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EPA Victoria and City West Water LCA of Clothes Washing Options for City West Water's Residential Customers Life Cycle Assessment - Final Technical Report Black

EPA Victoria and City West Water LCA of Clothes Washing Options for City West Water's Residential Customers Life Cycle Assessment - Final Technical Report

May 2010

Arup

Arup Pty Ltd ABN 18 000 966 165

This report takes into account the

particular instructions and requirements

of our client.

It is not intended for and should not be

relied upon by any third party and no

responsibility is undertaken to any third

party Arup

Level 17 1 Nicholson Street, Melbourne VIC 3000

Tel +61 3 9668 5500 Fax +61 3 9663 1546 www.arup.com

Job number 206853-00

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Arup Issue 24 May 2010

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Contents

Page

Executive summary i

Glossary iv

1 Introduction 1

2 Project context 3

3 Life cycle assessment approach 4

4 Goal and scope 5

4.1 Overview 5

4.2 Goal of the study 5

4.3 Scope of the study 6

5 Life cycle inventory analysis 11

5.1 Overview 11

5.2 Data collection procedures 11

5.3 Databases 13

5.4 Validation of data 14

5.5 Allocation and credit principles and procedures 14

6 Life cycle impact assessment 16

6.1 Impact assessment models 16

6.2 Impact categories 16

6.3 Definition of categories 16

7 Results and interpretation 20

7.1 Base case 20

7.2 Scenario analysis 44

7.3 Machine replacement 78

8 Conclusions and recommendations 79

8.1 Summary of results and significant issues 79

8.2 Recommendations for LCA model improvement 82

9 References 83

Tables

Table 1 Definition of Base Case Table 2 Clothes Washing LCA Scenarios Table 3 Energy Consumption for Water Supply and Sewage Treatment Table 4 Water use impact category definition Table 5 Energy consumption impact category definition Table 6 Global warming impact category definition Table 7 Eutrophication impact category definition Table 8 Non renewable resource depletion impact category definition Table 9 Land use impact category definition




Table 10 Water use impacts Table 11 Energy use impacts Table 12 Global warming impacts Table 13 Eutrophication impacts Table 14 Fossil fuels depletion impacts Table 15 Minerals depletion impacts Table 16 Land use impacts Table 17 Sensitivity to Analysis Options Table 18 Impact of future machines and current market leader Table 19 Machine Parameters for Top Loader/Front Loader Scenario Table 20 Machine Parameters for Top Loader Varying Energy Rating Scenario Table 21 Impact of varying energy rating for top loading machines Table 22 Machine Parameters for Front Loader Varying Energy Rating Scenario Table 23 Impact of varying energy rating for front loading machines Table 24 Machine Parameters for Top Loader Varying WELS Rating Scenario Table 25 Impact of varying WELS rating for top loading machines Table 26 Machine Parameters for Front Loader Varying WELS Rating Scenario Table 27 Impact of varying WELS rating for front loading machines Table 28 Impact of varying hotwater system type for 20°C wash temperature Table 29 Impact of varying hotwater system type for 60°C wash temperature Table 30 Washing machine temperature relationships Table 31 Impact of varying detergent type Table 32 Impact of fabric softener use Table 33 Impact of drying Table 34 Detergent overfill relationships Table 35 Washing machine loading Table 36 Impact of greywater reuse for irrigation Table 37 Impact of disposal of washing machine at end of life Table 38 Life cycle impacts comparison between machines Table 39 Recommendations for additional data collection Table 40 Machine Parameters for Top Loader/Front Loader Scenario Table 41 Machine Parameters for Top Loader Varying Energy Rating Scenario Table 42 Machine Parameters for Front Loader Varying Energy Rating Scenario Table 43 Machine Parameters for Top Loader Varying WELS Rating Scenario Table 44 Machine Parameters for Front Loader Varying WELS Rating Scenario Table 45 Comparison to results from Bole 2006 Table 46 Comparison with Australian Consumer's Association study Table 47 Uncertainty Analysis Table 48 Lifecycle Components Uncertainty – Water Use (L H2O) Table 49 Lifecycle Components Uncertainty – Energy Use (kJ eq) Table 50 Lifecycle Components Uncertainty – Global Warming (kg CO2 eq) Table 51 Lifecycle Components Uncertainty – Eutrophication (kg PO4 eq) Table 52 Lifecycle Components Uncertainty – Fossil Fuels Depletion (kJ Surplus) Table 53 Lifecycle Components Uncertainty – Minerals Depletion (kJ Surplus) Table 54 Lifecycle Components Uncertainty – Land Use (m

2)

Figures

Figure 1 Four phases of a LCA Figure 2 Process flow diagram Grey boxes represent different phases while boxes represent unit

processes. Figure 3 Water use impact base case Figure 4 Water use impact base case with drying Figure 5 Energy use impact base case Figure 6 Energy use impact base case with drying




Figure 7 Global warming impact base case Figure 8 Global warming impact base case with drying Figure 9 Eutrophication base case Figure 10 Eutrophication base case with drying Figure 11 Fossil fuels depletion impact base case Figure 12 Fossil fuels depletion impact base case with drying Figure 13 Minerals depletion impact base case Figure 14 Minerals depletion impact with drying Figure 15 Land use impact base case Figure 16 Land use impact base case with drying Figure 17 Impacts of future machines and current market leader (% Difference from base case) Figure 18 Impact of varying loading type Figure 19 Impact of varying energy rating for top loading machines (% Difference from base case) Figure 20 Impact of varying energy rating for front loading machines (% Difference from base case) Figure 21: WELS Rating comparison of washing machine size Figure 22 Impact of varying WELS rating for top loading machines (% Difference from base case) Figure 23 Impact of varying WELS rating for front loading machines (% Difference from base case) Figure 24 Impact of varying hot water system type for 20°C wash temperature

(% Difference from base case) Figure 25 Impact of varying hot water system type for 60°C wash temperature

(% Difference from base case) Figure 26 Relationship between Wash Temperature and Water Use Figure 27 Relationship between Wash Temperature and Energy Use Figure 28 Relationship between Wash Temperature and Global Warming Potential Figure 29 Relationship between Wash Temperature and Eutrophication Potential Figure 30 Relationship between Wash Temperature and Fossil Fuels Depletion Figure 31 Relationship between Wash Temperature and Minerals Depletion Figure 32 Relationship between Wash Temperature and Land Use Figure 33 Detergent type impact scenarios Figure 34: Fabric softener impact scenario Figure 35 Drying impact scenario Figure 36 Relationship between machine loading and water use Figure 37 Relationship between machine loading and energy use Figure 38 Relationship between machine loading and global warming potential Figure 39 Relationship between machine loading and eutrophication potential Figure 40 Relationship between machine loading and fossil fuels depletion Figure 41 Relationship between machine loading and minerals depletion Figure 42 Relationship between machine loading and land use Figure 43 Grey water scenario analysis Figure 44: The impact of disposal scenarios Figure 45 Impact of varying loading type (Constant Wash Size) Figure 46 Impact of varying energy rating for top loading machines (Constant Wash Size) Figure 47 Impact of varying energy rating for front loading machines Figure 48 Impact of varying WELS rating for top loading machines (Constant Wash Size) Figure 49 Impact of varying WELS rating for front loading machines (Constant Wash Size)

Appendices

Appendix A

Sensitivity Analysis for Option 2

Appendix B

Uncertainty Analysis

Appendix C




CWW and EPA Sustainability Covenant

Appendix D

CWW LCA Process Flow Maps



Page i Arup Issue 24 May 2010

Executive summary

The third Sustainability Covenant between Environment Protection Authority (EPA) Victoria and City

West Water (CWW), known as the EPA Sustainability Covenant (the Covenant), was signed in

2009. The Covenant defines three programs which aim to achieve resource efficiencies and reduce

operational ecological impacts.

Through Program 1 – Sustainable Clothes Washing, EPA Victoria and CWW committed to

partnering to develop and implement a sustainability program that enhances the resource efficiency

and reduces environmental impacts associated with domestic clothes washing. The washing of

clothes by domestic washing machines is a focus of the Covenant due to its range of environmental

impacts. Program 1 is divided into three phases, two development phases (Phase 1 – Conceptual

Design and Phase 2 – Detailed Design) and an implementation phase (Phase 3 - Implementation).

The detailed life cycle assessment (LCA) represents a key deliverable of Phase 2 and this report,

LCA of Clothes Washing Options for City West Water’s Residential Customers: Life Cycle

Assessment – Final Technical Report provides the EPA Victoria and CWW with quantified

information on the environmental impacts of clothes washing to inform a business decision

regarding the value of future machine technologies and engaging households in a behaviour change

program, centred on the clothes washing process.

The LCA separated the clothes washing process into three phases; upstream, use and downstream.

The upstream phase involves those processes relating to the production and delivery of key

products involved in the washing process. The use phase, as defined in this study involves the

regular use of products and equipment by households to wash and dry their clothes, it involves all

the resources consumed during the process including amongst others, water, detergent and energy.

The downstream phase relates to the disposal and treatment of all the materials and chemicals

produced during the process including the machines, detergents and waste water. Within each

phase are a series of individual ‗unit processes‘ (the smallest element considered in the life cycle

inventory analysis for which input and output data are quantified) and can be seen in Figure 2.

Process flows produced by CWW and EPA Victoria for washing machine manufacture, detergent

manufacture, wastewater treatment and water supply are included in Appendix D.

A key determinant in relation to the decision to proceed with implementation of the behaviour

change program or recommend investment in new technologies is the proportion of environmental

impacts that occur during the use phase, as it is this phase that CWW will be most able to influence

through such a program.

The LCA was undertaken by determining the overall and percentage contribution of each phase and

unit process to the relevant environmental impact category. The environmental impact categories

assessed for the LCA were:

• water use;

• energy consumption;

• Global warming (IPCC, 100 years);

• eutrophication

• non renewable resource depletion, including fossil fuel use and minerals; and

• land use.

The assessment was initially undertaken for the most common domestic clothes washing scenario

within the CWW region. All results related to the lifecycle impacts required to produce 1 kg of clean

dry clothes (the functional unit of the study). A range of realistic scenarios that varied from this base

case were then modelled to determine the change in contribution to each impact category.

The LCA determined that the use phase of the washing process has the largest proportion of

environmental impacts due to the frequency of operation of the machines and utilisation of the

detergents. The use phase contributes to impacts across:



Page ii Arup Issue 24 May 2010

• water use (92% of the life cycle impact);

• energy use (60% of the life cycle impact);

• global warming potential (73% of the life cycle impact); and

• fossil fuel depletion (62% of the life cycle impact).

Of the 92% life cycle impact, 91% is attributable to the washing machine water consumption, which

represents a significant opportunity area for CWW in forming a behaviour change program. In

regards to global warming, 30% of impacts are associated with the mechanical energy of the

washing machine and 25% with standby power. There is potential to reduce the contribution to

global warming by influencing household behaviour regarding standby power. It is also noted that

for energy use, 32% of impacts are associated with the upstream manufacture of detergent.

The LCA determined that the addition of a dryer, either electric or condenser, to the base case

scenario lead to increases in the environmental impact categories of energy use, global warming

potential and fossil fuel depletion land use. Rationalising the use of dryers with the CWW region

presents an opportunity to reduce a number of environmental impacts.

The investigation of future washing machine technologies (outlined in Box 1 and Box 2) highlighted

that they can reduce the environmental impacts of the washing process, particularly in respect to

eutrophication, land use and water use, although it is important to realise that in some

circumstances, impacts can also be higher than current market leading technologies. The

environmental impacts of new technologies should be considered in equilibrium, ensuring impact

savings tradeoffs are well considered.

