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The International Journal of Life
Cycle Assessment
ISSN 0948-3349
Int J Life Cycle Assess
DOI 10.1007/s11367-018-1538-5
Effect of methodological choice on the
estimated impacts of wool production
and the significance for LCA-based rating
systems
Stephen G.Wiedemann, Aaron
Simmons, Kalinda J.L.Watson & Leo
Biggs
1 23
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LCI METHODOLOGY AND DATABASES
Effect of methodological choice on the estimated impacts of wool
production and the significance for LCA-based rating systems
Stephen G. Wiedemann
1
&Aaron Simmons
2
&Kalinda J. L. Watson
1
&Leo Biggs
1
Received: 14 May 2018 /Accepted: 4 October 2018
#Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
Purpose One aim of LCA-based rating tools developed by the apparel industry is to promote a change in demand for textiles by
influencing consumer preferences based on the environmental footprint of textiles. Despite a growing consensus that footprints
developed using attributional LCA (aLCA) are not suitable to inform decisions that will impact supply and demand, these tools
continue to use aLCA. This paper analysesthe application of the LCA methods to wool production, specifically the application of
aLCA methods that provide a retrospective assessment of impacts and consequential (cLCA) methods that estimate the impacts
of a change.
Methods Attributional and consequential life cycle inventories (LCIs) were developed and analysed to examine how the different
methodological approaches affect the estimated environmental impacts of wool.
Results and discussion Life cycle impact assessment (LCIA) of aLCI and cLCI for wool indicates that estimated global warming
and water stress impacts may be considerably lower for additional production of wool, as estimated by cLCIA, than for current
production as estimated by aLCIA. However, fossil resource impacts for additional production may be greater than for current
production when increased wool production was assumed to displace dedicated sheep meat production.
Conclusions This work supports the notion that the use of a retrospective assessment method (i.e. aLCA) to produce information
that will guide consumer preferences may not adequately represent the impacts of a consumer’s choice because the difference
between aLCIA and cLCIA results may be relatively large. As such, rating tools based on attributional LCA are unlikely to be an
adequate indicator of the sustainability of textiles used in the apparel industry.
Keywords Apparel .Attributional life cycle assessment .cLCA .Consequential life cycle assessment .Fabric .Wool .Higg MSI
aLCA .Life cycle assessment
1 Introduction
Environmental impacts are an unavoidable effect of any in-
dustry, with many industries striving for ongoing improve-
ment required to meet legislative requirements and consumer
expectations. The global textile and apparel industry con-
sumes significant volumes of natural resources and fossil fuels
for raw material production, processing and use of apparel that
generate environmental impacts across the product life cycle.
The environmental impact of textiles is dependent on the type
of fibre from which the apparel is made (Muthu 2015), the
manufacturing and processing techniques and the length of
time that apparel spend in use prior to disposal. Because of
the long supply chain and its ability to provide a comprehen-
sive view of the environmental aspects of the product and/or
processes, life cycle assessment (LCA) is a useful tool for
understanding and improving the sustainability of the textile
and apparel supply chain. Accordingly, the LCA approach has
been adopted as a primary means for determining the sustain-
ability of apparel, with non-government organisations (e.g.
Sustainable Apparel Coalition (SAC) and MADE-BY),
implementing LCA as the basis for their respective rating
systems. These rating systems are used to determine which
textiles are environmentally ‘superior’or ‘inferior’fabric
types based on their environmental impacts (Sustainable
Responsible editor: Adriana Del Borghi
*Aaron Simmons
aaron.simmons@dpi.nsw.gov.au
1
Integrity Ag and Environment, 36a Highfields Road,
Highfields, QLD 4352, Australia
2
NSW Department of Primary Industries, Orange Agricultural
Institute, 1447 Forest Rd, Orange, NSW 2800, Australia
The International Journal of Life Cycle Assessment
https://doi.org/10.1007/s11367-018-1538-5
Author's personal copy
Apparel Coalition 2018b). The commendable objective of
these rating systems is to improve the environmental and so-
cial impacts of the apparel industry by providing information
that can guide procurement decisions of apparel manufac-
turers and consumers. To achieve this objective, ratings devel-
oped by these systems have been used to inform recommen-
dations for dramatic changes in the type of fibre used in the
apparel sector. For example, the ratings from the SAC Higg
Materials Sustainability Index (MSI) dataset was utilised to
support recommending a 30% reduction in global cotton use
(Global Fashion Agenda 2017). Other programs initiated by
governments (e.g. the Product Environmental Footprint
(PEF)) also apply LCA to communicate environmental infor-
mation about products, including apparel, to consumers
(European Commission 2013). However, debate over the ap-
propriate LCA method and data requirements to inform these
systems continues.
