumed to
be fibres of the same fibre type as the recycled garment. GHG emissions associated with this
benefit were calculated using cradle-to-gate carbon footprints of fibres from this study. The
calculation assumes that clothing collected for closed loop recycling displaces 90% of new fibre
(WRATE, 2010). GHG emissions associated with the process of closed loop recycling were
calculated using values from WRATE (2010) of 37.8 kWh per tonne for sorting and 400 kWh
per tonne for recycling. Clothing was assumed to be transported 50 km to a sorting facility and
250 km to a reprocessing plant.

Open Loop Recycling – The displaced product from open loop recycling was assumed to be
a low grade product (such as a wiping cloth) made from a 50:50 mix of cotton and polyester
fibres. This assumption was based on information from studies by ERM, 2006 and Oakdene
Hollins, 2009. GHG emissions associated with this benefit were calculated using cradle-to-gate
carbon footprints of 50% cotton fibre and 50% polyester fibre from this study. The calculation
assumes that clothing collected for open loop recycling displaces 90% of low grade fibre
(WRATE, 2010). GHG emissions associated with the process of open loop recycling were
calculated using values from WRATE (2010) of 37.8 kWh per tonne for sorting and 700 kWh
per tonne for recycling. Clothing was assumed to be transported 50 km to a sorting facility and
transported 50 km to an RDC.

Incineration – Default values from WRATE (2010) were extracted for natural and synthetic
fibres.

Landfill – Default values from WRATE (2010) were used.
25
Fate of Waste
Carbon Footprint (kg
CO2e per tonne
waste) (1)
References
Reuse in UK (cotton)
-14,904
Reuse in UK (wool)
-27,083
Reuse in UK (silk)
-13,429
Reuse in UK (flax/Linen)
-6,604
Reuse in UK (viscose)
-16,447
Reuse in UK (polyester)
-10,680
Reuse in UK (acrylic)
-21,872
Reuse in UK (polyamide)
-12,658
Reuse in UK (polyurethane)
Reuse abroad (all fibres)
Closed loop recycling (cotton)
Closed loop recycling (wool)
Closed loop recycling (silk)
Closed loop recycling (flax/linen)
Informed by Farrant (2008), WRATE
(2010)
-9,674
0
-1,280
-18,412
-1,529
-3
Informed by Oakdene Hollins (2009),
WRATE (2010)
Closed loop recycling (viscose)
-1,607
Closed loop recycling (polyester)
-4,522
Closed loop recycling (acrylic)
-6,520
Closed loop recycling (polyamide)
-6,964
Closed loop recycling (polyurethane)
-2,488
Open loop recycling (all fibres)
-2,259
Informed by ERM (2006), Oakdene
Hollins (2009), WRATE (2010)
Incineration (synthetic fibres)
1006
WRATE (2010)
Incineration (natural fibres)
-433
WRATE (2010)
222
WRATE (2010)
Landfill (all fibres)
Table 16: Carbon footprint associated with the treatment of clothing via various routes
3.8
Reduction Measures
Table 17 describes the reduction measures considered in the study.
3.9
Baseline and Future Scenarios
The three defined scenarios considered in this study, as previously described in this report, are a
baseline scenario, a Central scenario and a ‘What If?’ scenario. Table 17 describes the data used for
each scenario with regard to each reduction measure. Where possible, values for realistic (central) and
optimistic (‘What If?’) reductions were taken from the URS (2011) water footprint report for consistency.
References for each data source are provided in the table.
In addition to the reduction measures provided for each reduction scenario, an extra intervention was
also considered. This addressed the issue of the transportation method used to import garments to the
UK. The baseline scenario that has been used for this study assumes 92% sea freight and 8% air
freight, based on data extracted from the Biointelligence (2009). In order to calculate the reductions
achievable by reducing air transportation, two reduction measures were modelled. The Central scenario
is that the proportion of clothing transported by air is reduced by 10% (ie 92.8% sea freight and 7.2%
air freight). The ‘What If?’ scenario is that the proportion of clothing transported by air is reduced by
50% (ie 96% sea freight and 4% air freight). Results are presented separately from the other reduction
measures, as it was thought the potential to influence a reduction is lower than the other ten reduction
measures.
(1) Note that the emission factors used in the study relate are by fibre type and relate to the calculated upstream impacts for fibre type. These
are different from those in WRAP 2011 Benefits of Reuse research. The UK reuse factors include preparation for reuse burdens and displace UK
clothing.
26
Clothing
Working
Group
Design &
production
Design &
production
Design &
production
In use
In use
Principal
stakeholders
Message
Reduction
Measure
Lean
production
Eco efficiency
across supply
chain
(production,
distribution
and retail)
Baseline comprises
production based on most
recent published
ecoinvent data and 2010
energy mix (per country)
Longer
product
lifetime
Design for
Durability
[and product
lifetime
optimisation]
A weighted lifetime of
clothing in the UK is
taken as 2.2 years.
Considering both lifetime
of each garment type and
proportion of total UK
clothing each garment
represents.
Manufacturer
/ retailer /
consumer
Buy
differently
Shift in
market to
higher
proportion of
synthetic
fibres
~45% of fabric used in
the UK is synthetic
Consumer /
retailer
Reduce
consumer
footprint
through
behavioural
change
Consumer /
retailer
Reduce
consumer
footprint
through
behavioural
change
Manufacturer
/ retailer
Manufacturer
/ retailer /
consumer
What If? Scenario
(Optimistic)
References
30% reduction in GHG emissions
for all fibres and across supply
chain
Central scenario - trade associations,
manufacturers and retailers have
committed to various qualitative
work/objectives/eco innovation.
What If? 30% Tesco commitment to
reduce supply chain carbon footprint
by 30% by 2020
33% longer lifetime of clothing,
same end of life
Central: both Biointelligence, 2009,
Defra 2009. URS report
What If? from WRAP Resource
efficiency GHG. Quick win scenario for
Product lifetime optimisation
Replace 10% of cotton fabric
with a 50:50 poly-cotton
blended fabric
40% of cotton replaced
Baseline from Defra 2010 report on
Emerging Fibres
Central: Biointelligence and URS
What If?: Beyond best practice WRAP
(2010) RE and GHG
Clean
clothing less
100% washing machine
use (as opposed to hand
washing). Weighted
average lifetime washes
(52.7)
Washing machine use
unchanged. Lifetime washes
reduced by 10%.
Washing machine use
unchanged. Lifetime washes
reduced by 15%.
Baseline: Biointelligence, 2009.
Central: URS. What If? ERM
assumption
Wash at
lower
temperature
Weighted average wash
temperature for Europe is
46oC (ie weighted
averaged behaviour for
20oC, 30oC, 40oC, 60oC,
90oC)
Weighted average wash
temperature for Europe is
39.3oC (6% at 20oC, 55% at
30oC, 9% at 40oC, 30% at 60oC)
Weighted average wash
temperature for Europe is 32.9oC
(40% at 20oC, 29% at 29oC, 12%
at 40oC, 6% at 50oC, 11% at
60oC, 2% at 90oC)
Baseline, Central and What If?:
washing temperature for Europe,
Biointelligence, 2009.
Baseline Scenario
Central Scenario
5% reduction in GHG emissions
for all fibres and across each
stage of supply chain
10% longer lifetime of clothing,
same end of life
27
Consumer /
retailer
In use
Consumer /
retailer
In use
Reuse &
recycling
Reuse &
recycling/
Design &
Production
Reuse &
recycling
Consumer /
retailer
Manufacturer
/ retailer /
consumer
Consumer /
retailer
Reduce
consumer
footprint
through
behavioural
change
Reduce
consumer
footprint
through
behavioural
change
Reuse more
Increase size
of washing
and drying
loads
3.7 kg both wash and dry (8.8%
increase)
4 kg both wash and dry
(17.6% increase on baseline)
Baseline, Central and What If?: from
Biointelligence, 2009.
Use the
tumble dryer
less
32% of clothing is dried
in a tumble dryer. The
rest being either dried on
clothes lines, balconies,
clothes horses, radiators
etc.
For six months of the year (ie
summer and spring) a 30%
reduction from baseline is
achieved by an increase in the
proportion of clothing dried on
washing lines etc. For the other
half of the year (ie autumn and
winter) there is no change from
the baseline.
For six months of the year (ie
summer and spring) a 50%
reduction from baseline is
achieved by an increase in the
proportion of clothing dried on
washing lines etc. For the other
half of the year (ie autumn and
winter) there is a 15% reduction
from the baseline achieved by an
increase in the proportion of
clothing dried on radiators etc.
Baseline: Defra, 2009. Central and
What If?: from Biointelligence, 2009.
Dispose less reuse more
It has been estimated
~47.6% of clothing is
ultimately reused (13.9%
is reused in the UK).
52.6% of clothing ultimately
reused (15.4% is reused in the
UK). This is in addition to
baseline end of life for reuse
and disposal.
62.6% of clothing reused (18.3%
is reused in the UK). This is in
addition to baseline end of life for
reuse and disposal.
Baseline: WRAP 2011 Benefits of
Reuse), ERM. Central and What If?
ERM assumptions.
Currently little or no
clothing is closed loop
recycled (0% for the
baseline).
5% of all fibres are recycled
(closed loop) resulting in
reduction of production burden
(1:1 basis assumed). This is in
addition to baseline end of life
for reuse and disposal.
10% of all fibres are recycled
(closed loop) resulting in
reduction of production burden.
This is in addition to baseline end
of life for reuse and disposal.
Baseline: WRAP, ERM. Central and
What If? ERM assumptions.
~14.5% of clothing is
recycled (open loop).
19.5% of all clothing recycled
(open loop). This is in addition
to baseline end of life for reuse
and disposal.
24.5% of all fibres are recycled
(open loop). This is in addition
to baseline end of life for reuse
and disposal.
Baseline: WRAP, ERM. Central and
What If? ERM assumptions.
Recycle
more
Start closed
loop recycling
of synthetic
fibres
Recycle
more
Dispose less recycle more
(open loop)
3.4 kg for both washing
and drying
Table 17: Defined scenarios for each reduction measure (included in this study)
28
3.10
Data Quality
All assessments of this type will have data quality issues and it is important that these are communicated.
Due to the strategic-level nature of this study, a formal data quality review as required by ISO 14044 (1)
or PAS 2050 is out of the scope. However, ISO 14044 or PAS 2050 have been used to help to define the
data quality criteria that consideration should be given to, which are:






reliability;
precision;
completeness;
temporal specificity;
geographical specificity; and
technological specificity.
Each data set (rather than individual data points) has been assessed against these data quality criteria
and ranked according to a simple traffic light system (eg red = poor quality; amber = moderate quality;
and green = good quality). The criteria assessment is based on the lowest quality data point within the
data set. The resulting matrix below (Table 18) provides a quick guide to the likely uncertainty which
may be associated with the data set.
Life Cycle
Stage
Reliability
Precision
Completeness
Temporal
Correlation
Geographical
Correlation
Technological
Correlation
Fibre
production
Yarn
production
Fabric
production
Fabric
treatment
Garment
production
Packaging
Distribution
to the UK
Storage at
RDC
Storage at
retail outlet
Washing
Drying
Ironing
End of life
Table 18: Data quality assessment matrix
Data highlighted as having poor data quality are as follows.

Natural fibres - Where secondary data for production of a natural fibre were not specific to
the country modelled. Agricultural inputs were not changed due to the limited scope of this
study. It is likely that agricultural inputs and outputs will vary between countries and therefore
it is seen as a limitation that the model does not consider these.
(1) ISO14040 series of life cycle assessment standard. Reference ISO 14044:2006 Environmental management -- Life cycle assessment -Requirements and guidelines. http://www.iso.org
29

Production of silk fibre - The inventory was created using economic input output data (I/O),
which have a large inherent uncertainty due to their generic nature.

Production of polyamide fibre – Secondary data used for the production of polyamide fibre
were taken from the Australasian database, which contains data from as early as 1993.
Despite the age of these data, it was felt to be the most appropriate available, as its format
allowed grid electricity mixes to be altered to be specific to the country of fibre production.

Lifetime of clothing – The best data available were used to model the lifetime of clothing.
However, the variability between the lifetimes of individuals’ clothing is large and therefore it
may be difficult to capture this in an ‘average’ value.

Washing frequency - The data used for the number of times clothing is washed per year are
uncertain. There is variability in washing frequency between households, different garment
types and even within the same garment group (ie occasional wear versus every day wear).
Therefore, it is very difficult to represent the typical washing frequency of all clothing in the
UK.

Ironing – As with clothing lifetime data, data on the proportion of clothing ironed are highly
variable. It is noted that, despite the best data available being used, this is a potential data
limitation. However, ironing does not contribute to a significant proportion of life cycle GHG
emissions (see results section).