The scenario analysis assessed changes to the base case environmental impacts through alteration

of individual unit processes.

In relation to washing machines the LCA indicated that the water consumption impacts for front

loading and top loading machines are consistent with the star rating. This is because the use phase

water use impacts dominate the lifecycle such that a better water rating results in reduced water

consumption as expected.

For energy use, impacts are more closely linked to machine size than energy rating. This is

because the majority of impacts are related to detergent manufacture, which is based on the

manufacturers‘ recommendations per wash. In contrast, a change in energy star rating affects the

thermal and mechanical energy requirements of the machine only.

The LCA indicates that the lowest impacts are associated with cold washing and that an increase of

a relatively minor 10 C can lead to a disproportional increase in environmental impacts. If a

household determines there is a need to wash at elevated temperatures than the off peak electric

and 3 star gas storage perform worse across all impact categories, with solar gas split system

having the lowest environmental impacts across a range of categories.

With regard to loading, the LCA determined that environmental impacts increase exponentially as

washing machine loading decreases, such that very small loads have a disproportionately high

impact on the environment.

For detergent, the ‗generic‘ brand provided the highest contribution across all categories when

compared to the base case of concentrated top loader powder and the alternative scenarios of top

loader liquid and an eco-powder brand. This result stemmed from the reduce level of concentration

of certain chemicals in the generic brand, requiring high volumes to obtain the same level of

cleaning. The LCA also demonstrated that the impact of overfilling detergent by even 1% led to

increased impacts across every impact category and is a potential key message for households.

The addition of fabric softener to the clothes washing process was shown to increase all

environmental impacts.

The LCA model found that use of a normal household grey water system can have positive

environmental impacts through the reduction in water use and the decreased potential for



Page iii Arup Issue 24 May 2010

eutrophication. However it did not address other negative environmental impacts which may be

associated with the use of grey water.

It is important to note when considering the results that the availability of data relating to certain

domestic clothes washing LCA processes within Australia was in some cases, very limited.

Specifically, the accuracy of the LCA could be improved with sourcing further data on washing

machine and dryer manufacture, emerging technologies and detergent and fabric softener

manufacture. In order to further the project‘s aims of considering sustainable solutions and reducing

environmental impacts associated with domestic clothes washing, assumptions draw from this

limited information will be supported with a confidence level based on working with limited data.



Page iv Arup Issue 24 May 2010

Glossary

Allocation Partitioning the input or output flows of a unit process to the product of

interest.

By-Products

An incidental product derived from a manufacturing process or chemical

reaction, and not the primary product or service being produced. A by-

product can be useful and marketable, or may have negative ecological

consequences.

Category Endpoint Attribute or aspect of natural environment, human health, or resources,

identifying an environmental issue giving cause for concern.

Characterisation

Characterisation is the second step of an impact assessment and

characterises the magnitude of the potential impacts of each inventory flow

to the corresponding environmental impact.

Characterisation Factor Factor derived from a characterisation model which is applied to convert the

assigned LCI results to the common unit of the category indicator.

Classification Classification is the first step of an impact assessment. It is the process of

assigning inventory outputs to specific environmental impact categories.

Condenser Dryer Condenser dryers extract water from the clothes and condense the water on

an air-cooled heat exchanger.

Cut-off Criteria

Specification of the amount of material or energy flow, or the level of

environmental significance associated with unit processes or product

system to be excluded from a study.

Data Quality Characteristics of data that relate to their ability to satisfy stated

requirements.

Detergent Overfilling The use of additional detergent in excess of the manufacturers

recommended dose.

Downstream Phase

The downstream phase relates to the disposal and treatment of all the

materials and chemicals produced during the process including the

machines, detergents and waste water.

EcoInvent

A Swiss-developed database that contains international industrial life cycle

inventory data on energy supply, resource extraction, material supply,

chemicals, metals, agriculture, waste management services, and transport

services.

Electric Tumble Dryer Electric tumble dryers (or evaporative dryers) heat the clothes within using

an electric resistance element. These dryer types do not consume water.

Environmental Aspect An element of an organization's activities, products or services that can

interact with the environment.

Environmental Loadings Releases of pollutants to the environment such as atmospheric and

waterborne emissions and solid waste.



Page v Arup Issue 24 May 2010

Environmental Mechanism

A system of physical, chemical and biological processes for a given impact

category, linking the life cycle inventory analysis results to category

indicators and to category endpoints.

Eutrophication

The increase of excess nutrients into a water body or system, leading to

excessive plant growth. This can cause a reduction in the level of oxygen

available within the water body and cause the death of water organisms.

Evaluation

An element within the life cycle interpretation phase intended to establish

confidence in the results of the life cycle assessment. Evaluation includes a

completeness check, a sensitivity check, a consistency check, and any

other validation that may be required according to the goal and scope

definition of the study.

Fugitive Emission An unintended environmental release.

Functional Unit

The measure of the function of the studied system providing a reference to

which the inputs and outputs can be related. It is the unit of comparison

that assures that the products being compared provide an equivalent level

of function or service.

Fuzzy Logic

The technology by which washing machines sense the contents within the

machine and apply the correct amount of water and / or detergent required

to clean the load.

Impact Assessment

The assessment of the environmental consequences of energy and natural

resource depletion and waste releases associated with an actual or

proposed action.

Impact Categories Classifications of human health and environmental effects caused by a

product throughout its life cycle.

Impact Indicators Impact indicators measure the potential for an impact to occur rather than

directly quantifying the actual impact.

Input

A product, material or energy flow that enters a unit process.

NB: Products and materials include raw materials, intermediate products

and co-products.

Interpretation

The evaluation of the results of the inventory analysis and impact

assessment to reduce environmental releases and resource use with a

clear understanding of the uncertainty and the assumptions used to

generate the results.

Intermediate Flow A product, material or energy flow occurring between unit processes of the

product system being studied.

Intermediate Product An output from a unit process that is input to other unit processes that

require further transformation within the system.

Life Cycle Consecutive and interlinked stages of a product system, from raw material

acquisition or generation from natural resources, to final disposal.

Life Cycle Assessment (LCA) The compilation and evaluation of inputs, outputs and the potential

environmental impacts of a product system throughout its life cycle.

Life Cycle Impact

Assessment (LCIA)

A phase of life cycle assessment aimed at understanding and evaluating the

magnitude and significance of the potential environmental impacts for a

product system throughout the life cycle of the product.

Life Cycle Inventory Analysis

(LCI)

A phase of life cycle assessment involving the compilation and

quantification of inputs and outputs for a product throughout its life cycle.



Page vi Arup Issue 24 May 2010

Lower Heating Value (LHV)

Also known as net heating value, LHV is the amount of energy available

from the combustion of a fuel without recovering energy associated with

water condensing vapour produced in the combustion process.

MEPS

Minimum energy and performance standards. MEPS establish standards

for energy performance that products must meet or exceed before they can

be sold to consumers. MEPS is based on a six star energy efficiency rating

system which enables households to compare the energy efficiency of

domestic appliances.

Monte Carlo Analysis A method of statistical analysis which allows the level of confidence in each

indicator result to be calculated. Refer to Appendix B for more details.

Output

A product, material or energy flow that leaves a unit process.

NB: Products and materials include raw materials, intermediate products,

co-products and releases.

Pedigree Matrix A tool used to determine an appropriate standard deviation to allow for

certainty in a calculation. Refer to Appendix E for more details.

Process Energy

The energy input required for operating the process or equipment within a

unit process excluding energy inputs for the production and delivery of

energy itself.

Product Flow Products entering or leaving a product system.

Product System

A collection of unit processes with elementary and product flows which

perform one or more defined functions, and which model the life cycle of a

product.

Reason Washing Machine

An innovative future washing machine that utilises fuzzy logic and design

features to reduce the environmental impact of washing clothes. Features

include a water ballast tank for machine balance, new detergent, use of

room temperature water and fuzzy logic sensors for water and detergent

quantities. (see Box 1 for more details)

Reference Flow A measure of the outputs from processes in a given product system

required to fulfil the function expressed by the functional unit.

Releases Emissions to air and discharges to water and soil.

Scope 1 Emissions Greenhouse gas emissions generated as a direct result of an activity

undertaken by a corporation.

Scope 2 Emissions

Greenhouse gas emissions generated by a second organization in the

process of producing energy (electricity, heat or steam) for the use of the

primary corporation.

Scope 3 Emissions

Greenhouse gas emissions (other than scope 2 emissions) arising from

activities such as air travel and waste disposal generated in the wider

economy as a consequence of a corporation‘s activities.

Sensitivity Analysis A systematic evaluation process for describing the effect that variations in

inputs have on an output.

SimaPro A software based tool to collect, analyse and monitor the environmental

performance of products and services.

System Boundary A set of criteria specifying which unit processes are part of a product

system.

System Flow Diagram A depiction of the inputs and outputs of a system and how they are

connected.



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Unit Process The smallest element considered in the life cycle inventory analysis for

which input and output data are quantified (see Figure 2).

Upstream Phase Involves those processes relating to the production and delivery of a

washing machine; from manufacture through to eventual sale.

Use Phase

The regular use of the machine by households to wash and dry clothes

involving all the resources consumed during the process including water,

detergent and energy.

Water Extraction Index

Water Extraction Index is the ratio of the mass of water remaining in the

clothes following completion of the wash cycle compared to the dry mass of

the clothes expressed as a percentage.

Waterless Washing Machine

An innovative future washing machine that utilises nylon bead (instead of

water and detergent) to attract dirt and remove stains from clothing during

the washing process. (see Box 2 for more details)

WELS Water Efficiency and Labelling Scheme. The WELS Scheme labels a range

of products for water efficiency based on a 6 star system.



Page 1 Arup Issue 24 May 2010

1 Introduction

City West Water (CWW) is one of three retail water businesses in metropolitan Melbourne

owned by the Victorian Government. It provides drinking water, sewerage, trade waste and

recycled water services to approximately 310,000 residential and 34,000 non-residential

(industrial and commercial) customers in Melbourne‘s Central Business District and inner

and western suburbs.

CWW is committed to sustainability and considers environmental, social and economic

aspects in all its operations. CWW recognises that to achieve this, it needs to invest in a

number of programs and activities designed to promote sustainability within the business, as

well as providing its core services of water, sewerage, trade waste and recycled water in a

sustainable way.

The Environment Protection Authority (EPA) Victoria – has been protecting, caring for and

improving the environment since 1971. EPA Victoria was established as an independent

statutory authority under the Environment Protection Act 1970. The Act defines EPA‘s

powers, duties and functions, and provides a framework for the prevention and control of air,

land and water pollution and industrial noise. The vision of EPA Victoria is "The Victorian

community living sustainably," which to EPA Victoria is a community that knows the impact

of the decisions it makes and the actions it takes on the environment. Sustainability

covenants, such as the one with CWW, work to achieve this vision.

The third Sustainability Covenant between EPA Victoria and City West Water (CWW),

known as the EPA Sustainability Covenant, was signed in 2009. The Covenant defines

three programs which aim to achieve resource efficiencies and reduce operational

ecological impacts.

As part of the Covenant, CWW and EPA Victoria commissioned Arup to conduct a life cycle

assessment (LCA) of clothes washing options for CWW‘s households. The LCA was

undertaken in accordance with ISO 14040:2006 Environmental management – Life cycle

assessment – Principles and framework, and ISO 14044:2006 Environmental management

– Life cycle assessment - Requirements and Guidelines. All results related to the lifecycle

impacts required to produce 1 kg of clean dry clothes; the functional unit for the study.