The ratings that are produced by the systems developed by
the apparel industry to determine the sustainability of a mate-
rial (e.g. SAC Higg MSI) constitute a ‘footprint’based on
multiple environmental impacts. Footprints, using retrospec-
tive analyses based on attributional LCA (aLCA) methods,
may mislead policy or procurement decisions (Perry 2014;
Plevin et al. 2014;Reapetal.2008) because they provide an
estimate of the environmental impacts of the status quo as
opposed to analysing the impacts of a change where a change
in supply and/or demand may be expected (Brandão et al.
2014;Brander2017; Brander et al. 2008; Plevin et al. 2014;
Wardenaar et a l. 2012). Rather, the impacts of a change need
to be estimated using a consequential LCA (cLCA) approach.
Differences between aLCA and cLCA and a review of the
appropriate use and interpretation of attributional and conse-
quential LCA can be found in existing literature (Brander et al.
2008;EUJRC2010; Finnveden et al. 2009). Briefly, a con-
sequential analysis estimates the consequences of a decision
by considering the market effects of a decision. It seeks to
include these effects by using system expansion to deal with
co-production, assuming that system inputs from constrained
suppliers come from marginal production systems and also
assuming that where production is increased, it avoids the
production of the most appropriate substitute.
Previous research has demonstrated that the results from
aLCA may differ considerably to a cLCA of the same product.
For example, Sheehan et al. (1998) used attributional methods
to compare the benefits of replacing fossil fuel-derived diesel
use in buses with soybean-derived biodiesel and concluded
that the Buse of biodiesel to displace petroleum diesel in urban
buses is an extremely effective strategy for reducing CO
2
emissions^. However, a later study by García Sánchez et al.
(2012) that applied consequential methods showed that the
production and use of biodiesel can have greater emissions
than fossil fuel diesel when land use change associated with
increased demand for biofuel feedstocks are included. Similar
trends occurred when assessing the climate impacts of dairy
production. Gollnow et al. (2014) concluded, using attribu-
tional methods, that increasing milk production per dairy
cow reduced the GHG emission intensity of milk production,
whereas a consequential analysis (Cederberg and Stadig 2003)
found that increasing milk production per dairy cow had no
impact on emission intensity because of induced changes in
the co-production of beef. Zehetmeier et al. (2012)alsofound
that mitigation strategies focussing on milk production using
attributional methods resulted in erroneous conclusions be-
cause the method failed to consider market effects in the co-
product system (i.e. red meat production). The differences
between aLCA and cLCA discussed above can be attributed
to cLCA including market effects of changes in production of
the functional unit as well as co-products of the system. They
demonstrate that the analysis of a change in a system needs to
include these market effects.
This study assessed how LCA methodological choice can
affect the perceived environmental sustainability of wool pro-
duction. It did this by producing three life cycle inventories (one
attributional life cycle inventory (LCI) and two consequential
LCIs) for the production of 500 g of clean fine wool fibre
produced in the NSW high rainfall zone (HRZ) and processed
in Asia ready for textile production. Results from the analyses
of these LCIs were then used to calculate a single score using
the components of the Higg MSI methods that are publically
available to assess whether single scores could change in re-
sponse to the implementation of a different methodology. The
results from the study are discussed in the context of using LCA
to improve the sustainability of the apparel industry.
2 Materials and methods
2.1 Functional unit and boundaries
The functional unit for the study was 500 g of 100% superfine
Merino wool, clean and suitable for processing into a textile. The
mass was chosen to reflect wool requirements for the manufac-
ture of an outer garment such as a sweater. The analysis boundary
was cradle to mill, and included all processes and impacts asso-
ciated with wool production and pre-processing (i.e. scouring).
Three LCIs were developed: an aLCI and two cLCIs. Two cLCIs
were developed to assess the impact of the product substituted in
the system expansion process on the environmental impacts of
additional wool production.
2.2 Systems description
2.2.1 Wool production
Wool was produced in a fine wool Merino production system
in the high rainfall zone of NSW, as described by Wiedemann
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et al. (2016c). Wool was then assumed to be transported to
India or China for processing after which it was ready to be
formed into yarn.