Transportation of finished garments to the UK – Assumptions on the transportation
routes were made to build a model of the distribution of clothing to (and within) the UK. There
is inherent uncertainty within these assumptions that can only be reduced with detailed
modelling of transport routes into the UK.
A hotspot analysis of results was carried out to identify those life cycle stages that make the greatest
contribution to the total carbon footprint. Washing, drying and fabric production were identified as
major hotspots. For each of these life cycle stages, ‘good’ or ‘moderate’ quality data were used.
Therefore, despite the use of ‘poor’ quality data for certain aspects of the life cycle, the overall quality of
data can be considered to be reasonable, and at an appropriate level for the aims of this study.
30
4.0
Impact Assessment
This section provides description and interpretation of the main results of this study. A separate Excel
model is also provided alongside this report, which contains all the results of this study.
Note: values for the use-phase impacts are presented for all clothing in use in the UK in 2009, not just for
new clothing in use bought in 2009. If values for the use phase impacts of only new clothes in use are
required, then the use phase impact values presented in this section should be divided by the factor
2.177.
4.1
Baseline Scenario
4.1.1 Carbon Footprint of all Clothing in Use in the UK in 2009, whether manufactured in or
imported to the UK – UK Total
Table 19 below displays the baseline carbon footprint results of all clothing in use in the UK in 2009,
whether manufactured in or imported to the UK. Results are presented as a total for the UK and broken
down by both life cycle stage and fibre type. This is shown graphically in Figure 9.
The contribution of each life cycle stage and fibre type to the total baseline carbon footprint is shown in
Figure 10.
31
Carbon Footprint (tCO2e)
Fibre Type
Cotton
Wool
Silk
Flax / linen
Fibre
production
Yarn
production
Fabric
production
Garment
production
Distribution
Retail
Use washing
Use drying
Use ironing
End of life
TOTAL
862,591
3,933,342
6,738,624
328,311
756,870
227,091
2,479,140
1,638,070
104,532
-1,161,070
15,907,502
2,138,740
885,909
1,472,743
67,392
155,982
47,531
518,890
342,852
21,879
-417,159
5,234,758
23,211
79,600
139,099
7,510
15,480
5,281
57,654
38,095
2,431
-24,658
343,704
5,253
76,659
131,673
14,673
39,079
10,562
115,309
76,189
4,862
-27,628
446,632
Viscose
217,837
1,907,296
534,496
66,029
175,854
47,531
518,890
342,852
21,879
-254,415
3,578,248
Polyester
979,687
493,853
1,496,878
117,384
312,630
84,499
922,471
609,514
38,896
-305,691
4,750,120
Acrylic
779,454
1,902,845
901,967
66,029
175,854
47,531
518,890
342,852
21,879
-331,992
4,425,307
Polyamide
737,950
246,673
801,748
58,692
156,315
42,250
461,235
304,757
19,448
-177,984
2,651,085
Polyurethane /
polypropylene
106,194
92,597
300,656
22,010
58,618
15,844
172,963
114,284
7,293
-52,521
837,937
5,850,917
9,618,774
12,517,884
748,029
1,846,682
528,120
5,765,441
3,809,464
243,098
-2,753,116
38,175,293
TOTAL
Table 19: Carbon footprint all clothing in use in the UK in 2009, whether manufactured in or imported to the UK, represented as a total for the UK, broken down per fibre type
Figure 9: Carbon footprint all clothing in use in the UK in 2009, whether manufactured in or imported to the UK, represented as a total for the UK, broken down per fibre type
32
Figure 10: Contribution to the total carbon footprint of each life cycle stage and fibre type
33
From Table 19, Figure 9 and Figure 10, the following points are evident.

The total annual carbon footprint of all garments, both new and existing, in use in the UK in
2009 (ie the volume consumed, and the actively worn quantity) is approximately 38 million
tonnes of CO2e (~0.6 tonnes per person per year). Because the majority of clothing is
manufactured outside the UK, it is estimated that ~32% occurs within the UK (contributing to
the UK’s direct carbon footprint) and ~68% occurs abroad. Based on this estimate, the direct
impact of clothing in the UK can be estimated to be ~12 million tonnes of CO2e.

To put the direct carbon footprint of UK clothing into context, the total direct GHG emissions in
the UK in 2009 were reported to be 566 million tonnes of CO2e (DECC, 2011). It should be
noted that this total for the UK does not include GHG emissions associated with imported
goods or services, or international travel. Therefore, the direct carbon footprint contributes
approximately 2% to the UK’s total direct carbon footprint.

The carbon footprint of new garments ONLY, in use in the UK in 2009, can also be calculated
by dividing the carbon footprint of both new and existing clothing by its anticipated lifetime.
This figure is approximately 17 million tonnes of CO2e.

The dominant life cycle stage is fabric production (comprising weaving/knitting etc. and
treatment of fabric), representing 33% of total life cycle GHG impacts.

The second most dominant life cycle stage is use, representing 26% of total life cycle GHG
impacts. Of the activities in the use phase, washing represents is the largest contributor
(15%), followed by drying (10%) and ironing (1%).

Of all life cycle stages, garment production, distribution and retail contribute the least to the
total carbon footprint: contributing 2%; 5%; and 1%, respectively.

Of all the fibre types, the contribution of cotton to the total carbon footprint is the largest
(42%), primarily due to the large proportion of cotton used in the UK (43%). It is worth
noting that this baseline calculation does not take into account potential differences in laundry
impacts between fibre types, which is examined further in Section 4.6.
4.1.2 Carbon Footprint of all Clothing in Use in the UK in 2009, whether Manufactured in or
Imported to the UK – per person
Table 20 below displays the baseline carbon footprint results of all clothing in use in the UK in 2009,
whether manufactured in or imported to the UK. Results are represented as per person figures (UK
population of 62.262 million) and broken down by both life cycle stage and fibre type. This is shown
graphically in Figure 11.
From Table 20 and Figure 11, the following points are evident.

The per person per annum carbon footprint of all garments, both new and existing, in use in
the UK in 2009 is around 0.6 tonnes of CO2e.

The general comments made on Table 19, Figure 9 and Figure 10 also apply to these results.
34
Carbon Footprint (kgCO2e) per person
Fibre Type
Fibre
production
Yarn
production
Fabric
production
Garment
production
Distribution
Use washing
Retail
Use drying
Use ironing
End of
life
TOTAL
Cotton
13.9
63.2
108.2
5.3
12.2
3.6
39.8
26.3
1.7
-18.6
255.5
Wool
34.4
14.2
23.7
1.1
2.5
0.8
8.3
5.5
0.4
-6.7
84.1
Silk
0.4
1.3
2.2
0.1
0.2
0.1
0.9
0.6
0.0
-0.4
5.5
Flax / linen
0.1
1.2
2.1
0.2
0.6
0.2
1.9
1.2
0.1
-0.4
7.2
Viscose
3.5
30.6
8.6
1.1
2.8
0.8
8.3
5.5
0.4
-4.1
57.5
Polyester
15.7
7.9
24.0
1.9
5.0
1.4
14.8
9.8
0.6
-4.9
76.3
Acrylic
12.5
30.6
14.5
1.1
2.8
0.8
8.3
5.5
0.4
-5.3
71.1
Polyamide
11.9
4.0
12.9
0.9
2.5
0.7
7.4
4.9
0.3
-2.9
42.6
1.7
1.5
4.8
0.4
0.9
0.3
2.8
1.8
0.1
-0.8
13.5
94.0
154.5
201.1
12.0
29.7
8.5
92.6
61.2
3.9
-44.2
613.1
Polyurethane /
polypropylene
TOTAL
Table 20: Carbon footprint all clothing in use in the UK in 2009, whether manufactured in or imported to the UK, represented per person, broken down per fibre type
Figure 11: Carbon footprint all clothing in use in the UK in 2009, whether manufactured in or imported to the UK, represented per person, broken down per fibre type
35
4.1.3 Carbon Footprint of all Clothing in Use in the UK in 2009, whether Manufactured in or
Imported to the UK – per tonne
Table 21 below displays the baseline carbon footprint results of all clothing in use in the UK in 2009,
whether manufactured in or imported to the UK. Note: use phase impacts relate to all clothing in use for
one year’s use. Results are represented as per tonne and broken down by both life cycle stage and fibre
type. The data do not differentiate the ‘in-use’ carbon footprint by fibre type – which may underestimate the carbon footprint of water-retaining fibres, notably cotton. The baseline analysis does not
examine the effect of uncertainties which are considered in further analysis (Section 4.6). These results
are shown graphically in Figure 12.
From Table 21 and Figure 12, the following points are evident.

The total carbon footprint of a tonne of clothing in 2009 ranges from around 15 to 46 tonnes
CO2e, depending on the fibre type of the garment.

The life cycle stages garment production, distribution, retail and use have the same associated
GHG impact for each fibre type (see also research suggestions in Section 5.7 and sensitivity
analyses of Section 4.6.2 and 4.6.3).

Wool displays the largest carbon footprint of all the fibre types (around 46 tonnes of CO2e per
tonne across the entire life cycle), with fibre production contributing most to the carbon
footprint (45% of total life cycle impacts). This is explained by the large impact of agriculture
inputs and outputs associated with livestock (eg methane emissions from manure
management, nitrous oxide emissions from fertilisers etc.). It is acknowledged that the
assumption that all clothing is washed and dried at the same frequency and temperature
regardless of fibre type is uncertain. It is possible that some fibre types, for example wool,
may be washed less frequently and at lower temperatures than others and may not be tumbledried. However, data on washing behaviour by fibre type is lacking and it cannot be assumed
that washing and drying recommendations of clothing manufacturers will be followed.

Flax/Linen displays the smallest carbon footprint of all the fibre types (around 15 tonnes of
CO2e). The main reason for this is the relatively small carbon footprint allocated in the
production of fibre (335 kg CO2e per tonne), which can be explained by the fact that linen is a
low value co-product from the production of a higher-value product, linseed oil.
36
Carbon Footprint (kgCO2e) per tonne of fibre
Fibre Type
Cotton
Fibre
production
Yarn
production
Fabric
production
Garment
production
Distribution
Use washing
Retail
Use drying
Use ironing
End of
life
TOTAL
1,755
7,961
13,710
668
1,540
462
2,317
1,531
98
-2,362
27,679
20,790
8,654
14,316
655
1,516
462
2,317
1,531
98
-4,055
46,284
2,031
6,964
12,169
657
1,354
462
2,317
1,531
98
-2,157
25,425
335
3,353
5,760
642
1,709
462
2,317
1,531
98
-1,209
14,999
Viscose
2,118
18,540
5,196
642
1,709
462
2,317
1,531
98
-2,473
30,139
Polyester
5,357
2,700
8,185
642
1,709
462
2,317
1,531
98
-1,671
21,329
Acrylic
7,577
18,551
8,768
642
1,709
462
2,317
1,531
98
-3,227
38,427
Polyamide
8,070
2,700
8,768
642
1,709
462
2,317
1,531
98
-1,946
24,351
Polyurethane /
polypropylene
3,097
2,700
8,768
642
1,709
462
2,317
1,531
98
-1,532
19,792
Wool
Silk
Flax / linen
Table 21: Carbon footprint all clothing in use in the UK in 2009, whether manufactured in or imported to the UK, represented per tonne, broken down per fibre type
Figure 12: Carbon footprint all clothing in use in the UK in 2009, whether manufactured in or imported to the UK, represented per tonne, broken down per fibre type
37
4.1.4 Carbon Footprint of all Clothing in Use in the UK in 2009, whether Manufactured in or
Imported to the UK – per garment
Table 22 below displays the baseline carbon footprint results of all clothing in use in the UK in 2009,
whether manufactured in or imported to the UK. Results are represented as per garment and broken
down by life cycle stage. These results are shown graphically in Figure 13.
From Table 22 and Figure 13, the following points are evident.

The carbon footprint of each garment, both new and existing, in use in the UK in 2009 ranges
from around 1 to 17 kg CO2e.

The garment types displaying the largest carbon footprint are suits and ensembles (17 kg
CO2e), dresses (15 kg CO2e) and jackets (13 kg CO2e), which can be explained by their
relatively large mass.

Those garments displaying the smallest carbon footprint are gloves (1 kg CO2e), scarves,
shawls, ties etc. (2 kg CO2e) and swimwear (2 kg CO2e), which can be explained by their
relatively small mass.
38
Carbon Footprint (kgCO2e) per garment
Garment Type
Tops
Underwear, nightwear and hosiery
Bottoms
Jackets
Dresses
Suits and ensembles
Gloves
Sportswear
Swimwear
Scarves, shawls, ties etc.
Fibre
production
Yarn
production
Fabric
production
Garment
production
Distribution
1.1
1.8
2.1
0.1
0.2
0.2
0.9
0.6
0.0
-0.5
6.5
0.3
0.4
0.7
0.0
0.1
0.1
0.3
0.2
0.0
-0.1
1.9
1.1
2.4
2.8
0.2
0.2
0.3
1.3
0.9
0.1
-0.6
8.7
3.7
2.5
3.9
0.2
0.3
0.5
1.9
1.3
0.1
-1.0
13.3
2.2
3.7
4.0
0.3
0.5
0.6
2.6
1.7
0.1
-1.0
14.8
5.4
3.2
4.7
0.3
0.4
0.5
2.1
1.4
0.1
-1.3
16.8
0.3
0.3
0.3
0.0
0.0
0.0
0.1
0.1
0.0
-0.1
1.1
1.3
0.8
1.9
0.1
0.2
0.3
1.1
0.7
0.0
-0.4
6.1
0.4
0.2
0.6
0.0
0.1
0.1
0.3
0.2
0.0
-0.1
1.8
0.5
0.3
0.5
0.0
0.0
0.1
0.2
0.2
0.0
-0.1
1.8
Retail
Use washing
Use drying
Use ironing
End of
life
TOTAL
Table 25: Carbon footprint all clothing in use in the UK in 2009, whether manufactured in or imported to the UK, represented per garment, broken down per garment type
Figure 13: Carbon footprint all clothing in use in the UK in 2009, whether manufactured in or imported to the UK, represented per garment, broken down per garment type
39
4.2
Savings Achieved in the Central Scenario
Table 23 and Figure 14 below display the carbon saving from the baseline generated by each reduction
measure of the Central scenario. The baseline is the total carbon footprint of all garments, both new
and existing, in use in the UK in 2009 (ie the volume consumed, and the actively worn quantity), given
in tonnes of CO2e. The figure in the next column is the reduction in total carbon footprint of all
garments after the reduction measure is put in place.
Baseline
(t CO2e)
Reduction
(t CO2e)
Reduction %
Eco-efficiency across supply chain
(production, distribution and retail) Central scenario - 5% reduction for all
fibres across supply chain
38,175,293
1,563,219
-4.1%
Design for Durability (and product lifetime
optimisation) - Central scenario - 10%
longer lifetime of clothing
38,175,293
2,941,203
-7.7%
Shift in market to higher proportion of
synthetic fibres - Central scenario replace 10% of cotton with 50:50 polycotton. [Data exclude in-use savings]
38,175,293
164,150
-0.4%
Clean clothing less - Central scenario washes per year reduced by 10%
38,175,293
989,905
-2.6%
Wash at lower temperature - Central
scenario - weighted average wash
temperature of 39.3C
38,175,293
549,604
-1.4%
Increase size of washing and drying loads
- Central scenario - load increases to
3.7kg
38,175,293
531,538
-1.4%
Use the tumble dryer less - Central
scenario - 30% reduction in tumble dryer
use in summer
38,175,293
430,367
-1.1%
Dispose less - reuse more - Central
scenario – 15.4% of clothing ultimately
reused in the UK
38,175,293
272,063
-0.7%
Start closed loop recycling of synthetic
fibres - Central scenario - 5% of all
clothing is recycled (closed loop)
38,175,293
352,144
-0.9%
Dispose less - recycle more (open loop) Central scenario - 38% of all clothing is
recycled open loop
38,175,293
195,729
-0.5%
7,989,921
-20.9%
Reduction measure
Cumulative reduction
Table 23: Savings achieved by each reduction measure of the Central scenario
40
Figure 14: Savings achieved by each reduction measure of the Central scenario
From the estimates presented in Table 23 and Figure 14, the following points are evident.