The purpose of the LCA was to provide CWW and EPA Victoria with quantifiable data on the

environmental impacts of domestic clothes washing based on the functional unit, which

would allow informed decisions relating future machine technologies and household

behavioural change to support an increase in resource efficiency and reduction in the

environmental impact of household clothes washing.

This LCA of Clothes Washing Options for City West Water’s Residential Customers: Life

Cycle Assessment – Final Technical Report (Final Technical Report) is the third report

produced over the course of the project. The preceding reports were LCA of Clothes

Washing Options for City West Water’s Residential Customers: Life Cycle Assessment –

Goal and Scope Report (Goal and Scope Report), and LCA of Clothes Washing Options for

City West Water’s Residential Customers: Life Cycle Assessment - Life Cycle Inventory

Report (Life Cycle Inventory Report).

The Goal and Scope Report defined the EPA Victoria and CWW goals to be achieved by

undertaking the LCA and the performance characteristics of the domestic clothes washing

process to be studied. The Life Cycle Inventory Report specified the data sources, data

collection, validation and allocation processes applied in preparing the life cycle model.

Both reports provided key input for the Final Technical Report.

This Final Technical Report includes:

an overview of the life cycle assessment approach;

a summary of the Goal and Scope Report and Life Cycle Inventory Report; and




the presentation and interpretation of all results from the LCA in accordance with

the requirements of ISO 14044:2006 Environmental management – Life cycle

assessment - Requirements and guidelines.

A fourth report, LCA of Clothes Washing Options for City West Water’s Residential

Customers: Life Cycle Assessment – Communications Report (The Communications

Report) has also been produced. The Communications Report highlights study results of

interest (as nominated by CWW and EPA Victoria), in a form that can be readily adapted for

communication publications.




2 Project context

Sustainability covenants are public agreements between EPA Victoria and a company or

group of companies, and are established to explore creative ways to reduce environmental

impacts and increase resource efficiency. Covenants were formed as an alternative

mechanism to the legislative instruments available to the EPA Victoria under the

Environment Protection Act 1970 and provide a means by which a broader set of

environmental objectives can be achieved.

CWW and EPA Victoria have a history of cooperation to achieve environmental outcomes;

the current EPA Sustainability Covenant being the third covenant between the two

organisations. Signed in 2009, it defined three programs of work aimed at increasing

resource use efficiency and reducing the ecological impact of the water industry. This aim

aligns closely with the CWW commitment to sustainability involving the consideration of the

environmental, social and economic aspects of its operations.

Through Program 1 – Sustainable Clothes Washing, EPA Victoria and CWW committed to

partnering to develop and implement a sustainability program that enhances the resource

efficiency and reduces environmental impacts associated with domestic clothes washing.

The washing of clothes by domestic washing machines is a focus of the Covenant due to

the range of associated environmental impacts. With regard to household water

consumption, approximately 15 per cent of household water use is related to clothes

washing. CWW views the need to address water conservation as a critical challenge to be

actively marketed to households to encourage behaviour change (City West Water, 2008).

CWW already operate a range of programs to assist households to reduce their water

consumption, such as the showerhead exchange program, water conservations solutions

program and community support program. Program 1 has the potential to lead to the

creation of an additional program focused on key messages around households during the

clothes washing process.

In addition to water consumption, the domestic washing of clothes contributes to

greenhouse gas emissions and leads to the production of wastewater that reduces the

ability to recycle water without energy intensive processes. The CWW Sustainability Policy

has an objective to achieve significantly more with significantly less and within this to

maximise the sustainable reuse of water (City West Water, 2009).

Program 1 is divided into three phases; two development phases (Phase 1 – Conceptual

Design and Phase 2 – Detailed Design) and an implementation phase (Phase 3 -

Implementation).

The detailed LCA represents a key deliverable of Phase 2 and the Final Technical Report

provides the EPA Victoria and CWW with data and information on the environmental

impacts of clothes washing. The purpose of the LCA was to provide CWW and EPA

Victoria with quantifiable data on the environmental impacts of domestic clothes washing to

consider emerging technologies and inform a decision regarding the business value of

developing a behaviour change program for households.

The decision to develop a new program focused on domestic clothes washing and future

machine technologies during Phase 3 is predicated on a demonstration that the new

program will provide sufficient business value. That is the information provided by CWW to

households during the use phase will be sufficient to provide a demonstrable improvement

in resource efficiency and reduce ecological impacts.




3 Life cycle assessment approach

LCA is a technique for assessing the environmental impacts associated with a product or

process. An LCA approach to the measurement of environmental impacts differs from other

environmental management approaches as it focuses on the measurement and calculation

of impacts which are normalised per unit of output i.e. one functional unit.

The normalised functional unit measure in this particular study was agreed to be 1kg of

clean dry clothes as this was deemed to be easily understood in a domestic context.

Arup undertook the study for CWW households in accordance with the relevant international

standards:

ISO 14040:2006 Environmental management – Life cycle assessment – Principles and

framework (ISO14040); and

ISO 14044:2006 Environmental management – Life cycle assessment - Requirements

and Guidelines (ISO14044).

Reference to these standards is made throughout the report and excerpts from the

standards are provided to introduce key requirements, definitions and concepts.

In accordance with ISO14044:2006, the LCA was undertaken in four phases (Figure 1):

a) goal and scope;

b) inventory analysis;

c) impact assessment; and

d) interpretation.

This Final Technical Report is the key output of the fourth phase in Figure 1; providing an

interpretation of the results generated in the third phase, Impact Assessment. This report

also provides commentary on aspects of the first two phases of the project.

Figure 1 Four phases of a LCA




4 Goal and scope

4.1 Overview

LCA of Clothes Washing Options for City West Water’s Residential Customers: Life Cycle

Assessment – Goal and Scope Report addresses the detailed requirements of ISO14040

and ISO14044 in relation to the goal and scope phase.

The following is a summary of the key points for the purposes of providing context to the

Final Technical Report.

4.2 Goal of the study

The goal of an LCA study shall unambiguously state the intended application, the reasons for

carrying out the study, the intended audience, i.e. to whom the results of the study are intended to

be communicated and whether the results are intended to be used in comparative assertions

intended to be disclosed to the public.

ISO 14044:2006 Section 4.2.2

The goals of this LCA were to:

quantify the level of water consumption associated with washing and drying clothes

across the lifecycle;

quantify the other environmental impacts associated with washing and drying clothes

across the lifecycle, such as greenhouse gas emissions, energy use and eutrophication;

quantify the environmental benefits of changes in key variables within the life cycle of

clothes washing and drying:

hot water system type;

washing machine type;

washing machine temperature;

washing machine settings;

detergent type;

detergent overfill;

use of fabric softener;

dryer use;

waste water disposal;

washing machine disposal; and

washing machine replacement period .

understand the dependent relationships between each of the key variables;

understand from existing literature the optimum time within the life of a washing

machine at which to replace it from the perspective of minimising the environmental

impact; and

enable CWW to prioritise strategies and actions for communicating the preferred

approaches to clothes washing and drying which enhance the resource efficiency and

reduce environmental impacts as part of Phase 3 of Program 1 of the Covenant.




4.3 Scope of the study

The scope of an LCA shall clearly specify the functions (performance characteristics) of the

system being studied. The functional unit shall be consistent with the goal and scope of the study.

One of the primary purposes of a functional unit is to provide a reference to which the input and

output data are normalised (in a mathematical sense). Therefore the functional unit shall be clearly

defined and measurable.

Having chosen the functional unit, the reference flow shall be defined. Comparisons between

systems shall be made on the basis of the same function(s), quantified by the same functional

unit(s) in the form of their reference flows.

If additional functions of any of the systems are not taken into account in the comparison of

functional units, then these omissions shall be explained and documented. As an alternative,

systems associated with the delivery of this function may be added to the boundary of the other

system to make the systems more comparable. In these cases, the processes selected shall be

explained and documented.

ISO 14044:2006 Section 4.2.3.2

4.3.1 Function, functional unit and reference flows

The function, functional unit and reference flows for the purposes of this project are defined

below and more general definitions can be found in the glossary.

Function: Cleaning and drying of clothes

Functional Unit: 1kg of clothes, cleaned and dried

Reference Flows: Washing machine materials (mass)

Water input (mass/volume)

Thermal energy inputs (washing) (energy)

Electrical energy inputs (washing) (energy)

Detergent input (mass/volume)

Detergent packaging (mass/volume)

Fabric softener (mass/volume)

Fabric softener packaging (mass/volume)

Waste water outputs (mass/volume)

Detergent packaging waste outputs (mass/volume)

Fabric softener waste outputs (mass/volume)

End of life washing machine waste (mass/volume)

Dryer materials (mass)

Electrical energy inputs (drying) (energy)

End of life dryer waste (mass/volume)




4.3.2 System boundaries

The system boundaries established as part of the LCA are outlined in Figure 2.

This diagram highlights the three phases (upstream, use and downstream) within the

clothes washing process and the individual unit processes within each phase. Unit

processes are the smallest element considered in the life cycle inventory analysis for which

input and output data are quantified.

Each of the three phases defined in this study and illustrated in Figure 2 contain different

unit processes. The upstream phase involves those processes relating to the production

and delivery of a washing machine; from manufacture through to eventual sale. The use

phase involves the regular use of the machine by households to wash and dry clothes,

including all the resources consumed during the process such as water, detergent and

energy. The downstream phase relates to the disposal and treatment of the materials and

chemicals produced during the process including the machines, detergents and waste

water.

Figure 2 Process flow diagram Grey boxes represent different phases while boxes represent unit processes.

3. Downstream1. Upstream

1.3 Washing Machine

Manufacture

1.4 Detergent Manufacture

1.2 Water Supply

1.8 Energy (Electricity)

Supply

1.1 Energy (Hotwater) Supply

• hotwater supply type

2. Use Phase

2.1 Washing

• machine type

• machine temperature

• machine settings

• detergent type

• detergent fi l l ing

• use of fabric softener

• machine replacement period

2.2 Drying

• line/machine type

• machine replacement period

3.1 Wastewater Treatment

3.4 Waste Treatment of

Washing Machine at End of

Life


Detergent Packaging

1 kg clothes

x kg water

1.5 Detergent Packaging

Manufacture

1.9 Drying Machine

Manufacture

1 kg clean dry clothes

1.6 Fabric Softener

Manufacture

1.7 Fabric Softener

Packaging Manufacture


Drying Machine at End of

Life


Fabric Softener Packaging




4.3.3 Cut-off criteria

Cut off criteria provide a series of filters for making a decision on whether to include a

process/material in the study. Decisions are made within a framework based on the

following:

1. mass;

2. energy; and

3. environmental relevance.

The LCA specifies a significance cut off value of 1%. Processes which fall below this value

for all scenarios and for all impact categories include:

detergent packaging manufacture;

fabric softener packaging manufacture;

waste treatment of detergent packaging; and

waste treatment of fabric softener packaging.

All other processes identified in Figure 2 make a contribution of greater than 1% for at least

one impact category and one scenario. Effort was made to address all aspects of the

clothes washing lifecycle, including those processes which fall below the 1% cut off for all

categories. For example, detergent packaging was included in the LCA on request of CWW

and EPA Victoria despite its low contribution to the impacts.

4.3.4 Base case

The base case represents the most common (mode) clothes washing scenario for

households within the CWW region (Table 1). The mode is based on existing data supplied

by CWW, the Australian Bureau of Statistics, previous LCA reports and other publicly

available information.