2.2.2 Co-production and/or market effects
Major co-products of the wool production system are lambs and
mutton (sheep meat) from the on-farm stage, and sheep meat is a
major high-value co-product from sheep systems. While the
Merino breed has been selected specifically for high-value wool,
lamb production is a major contributor to farm revenue and on a
mass basis represents a greater biological output from the system.
For cLCI that represented an increase in wool production, co-
production was handled by system expansion and we decided to
test the sensitivity of the cLCIA to the chosen substitute by using
either a dedicated cross-bred meat sheep enterprise or a beef
production enterprise.
Sheep meat is a global specialty meat representing ~ 5% of
global meat production (FAO 2017), and global demand is
expected to either remain constant or increase (FAO 2016).
It is possible that additional sheep meat produced in response
to increased wool production may displace other meat prod-
ucts such as beef, pork or chicken. However, the strong pre-
dicted global demand, the observable market demand in
Australia (Meat and Livestock Australia 2018)andthecultur-
al preferencefor sheep meat indicates that an increase in sheep
meat production from wool focussed Merino systems are un-
likely to displace other meats. In addition, Australia is also the
most affected supplier of lamb (Colby 2015), which is pre-
dominantly supplied from cross-bred sheep systems.
Considering the above, the first cLCI assumed that an increase
in fine wool Merino production would occur by farmers re-
placing their dedicated cross-bred meat sheep that produced
wool unsuitable for garment production (i.e. coarse wool suit-
able for interior textiles) with Merino sheep (cLCI-SM). This
substitution is analogous to the substitution of beef from dairy
cattle systems with beef from purpose-grown beef herds in
response to production changes (Cederberg and Stadig 2003;
Thomassen et al. 2008; Zehetmeier et al. 2012). In addition to
changes in sheep meat production, the cLCI-SM also resulted
in a reduction of coarse wool produced in cross-bred meat
sheep systems. This change was handled by substituting
coarse wool with nylon, a substitution fibre for interior textiles
(Jackman and Dixon 2003). Beef was chosen as an alternative
substitute because, being a red meat, there are fewer cultural
barriers than other alternative meats in key sheep meat export
markets such as the Middle East. Globally, Australia and
Brazil are two of the world’s largest beef exporters and are
reasonably chosen to be the most affected suppliers of beef by
changes in the global beef market (Meat and Livestock
Australia 2016). In the present analysis, we assumed that
Australian beef production was the most affected supplier.
Accordingly, the second LCI assumed that, on average, an
increase in Merino sheep production in the NSW HRZ would
displaced some of the beef cattle herd (cLCIA-BM) and meant
that some regional beef herd was replaced by sheep meat and
fine wool production.
The wool processing stage also results in co-production,
namely lanolin from the scouring process that is primarily
used for body lotions and creams. Lower grade lanolin is also
used as a lubricant and corrosion inhibitor in marine, heavy
industrial and commercial applications; however, this is
sourced from Australian scouring operations (Lanotec 2018),
and because the inventory was from Chinese and Indian pro-
cessors, it was not deemed to be a relevant substitute. Co-
production of lanolin was, therefore, handled by system ex-
pansion to include avoided production of raw coconut oil (on
a 1:1 mass basis).
2.3 Inventory
2.3.1 Animal production data and substitutes
For the purposes of this scoping study, it was assumed
that relatively small changes in wool demand were ex-
pected to influence all producers across the NSW HRZ,
so inventory was based on average production of fine
wool in the region as detailed in Wiedemann et al.
(2016c). A dataset of the most affected producers was
not available at the present time but will be investigated
as part of ongoing research by the authors. For the aLCI,
co-production was handled by allocating impacts on a
protein mass basis as per Wiedemann et al. (2016c).
System expansion was implemented for cLCI and as-
sumed that additional fine wool Merino sheep production
avoided either cross-bred meat sheep and coarse wool
production or beef production, as discussed above. Data
used for avoided production of cross-bred sheep meat
production were taken from Wiedemann et al. (2016b),
while beef production inventory data (cLCI-BM) were
taken from Wiedemann et al. (2016a), with some modifi-
cation of land and water supply processes to account for
the likely expansion of lamb production on farms previ-
ously used for wool. In both instances, the most affected
suppliers that were increasing either sheep meat or beef
were expected to have performance levels similar to the
regional average. The increase in nylon associated with
the reduction in the supply of coarse wool to the market
was represented by the relevant Ecoinvent v3.0 (Weidema
et al. 2013) inventory for global production on a 1:1 mass
basis.