A potential 21% reduction in the carbon footprint of UK clothing would occur if all reduction
measures considered for the Central scenario were achieved.

The largest carbon footprint reductions are achieved by extending product lifetime (8%), ecoefficiency across the supply chain (4% reduction) and washing clothing less (3% reduction).

As calculated, reduction measures resulting in minimal reductions in carbon footprint include
increasing open loop recycling, increasing reuse and a shift in the market to a larger proportion
of synthetic fibres.

An eco-efficiency measure across the supply chain (production, distribution and retail) of 5%
for all fibres results in reduction in the total carbon footprint of 4.1%.

Increasing the lifetime of clothing by 10% results reduction in carbon footprint of 7.7%.
However, as an indirect negative consequence of this measure, it is possible that a longer
lifetime might result in poorer quality clothing at the end of life. As a consequence, there
would be less benefit for reuse items, although this was not examined in the study. This
potential indirect effect is more relevant to a situation where clothing lifetime has increased
through a behavioural change rather than a technological one.

A shift to synthetic fibres by replacing 10% of cotton with 50:50 poly-cotton only results in a
very small reduction in carbon footprint (0.4%). The savings are a result of the lower
embodied carbon value associated with polyester production in comparison to cotton.
However, the difference between these two values is small. It is possible that more significant
savings may result from this shift to synthetics, due to their relative physical properties
resulting in less energy required for washing, drying and ironing. This thesis is tested in a
sensitivity analysis in Section 4.6 of this report.

A 10% reduction in the number of washes per year results in significant carbon footprint
savings (2.6%). This is a consequence of the use phase representing such a large proportion
of total life cycle emissions and the fact that both drying and ironing are reduced by 10% when
the number of washes is reduced.

A reduction of average washing temperature from 46oC to 39.3oC results in a carbon footprint
reduction of 1.4%. This is an example of where a reduction in input results in a significant
reduction in overall carbon footprint, due to the significance of the life cycle stage.
41

An increase in washing and drying load sizes from 3.4 kg to 3.7 kg results in a carbon footprint
reduction of 1.4%.

A reduction in machine drying by 30% in the summer results in a modest reduction in carbon
footprint of 1.1%. Although machine drying is very energy-intensive and represents a
significant proportion of total life cycle impact, machine dryer use in the UK is already quite low
(around 32%) and therefore a 30% reduction in use does not translate into a large absolute
reduction in use. In terms of indirect effects of this measure, there may be a positive knock-on
effect of increasing the clothing lifetime through less shrinkage or other damage to clothing
whilst machine drying.

An increase in the final reuse of clothing in the UK from 13.9% to 15.4% results in a modest
carbon footprint reduction of 0.7%. It is noted here that the proportion of UK clothing reused
in the UK is around one third, which explains the relatively small saving achieved by increases
reuse as only the UK reused fraction is given a displacement benefit in this study. On a ‘per
tonne of clothing’ basis reuse results in significant carbon reduction and is generally favoured
as a waste management option from a carbon footprint perspective1.

An increase in closed loop recycling from 0% to 5% results in a carbon footprint reduction of
0.9%.

As with closed loop recycling, increasing open loop recycling from 14.5% to 19.5% does not
result in significant savings in carbon footprint.

It should also be noted that the number of decimal places of results displayed in Table 26 and
Table 27 does not represent the level of precision, rather these are illustrative, to allow for
distinction between reduction measures.

The above reduction measures can be grouped into potential savings from consumer actions,
potential savings as a result of business actions or combination of the two. The total potential
reduction in the carbon footprint of UK clothing is broken down into these three groups:



Potential savings as a result of consumer actions – 8.7%
Potential savings as a result of business actions – 4.1%
Potential savings as a result of both consumer and business actions – 8.1%
In addition, further savings can be achieved from encouraging the use of a particular fibre type
due to the differentiation in use phase impacts between fibre type (see sensitivity analyses in
Section 4.6.2 and Section 4.6.3).

Due to the potential for large savings to be achieved through consumer actions at the use
phase, the significance of in-use interventions has been tested further in this study (see
Section 4.5).
In addition, through reducing the proportion of clothing that is imported to the UK by air by 10% (ie to
7.2%), the total carbon footprint of UK clothing is reduced by 0.2%, or 90,770 t CO2e.
(1) Note that the benefits given to ‘recycling’ collection in some carbon reporting metrics (eg Defra/Decc GHG Protocol and the Scottish Carbon
Metric) are dominated by the proportion of clothes collected for ‘recycling’ which are subsequently separated for reuse. To the greatest extent
the benefit given is dependent on the proportion that is reused, and in this study in particular, the proportion that is reused in the UK. To a
lesser extent, the benefit given for recycling is associated with that remainder which is materials recycled or recovered.
42
4.3
Savings Achieved in the ‘What If?’ Scenario
Table 24 and Figure 15 below display the carbon saving from the baseline generated by each reduction
measure of the ‘What If?’ scenario. The baseline is the total carbon footprint of all garments, both new
and existing, in use in the UK in 2009 (ie the volume consumed, and also the quantity actively worn),
given in tonnes of CO2e.
Baseline
(t CO2e)
Reduction
(t CO2e)
Reduction %
Eco-efficiency across supply chain
(production, distribution and retail) - 'What
If?' - 30% reduction for all fibres across
supply chain
38,175,293
9,338,795
-24.5%
Design for Durability (and product lifetime
optimisation) - 'What If?' - 33% longer
lifetime of clothing
38,175,293
10,432,516
-27.3%
Shift in market to higher proportion of
synthetic fibres - 'What If?' - replace 40%
of cotton with 50:50 poly-cotton [Data
exclude in-use savings]
38,175,293
632,288
-1.7%
Clean clothing less - 'What If?' - washes per
year reduced by 15%
38,175,293
1,480,805
-3.9%
Wash at lower temperature - 'What If?' weighted average wash temperature of
32.9C
38,175,293
1,032,401
-2.7%
Increase size of washing and drying loads 'What If?' - load increases to 4kg
38,175,293
1,030,254
-2.7%
Use the tumble dryer less - 'What If?' 50% reduction in tumble dryer use in
summer, 15% reduction in winter
38,175,293
1,072,163
-2.8%
Dispose less - reuse more - 'What If?' –
18.3% of clothing reused in the UK
38,175,293
799,980
-2.1%
Start closed loop recycling of synthetic
fibres - 'What If?' - 10% of all clothing is
recycled (closed loop)
38,175,293
696,184
-1.8%
Dispose less - recycle more (open loop) 'What If?' - 43% of all clothing is recycled
open loop
38,175,293
383,353
-1.0%
26,898,739
-70.5%
Reduction measure
Cumulative reduction
Table 24: Savings achieved by each reduction measure of the ‘What If?’ scenario
43
Figure 15: Savings achieved by each reduction measure of the ‘What If?’ scenario
From the estimates presented Table 24 and Figure 15, the following points are evident.

A 71% reduction in the carbon footprint of UK clothing will occur if all reduction measures
considered by the ‘What If?’ scenario are achieved.

The largest carbon footprint reductions are achieved by extending product lifetime (27%
reduction), eco-efficiencies across the supply chain (24% reduction) and washing less (4%
reduction).

Reduction measures resulting in the smallest reductions in carbon footprint include increasing
closed loop recycling, increasing open loop recycling and a shift to a higher proportion of
synthetics.

An eco-efficiency measure across the supply chain (production, distribution and retail) of 30%
for all fibres results in a reduction in carbon footprint of 24.5%.

Increasing the lifetime of clothing by 33% results in a large reduction in carbon footprint of
27.3%.

A shift to synthetics by replacing 40% of cotton with 50:50 poly-cotton only results in a very
small reduction in carbon footprint (1.7%). However, this result excludes savings during the
in-use stage.

A 15% reduction in the number of washes per year results in significant carbon footprint
savings (3.9%).

A reduction of average washing temperature from 46oC to 32.9oC results in a carbon footprint
reduction of 2.7%.

An increase in washing and drying load sizes from 3.4kg to 4kg results in a carbon footprint
reduction of 2.7%.

A reduction in machine drying by 50% in the summer and 15% in the winter results in a
reduction in carbon footprint of 2.8%.

An increase in the direct reuse of clothing from 13.9% to 18.3% results in a modest carbon
footprint reduction of 2.1%. It is noted here that the proportion of UK clothing reused in the
44
UK is around one third, which explains the relatively small saving achieved by increases reuse
as only the UK reused fraction is given a displacement benefit in this study. On a ‘per tonne of
clothing’ basis reuse results in significant carbon reduction and is generally favoured as a waste
management option from a carbon footprint perspective1.

An increase in closed loop recycling from 0% to 10% results in a carbon footprint reduction of
1.8%.

As with closed loop recycling, increasing open loop recycling from 14.5% to 24.5% does not
result in significant savings in the carbon footprint.

The above reduction measures can be grouped into potential savings from consumer actions,
potential savings as a result of business actions or combination of the two. The total potential
reduction in the carbon footprint of UK clothing is broken down into these three groups:



Potential savings as a result of consumer actions – 17.0%
Potential savings as a result of business actions – 24.5%
Potential savings as a result of both consumer and business actions – 29.0%
In addition, further savings can be achieved from encouraging the use of a particular fibre type
due to the differentiation in use phase impacts between fibre type (see sensitivity analyses in
Section 4.6.2 and Section 4.6.3).