The washing machine parameters for standard energy consumption, water consumption,

rated capacity and water extraction are representative of the averages (mean) for machines

currently on the market with the predetermined base case properties for energy and water

star ratings.

Table 1 Definition of Base Case

Variable Base Case Value

Base Case

Water Heating System Type 5 star energy rating (MEPS) gas storage water heater.

Washing Machine Type

(WELS/energy star rating)

Top loading washing machine, 3 star WELS, 2 star energy

rating and both hot and cold water connections.

Washing Machine Settings Normal / Default

Washing Machine Lifespan 14 years

Washes per year 213

Washing Machine Temperature Cold (20oC)

Washing Machine Rated Capacity 7.03kg

Washing Machine Load Factor: 50% of rated load

Washing Machine Load: 3.52kg

Washing Machine Standard Test

Energy

1.59 KWh per wash




Washing Machine Standard Test

Water Consumption

97.29 L

Water Extraction Index 0.69

Detergent Type Omo top loader powder concentrate.

Detergent Filling 100% detergent dose recommendation.

Use of Fabric Softener No.

Line Drying / Dryer Use Line dry.

Washing Machine Disposal Recycle only machine metals.

Waste Water Disposal Disposal to sewer.

Base Case with Dryer

Line Drying / Dryer Use Electric Dryer.

Dryer Lifespan 20 years.

Cycles per year 52

Dyer Capacity 4.5kg

Dryer Loading 100% of rated load.

Dryer Standard Test Energy 4.84 kWh per cycle.

4.3.5 Scenario analysis

For each of the variables identified within the base case, a number of alternative values

were investigated.

This was done to gauge the level of impact that a selected variable may have on the

environment. Alternative modes of behaviour, choice or equipment which are relevant to

CWW‘s households were considered.

The alternatives are represented in the Table 2.

Table 2 Clothes Washing LCA Scenarios

Variable Alternative Values

Water System Type 3 star energy rating gas instantaneous

5 star energy rating gas instantaneous

Off peak electric single element

Off peak electric dual element

Solar electric split system

Solar electric thermosyphon (high efficiency)

Solar electric thermosyphon (minimum efficiency)

3 star energy rating gas storage

Solar gas split system

Solar preheat with gas instantaneous




Variable Alternative Values

Washing Machine Rating - Top

Loaders

Varying Energy Rating

3 star WELS / 1 star energy rating.

3 star WELS / 1.5 star energy rating.




Varying WELS Rating

1.5 star WELS / 2 star energy rating.




Washing Machine Rating – Front

Loaders

Varying Energy Rating





Varying WELS Rating


Machine Temperature Trend analysis was used across a range of temperatures

(20-95°C).

Detergent Type Top loading liquid concentrate.

Eco powder concentrate.

Generic brand powder concentrate.

Detergent Filling Trend analysis was used across a range of filling capacities

(100-200%).

Use of Fabric Softener Fabric softener used.

Line Drying / Dryer Use Electric dryer.

Condenser dryer.

Washing Machine Disposal Recycling of metals, plastics and concrete.

Machine sent to landfill.

Waste Water Disposal Use of grey water.

Future Machines Reason machine.

Waterless washing machine.




5 Life cycle inventory analysis

5.1 Overview

The requirements of ISO14040 and ISO14044 in relation to the inventory analysis phase of

an LCA are addressed in LCA of Clothes Washing Options for City West Water’s

Residential Customers: Life Cycle Assessment – Life Cycle Inventory Report.

The following provides a summary of this report.

5.2 Data collection procedures

The qualitative and quantitative data for inclusion in the inventory shall be collected for each unit

process that is included within the system boundary. The collected data, whether measured,

calculated or estimated, are utilised to quantify the inputs and outputs of a unit process.

ISO 14044:2006 section 4.3.2

Between August 2009 and February 2010 data was collected in partnership with CWW and

the EPA Victoria in accordance with Section 4.3.2 of ISO14044:2006.

The following specifies the data sources used in the LCA for each unit process in Figure 2.

5.2.1 Upstream processes

Information for upstream processes (such as energy and water supply, washing machine,

dryer, detergent and fabric softener manufacture) was collected from previous LCA reports,

government databases, and data embedded in the SimaPro databases. Arup, CWW and

EPA Victoria collectively approached business stakeholders and organisations to obtain

useful and relevant reports, research and market analysis to help feed into the LCA.

The unit processes involved in the upstream phases of the life cycle are shown in Figure 2

and explained in more detail below.

Energy (hot water) supply

The supply of thermal energy in the form of hotwater to the washing process from the

household hotwater system. Information on water system energy use was taken directly

from the Australian LCA Database, 2009.

Electricity supply

The supply of grid electricity used during the washing and drying processes. The LCI for the

supply of grid electricity was taken directly from the Australian LCA Database, 2009 LCI for

grid electricity in Victoria.

Water supply

The supply of reticulated water used during the washing process. The process for the

supply of water is taken directly from the Australian LCA Database, 2009 LCI for reticulated

water in Victoria. It includes impacts from water treatment chemicals for supply as well as

energy for both supply of water to the consumer and transportation of water to wastewater

treatment (based on average values for Melbourne). CWW also provided specific data

regarding its own energy consumption for supply of water and sewage services.

Unfortunately this data could not be directly incorporated into the model as the energy

consumption sources were not specified i.e. electricity, gas etc. Never the less, for

completeness this information is provided in Table 3.




Table 3 Energy Consumption for Water Supply and Sewage Treatment

Source Data Energy Consumption (GJ/ML)

Water Supplied Sewage Treated Total

Aus LCA Database n/a n/a 0.506

CWW 06/07 0.1979 3.2084 3.4063

CWW 07/08 0.4135 3.3015 3.7150

CWW 08/09 0.0057 0.2231 0.2288

CWW average 0.2057 2.2443 2.45

Washing machine manufacture

Due to lack of supply of information from manufacturers, manufacturing data for each

individual machine model was not obtained and instead machines were split into two main

types (top loading and front loading) with generic values sourced from previous LCA reports

and assigned to each. The difference between the type and quantity of materials used in

washing machine types is mainly due to mechanical differences between the operations of

each machine type resulting from the different orientation of the washing barrel.

Detergent manufacture (including detergent packaging manufacture)

Detergent manufacturing data to inform the unit process calculations is difficult to obtain as

the mix of constituents is often not disclosed by the manufacturer for commercial reasons.

The scarce data available is generally in the form of concentration ranges in a company

Materials Specification Data Sheet (MSDS) or from some independent studies. Some

international data is also available and utilised in this study however Australian detergents

are specially formulated for local conditions and are significantly different compared to other

parts of the world.

Regardless, from the available information it is clear that the specific detergent formulation

for each product varies widely in terms of chemicals and concentrations.

For the purposes of the LCA, the study has utilised the midpoint of these concentration

ranges and the percentage contribution weighted accordingly. Although there is a high

degree of uncertainty in this method, at this stage it is the most robust methodology

regarding the formulation and origins of chemicals.

Fabric softener manufacture (including packet manufacture)

The purpose of adding ‗fabric softeners‘ at the end of the washing process is to soften

clothes by neutralising the very small amounts of residual detergents left in the clothes and

preventing static electricity, especially for fabrics with a high content of synthetic fibres.

Fabric softeners also often include small amounts of fragrance. The esterquat group of

substances contains the main active ingredients of today‘s fabric softeners and are readily

degradable and have low toxicity to aquatic organisms. Esterquat based fabric softeners

were assumed in this LCA.

Both the fabric softener and fabric softener packaging manufacture were included within this

unit process. Data for fabric softeners was taken from MSDSs using the same methodology

adopted for detergents above and subject to the same limitations.

Drying machine manufacture

Drying machine manufacture relates to the manufacture of both electric tumble dryers and

condenser dryers. Due to the relatively small impact (less than 1% cut off value) of machine

manufacture and large degree of similarity of materials and construction processes, drying

machine manufacture is estimated by the same process as for a top loading washing

machine. Front loading machines perform worse than top loaders in terms of upstream




impacts for all impact categories and were therefore not selected to represent the dryer

manufacturing process.

5.2.2 Use phase

The use phase of the project relates to the washing and drying process that is undertaken

by households using their washing machines and tumble dryers or clothes lines. Arup

collected use phase data from previous LCAs, publicly available information on appliance

energy and water ratings and from household use information supplied by City West Water.

Arup made use of work conducted by Alan Pears for the EPA Australian Greenhouse

Calculator on washing machines for this phase of the data collection process.

The unit processes involved in the use phase of the life cycle are depicted in Figure 2 and

explained in more detail below.

Washing

This relates to the washing of clothes by a washing machine.

Drying

The drying of clothes using a tumble electric or condenser dryer.

5.2.3 Downstream processes

Downstream processes involve the treatment of waste water, as well as the treatment and

disposal of the packaging and machinery utilised during the use phase of the project. Data

for this phase was collected from CWW directly, who provided data relevant to their service

area. Additional data sources were also utilised when required, along with SimaPro

databases.

The unit processes involved in the downstream phases of the life cycle are depicted in

Figure 2 and explained in more detail below.

Wastewater disposal

The disposal of wastewater through the Western Treatment Plant in Victoria. The

wastewater treatment is separated into two units processes to reflect the impacts that are

dependent on volume of wastewater and impacts that are dependent on quality of

wastewater (i.e. detergent loading). For the volume dependent impacts, data was taken

from Melbourne Water‘s Annual Environmental Reports for 2006/07and is therefore based

on these annual values.

For the pumping energy associated with transportation of the water to the treatment plant,

values were taken from the Aus LCA Database (see water supply above).

Waste treatment for packaging

The treatment of packaging waste from clothes washing detergent and fabric softener

packaging.

Waste treatment of washing machine/dryer at end of life

The treatment of waste associated with the disposal of a washing machine and dryer.

5.3 Databases

In certain circumstances, where product-specific data was not available for upstream,

downstream and use processes, data was sourced from a number of databases within the

SimaPro software. These databases combine previous LCA information for the production

of a number of common material and energy inputs globally.

Australian LCA database 2009

The Australian LCA database was initially developed as part of a joint project between the

Centre for Design at RMIT and the Co-operative Research Centre for Waste Management

and Pollution Control. Since its initial development, data has been updated and new data




added from work undertaken at the Centre for Design and by other SimaPro users around

Australia.

The database includes information on fuels, electricity, transport, building and packaging

materials, waste management and some data on agricultural production. The database also

includes uncertainty data for energy processes to enable Monte Carlo analysis (See

Appendix B).

Ecoinvent

The Ecoinvent database v2.0 contains international industrial life cycle inventory data on

energy supply, resource extraction, material supply, chemicals, metals, agriculture, waste

management services, and transport services. The Ecoinvent database covers nearly 4000

processes predominantly within Switzerland and Western-Europe.

5.4 Validation of data

A check on data validity shall be conducted during the process of data collection to confirm and

provide evidence that the data quality requirements for the intended application have been fulfilled.

Validation may involve establishing, for example, mass balances, energy balances and/or

comparative analyses of release factors.

ISO 14044:2006 section 4.3.3.2

5.4.1 Data quality assessment

Data inputs into the SimaPro model were cross-checked under Arup‘s quality assurance

protocols. Where possible, uncertainty was assigned to raw data to facilitate the uncertainty

analysis which was carried out as part of the Life Cycle Impact Assessment phase of the

project. In addition, LCA experts from the University of New South Wales ( UNSW) peer

reviewed both the LCA process and the Final Technical Report.

5.5 Allocation and credit principles and procedures

The inputs and outputs shall be allocated to the different products according to clearly stated

procedures that shall be documented and explained together with the allocation procedure.