2.3.2 Wool processing data and substitutes
Wool processing data were taken from the wool industry
LCI database (unpublished data), and included average
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data from eight wool scouring plants, predominantly in
China and India. Primary data were collected over a 12-
monthperiodin2016and2017.Datawereaveraged
across all processing plants without selecting the most
affected suppliers in this scoping level research (Table 1)
and may be expected to be a conservative approach. This
is not taking into account that improvement in water and
energy use in the processing sector is ongoing and more
modern, cost-competitive processors are expected to have
lower energy and water requirements (Blackburn 2009;
Kocabas 2008;Muthu2015). Throughout the processing,
inputs with inventories available in Ecoinvent v3.0 (i.e.
LPG, diesel, sodium chloride, electricity, heat) used the
respective attributional (i.e. APOS) or consequential (i.e.
Conseq) inventory. Where Ecoinvent inventories were not
available, inventories from AusLCI (Grant et al. 2017)
were used. Ecoinvent consequential LCI that represented
global demand of coconut oil was used to represent pro-
duction avoided by the co-production of lanolin.
2.3.3 Land transformation
Land transformation was not required to be included in fore-
ground processes because all production changes occurred
within existing enterprises. Land transformation impacts in
background Ecoinvent processes used were included in the
assessment.
2.3.4 Emission calculations
Emission calculations built into the inventory were consistent
with those described by in previous research (Wiedemann
et al. 2016a,b,c).
2.4 Impact assessment
All LCIs were developed and analysed in SimaPro (PRé
Sustainability 2016). Impact assessments were global
warming using the IPCC GWP 100a v 1.3 indicator set based
on the AR5 report (IPCC 2013). Fossil energy demand was
assessed using a modified ReCiPe midpoint (H) indicator set v
1.13 (Goedkoop et al. 2009) that removed normalisation to
provide raw estimates. Estimates were converted from kilo-
gram oil equivalent to megajoules (MJ) with lower heating
values (LHV). Stress-weighted water use was assessed using
the water stress index (WSI) of Pfister et al. (2009) and report-
ed in water equivalents (H
2
O-e) after Ridoutt and Pfister
(2010).
2.5 Calculation of impacts using Higg MSI
methodology
The Higg MSI methodology calculates a single score with
which to compare the relative impacts of fabrics used in the
apparel industry (Sustainable Apparel Coalition 2016). The
single score is developed by weighting and normalising global
warming, water scarcity, eutrophication, abiotic (fossil fuel)
depletion and chemistry impacts. The present study used the
methods of the Higg MSI to calculate a single score based on
global warming, fossil fuel depletion and water scarcity im-
pacts for aLCIA, cLCIA-SM and cLCIA-BM. Results from
each LCIA were weighted and normalised using the same
values for each impact category presented in the Higg MSI
methodology (Sustainable Apparel Coalition 2018a) to a val-
ue that represented the global warming, fossil fuel depletion
and water scarcity impacts. These values were then normal-
ised and summed to calculate a single score. It should be noted
that, although the Higg MSI score includes eutrophication
impacts, we were unable to include eutrophication impacts
in our calculations because of a lack of suitable inventory data.
Table 1 Inventory for the scouring and combing of 1 kg of wool (see
Section 2.3.2 for source)
Material/process Amount Unit
Input Australian greasy wool 1.390 kg
Wat er 17 .8 43 l
Diesel 0.00021 kg
Liquid petroleum gas 0.00051 kg
Detergent 0.00425 kg
Surfactant 0.00130 kg
Flocculent 0.00039 kg
Sodium bicarbonate 0.00509 kg
Sodium chloride 0.00384 kg
Metal salt 0.00374 kg
Nylon packaging 0.00063 kg
Steel strap packaging 0.00068 kg
Polyethelene packaging (plastic bags) 0.00198 kg
Articulated truck B-double transport 0.834 tkm
Freight ship transport 11.775 tkm
Articulated truck (urban freight) 0.274 tkm
Electricity—high voltage 0.209 kWh
Energy from natural gas 6.266 MJ
Process steam from light fuel oil 0.013 MJ
Output Clean wool (scoured) 1.000 kg
Lanolin 0.077 kg
Wool for recovery
a
0.004 kg
Water lost to evaporation 2.399 l
Solid waste treatment (municipal) 0.310 kg
Scouring wastewater treatment 15.444 l
a
Wool for recovery was treated as a residual (primary data)
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3 Results
3.1 LCIA
The impacts associated with the production of 500 g of clean
fine Merino wool ready for textile processing differed be-
tween cLCIA-SM, cLCIA-BM and aLCIA scenarios. GHG
emissions were 9.11 and 6.82 kg CO
2
-e for the production
of an additional 500 g wool under the cLCIA-SM and
cLCIA-BM scenarios, respectively. The estimated impacts of
wool production using aLCIAwere 15.75 kg CO
2
-e for 500 g
of clean wool. Figure 1shows the contribution that expanding
the system to include avoided sheep meat or beef production
made to the total emissions for cLCIA scenarios. It also shows
that the emissions for avoided coconut oil production (to han-
dle additional co-production of lanolin, where relevant) and
emissions associated with wool processing contributed very
little to the overall emission profile for wool.