Due to the potential for large savings to be achieved through consumer actions at the use
phase, the significance of in-use interventions has been tested further in this study (see
Section 4.5).
In addition, through reducing the proportion of clothing that is imported to the UK by air by 50% (ie to
4%), the total carbon footprint of UK clothing is reduced by 1.2%, or 453,850 t CO2e.
4.4
Benchmarking Against Other Studies
The results generated in this study were compared to a selection of published reports, in order to sensecheck the results. It should be noted here that often a direct comparison with other studies is difficult as
there may be differences in the scope; in particular the chosen functional unit.
A study from the Carbon Trust (2011) gives a value of the production of new clothing at around 150 kg
CO2e per person footprint, albeit the study has a different functional unit. To allow a fair comparison,
we must adjust this value to reflect both new and existing clothing over the entire life cycle. In order to
do this, we can assume production represents 75% of life cycle impacts and new clothing only
represents 45% of total clothing in use (based on a lifetime of 2.2 years). The resulting carbon footprint
for all clothing over the entire life cycle is 435 kg CO2e per person, which is closer to the results of this
study (613 kg CO2e per person).
All studies considered highlight the use phase as being dominant. Although the use phase contributes a
large proportion (~25%) of the GHG impact of clothing in this study, it is a considerably lower proportion
than suggested in other studies. A study by Business for Social Responsibility (2009) suggests that, in
most studies, the use phase accounts for 40-80% of life cycle GHG impacts. For example, a study by
the University of Cambridge (2006) calculates that the use phase of T-shirts represents 60% of energy
impacts. It is thought that washing frequency is a major contributor to these differences. In the studies
with which this assessment has been compared, the washing frequency data come from
product/garment level studies and assume that new clothing is bought and used. However, in this
study, an average cleaning frequency was calculated top down, based on average washing loads and
load size (see Section 3.6.1). The resultant wash frequency is less than predecessor studies and
(1) Note that the displacement benefit given for closed loop and open loop recycling in this study refers to materials that are recycled. The
benefits given to ‘recycling’ in some carbon reporting metrics (eg Defra/Decc GHG Protocol and the Scottish Carbon Metric) are dominated by the
proportion of clothes collected for ‘recycling’ which are subsequently separated for reuse. To a lesser extent, the benefit given for recycling is
associated with that remainder which is materials recycled or recovered.
45
suggests a significant proportion of all new clothing (perhaps half) is bought and then not used, or is
infrequently used.
In terms of impact per tonne of finished garment per fibre type, the Biointelligence (2009) report
provides values of 22.5 and 27.2 kg CO2e per kg for the production of cotton and polyester garments,
respectively (up to the garment manufacturer gate). The values given in this study for cotton and
polyester garments are 27.7 and 21.3 kg CO2e per kg, respectively. For polyester fibre, a study carried
out by Shen et al. (2010) of Utrecht University provides a value of 4.1 kg CO2e per kg, which is in line
with the value from this study of 5.3 kg CO2e per kg.
4.5
Further Analysis
4.5.1 Further In-use Interventions
The SCAP ‘In-use’ group identified the use phase as an area warranting further analysis, given its
contribution to the carbon footprint. Therefore, in addition to the reduction measures presented in the
scenarios above, a series of consumer interventions were tested for their influence on carbon footprint
results. To this end, the impact of 10 in-use interventions was tested through a behavioural change shift
in 10% of the population for each reduction measure (ie 10% of the UK population adopt each
measure). These measures are described in Table 25.
The purpose of this analysis is to compare the effectiveness of a variety of measures to change
consumer behaviour during the use phase of clothing after the clothing was purchased.
Reduction Measure
Reduction Scenario
Increase size of washing and
drying loads
10% shift in population from 3.4kg loads to 4kg loads
Clean clothing less
10% shift from weighted average of 21.6 to 18.4 lifetime
washes (ie 15% reduction)
Use the tumble dryer less
10% shift in population from 32% dryer use (year round) to
16% in the summer and 27% in the winter.
Wash at 30oC (effect of a wash at
a lower temperature campaign)
10% shift in population from average wash temperature of 46oC
to 32.9oC, where no bio fouling prevention is undertaken
Wash at 30oC and periodic biofouling service wash (variant of
above)
10% shift in population from average wash temperature of 46oC
to always washing at 32.9oC, bio fouling ‘service’ wash (90oC)
every six weeks
Wash at 30oC and periodic biofouling service wash and descaling/detergent removal (variant
of above)
10% shift in population from average wash temperature of 46oC
to always washing at 32.9oC with monthly bio fouling ‘service’
wash (90oC with integrated lime scale and detergent remover
and additional machine efficiency benefit of this)
Enhanced energy efficiency of
washing - improved appliance
rating of washing machines OR
achieved by more use of existing
economy wash setting for washing
machine
10% shift in population from A rated to A+ rated washing
machines OR a 10% in consumer behaviour to use economy
setting resulting in equivalent carbon saving
Increased spin drying of washing
machine
10% shift in population from spin cycle of 1000rpm to 1600rpm
with positive consequences for drying1
Reduced detergent use
10% shift in population to reduce detergent use by 10%
1
Increasing the speed of spin drying of washing machines reduces the moisture content of clothing and therefore reduces
energy consumption of dryers. A study by Defra (2009) entitled ‘Reducing the environmental impact of clothing’, states that an
increase in washing machine spin drying speed from 1000 rpm to 1600 rpm can reduce drying energy by 13%.
46
Better washing and drying
behaviour results increase in
lifetime of clothing
10% of the population adopt better washing and drying
behaviour, which increases the in use life of their clothing by 1
year. Behavioural changes comprise washing at lower
temperature, washing clothing less, using the tumble dryer less
and performing ‘service washes’ to reduce bio fouling.
Table 25: In-use interventions considered in this study
Bio-Fouling
The prevention of bio-fouling (the build-up of unsightly and odorous residues and mould in washing
machines) may have benefits in terms of appliance longevity and efficiency. Washing machine
manufacturers generally recommend that high temperature, no load ‘service’ washes should be run
periodically to minimise bio-fouling, blockages etc. In these service washes, consumers may also include
limescale/detergent removal tablets to help to prevent the build-up of limescale in hard water areas
and/or to remove detergent. In addition to the benefits achieved from the prevention of bio-fouling,
there are also associated burdens, principally resulting from the energy used in the service wash.
Data from a 2009 WRAP report carried out by ERM entitled ‘ Environmental Life Cycle Assessment (LCA)
Study of Replacement and Refurbishment options for domestic washing machines’ suggests that 77% of
the energy consumption of a washing machine is associated with heating and that, over time, limescale
build up can reduce heating element efficiency by 10% (i.e. a 7.7% overall reduction in efficiency is a
result of limescale build up). However, the washing temperature modelled in the 2009 WRAP study was
higher than the average washing temperature of this study, and therefore savings were scaled down to
an illustrative 4%. No data were found on the reduction of efficiency associated with the build-up of
detergent and mould within the machine, and therefore an illustrative 1% was assumed as the
reduction in efficiency caused by this build up.
In this assessment, it was assumed that a service wash is undertaken at a recommended high
temperature (90oC) every six weeks. This was modelled using data on the power consumption of an A
rated washing machine, given in Table 13. The embodied carbon of limescale remover was assumed to
be equal to that of washing powder; 50g was assumed to be added to every service wash.
Table 26 below displays the carbon saving generated by a 10% shift in population towards each in-use
intervention.
47
In-use intervention
% Reduction in total
carbon footprint
Rank
10% shift in the washing and drying behaviour of population, increase the
length of time clothing is use for by 1 year. Behavioural changes comprise
washing at lower temperature, washing clothing less, using the tumble
dryer less and performing ‘refresh washes’.
-5.44%
1
10% shift from weighted average of 21.6 to 18.4 lifetime washes (ie 15%
reduction)
-0.41%
2
10% shift in population from 32% dryer use (year round) to 16% in the
summer and 27% in the winter.
-0.30%
3
10% shift in population from 3.4kg loads to 4kg loads
-0.29%
4
10% shift in population from average wash temperature of 46oC to 32.9oC,
where no bio fouling prevention is undertaken
-0.18%
5
10% shift in population from spin cycle of 1000rpm to 1600rpm with
positive consequences for drying
-0.15%
6
-0.15%
7
-0.14%
8
-0.07%
9
-0.07%
10
10% shift in population from average wash temperature of 46oC to always
washing at 32.9oC, with monthly bio fouling ‘service’ wash (90 oC)
10% shift in population from average wash temperature of 46oC to always
washing at 32.9oC with monthly bio fouling ‘service’ wash (90oC with
integrated lime scale and detergent remover and additional machine
efficiency benefit of this)
10% shift in population to reduction in detergent that results in 10%
reduction in impact
10% shift in population from A rated to A+ rated washing machines OR a
10% in consumer behaviour to use economy setting resulting in equivalent
carbon saving
Table 26: Savings achieved by each in-use intervention
From the results presented in Table 26, the following points are evident.
4.6

The most significant in-use intervention is a behavioural shift resulting in an increase in
clothing lifetime by one year (reduction in overall carbon footprint of 5.4%). This behaviour
shift reduces both in-use and production impacts of clothing.

Cleaning clothing less, increasing the size of washing loads and reducing tumble dryer use
result in combined carbon footprint savings of greater than 1%. Other interventions have a
less significant impact.
Sensitivity Analyses
In this study, a number of uncertainties were identified that warranted further investigation through
sensitivity analyses. Sensitivity analysis is a technique to explore the sensitivity of results and
conclusions to a change in a particular assumption or data point.
The sensitivities examined are: the influence of the likely decarbonisation of grid electricity; the influence
of fibre type on washing and drying; the influence of product lifetime; the influence of washing
frequency; and the influence of UK fibre mix.
4.6.1 Decarbonisation of Grid Electricity (Sensitivity 1)
48
The Government has committed to reducing emissions of UK greenhouse gases by at least 80% by 2050
and 34% by 2020, on 1990 levels. In the latest Carbon Plan1 it sets out plans for achieving the target.
A fourth carbon budget is established in the plan, which covers the period 2023–2027, and requires
overall emissions to be cut by 50% on 1990 levels.
The contribution that the UK energy sector can make to these targets is fundamentally dependent on
two factors: future energy demand (economic growth/energy efficiency); and how clean is the future
electricity supplied (either through change in technology/grid efficiency or through carbon
mitigation/capture technologies).
The Carbon Plan gives an Emissions Performance Standard for new electricity production of 450 gCO2
per kWh in 2013, but does not set targets for each energy generation technology or a future
decarbonisation target for the energy sector overall. Hence, the sensitivity examined in this study uses
the Climate Change Committee (2008) recommendation that the overall carbon intensity of UK grid
needs to be reduced to 70 gCO2 per kWh in 2030. Figure 16 shows the potential pathway to achieving
this proposed reduction, which was extracted from chapter 5 of a 2008 report by the Climate Change
Committee entitled Building a low carbon economy.
Figure 16: Potential pathway to decarbonisation of grid electricity in the UK
The purpose of this sensitivity analysis is to assess the sensitivity of results and conclusions to the
anticipated decarbonisation of grid electricity. In particular, does the priority of reduction measures
change when considered in the context of a future UK with decarbonised electricity? Electricity is used
in many stages over the life cycle of clothing (some within the UK and some outside the UK). However,
in terms of contribution to the overall carbon footprint, the electricity used in the use phase is the most
important. Therefore, this sensitivity analysis only considers the change in UK grid electricity emissions
for the use phase.
In this study, the carbon intensity of 2009 UK grid electricity is taken from ecoinvent and calculated to
be 610 gCO2e per kWh. For this sensitivity analysis, the carbon intensity of UK grid electricity used in
the use phase of clothing was reduced to 70 g CO2 per kWh (ie recommended 2030 levels). Table 27
presents the main results of this study recalculated using this reduced emission factor for grid electricity.
Use Phase Grid
1
Use Phase Grid
% Difference
The Carbon Plan (2011) is accessible at http://www.decc.gov.uk/en/content/cms/tackling/carbon_plan/carbon_plan.aspx
49
Electricity
Emissions Factor
610 g CO2e per
kWh
Electricity
Emissions Factor
70 g CO2e per
kWh
Fibre production
5,850,917
5,850,917
0%
Yarn production
9,618,774
9,618,774
0%
12,517,884
12,517,884
0%
Fabric production
Garment production
748,029
748,029
0%
1,846,682
1,846,682
0%
528,120
528,120
0%
Use - washing
5,765,441
2,731,655
-52.6%
Use - drying
3,809,464
437,600
-88.5%
Use - ironing
243,098
27,925
-88.5%
Distribution
Retail
End of life
TOTAL
-2,753,116
-2,753,116
0%
38,175,293
31,554,469
-17.3%
Table 27: Sensitivity analysis to access the effect of a decarbonised grid on use phase impacts
From the results of the sensitivity analysis presented Table 27, the following points are evident.

With the reduced carbon intensity of grid electricity applied to the use phase, the total carbon
footprint for UK clothing is reduced by 17%. The value is comparable in scale to the majority
of potential reductions across the supply chain estimated for the Central scenario;

The largest contributor in percentage terms is the reduction in drying impacts (reduced by
88.5%);

The reduction in carbon footprint of washing is only 52.6%, due to the influence of washing
detergent, which is not affected by the decarbonisation of UK grid electricity in the sensitivity
analysis. However, the reduction in terms of tonnes CO2e is roughly equivalent to that
achieved in the drying phase (Figure 17);

The significance of the use phase decreases relative to upstream life cycle stages (ie such as
manufacture) and also at end of life. It should be noted here that only use phase grid
electricity decarbonisation was modelled in this sensitivity. However, as the majority of
clothing is manufactured outside of the UK, it is anticipated that modelling decarbonisation in
all life cycles stages would not alter this conclusion significantly;

With electricity being entirely responsible for the carbon footprint for drying and ironing, a
reduction in the carbon intensity of electricity results in a direct reduction in carbon footprint.
Sensitivity analysis 1 indicates that the relative importance of non-use life cycle stages is increased
when electricity is decarbonised. Therefore, when considering reduction initiatives, some non-use phase
measures increase in importance when grid electricity is decarbonised. As it is likely that UK grid
electricity will be decarbonised in the future, both use phase and non-use phase reduction measures
should be considered. This is considered further in Table 28, which displays the difference in ranking of
carbon reduction measures where UK grid electricity is decarbonised.
50
Use Phase Grid Electricity Emissions Factor 610 g CO2e per kWh
Use Phase Grid Electricity Emissions Factor 70 g CO2e per kWh
Reduction
due to
measure
from
baseline
(%)
Cumulative
Reduction
(%)
1,563,219
-4.1%
-4.1%
2
31,554,469
29,991,250
1,563,219
-5.0%
-5.0%
2
33,670,871
4,504,422
-7.7%
-11.8%
1
31,554,469
27,116,255
4,438,214
-9.1%
-14.1%
1
38,175,293
33,506,721
4,668,572
-0.4%
-12.2%
10
31,554,469
26,952,105
4,602,364
-0.5%
-14.6%
9
38,175,293
32,516,816
5,658,477
-2.6%
-14.8%
3
31,554,469
26,624,283
4,930,186
-1.0%
-15.6%
4
Wash at lower
temperature - Central
scenario - weighted
average wash temperature
of 39.3C
38,175,293
31,967,212
6,208,081
-1.4%
-16.3%
4
31,554,469
26,359,617
5,194,852
-0.8%
-16.5%
6
Increase size of washing
and drying loads - Central
scenario - load increases
to 3.7kg
38,175,293
31,435,674
6,739,619
-1.4%
-17.7%
5
31,554,469
26,140,377
5,414,092
-0.7%
-17.2%
7
Total
Baseline
Carbon
Footprint
(t CO2e)
Total
Reduced
Carbon
Footprint
(t CO2e)
Eco-efficiency across
supply chain (production,
distribution and retail) Central scenario - 5%
reduction for all fibres
across supply chain
38,175,293
36,612,074
Design for Durability (and
Product lifetime
optimisation) - Central
scenario - 10% longer
lifetime of clothing
38,175,293
Shift in market to higher
proportion of synthetic
fibres - Central scenario replace 10% of cotton
with 50:50 poly-cotton
Clean clothing less Central scenario - washes
per year reduced by 10%
Cumulative
Reduction
(t CO2e)
Rank
Total
Baseline
Carbon
Footprint
(t CO2e)
Total
Reduced
Carbon
Footprint
(t CO2e)
Cumulative
Reduction
(t CO2e)
Reduction
due to
measure
from
baseline
(%)
Cumulative
Reduction
(%)
Rank
51
Use the tumble dryer less
- Central scenario - 30%
reduction in tumble dryer
use in summer
38,175,293
31,005,308
7,169,985
-1.1%
-18.8%
6
31,554,469
26,083,767
5,470,702
-0.2%
-17.3%
10
Dispose less - reuse more
- Central scenario – 15.4%
of clothing ultimately
reused in UK
38,175,293
30,733,245
7,442,048
-0.7%
-19.5%
8
31,554,469
25,811,704
5,742,765
-0.9%
-18.2%
5
Start closed loop recycling
of synthetic fibres Central scenario - 5% of
all clothing is recycled
(closed loop)
38,175,293
30,381,101
7,794,192
-0.9%
-20.4%
7
31,554,469
25,459,560
6,094,909
-1.1%
-19.3%
3
Dispose less - recycle
more (open loop) - Central
scenario - 38% of all
clothing is recycled open
loop
38,175,293
30,185,372
7,989,921
-0.5%
-20.9%
9
31,554,469
25,263,832
6,290,638
-0.6%
-19.9%
8
Table 28: Sensitivity analysis to access the impact on the central reduction scenario of grid decarbonisation
52
From the results of the sensitivity analysis presented in Table 28, the following points are evident.