The sum of the allocated inputs and outputs of a unit process shall be equal to the inputs and

outputs of the unit process before allocation.

Whenever several alternative allocation procedures seem applicable, a sensitivity analysis shall be

conducted to illustrate the consequences of the departure from the selected approach.

ISO 14044:2006 section 4.3.4

Where two or more product outputs exist in any one process, allocation rules must be

assigned to distribute the upstream impacts.

Similarly, where two or more waste products are treated in one waste treatment process,

rules must be assigned to distribute the downstream impacts to the waste. There were no

occurrences of two or more products or waste products being processed or treated during

this study and therefore no allocation issues were indentified in the project.

It is assumed that the recycling of washing machine and dryer materials results in an

avoided need for the production of equivalent material (e.g. steel, aluminium etc. As such

the recycling of machines is assigned a credit for the avoided production).

Credits were used in the following three processes:

recycling of washing machine materials;

recycling of dryer materials; and

use of greywater.




For grey water, it is assumed that the use of greywater for irrigation of gardens avoids the

need for the supply of an equivalent amount of irrigation water. As such, grey water usage

is assigned a credit for avoided supply of potable water. Where households would not

otherwise irrigate or where the source of irrigation water is other than mains (eg rainwater,

other sources of greywater) this would represent an overestimation of the benefits of grey

water.




6 Life cycle impact assessment

6.1 Impact assessment models

The LCA was modelled in SimaPro 7.1 LCA software. SimaPro stands for "System for

Integrated Environmental Assessment of Products". Its generic setup means that its use has

expanded from product analysis to analysis of processes and services. SimaPro 7.1

provides a tool to collect, analyse and monitor the environmental performance of products

and services. Complex life cycles can be modelled in a systematic and transparent way,

following the ISO 14040 series recommendations.

First released in 1990, SimaPro is used by major industries, consultancies and universities

for 1SO14040 compliant LCA studies with nearly one thousand user licenses sold in 50

countries.

6.2 Impact categories

Using the LCA software modelling program, SimaPro 7.1, the impact assessment modules

were used to report on the following impact categories:

The list of impact assessment categories included:

water use;

energy consumption;

global warming (IPCC, 100 years);

eutrophication;

non renewable resource depletion, including fossil fuel use and minerals; and

land use.

Air acidification and photochemical oxidant were initially included in the range of impact

categories. However the LCI databases adopted for use in this report did not contain a

sufficient level of data on either of these category indicators especially for the Australian

context. The results for air acidification and photochemical oxidant impact categories are

therefore skewed towards disproportionately high impacts for the detergent manufacture

processes which adopt European data. The results for both these impact categories were

therefore considered inaccurate and misleading and were excluded.

6.3 Definition of categories

Each of the impact categories outlined in Section 6.2 are defined in Table 4 to Table 9.




Table 4 Water use impact category definition

Water Use Impact Category Definition

General description Total volume of water extracted from natural sources to produce one

kilogram of clean dry clothes, includes water consumed in upstream and

downstream processes (such as at power plants during energy generation,

during detergent manufacture and at the wastewater treatment plant) in

addition to water consumed by the washing machines

LCI results Consumption of water resources including by end-use

Characterisation

model

Not applied (i.e. no consideration of the relative importance of one water

source compared to another or compared to existing quantity of water

reserves)

Category indicator Decrease in the quantity of water reserves

Characterisation

factor

No characterisation factor applied

Category indicator

result

KL H2O consumed from natural sources per functional unit

Category endpoints Water reserves

Environmental

relevance

The depletion of water reserves leads to depletion of local water resources

potentially lowering their availability to other human, aquatic and terrestrial

ecosystems.

Table 5 Energy consumption impact category definition

Energy Consumption Impact Category Definition

General description Total energy resources consumed to produce one kilogram of clean dry

clothes, includes energy consumed in upstream and downstream processes

(such as at power plants during energy generation, during detergent

manufacture and at the wastewater treatment plant) in addition to energy

consumed by the washing machines

LCI results End-use of energy for transport, process heat, fuel extraction and delivery,

electricity delivered and electricity lost

Characterisation

model

Not applied (i.e. no consideration of the relative importance of one energy-

end use compared to another or compared to the energy supply available)

Category indicator Decrease in energy available

Characterisation

factor

No characterisation factor

Category indicator

result

MJ LHV required per functional unit

Category endpoints Energy supply networks and the reserves and infrastructure required to meet

demand

Environmental

relevance

The energy demand decreases the amount of energy available for other

useful work. For electricity the energy demand increases the amount of

energy to be supplied to the grid from external sources.




Table 6 Global warming impact category definition

Global Warming Impact Category Definition

General description Total global warming potential of the greenhouse gases emitted to produce

one kilogram of clean dry clothes

LCI results Emissions of greenhouse gas per functional unit

Characterisation

model

Baseline model of 100 years of the Intergovernmental Panel on Climate

Change

Category indicator Infrared radiative forcing (W/m2)

Characterisation

factor

Global warming potential (GWP100) for each greenhouse gas (kg CO2-

equivalents/kg emission)

Category indicator

result

Kilograms of CO2-equivalents per functional unit

Category endpoints Coral reefs, forests, crops, urban settlements

Environmental

relevance

Infrared radiative forcing is a proxy for potential effects on the climate,

depending on the integrated atmospheric heat adsorption caused by

emissions and the distribution over time of the heat absorption

Table 7 Eutrophication impact category definition

Eutrophication Impact Category Definition

General description Total eutrophication potential of nutrients (such as phosphates and nitrates)

released to water bodies to produce one kilogram of clean dry clothes

LCI results Emissions of eutrophying substances into the air, water or soil per functional

unit

Characterisation

model

Based on the stoichiometric calculations of Heijungs (1992) which identify the

equivalence between N and P for both terrestrial and aquatic systems

Category indicator Deposition/N/P equivalents in biomass

Characterisation

factor

Eutrophication potential for each eutrophying emission to the air, water or soil

(in kg PO4-equivalents/kg emission)

Category indicator

result

Kilograms of PO4-equivalents per functional unit

Category endpoints Water bodies

Environmental

relevance

Nutrients (phosphorous and nitrogen) enter water bodies, such as lakes,

estuaries and slow-moving streams, causing excessive plant growth and

oxygen depletion.




Table 8 Non renewable resource depletion impact category definition

Non Renewable Resource Depletion Impact Category Definition

General description The additional energy required to extract new resources (which are more

difficult to access, extract and process) as a result of depletion of existing

reserves to produce one kilogram of clean dry clothes

LCI results Consumption of minerals, ores and fossil fuel resources which cannot be

renewed in human relevant periods of time per functional unit

Characterisation

model

Eco-indicator 99 method, egalitarian version based on Chapman and

Roberts (1983) assessment procedure for the seriousness of resource

depletion

Category indicator Decrease in the concentration of reserves

Characterisation

factor

Additional energy requirement to extract an equivalent amount of resources

at some time into the future based on the lower resource concentration or

other unfavourable reserve characteristics1

Category indicator

result

MJ surplus energy required per functional unit

Category endpoints Mineral, ore and fossil fuel reserves

Environmental

relevance

The use of minerals, ore and fossil reserves as material and energy sources

leads to depletion of global reserves thereby lowering their availability for

future generations.

Table 9 Land use impact category definition

Land Use Impact Category Definition

General description The amount of land required to produce the inputs necessary per kilogram of

clean dry clothes

LCI results Occupation of land

Characterisation

model

Not applied (i.e. no consideration of the relative importance of one land

occupation type compared to another or compared to existing land reserves)

Category indicator Decrease in quantity of occupiable land reserves

Characterisation

factor

No characterisation factor applied

Category indicator

result

m2 occupiable land used per functional unit

Category endpoints Land reserves

Environmental

relevance

The use occupiable leads to depletion of total occupiable land potentially

reducing availability to other human, aquatic and terrestrial ecosystems.




7 Results and interpretation

This section provides results from the Life Cycle Impact Assessment (LCIA) for the impact

categories identified in Section 6.2. Section 7.1 analyses the base case with and without

drying to determine which aspects of the life cycle have the largest environmental impacts.

Scenario analysis is then provided in Section 7.2, which explores the impact of changes in

various base case parameters. The aim of the scenario analysis is to determine which

variables have the largest impacts on the clothes washing lifecycle.

7.1 Base case

The following represents the impacts for the base case including line drying as previously

defined in Section 4.3.4. Each impact category includes impacts which occur during the

upstream, use and downstream phases of the process. The impacts are presented in

absolute terms as well as percentage breakdowns in the corresponding tables and pie

charts. The pie charts include:

a breakdown by phase for each scenario (the large pie chart on the left of each figure);

and

a further breakdown of the pie chart by contributing unit processes within each phase

(the three pie chart wedges on the right of each figure).

When interpreting these results, it should be noted that negative impacts are observed

where credits are given for avoided impacts (e.g. recycled steel). For the purposes of visual

representation these have been assigned a zero value within the pie charts.

7.1.1 Water

Results indicate that the lifecycle impacts of clothes washing on water use are

approximately 30.4 L H2O per kg of dry clothes for the base case and 31.9 L H2O per kg of

dry clothes for the base case with electric tumble drying. The contributions of the various

lifecycle phases for the base case are presented in Table 10 and Figure 3. The

contributions of various lifecycle phases for the base case with drying are presented in

Table 10 and Figure 4.




Table 10 Water use impacts

Lifecycle Phase

Base Case

(L H2O per kg

Dry Clothes)

Base Case with dryer

(L H2O per kg

Dry Clothes)

UP

ST

RE

AM

Detergent Manufacture 2.49 2.49

Detergent Packaging Manufacture 0.01 0.01

Washing Machine Manufacture 0.04 0.04

Dryer Manufacture n/a 0.38

Total Upstream Phase 2.54 2.91

US

E

Washing Machine Thermal Energy 2 x10-3

2 x10-3

Washing Machine Mechanical Energy 0.10 0.10

Washing Machine Water Consumption 27.68 27.68

Dryer Energy n/a 1.24

Standby Power 0.06 0.06

Total Use Phase 27.9 29.1

DO

WN

ST

RE

AM

Wastewater Treatment 0.01 0.01

Detergent Packaging Disposal 1 x10-3

1 x10-3

Washing Machine Disposal -0.02 -0.02

Dryer Disposal n/a -0.09

Total Downstream Phase -4 x10-3

-0.09

TOTAL 30.4 31.9

*Note that where negative impacts are observed (and credits given for avoided impacts), these have

been assigned a zero value for the purposes of generating the pie charts.

The consumption of water within the use phase is unsurprisingly the main cause of water

consumption. As shown in Figure 3, a substantial 91.1% of the water impact is attributed to

the water consumption of washing machines in the base case with only 8% occurring in the

upstream phase during detergent manufacture. All other processes consume relatively little

water and as such do not impact on the scenario.

The story is similar when an electric tumble dyer is used, with washing machine use phase

consumption accounting for 86.5%, detergent manufacture representing 7.8% and dryer

energy approximately 4%. This result occurs because of the large amounts of electricity

used during the drying process. Electricity generation consumes large amounts of water

particularly at coal fired power plants and any process using a large amount of electricity will

have embodied water use impacts.




Figure 3 Water use impact base case




Figure 4 Water use impact base case with drying




7.1.2 Energy use

The results indicate that the lifecycle impacts of clothes washing on energy use are

approximately 2,476 kJ eq per kg of clothes washed for the base case and 12,196 kJ eq for

the base case with electric drying. The contributions of the various lifecycle phases for the

base case are presented in Table 11 and Figure 5. The contributions of various lifecycle

phases for the base case with drying are presented in Table 11 and Figure 6.