Fossil energy requirements for the production of an addi-
tional 500 g of clean fine Merino wool were 36.15 and
13.13 MJ for the cLCIA-SM and cLCIA-BM, respectively,
and that the energy requirements for the production of 500 g
of clean wool assessed by aLCIA were 19.32 MJ. Energy use
for the processing phase made a greater contribution to total
energy use than for global warming impacts, and the contri-
bution that avoided emissions associated with co-production
in cLCIA scenarios made also varied considerably.
Total water stress associated with the production of an ad-
ditional 500 g of clean fine Merino wool was 14.64 and −
52.90 water stress index equivalents (WSI-e) for cLCIA-SM
and cLCIA-BM, respectively, and the water stress associated
with existing production of 500 g of wool was 52.12 WSI-e.
Wool production made the greatest contribution to the total
water stress impacts for wool regardless of the methodological
choice and co-production of lanolin, and wool processing
made only minor contributions to total water stress impacts.
Using consequential Ecoinvent v3.0 inventories to repre-
sent marginal producers in cLCI resulted in a 0.4 and 2%
decrease in global warming and fossil energy impacts, respec-
tively, and a 0.7% reduction in water stress impacts (data not
shown).
3.2 Higg MSI score
Global warming, fossil resource use and water scarcity im-
pacts as calculated by the Higg MSI methodology for each
LCIA are presented in Table 2. Global warming and fossil
resource use impacts of wool production were greatest for
the aLCI. In contrast, water scarcity impacts were greatest
for cLCIA-BM. The single scores as calculated by the
Fig. 1 Global warming (a), fossil energy demand (b)andwaterstress(c)
impacts for the production of 500 g of clean, Merino wool when
estimated using cLCI with the system expanded using sheep meat
(cLCI-SM) or beef (cLCI-BM), or when estimated using aLCI
Table 2 Single scores for global warming, fossil energy and water
scarcity impacts for the production of 500 g of clean fine Merino wool
ready for textile production as calculated using the methods of the Higg
MSI (Sustainable Apparel Coalition 2016)
LCIA
aLCIA cLCIA-
SM
cLCIA-
BM
Global warming 15.1 8.7 6.5
Fossil energy 1.4 2.7 1.0
Water scarcity 1.0 0.3 −1.0
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modified Higg MSI methodology were 39, 27 and 21 for the
aLCIA, cLCIA-SM and cLCIA-BM, respectively.
4 Discussion
This study is the first to show that the impacts of producing
additional fine Merino wool production used in the apparel
industry in response to additional demand were lower than
those for current wool production (Fig. 1). This was evident
in results from LCIA and also when those results were used to
calculate individual impacts using the Higg MSI methodology
(Table 2). Calculating single scores using the available Higg
MSI methodology showed that single scores from cLCIA re-
sults were 31 and 47% lower than those calculated using
aLCIA results. Differences between aLCIA and cLCIA were
primarily related to the displaced product system, rather than
the impacts of marginal inputs (where used). These results
demonstrate that, in addition to aLCA being a technically
incorrect methodology to use where there is an inherent drive
to alter fibre choices and thereby influence supply and demand
(Brander et al. 2008;Plevinetal.2014), the use of aLCA by
the Higg MSI is likely to have overestimated the perceived
impacts of apparel made with fine Merino wool. Previous
research has also shown that the results of aLCIA and
cLCIA can differ substantially. For example, a comparison
between cLCI and aLCI from Ecoinvent v3.0 found that over
66% of the LCIs deviate by more than 10% (Weidema and
Moreno 2013). This means that it is quite likely that other
Higg MSI scores, and the perceived sustainability of all ma-
terials rated by the MSI, would change if a consequential
approach was systematically applied to calculate scores. The
differences reported here for wool between methodological
approaches provide a strong justification for the implementa-
tion of a consequential method in rating systems and strongly
suggest that the Higg MSI scores and other apparel sustain-
ability indicators (e.g. MADE-BY) that currently use aLCA in
their assessment cannot be considered a reliable indicator of
the sustainability of materials used in the apparel industry.