The reduction achieved through the following measures is increased by the shift to a
decarbonised grid: eco-efficiency across the supply chain; open and closed recycling; reuse
more; shift towards synthetic fibres; and design for durability.

The reduction achieved through the following measures in decreased by the shift to a
decarbonised grid: cleaning clothing less; using the tumble dryer less; increased load sizes; and
lower washing temperature.

Therefore, in a decarbonised UK, non-use phase reduction measures become more important
relative to use phase reduction measures.
4.6.2 Influence of Fibre Type on Drying (Sensitivity 2a and 2b)
Consistent with the findings of previous environmental analyses of clothing, machine tumble drying has
been identified in this study as a significant aspect of the life cycle with respect to the carbon footprint
of clothing. Drying is also one stage of the life cycle with scope for potential intervention/innovation, ie
technological innovation (either in tumble drier functionality, or indirectly through alternative fibres with
faster-drying properties), or through consumer behavioural change (ie better separation, or selective
drying and use).
Also consistent with the previous research, the carbon footprint model of this study allocates the burden
associated with using a tumble dryer to clothing on a mass basis, regardless of the type of fibre of
which the clothing is made. For example, a kilogram of cotton shirts is ascribed the same GHG
emissions for drying as a kilogram of polyester shirts. The rationale behind this allocation choice is that
there is considerable uncertainty in the data available with regard to fibre type and drying properties.
In particular, the influence of the combined physical properties of different fibres when dried in mixed
loads is uncertain. A second argument for the mass allocation is that clothes are dried as mixed fibre
loads.
However, this mass allocation choice carries a level of uncertainty with evidence in the public domain
that different fibre types possess significantly different drying properties. Therefore, the sensitivity of
results to this allocation choice was tested through two sensitivity analyses:

Sensitivity 2a - An index of relative drying rates was created for each fibre type, which was in
turn used to allocate the impacts of drying to clothing of each fibre type, based on how quickly
each fibre type dries. In this model, there is an assumption that either clothing will be
separated by the consumer according to fibre type, or that dryers have appropriate technology
/ sensors such that drying time will reflect the fibre types being dried. Where different fibre
types are present in the dryer, it will be assumed in this analysis that they have no influence on
each other’s’ drying rate. The resulting total carbon footprints are compared with those where
a mass-based allocation was used to determine the influence on results.

Sensitivity 2b - Using the index of relative drying rates for each fibre type, further data were
collected and used to examine the influence fibres have on each other’s drying rate. Resulting
carbon footprints were compared with those of the current model.
For sensitivity 2a, two approaches were considered for the construction of a relative index of drying
times. The first was to gather data on the physical properties of fibre types in relation to the diffusion
of water through clothing. Some outputs of this research are summarised below:

A publication by Manich et al. (2005) describes that the key parameters determining drying
kinetics are the amount of moisture retained on the outside of the fibres and the diffusion of
moisture within the fibres.

The rate of drying from the surface of clothing is dependant largely on the temperature,
humidity and mass of the air in the dryer doing the drying, but not fibre type. Therefore, there
is effectively no difference in drying rate at the surface between fibre types.
53

Conversely, the process of diffusion of water within fibres is strongly determined by fibre type
and it is this parameter that affects the relative drying times of different fibre types most
significantly.

Of the fibres tested by Manich et al., polypropylene had the lowest water retention (4.1%) and
quickest drying rate (7.5% per minute), viscose had the highest water retention (83.3%) and
modal had the slowest drying rate (3.2% per minute) (cotton or wool were not assessed).

There are many other studies concerning the drying kinetics of one fibre type or comparing
two fibre types, but Manich et al. was the most comprehensive study identified. However,
Manich et al. did not access all fibre types required for this study.
A second approach to constructing a relative index of drying times was to gather data on the energy
requirements of a tumble dryer for different fibre types. It was thought that this approach was more
representative than considering the physical properties of fibre types and was therefore used in this
sensitivity analysis. Product guides for tumble dryers were reviewed for information on drying times
based on fibre type. As an example, a product guide for a Hotpoint tumble dryer is summarised in
Table 29 below.
Fibre type
Cottons
Synthetics
Drying
Time
(mins)
Temperature
130
High heat
80
Low heat
Table 29: Drying time of different fibre types
Based on the information on relative drying times provided in user manuals, an assumption was made
for this sensitivity analysis that all synthetic fibres should be given a relative value of 0.5 and all natural
fibres should be given a relative value of 1 (as per Table 30 below). In this sense, if separated, clothing
made of nature fibres is assumed to use twice the energy to dry in comparison to clothing made of
synthetic fibres. These relative values were applied to drying energy data for the first drying sensitivity
analysis (ie where clothing is separated for drying). It is recognised that there are uncertainties with
using this approach and further testing of tumble dryers by manufacturers is required to fill the gap in
available research.
Fate of Waste
Relative Index of Drying Time
Cotton
1
Wool
1
Silk
1
Flax / linen
1
Viscose
0.5
Polyester
0.5
Acrylic
0.5
Polyamide
0.5
Polyurethane /
polypropylene
0.5
Weighted average
0.78
Table 30: Relative index of drying time of different fibre types
For sensitivity analysis 2b (ie where mixed loads are dried together), all clothing was assumed to dry at
the same rate as natural fibres. Manich et al. (2005) suggest that some fibre will have more influence
on drying rates than others and that the relative quantity of fibres type in a load is also an important
factor. The influence different fibre types have on each other is a complex issue but becomes a less
important consideration if clothing is not typically removed from the dryer as it dries – in which case the
rate of drying of mixed loads is largely determined by the slowest drying fibre type.
54
A final consideration is that for some fibre types it is not recommended that they are tumble dried at all.
The Fabric Care Research Association Handbook (1988) suggests that wool should not be tumble dried
as it is prone to shrinkage, silk should not be tumble dried as it is too fragile and linen should not be
tumble dried as it creases very easily. Whilst it may be the case that some consumers follow washing
instructions, it is not known what proportion of the population do this. In addition, some delicate fibre
types have been improved by manufacturers, so it is less easy for consumers to damage them by drying
(eg easy care wool) and it is not known what proportion of these types of clothing are being tumble
dried. Therefore, for these sensitivity analyses, results are presented where considering both inclusion
and exclusion of delicates (wool, silk and linen) to the tumble dryer.
Table 31 presents results of sensitivity analysis 2a and Table 32 presents results of sensitivity analysis
2b. The proportion of the population that excluded delicates (ie wool, silk, flax) from tumble dryer loads
is not known; the results of the sensitivity analysis are presented both where delicates are tumble dried
and where they are excluded.
Total Carbon Footprint (t CO2e)
Drying energy
All fibres use
Drying energy
based on
based on
relative drying
relative drying
rate of fibres,
rate of fibres,
clothing
clothing
separated,
the same
separated,
energy to dry
delicates dried
Fibre Type
(baseline)
in tumble dryer
Cotton
15,907,502
16,383,070
5,234,758
Silk
Flax / linen
% Difference
delicates not
dried in
% Difference from
tumble dryer
baseline
3%
16,383,070
3%
5,334,296
2%
4,891,906
-7%
343,704
354,763
3%
305,609
-12%
446,632
468,752
5%
370,443
-21%
Viscose
3,578,248
3,456,591
-4%
3,456,591
-4%
Polyester
4,750,120
4,533,840
-5%
4,533,840
-5%
Acrylic
4,425,307
4,303,650
-3%
4,303,650
-3%
Polyamide
2,651,085
2,542,945
-4%
2,542,945
-4%
837,937
797,385
-5%
797,385
-5%
38,175,293
38,175,293
0%
37,585,440
-2%
Wool
Polyurethane /
Polypropylene
TOTAL
from
baseline
Table 31: Sensitivity analysis to assess the influence on relative drying times of fabric types on results
(where clothing is separated)
From the results of the sensitivity analysis presented in Table 31, the following points are evident.

Where drying energy is allocated according to the relative drying time of fibre types, the
carbon footprint increases from the baseline for natural fibres and decreases for synthetic
fibres. This can be explained by the hydrophobic nature of synthetic fibres causing them to
hold less water and therefore to dry more quickly (using less energy) than natural fibres.

Clearly, when the assumption is made that delicates are not dried in the tumble dryer, there is
a large reduction in carbon footprint relative to the baseline for fibre types considered delicates
(ie wool, silk and linen).

The overall carbon footprint of UK clothing remains the same, whether allocation of dryer
energy is carried out according to mass or relative drying time of fibre type.
55

Where delicates are not tumble dried, a small reduction of the overall carbon footprint of UK
clothing is observed, which is due to the removal of drying burden for three fibre types.
Total Carbon Footprint (t CO2e)
Drying energy
based on
Drying energy
relative drying
based on
rate of fibres,
relative drying
clothing
All fibres use
rate of fibres,
mixed,
the same
clothing mixed,
delicates not
energy to dry
delicates dried
Fibre Type
(baseline)
in tumble dryer
Cotton
15,907,502
dried in
% Difference
tumble dryer
% Difference
16,383,070
3%
16,383,070
3%
5,234,758
5,334,185
2%
4,891,906
-7%
Silk
343,704
354,751
3%
305,609
-12%
Flax / linen
446,632
468,727
5%
370,443
-21%
Viscose
3,578,248
3,677,675
3%
3,677,675
3%
Polyester
4,750,120
4,926,879
4%
4,926,879
4%
Acrylic
4,425,307
4,524,735
2%
4,524,735
2%
Polyamide
2,651,085
2,739,464
3%
2,739,464
3%
837,937
871,079
4%
871,079
4%
38,175,293
39,280,566
3%
38,690,861
1%
Wool
Polyurethane /
Polypropylene
TOTAL
Table 32: Sensitivity analysis to assess the influence on relative drying times of fabric types on results
(where clothing is mixed)
From the results of the sensitivity analysis presented in Table 32, the following points are evident.

Where loads are mixed and drying energy is based on the slowest drying item of clothing (ie
natural fibres), the carbon footprint for each fibre type increases from the baseline.

Where it is assumed that delicates are not tumble dried, there is a large reduction in carbon
footprint relative to the baseline for fibre types considered delicates.

The overall carbon footprint of UK clothing is increased from the baseline by virtue of the
increase in mixed load tumble drying.

The increase in the carbon footprint of UK clothing is less apparent where delicates are not
tumble dried, due to the reduction in impact for these fibre types.
4.6.3 Influence of Fibre Type on Washing (Sensitivity 3)
In addition to examining the effect of fibre type on drying, the SCAP in-use group is interested in the
effect of fibre type on washing. Physically, there are differences in the fibre properties (eg water and
detergent retention) that mean there may also be a rationale for differentiation in the allocation of
washing impacts in mixed loads, but this is even less certain than for drying. However, for some types
of clothing, such as white cotton, it is certainly traditional to wash these separately from other clothes at
a higher temperature.
56
In the current version of the carbon footprint model, data on washing method (eg temperature of wash)
does not consider any differences between fibre types, which can be seen as a limitation. Therefore,
this sensitivity analysis assesses how significant differentiation in washing method between fibre type
affects results, using the shift of market from cotton to poly-cotton as an example. It is anticipated that
the reduction achieved from a shift to poly-cotton will be greater when clothing is considered to be
separated for washing. This is because, in addition to lower manufacturing impacts associated with
poly-cotton in comparison to cotton, the washing energy required for synthetics is also likely to be
lower.
In a report in 2006 by the University of Cambridge entitled ‘Well Dressed?’, a typical washing
temperature for a cotton t-shirt in the UK was given as 60oC, whereas the typical washing temperature
for a viscose blouse was given as 40oC. In addition, the report also assumed the cotton t-shirt would be
machine dried and ironed whereas neither of these activities were considered necessary for the viscose
blouse. Based on information from the ‘Well Dressed?’ report, the sensitivity analysis considered in this
report assumes the temperature of a cotton only wash to be 60oC and that of a poly-cotton only wash to
be 45oC (ie the average European temperature of 46oC used in the current model for all clothing).
The following scenarios are considered for this sensitivity analysis.

Central scenario – a shift in market where 10% of cotton is replaced with 50:50 poly-cotton
mix, where clothing is not separated for washing and washed at an average temperature of
46oC.

Central scenario sensitivity - a shift in market where 10% of cotton is replaced with 50:50 polycotton mix, where clothing is separated for washing and washed at a temperature appropriate
for that fibre type (ie 60oC for cotton and 46oC for poly-cotton).

‘What If?’ scenario – a shift in market where 40% of cotton is replaced with 50:50 poly-cotton
mix, where clothing is not separated for washing and washed at an average temperature of
46oC.

‘What If?’ scenario sensitivity - a shift in market where 40% of cotton is replaced with 50:50
poly-cotton mix, where clothing is separated for washing and washed at a temperature
appropriate for that fibre type (ie 60oC for cotton and 46oC for poly-cotton).
Table 33 below displays the reduction from baseline achieved by a shift in the market towards
synthetics where, firstly, clothing is not separated for washing and washed at an average temperature
of 46oC and, secondly, where clothing is separated for washing and washed at a temperature
appropriate for that fibre type. The shift towards synthetics is illustrated with two scenarios: the Central
scenario of replacing 10% of cotton with 50:50 poly-cotton; and the ‘What If?’ scenario of replacing
40% of cotton with 50:50 poly-cotton.
57
Reduction % – where
clothing is not separated for
washing and washed at an
average temperature of 46oC
Reduction % – where
clothing is separated for
washing and washed at a
temperature appropriate for
that fibre type
Central scenario - replace 10% of
cotton with 50:50 poly-cotton
-0.4%
-0.7%
‘What If?’ scenario - replace 40% of
cotton with 50:50 poly-cotton
-1.7%
-2.8%
Reduction measure
Table 33: Sensitivity analysis to assess to test the influence of fibre type on washing impacts
From the results of the sensitivity analysis presented Table 33, the following points are evident.