Table 11 Energy use impacts

Lifecycle Phase

Base Case

(kJ eq per kg Dry

Clothes)


(kJ eq per kg Dry

Clothes)

UP

ST

RE

AM

Detergent Manufacture 805 805

Detergent Packaging Manufacture 24 24

Washing Machine Manufacture 92 92

Dryer Manufacture n/a 612

Total Upstream Phase 921 1,533

US

E

Washing Machine Thermal Energy 242 242

Washing Machine Mechanical Energy 751 751

Washing Machine Water Consumption 41 41

Dryer Energy n/a 9,283

Standby Power 471 471

Total Use Phase 1,506 10,788

DO

WN

ST

RE

AM

Wastewater Treatment 94 94

Detergent Packaging Disposal -5 -5

Washing Machine Disposal -40 -40

Dryer Disposal n/a -174

Total Downstream Phase 49 -125

TOTAL 2,476 12,196



As demonstrated in Figure 5, detergent manufacture was the most energy intensive process

for the base case scenario, representing 32% of the overall energy used to produce 1 kg of

clean dry clothes. This can be attributed to the fact that the chemicals used in detergents

are produced using large quantities of energy particularly those derived from

petrochemicals. Furthermore, because detergent is used for each and every wash, the

frequency of detergent use is high, meaning detergent manufacture is attributed to each

wash and is not just a one off event. Direct energy in the form of washing machine

mechanical energy and standby power were also significant contributors to energy use

representing 30% and 19% respectively.

Once a dryer is added, the energy use profile changes significantly, increasing by almost

400%. Figure 6 shows that dryer energy represents almost 75% of total energy use during

this scenario. This can be attributed to the energy intensity of dryers. Several activities that

were comparatively energy intensive in the base case without drying scenario, such as




detergent manufacture, washing machine mechanical energy and standby power,

individually represent less than 7% of the total energy use in this scenario.

It should be noted that standby power for dryer use has not been considered in the impact

assessment. Unlike washing machines, standby power for dryers is not included in the

Energy Rating databases. While some machines are likely to consume power in standby

mode, this will vary widely depending on machine type, with some dryers having very few

standby functions which consume power. Due to large range of uncertainty relating to power

use, standby power for dryers was not considered in the impact assessment. Households

should be aware that dryers with functions which operate in standby mode will have a

greater impact than those without.



Page 26 Arup Draft 1 29 March 2010

Figure 5 Energy use impact base case




Figure 6 Energy use impact base case with drying




7.1.3 Global warming

The results indicate that the lifecycle impacts of clothes washing on global warming are

approximately 0.21 kg CO2-e per kg of washed clothes for the base case and 1.20 kg CO2-e

for the base case with electric tumble drying. The contributions of the various lifecycle

phases for the base case are presented in Table 12 and Figure 7. The contributions of the

various lifecycle phases for the base case with drying are presented in Table 12 and Figure

8.

Table 12 Global warming impacts

Lifecycle Phase

Base Case

(kg CO2 per kg Dry

Clothes)


(kg CO2 per kg Dry

Clothes)

UP

ST

RE

AM






US

E

Washing Machine Thermal Energy 0.01 0.01


Washing Machine Water Consumption 4 x 10-3

4 x 10-3




DO

WN

ST

RE

AM


Detergent Packaging Disposal -9 x 10-5

-9 x 10-5

Washing Machine Disposal -3 x 10-3

-3 x 10-3


Total Downstream Phase -0.01 6 x 10-4

TOTAL 0.21 1.3



Global warming impacts are different to energy impacts, mainly due to the different

emissions intensity of various energy production sources. With a 39.0% impact, the

mechanical energy phase contributes the greatest amount to global warming due to the

direct energy use and the relative emission intensity of electricity generation in Victoria.

Although detergent manufacture was the greatest source of energy consumption, it is

reduced to 16.4% of global warming impacts as many of the chemicals are manufactured

overseas where electricity generation is less emission intensive or natural gas boilers are

used for energy supply at manufacturing plants. Other energy related phases (such as

standby power and thermal energy) also contribute a relatively large amount to the global

warming impact scenario.

Once a dryer is used, the contributions to global warming change significantly, with dryer

energy contributing close to 80% of all emissions. This is again a result of electricity use.

While washing machines derive some of their power from thermal energy supplied from




water heating (supplied by gas in the base case), dryers rely solely on electricity. Victoria‘s

electricity supply is a high emissions intensity energy source and as such, the use of a dryer

notably increases emissions and the impact on global warming.




Figure 7 Global warming impact base case

Downstream

Upstream

20%Use

73%

Downstream

7%Use

Washing

Machine

Thermal Energy

7%

Washing

Machine

Mechanical

Energy

39%

Washing

Machine Water

Consumption

2%

Standby Power

25%

Upstream

Detergent

Manufacture

17%

Washing

Machine

Manufacture

3%Detergent

Packaging

Manufacture

1%

Use

Total Lifecycle

Global Warming

Impacts

DownstreamWastewater

Treatment

7%

Upstream




Figure 8 Global warming impact base case with drying

Downstream

Use

Total Lifecycle

Global Warming

Impacts

UpstreamBase case with dryer - Global warming impacts

Downstream

1%Upstream

7%

Use

92%

Upstream

Detergent

Manufacture

2.7%Detergent

Packaging

Manufacture

0.1%Washing

Machine

Manufacture

0.5%Dryer

Manufacture

3.7%

Use

Dryer Energy

79.9%Standby Power

4.1%

Washing

Machine

Thermal Energy

1.2%Washing

Machine

Mechanical

Energy

6.5%

Washing

Machine Water

Consumption

0.3%

Downstream

Wastewater

Treatment

1.1%




7.1.4 Eutrophication potential

The results indicate that the lifecycle impacts of clothes washing on eutrophication potential

are approximately 1.17 g PO4 eq per kg of washed clothes for the base case and

1.3 g PO4 eq for the base case with drying. The contributions of the various lifecycle phases

for the base case are presented in Table 13 and Figure 9. The contributions of the various

lifecycle phases for the base case with electric drying are presented in and Table 13 and

Figure 10.

Table 13 Eutrophication impacts

Lifecycle Phase

Base Case

(g PO4 per kg Dry

Clothes)


(g PO4 per kg Dry

Clothes)

UP

ST

RE

AM


Detergent Packaging Manufacture 1 x 10-3

1 x 10-3

Washing Machine Manufacture 4 x 10-3

4 x 10-3



US

E

Washing Machine Thermal Energy 0.02 0.02


Washing Machine Water Consumption 1 x 10-3

1 x 10-3




DO

WN

ST

RE

AM


Detergent Packaging Disposal 3 x 10-6

3 x 10-6

Washing Machine Disposal -2 x 10-3

-2 x 10-3


Total Downstream Phase 1.10 1.09

TOTAL 1.17 1.3



As illustrated in Figure 9, the treatment of wastewater is the largest contributor to

eutrophication potential for the base case, with a 93.6% impact. This can be attributed to

the phosphorus content of the detergent output from the washing machine.

While some of this phosphorous is removed at Melbourne Water‘s Western Treatment

Plant, the remainder is disposed to Port Phillip Bay. All other stages involved in the washing

machine process impact minimally on this scenario with only some occurring upstream

during detergent manufacture and electricity generation.

Results for the base case with an electric tumble dryer (Figure 10) are similar to those of the

base case. In this scenario, treatment of wastewater is the main contributor to

eutrophication potential, representing 81% of the total impact; however dryer energy also

contributes to over 11%. The contribution to eutrophication by dryer energy results from

electricity consumption for the energy intensive dryer and the wastewater treatment

associated with electricity generation.




Figure 9 Eutrophication base case




Figure 10 Eutrophication base case with drying




7.1.5 Fossil fuels depletion

The results indicate that the lifecycle impacts of clothes washing on the depletion of fossil

fuels are approximately 153 kJ of surplus depletion per kg of washed clothes for the base

case and 739 kJ for the base case with drying. The contributions of the various lifecycle

phases for the base case are presented in Table 14 and Figure 11. The contributions of the

various lifecycle phases for the base case with electric drying are presented in Table 14 and

Figure 12.

Table 14 Fossil fuels depletion impacts

Lifecycle Phase

Base Case

(kJ Surplus per kg

Dry Clothes)


(kJ Surplus per kg

Dry Clothes)

UP

ST

RE

AM

Detergent Manufacture 45 45



Dryer Manufacture n/a 45

Total Upstream Phase 54 99

US

E

Washing Machine Thermal Energy 21 21

Washing Machine Mechanical Energy 45 45

Washing Machine Water Consumption 2.8 2.8

Dryer Energy n/a 553

Standby Power 28 28

Total Use Phase 96 649

DO

WN

ST

RE

AM


Detergent Packaging Disposal -0.4 -0.4


Dryer Disposal n/a -12

Total Downstream Phase 2.4 -9.7

TOTAL 153 739



Figure 11 illustrates the scenario of fossil fuel resource depletion and energy consumption

although some fossil fuels are given greater weighting to reflect the relative energy intensity

of their extraction. Aside from detergent packaging disposal, all washing machine processes

achieve a notable impact on this scenario. Detergent manufacturing and mechanical energy

achieve the largest impact results with 28.3% and 28.1% respectively.

When the use of a dryer is considered (Figure 12), the fossil fuel depletion impacts increase

considerably. Dryer energy accounts for close to three quarters (73.3%) of the total fossil

fuel depletion impacts, with dryer manufacture, detergent manufacture and washing

machine mechanical energy all representing almost 6% each of the total impacts for this

scenario. Again, the energy intensity of dryers and their reliance on electricity (which is

produced from fossil fuels) explain the results for this scenario.




Figure 11 Fossil fuels depletion impact base case




Figure 12 Fossil fuels depletion impact base case with drying




7.1.6 Minerals depletion

The results indicate that the lifecycle impacts of clothes washing on the depletion of

minerals are approximately 10.2 kJ of surplus depletion per kg of washed clothes for the

base case and 33.2 kJ for the base case with electric tumble drying. The contributions of

the various lifecycle phases for the base case are presented in Table 15 and Figure 13.

The contributions of the various lifecycle phases for the base case with drying are presented

in Table 15 and Figure 14.

Table 15 Minerals depletion impacts

Lifecycle Phase

Base Case

(kJ Surplus per kg

Dry Clothes)

Base Case

(kJ Surplus per kg

Dry Clothes)

UP

ST

RE

AM


Detergent Packaging Manufacture 2 x 10-4 2 x 10

-4




US

E

Washing Machine Thermal Energy 4.E-04 4 x 10-4


Washing Machine Water Consumption 1 x 10-3 1 x 10

-3




DO

WN

ST

RE

AM


Detergent Packaging Disposal -1 x 10-5 -1 x 10

-5



Total Downstream Phase -0.3 -1.7

TOTAL 10.2 33.2



Manufacturing processes provide the greatest impact on the scenario of minerals depletion

as illustrated in Figure 13. Detergent manufacturing contributes a 64.2% impact, with

washing machine manufacturing providing a further 32.3% contribution towards the total

minerals depletion in this scenario. Within these processes, the greatest proportion of

minerals is consumed in the manufacturing of machine parts and detergent chemicals.

Manufacturing processes still dominate when the base case is considered with the use of a

dryer (Figure 14). However the manufacture of the dryer outweighs the detergent and

washing machine contributions, accounting for close to 70% of the mineral depletion. The

dryer manufacture registers higher mineral depletion than the washing machine

manufacture even though the same manufacturing processes have been assumed. This is

because the dryer is used less frequently than the washing machine and so its impacts are

concentrated in fewer cycles over its useful life.