In addition to differences in results between attributional
and consequential approaches, results for impacts assessed by
cLCIA were sensitive to choice of substitute (Fig. 1). Similar
results have been found in other studies (Fig. 1; Cederberg and
Stadig 2003; Flysjö et al. 2011;Zehetmeieretal.2012), and
the sensitivity reported here is similar to the range in values
shown between different allocation methods for wool (see
Wiedemann et al. 2015). Importantly, this sensitivity is not a
justification for not using a consequential approach in rating
systems because sensitivity to co-production is already an
issue in aLCA. For example, McGeough et al. (2012)reported
that, depending on the allocation method used, the global
warming impacts for a kilogram of fat and protein corrected
milk ranged from 0.67 to 0.92 kg CO
2
-e. Research has also
examined the allocation issue in the context of bioenergy pro-
duction. Wardenaar et al. (2012) reported that when physical
allocation was used instead of economic allocation to handle
co-production associated with the production of bioelectricity
from rapeseed oil, the global warming impacts decreased from
0.604 to 0.477 kg CO
2
-e kWh. Further, Luo et al. (2009)also
showed that the estimated environmental impacts of corn
stover-based ethanol were highly dependent on whether mass
or economic allocation was used. There were a number of
reasons for differences between the results of cLCIA-SM
and cLCIA-BM. cLCIA-SM had greater global warming im-
pacts than cLCIA-BM because, even though cLCIA-SM
displaced the production of sheep, it also displaced the pro-
duction of coarse wool that needed to be substituted with
nylon. Similarly, the production of additional nylon was re-
sponsible for the high fossil energy demand for cLCIA-SM
relative to cLCIA-BM. In contrast, water stress impacts for
cLCIA-BM were greater than for cLCIA-SM. This was the
result of lower on-farm water demand for sheep production
compared to cattle, an established difference between the spe-
cies (Zonderland-Thomassen et al. 2014), so avoiding beef
production results in a much lower water stress. These results
demonstrate that differences in cLCIs developed for wool are
likely to occur due to market effects associated with the co-
products. Many other textiles are also produced from fibres
that are produced with a co-product. For example, cotton fi-
bres are co-produced with cotton seed, and petroleum-based
polyester is also co-produced with many other petroleum
products so the sensitivity of choice of substitute is unlikely
to be limited to materials made from wool. Sensitivity of re-
sults to substitution products means that selecting the most
appropriate substitute when implementing a consequential ap-
proach requires careful consideration by the apparel industry.
A proposed resolution to this issue is that rating tools ensure
that a consistent approach to determining the most appropriate
substitute for use in system expansion is implemented based
on the available guidance (EU JRC 2010).
5 Conclusions
Improved sustainability is a priority for the apparel sector, and
considerable effort has been placed on the development of
tools and rating systems to quantify impacts and enable im-
provements to be made. The results of this study indicate that
using aLCI to assess the impacts of a preference for woollen
materials by consumers may lead to an incorrect estimate of
these impacts. The potential for rating tools that use an attri-
butional approach to incorrectly estimate the impacts of a fibre
is not solely limited to wool production. Considering that the
purpose of these rating tools is to influence the demand for
different textiles based on perceived sustainability, it is clear
that a consequential approach is most appropriate. The
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implementation of a consequential approach to developing
LCI for assessing impacts would require a consistent approach
to determining the most appropriate substitute for use in the
system expansion process and identifying marginal suppliers.
The impacts of co-production demonstrated by this study sug-
gest that other animal or plant-based fibres for which co-
production occurs (e.g. cotton, flax, coir, kenaf, kapok and
leather) are also likely to change if assessed using cLCIA.
Funding information Funding for this study was provided by Australian
Wool Innovation Limited (AWI) and the NSW Department of Primary
Industries.
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