The reduction achieved from the shift towards synthetics in both the central and ‘What If?’
scenarios is over a third larger when the difference in washing temperature is also considered.
If the difference in washing temperature at which different fibre types are washed was considered in this
study, the carbon footprint results represented per fibre type and per garment type would be different.
However, the total carbon footprint results for UK clothing as a whole would remain the same, as the
weighted average temperature used considers all washing behaviour (including separating clothing
based on fibre type to wash at a higher or lower temperature).
4.6.4 Longer Product Lifetimes (Sensitivity 4a and 4b)
Of the reduction measures modelled in central and ‘What If?’ scenarios, one identified as warranting
further sensitivity analysis is the measure to extend the lifetime of clothing. This measure could be
brought about by a technological change (ie improving the durability of clothing through redesign)
and/or (solely) through behavioural change (ie consumers choosing to use their clothing for longer).
With the former approach, there may be an increased burden associated with manufacturing that is
necessary to bring about the extension in lifetime (eg heavier material, coating, dye etc).
Therefore, the sensitivity of the results to the requirement of an increase in manufacturing burdens to
extend the lifetime of clothing warranted testing through a sensitivity analysis. This sensitivity
compares the benefits of extending clothing lifetime where extra manufacturing is required with the
benefits of extending clothing lifetime where no extra manufacturing is required (ie current approach).
The following scenarios are considered:

Central scenario – a 10% longer lifetime of all clothing, where no increase in manufacturing
burdens is necessary;

Central scenario sensitivity - a 10% longer lifetime of all clothing, brought about by a 10%
increase in manufacturing burdens;

‘What If?’ scenario - a 33% longer lifetime of all clothing, where no increase in manufacture
burdens is necessary; and

‘What If?’ scenario sensitivity - a 33% longer lifetime of all clothing, brought about by a 10%
increase in manufacturing burdens.
In addition, there is also considerable uncertainty over the lifetime of clothing, which warrants further
investigation through a sensitivity analysis. In the current carbon footprint model, an ‘average’ weighted
lifetime of clothing in the UK of 2.177 years was used, which was based on data from the URS (2011)
report. However, it is well known that the variability between the lifetimes of individuals’ clothing is
large and therefore it may be difficult to capture this in an ‘average’ value. For example, an unpublished
Defra report undertaken by ERM presents results of a survey asking participants for how long they would
normally expect to use certain products. For jumpers, answers from one year to 17 years were given,
with the median result being two years.
58
The sensitivity of results to the choice of using 2.177 years as the weighted average lifetime of clothing
was considered in this sensitivity analysis. This was achieved by comparing the results where a
weighted average of 2.177 years was used to results where other data on clothing lifetime is used in its
place. Table 34 below provides the data on garment lifetime used to base weighted average lifetimes of
clothing in this sensitivity analysis.
Lifetime (Years)
Garment Type
URS (2011)
Defra / ERM
(2011)
Biointelligence
(2009)
Tops
2
1.5
3
Underwear, nightwear and hosiery
2
No data
2
Bottoms
2
2
2
Jackets
3
2
3
Dresses
3
No data
No data
Suits and ensembles
3
3
No data
Gloves
2
No data
2
Sportswear
3
No data
No data
Swimwear
3
No data
No data
Scarves, shawls, ties etc
3
No data
No data
2.177
1.770
2.493
Weighted average lifetime (years)
Table 34: Data on clothing lifetimes used in sensitivity analysis
The lifetime of clothing can be extended through either consumers retaining clothing for longer or
manufacturers increasing durability. In the latter scenario, it could be argued that an extra
manufacturing burden is required to allow clothing to be made more durable. However, in the current
carbon footprint model, it was assumed that no extra manufacturing burden would be required to extend
product lifetime. This sensitivity analysis tests the impact of this assumption.
Table 35 displays results of this sensitivity analysis; comparing the reduction from baseline achieved by
extending the lifetime of clothing where a 10% extra manufacturing burden is required in comparison to
where extra manufacturing burden is not required.
Reduction measure
Central scenario – a 10% longer
lifetime of all clothing
‘What If?’ scenario - a 33% longer
lifetime of all clothing
Reduction % – excluding
extra manufacturing burden
Reduction % – including
extra manufacturing burden
-7.7%
-1.0%
-27.3%
-22.4%
Table 35: Sensitivity analysis to access the impact of considering extra manufacturing burden required to
extend the lifetime of clothing
From the results of the sensitivity analysis presented Table 35, the following points are evident.

For both the central and ‘What if?’ scenarios, the reduction achieved from extending the
lifetime of clothing is less when a 10% extra manufacturing burden is required to achieve this
extension.

The decrease in reduction achieved is greater for the Central scenario than for the ‘What If?’
scenario. This is due to the fact the increase in manufacturing impact remains the same for
each scenario but the savings from extended lifetime are far greater for the ‘What If?’ scenario.
In addition to assessing the sensitivity to results of considering extra manufacturing in extending the
lifetime of clothing, another sensitivity analysis related to product lifetimes was carried out. This
considers the choice of using 2.177 years as the weighted average lifetime of clothing. Data from two
other sources was used to calculate two alternative weighted average lifetimes of clothing, as described
59
above. These lifetimes were applied to the carbon footprint model and results are presented in Table 36
below.
Carbon Footprint (t CO2e)
Lifetime of 2.177
years
Lifetime of 1.770 years
Lifetime of 2.493
years
Fibre production
5,850,917
7,196,298
5,109,285
Yarn production
9,618,774
11,830,548
8,399,547
12,517,884
15,396,290
10,931,181
748,029
920,033
653,213
1,846,682
2,271,314
1,612,606
528,120
649,557
461,178
Use - washing
5,765,441
5,765,441
5,765,441
Use - drying
3,809,464
3,809,464
3,809,464
Use - ironing
243,098
243,098
243,098
-2,753,116
-3,386,178
-2,404,145
38,175,293
44,695,867
34,580,867
Fibre Type
Fabric production
Garment production
Distribution
Retail
End of life
TOTAL
Table 36: Sensitivity analysis to access the impact on results of the clothing lifetime data choice
From the results of the sensitivity analysis presented Table 35, the following points are evident:

The choice of clothing lifetime data greatly influences results;

The longer the weighted average lifetime of clothing that is used, the lower the carbon
footprint;

The shorter the weighted average lifetime of clothing that is used, the higher the carbon
footprint;

Impacts from production, retail and end of life are allocated on a per annum basis and
therefore, the longer the clothing lifetimes the smaller the carbon footprint; and

Use phase impacts per annum remain the same regardless of the lifetime of clothing.
Table 37 below shows the carbon saving from the baseline generated by each reduction measure of the
Central scenario using a clothing lifetime of 2.177, 1.770 and 2.493 years. This table shows the
sensitivity of results of the central reduction central to the lifetime of clothing.
60
Lifetime of 2.177
years
Lifetime of 1.770
years
Lifetime of 2.493
years
%
Reduction
Rank
%
Reduction
Rank
%
Reduction
Rank
Eco efficiency across supply
chain (production, distribution
and retail) - Central scenario 5% reduction for all fibres
across supply chain
-4.1%
2
-4.3%
2
-3.9%
2
Design for Durability (and
product lifetime optimisation) Central scenario - 10% longer
lifetime of clothing
-7.7%
1
-8.0%
1
-7.5%
1
Shift in market to higher
proportion of synthetic fibres Central scenario - replace 10%
of cotton with 50:50 polycotton
-0.4%
10
-0.5%
10
-0.4%
10
Clean clothing less - Central
scenario - washes per year
reduced by 10%
-2.6%
3
-2.2%
3
-2.9%
3
Wash at lower temperature Central scenario - weighted
average wash temperature of
39.3C
-1.4%
4
-1.2%
4
-1.6%
4
Increase size of washing and
drying loads - Central scenario
- load increases to 3.7kg
-1.4%
5
-1.2%
5
-1.5%
5
Use the tumble dryer less Central scenario - 30%
reduction in tumble dryer use
in summer
-1.1%
6
-1.0%
6
-1.2%
6
Dispose less - reuse more Central scenario – 15.4% of
clothing ultimately reused in
the UK
-0.7%
8
-0.7%
8
-0.7%
8
Start closed loop recycling of
synthetic fibres - Central
scenario - 5% of all clothing is
recycled (closed loop)
-0.9%
7
-1.0%
7
-0.9%
7
Dispose less - recycle more
(open loop) - Central scenario
- 38% of all clothing is
recycled open loop
-0.5%
9
-0.5%
9
-0.5%
9
Cumulative reduction
-20.9%
-20.7%
-21.1%
Table 37: Sensitivity analysis to access the impact on the central reduction scenario of the clothing
lifetime data choice
From the results of the sensitivity analysis presented in Table 37, the following points are evident:

Although the absolute carbon reduction varies greatly when a different lifetime of clothing is
assumed, the percentage reduction for each reduction measure does not change greatly;

As a result, the order of effectiveness of reduction measures doesn’t change;

For use phase reduction measures, the longer the assumed lifetime of clothing, the greater the
reduction, which is due to use phase impacts remaining static relative to other life cycle stages
when clothing lifetime is changed; and
61