Figure 13 Minerals depletion impact base case




Figure 14 Minerals depletion impact with drying




7.1.7 Land use

The results indicate that the lifecycle impacts of land use are minimal, representing only an

approximate 3.9 x 10-3

m2 per kg of washed clothes for the base case and 6.8 x 10

-3 m

2 for

the base case with electric drying. The contributions of the various lifecycle phases for the

base case are presented in Table 16 and Figure 15. The contributions of the various

lifecycle phases for the base case with drying are presented in Table 16 and Figure 16.

Table 16 Land use impacts

Lifecycle Phase

Base Case

(m2 per kg Dry

Clothes)


(m2 per kg Dry

Clothes)

UP

ST

RE

AM

Detergent Manufacture 3.3 x 10-3

3.3 x 10-3

Detergent Packaging Manufacture 1.6 x 10-5

1.6 x 10-5

Washing Machine Manufacture 1.1 x 10-4

1.1 x 10-4

Dryer Manufacture n/a 5.0 x 10-4

Total Upstream Phase 3.5 x 10-3

4.0 x 10-3

US

E

Washing Machine Thermal Energy 3.9 x 10-6

3.9 x 10-6

Washing Machine Mechanical Energy 2.0 x 10-4

2.0 x 10-4

Washing Machine Water Consumption 1.1 x 10-5

1.1 x 10-5

Dryer Energy n/a 2.4 x 10-3

Standby Power 1.2 x 10-4

1.2 x 10-4

Total Use Phase 3.4 x 10-4

2.8 x 10-3

DO

WN

ST

RE

AM

Wastewater Treatment 3.0 x 10-5

3.0 x 10-5

Detergent Packaging Disposal 1.2 x 10-5

1.2 x 10-5

Washing Machine Disposal -8.2 x 10-6

-8.2 x 10-6

Dryer Disposal n/a -3.5 x 10-5

Total Downstream Phase 3.4 x 10-5

-1.2 x 10-6

TOTAL 3.9 x 10-3

6.8 x 10-3



Land use impacts for both the base case and the base case with an electric tumble dryer

are less than a square meter per kg of clean dry clothes. However, when the mass of

clothes washed over an annual period is considered, the land use impact becomes

significant. For example, under the base case, land use impacts without the use of a dryer

equate to approximately 2.9 m2 per year per household. Considering the number of

households in the CWW region collectively, this impact is very large.

Manufacturing processes provide the greatest contribution to land use in the base case, with

detergent manufacturing representing 86.8% of all land use as highlighted in Figure 15.

Detergent manufacturing uses a number of agricultural derived products and as such, its

impact on land use is the most significant during the base case scenario.

Once a dryer is considered (Figure 16) dryer energy contributes moderately (36%) to land

use impacts. This is due to its high reliance on electricity, the production of which causes

some land degradation.




Figure 15 Land use impact base case




Figure 16 Land use impact base case with drying




7.2 Scenario analysis

7.2.1 Washing machine selection scenarios

The comparative performance of washing machine types is dependent on a number of

variables including:

energy efficiency (represented by energy rating);

water efficiency (represented by water rating);

loading (front or top);

rated load capacity (size); and

machine loading (dependent on household behaviour when filling the machine).

To investigate the importance of any one of these variables in a scenario analysis, it is

important to hold all other variables constant. However, this sort of analysis is not realistic

as often there may be no actual machines on the market represented by a particular

scenario. For instance, a 7kg machine may only be available under a limited number of

energy star and WELS ratings. Therefore we have attempted to represent actual machines

available on the market, with information on this sourced from the government‘s Water

Efficiency and Labelling (WELS) Rating Scheme (Commonwealth of Australia, 2009) and

Energy rating scheme (DEWHA, 2009). Additionally, we have included the comparison of

future washing machine technologies to provide an indication of the environmental benefits

of emerging technology within the machine washing field.

Generally machines with the same energy or WELS star ratings vary widely in terms of size.

Therefore both size and machine loading cannot be simultaneously held constant. To

address this, two possible options were considered:

1. Keep the machine loading constant at 50% capacity (as per the base case) for all

options resulting in variations in kg clothes per wash (with smaller machines

washing less clothes mass than larger machines); and

2. Keep the kg clothes per wash at 3.52kg (the washing machine load as per the base

case) for all options resulting in variations for the machine loading with smaller

machines closer to full capacity than large machines.

Some lifecycle phases are sensitive to changes in percentage capacity and therefore vary

for Option 2, while others are dependent on wash mass and therefore vary for Option 1 as

shown in Table 17 below.

Table 17 Sensitivity to Analysis Options

Phase Option 1 – 50% machine loading Option 2 – 3.52kg machine loading

Small Machine Large Machine Small Machine Large Machine

Detergent

Consumption

Increased

impacts as

concentrated over

smaller wash size

Reduced impact

as dispersed over

larger wash size

No difference No difference

Energy

Consumption

No difference No difference Reduced impact

as less

underfilling

Increased impacts

as excess energy

use due to

underfilling

Water

Consumption


as less

underfilling

Increased impacts

as excess water

use due to

underfilling




Phase Option 1 – 50% machine loading Option 2 – 3.52kg machine loading

Small Machine Large Machine Small Machine Large Machine

Machine

Manufacture

Increased

impacts as

concentrated over

smaller wash size

Reduced impact

as dispersed over

larger wash size


Wastewater

Treatment

(detergent

loading)

Increased

impacts as

concentrated over

smaller wash size

Reduced impact

as dispersed over

larger wash size


Wastewater

Treatment

(energy and

greenhouse)


as less

underfilling

Increased impacts

as excess water

use due to

underfilling sent to

WWTP

For the base case approach of keeping the machine loading constant at 50% (Option 1),

Table 17 highlights that varying the machine size will concentrate the impacts associated

with detergent manufacture, machine manufacture and detergent loading in wastewater, but

keep the use phase energy and water consumption constant.

This outcome is a reflection of the amount of detergent used per wash being constant as

manufacturers‘ recommendations generally do not distinguish between small and large

machines or loads. Therefore for smaller machines, greater amounts of detergent are used

per kg of clothes such that embodied impacts of detergent (which are relatively high across

all impact categories except water use) are larger for smaller machines.

This is significant in the overall results because considerable impacts are related to

detergent manufacture, which is based on the manufacturers‘ recommendations per wash.

For example detergent manufacture accounts for 32% of the overall energy used to produce

1 kg of clean dry clothes.

The consequence of this is that when considering the environmental impacts associated

with the use of washing machines, results from the study indicate that energy use impacts

are more closely linked to machine size than energy rating, as this only affects the thermal

and mechanical energy requirements of the machine. By increasing the machine size a

reduced amount of detergent is used per kg of clean clothes.

To minimise the environmental impacts of clothes washing, consideration of the energy and

water efficiency (MEPS and WELS rating) should occur once the size of the machine has

been optimised to reflect the volume of clothes that require washing per week and the

capacity of the machine required for this volume.

In considering the type of machine and efficiency ratings the study has considered top

loading (base case) and front loader machines (3 star energy rating and 4 star WELS front

loading machines (the most common front loader available on the market)) and future

machines, namely the current market leader in terms of both energy and water star ratings

(front loading 4 star energy rating 4.5 star WELS rating) and the Reason and Waterless

machines.

The results show that the future machines consistently outperform the base case (except for

fossil fuel depletion) and also perform better than front loading machines. The front loader

outperforms the top loader for water use, but performs worse than the top loader across all

categories. This is primarily due to the front loader having, on average, a smaller capacity

than the top loader and, as indicated above, this has a significant bearing on the results. If




the front loader has the same capacity as the top loader it is expected it would have a

reduced environmental impact across the majority of impact categories.

In relation to future machines the current market leader produced the fewest emissions and

the Waterless machine used the least water. Fossil fuels depletion and energy use were

greater than the base case for the Waterless machine due to the embodied impacts in the

nylon beads. The Reason machine achieved less significant environmental savings than

the Waterless machine in areas such as land use, eutrophication and of course water use,

but it consistently outperformed the base case in all impact categories and had the most

consistent savings from a holistic impact perspective.

The following section provides detailed information on these findings, and Appendix A the

sensitivity analysis for the Option 2 scenario outlined in Table 17.




7.2.2 Future Machines

In order to understand the sustainable direction in which domestic clothes washing is

heading, the scenario analysis considered future machines as well as the current market

leader in terms of both energy and water star ratings (front loading 4 star energy rating 4.5

star WELS rating). The future machines nominated were the Waterless machine and the

Reason machine which are yet to be fully commercialised. The assumptions around the

future machines are based on manufacturers‘ claims and have not been verified.

Box 1: Future washing technologies: The Reason machine

The Reason Washing Machine

Andrew Reason, an Architect from the UK has developed a green washing machine that uses the

fuzzy logic system to reduce the impacts of clothes washing on the environment.

Water stored in the ‗balance tank‘ provides the machine‘s stability, enabling the elimination of

concrete, which most conventional washing machines use to balance the machine. This tank also

enables water to warm to room temperature, reducing the energy requirements of water heating

for warm or hot water loads. The creator has also developed special detergents to operate at 15

degrees that are beneficial to the environment and further reduce energy requirements for water

heating.

The fuzzy logic technology senses the exact amount of water, detergent and softener to add to

each load, further enhancing energy and water efficiency along eliminating the potential for

detergent overfilling.

Box 2: Future washing technologies: The Waterless Washing Machine

The Waterless Washing Machine

The Waterless washing machine developed by Xeros Ltd in the UK uses 90% less water than

conventional machines. The technology uses 20kg of nylon beads, which attract dirt and absorb

stains when mixed in the machine with a load of dirty washing.

Xeros' technology uses as little as a cup of water containing the detergent in each wash cycle,

without the need for a rinse or spin cycle. When finished, a grill at the bottom of the machine

opens to collect the chips, which can be re-used many times. The machine minimises water

consumption and through a reduction in the need to heat water, it also saves energy and

detergent.

The technology is planned for commercial release by the end of 2011 and could revolutionise the

way people wash clothes.

The results from investigating future machines are included in Table 18 and Figure 17.

Table 18 Impact of future machines and current market leader

Scenario

Impact Category

Wa

ter

Use

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l W

arm

ing

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

Dep

leti

on

(kJ

su

rplu

s)

Min

era

ls

Dep

leti

on

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case 30.4 2,476 0.21 1.2 152.56 10.18 3.9 x 10-3

Reason 10.0 1,247 0.11 0.4 76.47 5.90 1.5 x 10-3

Waterless 1.5 2,166 0.14 0.1 169.56 4.92 2.7 x 10-4

Current Market

Leader 10.8 1,576 0.11 1.2 94.42 12.82 3.9 x 10

-3




Figure 17 Impacts of future machines and current market leader (% Difference from base case)

All emerging technology machines performed better than the base case machine for energy

use, global warming and water use (implying they use less energy, produce fewer emissions

and use less water). The market leader produced the fewest emissions and the Waterless

machine used the least water. The market leader performed worse than the base case

machine for eutrophication and minerals depletion due to detergent impacts. This is

because the market leader is a relatively small machine and so detergent manufacture

impacts are concentrated over a smaller wash size compared to the base case and the

other future machines. Fossil fuels depletion and energy use were greater than the base

case for the Waterless machine due to the embodied impacts in the nylon beads. The

Reason machine achieved less significant environmental savings than the Waterless

machine in areas such as land use, eutrophication and of course water use, but it

consistently outperformed the base case in all impact categories and had the most

consistent savings from a holistic impact perspective.