For non-use phase reduction measures, the longer the assumed lifetime of clothing, the smaller
the reduction, which is due to impacts of these life cycle stages changing relative to the use
phase when clothing lifetime is changed.
4.6.5 Washing Frequency (Sensitivity 5)
The data used for the number of times clothing is washed per year are uncertain. There is variability in
washing frequency between households, different garment types and even within the same garment
group (ie occasional wear versus ‘everyday’ wear). Therefore, it is difficult to represent the typical
washing frequency of all clothing in the UK.
Currently, the number of washes per year is derived from a Defra study (2009). This study provides a
value 274 washes per household per year, which was extracted from a report by the Market
Transformation Programme (2006). The original source of this data point is from the research carried
out by the Oxford Environmental Change Institute (published in Lower Carbon Futures for European
Households, 2000).
As the carbon footprint is based on the mass of UK clothing, it was necessary to normalise washing
frequency to a metric of ‘number of washes per kilogram of clothing’. This was achieved by multiplying
the number of washes per household with the number of UK households (26,300,000) and the average
washing load size (3.43 kg), which provides the mass of clothing washed in the UK. This value was
subsequently divided by the mass of clothing in use in the UK (2.49 million tonnes), to provide a value
of 9.9 washes per kilogram of clothing per year.
In order to test the sensitivity of results to the washing frequency upper and lower values of 5 and 15
washes per kilogram of clothing per year were entered into the carbon footprint tool.
Where it is assumed clothing is washed 5 times per kilogram per year, the total carbon footprint is 13%
less than that of the current carbon footprint, where it is assumed clothing is washed 9.9 times. As a
consequence, the carbon reductions achieved through use phase improvement actions are less and
those of non-use phase improvement action are greater.
Where it is assumed clothing is washed 15 times per kilogram per year, the total carbon footprint is 13%
greater than that of the current carbon footprint. The carbon reductions achieved through use phase
improvement actions in this case are greater and those of non-use phase improvement action are less.
4.6.6 UK Fibre Mix (Sensitivity 6)
The UK fibre mix modelled may not be representative of the UK clothing market. The original source of
the fibre mix data is Biointelligence 2009, which reflects a European rather than UK specific fibre mix.
This data was first used in URS’ 2011 water footprint study and subsequently used in this study for
consistency.
In order to test the sensitivity of results to fibre mix data, the results for the baseline results and ‘What
if?’ scenario results extracted from the carbon footprint tool where a different mix was entered. This
fibre mix data is from a Carbon Trust (2011) report entitled ‘International Carbon Flows’, which is shown
in Table 38 below alongside Biointelligence data for comparison.
62
European Fibre Mix
(Biointelligence,
2009)
UK Specific Fibre Mix
(Carbon Trust)
Cotton
43%
32%
Wool
9%
2%
Silk
1%
2%
Flax / linen
2%
6%
Viscose
9%
4%
Polyester
16%
45%
Acrylic
9%
4%
Polyamide
8%
5%
Polyurethane / polypropylene
3%
0%
Fibre Type
Table 38: European fibre mix data used in this study in comparison to UK specific fibre mix data used for
sensitivity 6
The baseline carbon footprint total with the Carbon Trust fibre mix data is 12% less than the baseline
total where Biointelligence fibre mix data is used. For the ‘What if?’ scenario, the reduction achieved
where Carbon Trust fibre mix data is used is 11% less than the reduction achieved where Biointelligence
fibre mix data is used. Although the absolute reduction values change, the order of improvement
actions does not change with the new fibre mix.
4.7
Conclusions of Sensitivity Analyses
4.7.1 Decarbonisation of Grid Electricity (Sensitivity 1)
Figure 17 summarises the life cycle carbon footprint of UK clothing where an electricity emission factor
of 70 g CO2e per kWh is used for the use phase, in comparison to that where 610 g CO2e per kWh is
used.
Figure 17: Graph to compare the carbon footprint of UK clothing where an electricity emission factor of
70g CO2e per kWh is used for the use phase, in comparison to that where 610g CO2e per kWh is used
With the reduced carbon intensity of grid electricity applied to the use phase, the total footprint for UK
clothing is reduced by 17%. The result of this anticipated decarbonisation of grid electricity is that the
significance of the use phase will decrease relative to upstream life cycle stages (ie such as
63
manufacture) and also at end of life, further increasing the importance of production stages. It should
be noted here that only use phase grid electricity decarbonisation was modelled in this sensitivity
analysis. However, as the majority of clothing is manufactured outside of the UK, it is anticipated that
modelling decarbonisation in all life cycle stages would not alter this conclusion significantly.
The reduction achieved through the following measures is increased by the shift to a decarbonised grid:
eco-efficiency across the supply chain, open and closed recycling, reuse more, shift towards synthetic
fibres and design for durability. The reduction achieved through the following measures is decreased by
the shift to a decarbonised grid: cleaning clothing less; using the tumble dryer less; increased load sizes;
and lower washing temperature. Therefore, in a decarbonised UK, non-use phase reduction measures
become more important relative use phase reduction measure.
Sensitivity analysis 1 indicates that, with the decarbonisation of grid electricity, the use phase becomes
a smaller proportion of the total life cycle carbon footprint of UK clothing. Therefore, when considering
reduction initiatives, some non-use phase measures increase in importance when grid electricity is
decarbonised. As it is likely that UK grid electricity will be decarbonised in the future, both use phase
and non-use phase reduction measures should be considered.
4.7.2 Influence of Fibre Type on Drying (Sensitivity 2a and 2b)
Figure 18 below summarises the life cycle carbon footprint of UK clothing where different assumptions
are made on the allocation of drying energy according to fibre type.
Figure 18: Graph to compare the carbon footprint of UK clothing where different assumptions are made
on the allocation of drying energy according to fibre type
Where drying energy is allocated according to the relative drying time of fibre types, the carbon
footprint increases from the baseline for natural fibres (by ~2-5%) and decreases for synthetic fibres
(by ~3-5%), but the total remains the same. The shift of drying energy burden towards natural fibres
reflects the fact they typically take longer to dry than synthetics.
Where loads are mixed and drying energy is based on the slowest drying item of clothing (ie natural
fibres), the carbon footprint for each fibre type increases from the baseline. This reflects an increase in
the drying time of all fibre types caused by a natural fibre type being present in each load.
Sensitivity analysis 2 indicates that the choice of allocation approach in the drying stage affects the
carbon footprint of each fibre type but has little effect on the overall carbon footprint of UK clothing.
With regard to carbon reduction, any measure to encourage the use of synthetic fibres will result in a
reduction in the carbon footprint from drying, provided clothing is separated according to fibre type.
64
4.7.3 Influence of Fibre Type on Washing (Sensitivity 3)
Sensitivity analysis 3 shows that the reduction achieved from the shift towards synthetics in both the
central and ‘What If?’ scenarios is around a third larger when the difference in washing temperature is
also considered.
Based on this sensitivity analysis, more weight can be put on the benefits of shifting consumer
purchasing behaviour away from natural fibres and towards synthetic fibres. Combined with the
reduced impact in production and in drying, this sensitivity indicates that a shift towards synthetics is an
effective reduction measure. However, it should be noted that this study only considers the carbon
footprint of clothing. If other impact categories were considered, a recommendation to use synthetic
fibre may not still be valid. For instance, synthetic fibres are derived from finite hydrocarbon resources
and therefore encouraging their use would further increase resource depletion.
4.7.4 Longer Product Lifetimes (Sensitivity 4a and 4b)
Sensitivity 4a shows that for both the Central and ‘What if?’ scenarios, the carbon reduction achieved
from extending the lifetime of clothing is not as great when a 10% extra manufacturing burden is
required to achieve this extension. The decrease in reduction achieved is greater for the Central
scenario than for the ‘What If?’ scenario. This is due to the fact the increase in manufacturing impact
remains the same for each scenario but the savings from extended lifetime are greater for the ‘What If?’
scenario.
Therefore, when considering the carbon savings achieved through extending the lifetime of clothing by
increasing its durability, it is important to consider if extra manufacturing burdens are required. In
addition, this sensitivity also shows that rather than extending clothing lifetimes through better design it
may be more effective to encourage consumers to keep clothing for longer.
Figure 19 below summarises the life cycle carbon footprint of UK clothing where different assumptions
are made on the lifetime of clothing, which is an output of sensitivity 4b.
Figure 19 Graph to compare the life cycle carbon footprint of UK clothing where different assumptions
are made on the lifetime of clothing.
Sensitivity 4b shows that the longer the weighted average lifetime of clothing that is used, the lower the
carbon footprint and the shorter the weighted average lifetime of clothing that is used, the higher the
carbon footprint. In addition, impacts from production, retail and end of life are allocated on a per
annum basis and therefore, the longer the clothing lifetime, the smaller the carbon footprint, and use
phase impacts remain the same regardless of the lifetime of clothing. In terms of the effectiveness of a
65
reduction measure to increase clothing lifetime, in both central and ‘What If?’ scenarios this measure is
ranked the highest when either 1.770, 2.177 or 2.493 years are used.
From sensitivity 4b, it can be seen that the choice of data on clothing lifetimes is particularly important
as it influences the results greatly. Due to this importance, and to the uncertainty of the data available,
our recommendation to undertake primary research on clothing lifetimes (including sales and clothing
stock) is reinforced. In addition, sensitivity 4b indicates that consumers should be encouraged to retain
clothing for longer and the ‘disposable fashion’ end of the clothing market should be discouraged. This
initiative to retain clothing longer should be coupled with an initiative of encouraging reuse.
4.7.5 Washing Frequency (Sensitivity 5)
The results of sensitivity analysis 5 show that, where it is assumed clothing is washed 5 times per
kilogram per year, the carbon footprint is 13% less than that of the current carbon footprint. Where it is
assumed clothing is washed 15 times per kilogram per year, the carbon footprint is 13% greater than
that of the current carbon footprint. The change in carbon footprint is apparent only in the use phase,
when the burden is increased or decreased, respectively, with a decrease or increase in wash frequency.
The higher assumed washing frequency, the greater the carbon reduction achieved through use phase
improvement actions and the lesser the carbon reduction achieved through non-use phase improvement
actions. This is due to the increase in washes, increasing use phase burden so that it represents a larger
proportion of the total carbon footprint, relative to non-use phase life cycle stages.
4.7.6 UK Fibre Mix (Sensitivity 6)
Sensitivity analysis 6 shows that carbon footprint results for both baseline and improved scenarios are
affected by a change in fibre mix data. The carbon footprint total with the Carbon Trust fibre mix data is
12% less than the baseline total where Biointelligence fibre mix data is used. For the ‘What if?’ scenario,
the reduction achieved where Carbon Trust fibre mix data is used is 11% less than the reduction
achieved where Biointelligence fibre mix data is used. This result is to be expected as the carbon
footprint of each fibre type is different.
From sensitivity 6, it can be seen that the choice of fibre mix data is significant, as the results are
influenced greatly. However, as the order of improvement actions changes very little, the use of the
carbon footprint tool to compare improvement actions is unaffected by the use of this different fibre mix
data.
5.0
Conclusions
This section summarises the overall conclusions of the core study and provides suggestions for further
research.
5.1
Summary of this Study
This study provided a strategic-level carbon footprint of UK clothing over the entire life cycle using
secondary data available in the literature. A number of example reduction measures were considered
and potential savings in relation to a baseline were quantified for both a central reduction scenario and a
‘What If?’ reduction scenario. In addition, an Excel model was developed that allows the modeller to
examine new scenarios, where values for each reduction measure can be changed.
Carbon footprint results were represented in terms of the following functional unit:
The entire life cycle of all garments, both new and existing, in use in the UK in 2009.
The results provided in the study relate to the annual impacts associated with UK clothing. They include
the impacts associated with the quantity of clothes that are produced for the UK and consumed and
66
disposed of each year, but they also include the impacts associated with clothing that is actively worn
and cleaned each year (approximately 1.1 million tonnes of new clothing is consumed in the UK each
year, ~2.5 million tonnes is in active use - note that this is greater than the annual consumed clothing
because clothes last for more than one year).
As alternative expressions of this functional unit, carbon footprint results were also presented in terms of
one tonne of garments in use in the UK in 2009; garments used by one UK resident in 2009; and one
garment used in the UK in 2009.
5.2
5.3
Summary of Baseline Results

The total annual carbon footprint of all garments, both new and existing, in use in the UK in
2009 (ie the volume consumed, and the actively worn quantity) is approximately 38 million
tonnes of CO2e (~0.6 tonnes per person per year). Because the majority of clothing is
manufactured outside the UK, it is estimated that ~32% occurs within the UK (contributing to
the UK’s direct carbon footprint) and 68% occurs abroad. Based on this estimate, the direct
impact of clothing in the UK can be estimated to be ~12 million tonnes of CO2e. Note that this
baseline analysis does not examine the effect of uncertainties, which are considered further in
the sensitivity analysis section of the report (Section 4.6).

To put this carbon footprint of UK clothing into context, the total direct GHG emissions in the
UK in 2009 were reported as 566 million tonnes of CO2e (DECC, 2011). It should be noted that
this total for the UK does not include GHG emissions associated with imported goods or
services or international travel. Therefore, the direct carbon footprint of clothing contributes
approximately 2% to the UK’s total direct carbon footprint.

The carbon footprint of new garments ONLY, in use in the UK in 2009, can also be calculated
by dividing the carbon footprint of both new and existing clothing by its anticipated lifetime.
This figure is approximately 17 million tonnes of CO2e.

The most dominant life cycle stage is fabric production (comprising weaving/knitting etc. and
treatment of fabric), representing 33% of total life cycle GHG impacts.

The carbon footprint of a tonne of garments, both new and existing, in use in the UK in 2009
ranges from around 15 to 46 tonnes CO2e, depending on the fibre type of the garment.

The carbon footprint of each garment, both new and existing, in use in the UK in 2009 ranges
from around 1 to 17 kg CO2e.

The per person carbon footprint of all garments, both new and existing, in use in the UK in
2009 is around 0.6 tonnes of CO2e.
Summary of Reduction Scenarios
For the reduction measures examined in the Central scenario, the combined effect of the ten reduction
across the entire life cycle) is estimated to be 21%. In the aspirational ‘What If?’ scenario this is
increased - it is estimated a carbon reduction of 71% could be potentially achieved. However, it should
be noted that the study does not examine the practicability of implementing each option, nor assess
other non-carbon sustainability impacts for these options. It should also be noted that these reductions
do not include the potential decarbonisation of energy (electricity) production, which will also reduce the
carbon footprint of clothing in future.
Table 39 and Table 40 below rank reduction measures in order of effectiveness, for both the central and
‘What If?’ scenarios, respectively.
67
Rank
1
2
3
4
5
6
7
8
9
10
Reduction Measure
Stakeholder
Design for Durability (and Product lifetime optimisation) - central scenario - 10%
longer lifetime of clothing
Eco efficiency across supply chain (production, distribution and retail) - central
scenario - 5% reduction for all fibres across supply chain
Manufacturer/
consumer
Clean clothing less - central scenario - Washes per year reduced by 10%
Wash at lower temperature - central scenario - weighted average wash temperature
of 39.3C
Consumer
Increase size of washing and drying loads - central scenario - load increases to 3.7kg
Use the tumble dryer less - central scenario - 30% reduction in tumble dryer use in
summer
Start closed loop recycling of synthetic fibres - central scenario - 5% of all clothing is
recycled (closed loop)
Consumer
Dispose less - reuse more - central scenario - 15.4% of clothing reused in the UK
Dispose less - recycle more (open loop) - central scenario - 19.5% of all clothing is
recycled open loop
Shift in market to higher proportion of synthetic fibres - central scenario - Replace
10% of cotton with 50:50 poly-cotton
Consumer
Manufacturer
Consumer
Consumer
Consumer
Consumer
Manufacturer/
consumer
Table 39: Reduction measures of the Central scenario in order of effectiveness
As can be seen from Table 39, the most effective reduction measures of the Central scenario considered
are design for durability, eco-efficiency across the supply chain and cleaning clothing less frequently.
Rank
Reduction Measure
Stakeholder
1
Design for Durability (and Product lifetime optimisation) - 'what if?' - 33% longer
lifetime of clothing
Manufacturer/
Consumer
2
Eco efficiency across supply chain (production, distribution and retail) - 'what if?' 30% reduction for all fibres across supply chain
Manufacturer
3
Clean clothing less - 'what if?' - Washes per year reduced by 15%
Use the tumble dryer less - 'what if?' - 50% reduction in tumble dryer use in summer,
15% reduction in winter
Wash at lower temperature - 'what if?' - weighted average wash temperature of
32.9C
Consumer
Consumer
8
Increase size of washing and drying loads - 'what if?' - load increases to 4kg
Dispose less - reuse more - 'what if?' - 18% of clothing reused in the UK
Start closed loop recycling of synthetic fibres - 'what if?' - 10% of all clothing is
recycled (closed loop)
9
Shift in market to higher proportion of synthetic fibres - 'what if?' - Replace 40% of
cotton with 50:50 poly-cotton
Manufacturer/
Consumer
10
Dispose less - recycle more (open loop) - 'what if?' - 24.5% of all clothing is recycled
open loop
Consumer
4
5
6
7
Consumer
Consumer
Consumer
Consumer
Table 40: Reduction measures of the ‘What If?’ scenario in order of effectiveness
As is apparent from Table 40, the most effective reduction measures considered in the ‘What If?’
scenario are design for durability, eco-efficiencies across the supply chain and cleaning clothing less.
5.4
Further In Use Analysis Findings
Of the 10 potential in-use behavioural change interventions examined, the hybrid intervention, in which
a shift in consumer behaviour towards better clothing care also indirectly increased the lifetime of
clothing, showed the greatest benefit. The intervention reduced both the in-use and production impacts
and, as such, represents a potential ‘win:win’ reduction measure.
68
5.5
Findings from Sensitivity Analyses
Sensitivity analyses were undertaken to investigate key uncertainties of the study. These examined the
sensitivity of results and conclusions to a change in a particular assumption or data point. The
sensitivity analyses undertaken were: the influence of a future decarbonised electricity grid on the
impact of the use phase; the influence of fibre type on washing and drying impacts; and the influence of
product lifetime on results. The results of these sensitivity analyses indicate the following.
5.6

Future ‘decarbonisation’ of UK electricity will decrease the direct carbon footprint associated
with the cleaning of clothing. The significance of the use phase (primarily washing and drying)
impacts, relative to upstream life cycle stages (raw materials, manufacture and distribution,
retail) and end of life impacts will reduce.

Where the energy use impacts of tumble drying are allocated to clothing based on the relative
drying time of fibre types (rather than by its mass only as they are in the main analysis), the
carbon footprint increases from the baseline for natural fibres (by ~2-5%) and decreases for
synthetic fibres (by ~3-5%). The total remains the same.

Where loads are mixed and drying energy is based on the slowest drying item of clothing (ie
natural fibres), the carbon footprint for each fibre type increases from the baseline. This
reflects an increase in the drying time of all fibre types caused by a natural fibre type being
present in each load. This is in comparison to a baseline average energy usage where some
loads are mixed and some are separated.

When the difference in washing temperature is also considered, the reduction achieved from
the shift towards synthetics in both the central and ‘What If?’ scenarios is around a third larger.

The longer the lifetime of clothing (eg from clothing simply being retained in use by the
consumer for longer, design for durability, reuse, or from leasing or resale), the lower the
carbon footprint (reduced supply chain impacts, primarily) and the shorter the lifetime of
clothing that is used, the higher the carbon footprint.

Where it is assumed in the analysis clothing is washed 5 times per kilogram per year, the total
carbon footprint is 13% less than that of the main analysis (where it is assumed clothing is
washed 9.9 times). The carbon reductions achieved through use phase improvement actions
are less and those of non-use phase improvement action are greater. Where it is assumed
clothing is washed 15 times per kilogram per year, the total carbon footprint is 13% greater
than that of the main analysis carbon footprint. The carbon reductions achieved through use
phase improvement actions are greater and those of non-use phase improvement action are
less.

The baseline carbon footprint total with the alternative Carbon Trust fibre mix data is 12% less
than the baseline total where Biointelligence fibre mix data is used. For the ‘What if?’ scenario,
the reduction achieved where Carbon Trust fibre mix data is used is 11% less than the
reduction achieved where Biointelligence fibre mix data is used. Although absolute reduction
values change, the order of improvement actions changes less with the new fibre mix, with the
top three and bottom three improvement actions remaining the same with both fibre data.
Concluding Remarks
Overall, the analysis confirms the rationale for encouraging reduction measures at each and every stage
of the life cycle, including nudging consumer behaviour towards favourable outcomes. If UK electricity
is decarbonised, the sensitivity analysis undertaken indicates sustainable production and consumption
measures aimed at reducing the production impacts of clothing will increase further in importance over
time, relative to use phase interventions. The study provides initial analysis into the potential indirect
effects on the washing and drying footprint if the market is shifted towards one type of fibre over
69
another. The findings from the study sensitivity analysis indicate that, amongst other factors, the fibre
mix of UK clothing affects the magnitude of the footprint and the overall savings achievable, but has
less influence on the order of the reduction measures.
5.7
Suggested Next Steps
This study is a strategic level assessment of UK clothing and, as such, there are a number of
opportunities for improvement. Below is a list of suggestions.

This report is transparent with respect to the data sources used and assumptions taken to
calculate the carbon footprint. These data and assumptions would benefit from further review.
Therefore, it is suggested that interested stakeholders (eg consumer groups, detergent
manufacturers and washing machine manufacturers) be provided with a copy of the report in
order to validate the information and to provide new data if necessary, with the overall
aspiration of developing consensus around the data.

It is also noted that there is potential for improving the data quality of the study through the
collection of primary data from a representative number of manufacturers. In particular, a
more detailed assessment of the difference between the production of fibre, yarn, fabric and
garments from the most significant countries producing clothing for the UK would be desirable
to improve the representativeness of data used for production. This is particularly relevant to
the natural fibres, such as cotton and wool where agricultural inputs and outputs are likely to
vary significantly between countries.

There is currently an evidence gap with respect to the influence of the physical properties of
different fabrics on relative cleaning impacts (washing, drying and ironing). The use phase
contributes a significant proportion of life cycle impacts and therefore it is a sensible area to
target carbon footprint reduction measures. However, in this study, through allocating use
phase impacts on a mixed load, mass basis, each fibre type is given the same impact in the
main analysis. Despite this issue being examined in this study as a sensitivity analysis, more
primary research could be undertaken by manufacturers to gain a better understanding of
consumer behaviour, including the impact of modern low-temperature detergents and fabric
treatments on the size of use phase impacts.

It should also be noted that some preliminary research was carried out within this study on the
novel fibre polylactic acid (PLA). Reasonable quality data were gathered to model the
manufacture of clothing made from this material. However, as PLA fabric is currently produced
in very small quantities and the market share in the future in uncertain, it was not included in
this report. It would be valuable to undertake research into the market potential for innovative
fibres (PLA and others) and subsequently to evaluate their impacts in the use phase since
these fibres are often reported as having characteristic physical properties.

Improvements could be made on data relating to garment sizes, weights and lifetimes with the
aspiration of providing example data for industry baseline reporting and monitoring carbon
reduction.

Consequential impacts such as rebound effects are an area of potential research interest and
analysis may be appropriate for sustainable clothing, and more generally, for the impact
assessment of sustainable consumption and production policies.

As a final point, of the most effective reduction measures in each reduction scenario, most
require behavioural change. Therefore, the development of consensus UK-specific data on the
consumer cleaning behaviour and product lifetimes for clothing would be extremely
advantageous. A UK-relevant Product Category Rule (PCR) covering both synthetic and natural
fibre clothing, and its use, could be developed by industry and stakeholders.
70
6.0
References
ADEME (2006), An Environmental Product Declaration of Jeans http://www.ademe.fr/internet/ecojean/EPD_en_jeans_v2.pdf
BSI (2008). PAS 2050:2008 - Specification for the assessment of the life cycle greenhouse gas
emissions of goods and services http://www.bsigroup.com/Standards-and-Publications/How-we-canhelp-you/Professional-Standards-Service/PAS-2050
Biointelligence service (2009, Unpublished) EC-funded IMPRO project on “Environmental improvement
potential of textiles” (final report 2009, unpublished)
Biswal et al. (2010) entitled, ‘Global warming contributions from wheat, sheep meat and wool production
in Victoria, Australia – a life cycle assessment’ (available from:
http://www.sciencedirect.com/science/article/pii/S0959652610001769
Business for Social Responsibility (2009), Apparel industry life cycle carbon mapping.
http://www.bsr.org/reports/BSR_Apparel_Supply_Chain_Carbon_Report.pdf
Brook Lyndhurst (2010) Public Understanding of Product Lifetimes and Durability A DRAFT
CONFIDENTIAL report for Defra. Draft 1.0. May 2010. http://randd.defra.gov.uk
Carbon Trust (2011), International carbon flows: clothing.
http://www.carbontrust.co.uk/publications/pages/publicationdetail.aspx?id=CTC793
Carbon Trust (2006) The carbon emissions generated in all that we consume, London: Carbon Trust.
http://www.carbontrust.co.uk/Pages/Default.aspx
Climate Change Committee (2008) Building a low carbon economy: The UK’s contribution to tackling
climate change http://www.theccc.org.uk/pdf/7980-TSO%20Book%20Chap%205.pdf
Dahllhof (2003), Life Cycle Assessment Applied in the Textile Sector: the Usefulness, Limitations and
Methodological Problems – A Literature Review.
https://www.chalmers.se/ee/SV/forskning/forskargrupper/miljosystemanalys/publikationer/esarapportserie-1979/esa2005/downloadFile/attachedFile_4_f0/ESA20039.pdff
Danish Ministry for the Environment (2007) EDIPTEX - LCA of Textiles.
http://www2.mst.dk/Udgiv/publications/2007/978-87-7052-515-2/pdf/978-87-7052-516-9.pdf
DECC (2011) 2009 Final Figures.
http://www.decc.gov.uk/en/content/cms/statistics/climate_stats/gg_emissions/uk_emissions/2009_final/
2009_final.aspx
Defra-ERM (2007) Mapping of evidence of sustainable development impacts that occur in life cycles of
clothing”, available at
http://www.defra.gov.uk/environment/business/products/roadmaps/clothing/evidence.htm
Defra (2009), Reducing the environmental impacts of clothes cleaning.
http://randd.defra.gov.uk/Document.aspx?Document=EV0419_8628_FRP.pdf
Defra (2010) Sustainable Clothing Action Plan (update Feb 2010)
http://www.defra.gov.uk/environment/business/products/roadmaps/clothing/index.htm
Defra (2011) Sustainable Clothing Roadmap: Progress Report 2011.
http://www.defra.gov.uk/publications/2011/06/02/pb13461-clothing-roadmap/
Defra-ERM (2011) (Unpublished) Product lifetimes study
Ecoinvent, 2010. Swiss Centre for Life Cycle Assessment Ecoinvent database. http://www.ecoinvent.org
71
ENEA (2003) LCA of cotton polyester fabric in B05 company. http://www.enea.it/it
Environment Agency (2008) Life cycle assessment of disposable and reusable nappies in the UK.
http://www.environment-agency.gov.uk
Environment Agency (2011) Life Cycle Assessment of Supermarket Carrier Bags. Report: SC030148.
http://www.pdfio.com/k-357005.html
ERM (2006) Carbon Balances and Energy Impacts of the Management of UK Waste Streams - WR0602
Annex B (Textiles, Min quality - recycled as filler and max quality - recycled as cloth)
http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&Completed=0&Pro
jectID=14644
ERM (2011) Sustainable Clothing Procurement – Uniforms in the NHS. http://randd.defra.gov.uk
ERM (Unpublished) Longer Product Lifetimes Research. Scoping_LCA_T-shirt_Final draft for Defra.
http://randd.defra.gov.uk
ERM (2011) WRAP report on “Benefits of reuse”, to be published.
ERM (Unpublished). MTP Washing Machine calculator.
Farrant, L (2008) Environmental Benefits from reusing clothes
http://www.uffnorge.org/files/Laura%20Farrantenvironmental%20benefits%20from%20reusing%20clothes.pdf
FAO (2009) Annex I: Economics of silkworm rearing on one acre mulberry cultivation for seasons
2005-6; 2006-7; 2007-8 (all values in IN Rs).
http://www.fao.org/ag/againfo/programmes/en/pplpi/docarc/rep-0903_IndiaSericulture.pdf
FAO (2009) Technical Conversion Factors for Agricultural Commodities - agricultural co-products
guidance http://www.fao.org/fileadmin/templates/ess/documents/methodology/tcf.pdf
Fabric Care Research Associated (1988) Laundry Handbook http://www.scribd.com/doc/7843116/FabricCare-Research-Association-Laundry-Booklet
Hemp sys (2006). LCA of Hemp textile yarn.
www.rennes.inra.fr/umrsas/content/download/.../HEMPLCA_310506.pdf
ISO (2006). ISO 14044:2006 Environmental management -- Life cycle assessment -- Requirements and
guidelines. http://www.iso.org
Kazakevičiūtė at al (2004) Reducing Pollution in Wet Processing of Cotton/Polyester Fabrics,
Environmental research, engineering and management, 28 (2)
Kviseth K, Tobiasson T. S (2011) Pulling Wool over our Eyes: The Dirty Business of LCAs KEA
Conference Towards sustainability in Textiles and Fashion industry. Copenhagen 26th – 27th of April
2011. www.nicefashion.org
Manich et al. (2005) Application of the Thermal Analysis to the Determination of the Fine Structure of
Textile Fibre. http://centrum.tul.cz/centrum/itsapt/Summer2005/files/manich.pdf
Marks & Spencer plc (2002), Streamlined Life Cycle Assessment of Two Marks & Spencer plc Apparel
Products, February 2002.
http://aestivaltd.web.officelive.com/Documents/MandS%20LCA%20Final%20Report.pdf
Oakdene Hollins (2009) DEFRA Minimising Reuse and Recycling of UK Clothing and Textiles.
http://www.oakdenehollins.co.uk/pdf/defra_173_summary_issue_4.pdf
http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&ProjectID=16096
72
Outdoor clothing consortium. www.ecoindexbeta.org/
Oerlikon (2012) The Fibre Year 2009/10. A World Survey on Textile and NonWovens Industry. World
Man Made Fibre Volumes 2009
Tesco (Unpublished) Carbon Footprint of Jeans (2010). Conducted for Tesco by ERM
Shen and Patel (2010) LCA of Man-made cellulose fibres. http://igitur-archive.library.uu.nl/chem/20110401-200353/LB_88_2010_paper_1.pdf
Shen et al. (2010), Environmental impact assessment of man-made cellulose fibres, Resources,
conservation and recycling, 55
WRAP (Unpublished) Environmental Life Cycle Assessment (LCA) Study of Replacement and
Refurbishment options for domestic washing machines. Study conducted by ERM
http://www.wrap.org.uk
University of Cambridge Institute for Manufacturing (2006), Well dressed.
http://www.ifm.eng.cam.ac.uk/sustainability/projects/mass/uk_textiles.pdf
URS (2011, Unpublished) Review of Data on Embodied Water in Clothing and Opportunities for Savings.
Feedback Summary Report, Report for WRAP. June 2011
WRAP (Unpublished) The Clothes We Buy and Waste’ Preliminary draft. http://www.wrap.org.uk
WRAP (2009) Environmental Life Cycle Assessment (LCA) Study of Replacement and Refurbishment
options for domestic washing machines.
http://www.wrap.org.uk/recycling_industry/publications/washing_machines.html
WRAP (2010) Meeting the UK climate change challenge: The contribution of resource efficiency.
http://www.wrap.org.uk/downloads/AC_Exec_Summary_WEB_2.b02822b5.8035.pdf
WRATE (2010). Wrate v2.0.1.4. Environment Agency. www.environment-agency.gov.uk
73