7.2.3 Varying loading type (top loader and front loader)

Despite having knowledge that emerging technologies significantly reduce environmental

impacts, these machines are not yet commercially available and as such, there is a need to

better understand the machines currently available on the market. The base case (top

loading machine) was compared against front loading machines and emerging technologies

to provide general insight into the environmental impacts of different machine loading types.

The front loading machine is represented by the average of 3 star energy rating and 4 star

WELS front loading machines (the most common front loader available on the market). The

various parameters of these machines are outlined in Table 19 and Figure 18.

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

Global Warming Eutrophication Land use Water Use Fossil Fuels Depletion

Minerals Depletion

Energy Use

Base Case Reason Waterless Current Market Leader




Table 19 Machine Parameters for Top Loader/Front Loader Scenario

TL/FL

Energy

Star

Rating

WELS

Rating

Nu

mb

er

of

Ma

ch

ine

s

on

Ma

rke

t

Average Standard

Test Performance

Av

era

ge

Ra

ted

Ca

pa

cit

y

(kg

pe

r w

ash

)

Ca

pa

cit

y

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water

(L per

wash)

TL 2 star 3 star 34 1.59 97.29 7.03 50% 3.52

FL 3 star 4 star 22 0.99 66.23 6.93 50% 3.47

Reason n/a n/a n/a 0.04 9.73 10.00 50% 5.00

Waterless n/a n/a n/a 0.05 2.77 7.03 50% 3.52

Figure 18 Impact of varying loading type

The results show that the emerging technologies consistently outperform the base case

(except for fossil fuel depletion) and also perform better than front loading machines. The

front loader outperforms the top loader for water use which is to be expected given the

higher WELS rating and reduced use phase water consumption of the front loading

machines. However the results for the other impact categories are counterintuitive with the

higher energy rated front loader performing worse than the top loader across all categories.

This is due to two factors. Firstly, the algorithm in the lifecycle model assumes a greater

mechanical energy requirement (for the motor and controls) for front loading machines

compared to top loading machines with the same standard test energy based on previous

research conducted by Alan Pears for the EPA Australian Greenhouse Calculator.

Secondly, the front loader machine is slightly smaller which is of significance for impacts

associated with detergent use. Quantity of detergent used is constant per wash according to

detergent manufacturers‘ recommendations which generally do not distinguish between

small and large machines or loads. Therefore for smaller machines, greater amounts of

-100%

-80%

-60%

-40%

-20%

0%

20%


Minerals Depletion

Energy Use

Base Case (Top Loader 2*) Front Loader Base Case 3* Reason Waterless




detergent are used per kg of clothes such that embodied impacts of detergent (which are

relatively high across all impact categories except water use) are larger for smaller

machines.

7.2.4 Varying energy rating

Scenario analysis was conducted on top and front loading machines of various energy

ratings, maintaining the same WELS rating as the base case. This section looks at the

impact of varying energy ratings across top loading machines (and emerging technologies)

first and then considers the impacts of varying energy ratings across front loading machines

(and emerging technologies). These two different scenarios are then considered in a

combined commentary on the impacts of varying energy ratings in general.

Box 3: Energy Star Rating

Energy Star Ratings

The energy rating label enables households to compare the energy efficiency of domestic

appliances. It was first introduced in 1986 in NSW and Victoria and is now mandatory in all

states and territories for refrigerators, freezer, clothes washers, clothes dryers, dishwashers

and some air-conditioners.

The star rating of an appliance is determined from the energy consumption (CEC) and size /

capacity of the product. These values are measured under Australian Standards which define

test procedures for measuring energy consumption and minimum energy performance criteria.

The base energy consumption defines the "1 star" line for particular products. For clothes

washers, an additional star is awarded when the CEC of the model is reduced by 27%.

Energy consumption is measured on the program recommended for a normally soiled cotton

load at the rated capacity. The minimum wash temperature for energy labelling tests is 35°C.

The WELS rating of a clothes washing machine is then determined using the following

formula:

Where:

Star Rating Index = fractional star rating used to determine the number of stars to appear on the label, rounded down to the nearest half star

CEC = comparative energy consumption (energy that appears on the energy label)

BEC = Base Energy Consumption = 115 × C C = rated load capacity of clothes washer (kg) ERF = energy reduction factor – reduction in CEC for each additional star

(27%) = 0.27

Em =

F = 0.1 WEI = Water Extraction Index (kg water per kg dry clothes)

Eref =

WEIref = 1.03

As is indicated in Box 3, the energy rating takes into account the size of the washing

machine. In theory therefore, a large machine consuming more energy per wash will

receive a similar star rating to a smaller machine using less water per wash. However, this

is not necessarily observed as there are a number of other factors which contribute to the

star rating. Firstly, for clothes washers, the star rating index is influenced by energy

consumption as well as the spin performance of the machine, as it is assumed that some of

the load will be put into a dryer. Secondly, the quoted energy consumption under the test

rating varies depending on the test temperature which can vary from a minimum of 35C up




to 90C depending on the nominated setting recommended for a normally soiled cotton load.

It may be that the machine is actually capable of operating at much lower temperatures.

For our analysis we have used the comparative energy consumption values adjusted using

Alan Pears‘ correlations to determine actual energy consumption from standard energy

consumption and wash temperature. Due to the factors identified above, the actual energy

consumption per unit of clothes washed does therefore not actually correspond particularly

well to the star rating.

In addition a large percentage of lifecycle impacts for energy consumption are derived from

detergent manufacture. Detergent consumption is assumed to be constant irrespective of

washing machine size or type such that smaller machines use a larger quantity of detergent

per kg of clothes. This also affects the results for energy consumptions such that energy,

fossil fuel and global warming results are more affected by machine size than star rating.

Firstly, scenario analysis was conducted for top loading machines (with hot and cold

connections) of various energy ratings, maintaining the same WELS rating (3 star) as the

base case. For some combinations of energy star and WELS rating, there are no machines

currently on the market and therefore not all energy star ratings are represented. These

results were compared against emerging technologies (no energy star ratings) to provide an

indication of the type of environmental impacts which can be reduced in the future through

new machine technology.

The machines considered in this scenario analysis are presented in Table 20.

Table 20 Machine Parameters for Top Loader Varying Energy Rating Scenario

TL/FL

Energy

Star

Rating

WELS

Rating

Nu

mb

er

of

Rate

d

Ma

ch

ine

s o

n M

ark

et

Average Standard

Test Performance

Ave

rag

e R

ate

d

Cap

ac

ity

(kg

pe

r w

ash

)

Cap

ac

ity

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water

(L per

wash)

TL 1 star 3 star 2 2.53 113.00 8.50 50% 4.25

TL 1.5 star 3 star 5 2.11 111.80 8.20 50% 4.10

TL 2 star 3 star 34 1.59 97.29 7.03 50% 3.52

TL 2.5 star 3 star 2 0.98 90.50 6.25 50% 3.13

TL 3 star 3 star 1 1.31 104.00 8.00 50% 4.00

TL 3.5 star 3 star 1 0.78 92.00 7.00 50% 3.50

Reason n/a n/a n/a 0.04 9.73 10.00 50% 5.00


Note: The shaded row represents the top loader base case scenario.

The results of this scenario analysis are presented in Table 21 and Figure 19.




Table 21 Impact of varying energy rating for top loading machines

Scenario

Impact Category

Wa

ter

Us

e

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l

Wa

rmin

g

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case (2*

Energy Rating)

30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10-3

1* Energy

Rating Top

Loader

28.9 2,281 0.20 1.0 140.97 8.429 3.2 x 10-3

1.5* Energy

Rating Top

Loader

29.6 2,300 0.20 1.0 142.24 8.736 3.3 x 10-3

2.5* Energy

Rating Top

Loader

32.0 2,609 0.21 1.3 160.63 11.449 4.3 x 10-3

3* Energy

Rating Top

Loader

28.4 2,242 0.19 1.0 138.44 8.951 3.4 x 10-3

3.5* Energy

Rating Top

Loader

29.0 2,376 0.20 1.2 146.32 10.224 3.8 x 10-3

Reason 10.0 1,247 0.11 0.4 76.47 5.90 1.5 x 10-3

Waterless 1.5 2,166 0.14 0.1 169.56 4.92 2.7 x 10-4

Figure 19 Impact of varying energy rating for top loading machines (% Difference from base case)

-100%

-80%

-60%

-40%

-20%

0%

20%


Minerals Depletion

Energy Use

Base Case (Top Loader 2*) Top Loader 1* Top Loader 1.5* Top Loader 2.5*

Top Loader 3* Top Loader 3.5* Reason Waterless




Front loading machines were also included in the analysis based on the most common

machine rating in the front loader market (3 star energy rating and 4 star WELS rating).

Scenario analysis was conducted for front loading machines (with hot and cold connections)

of various energy ratings, maintaining the same WELS rating (4 star) as the front loading


currently on the market. Therefore not all energy star ratings are represented. Emerging

technologies have also been considered in this scenario. Despite lacking an energy star

rating, their results provide an indication of the type of environmental impacts which can be

avoided in the future. The machines considered in this scenario analysis are presented in

Table 22.

Table 22 Machine Parameters for Front Loader Varying Energy Rating Scenario

TL/FL

Energy

Star

Rating

WELS

Rating

Nu

mb

er

of

Ra

ted

Ma

ch

ine

s o

n M

ark

et

Average Standard

Test Performance

Av

era

ge

Ra

ted

Ca

pa

cit

y

(kg

pe

r w

ash

)

Ca

pa

cit

y

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water (L

per

wash)

FL 2 star 4 star 5 1.51 58.40 6.80 50% 3.40

FL 2.5 star 4 star 2 1.30 51.00 7.00 50% 3.50

FL 3 star 4 star 22 0.99 66.23 6.93 50% 3.47

FL 3.5 star 4 star 6 0.76 66.17 6.92 50% 3.46

FL 4 star 4 star 10 0.72 75.40 7.45 50% 3.73

FL 4.5 star 4 star 9 0.68 80.11 8.11 50% 4.06

Reason n/a n/a n/a 0.04 9.73 10.00 50% 5.00


Note: The shaded row represents front loader base case scenario


Table 23 Impact of varying energy rating for front loading machines

Scenario

Impact Category

Wa

ter

Use

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l

Wa

rmin

g

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

Dep

leti

on

(kJ

su

rplu

s)

Min

era

ls

Dep

leti

on

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case (2*

Energy Rating

Top Loader)

30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10-3

2* Energy

Rating Front

Loader

21.9 2,532 0.22 1.2 154.15 12.052 3.9 x 10-3

2.5* Energy

Rating Front

Loader

20.0 2,761 0.24 1.2 167.29 12.286 4.0 x 10-3




Scenario

Impact Category

Wa

ter

Us

e

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l

Wa

rmin

g

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

Front Loader

(3* Energy

Rating)

17.3 2,588 0.22 1.2 156.42 11.932 3.9 x 10-3

3.5* Energy

Rating Front

Loader

21.9 2,436 0.21 1.2 148.44 12.067 3.9 x 10-3

4* Energy

Rating Front

Loader

22.8 2,336 0.20 1.1 142.78 11.211 3.6 x 10-3

4* Energy

Rating Front

Loader

22.1 2,210 0.19 1.0 135.27 10.300 3.4 x 10-3

Reason 10.0 1,247 0.11 0.4 76.47 5.90 1.5 x 10-3

Waterless 1.5 2,166 0.14 0.1 169.56 4.92 2.7 x 10-4

Figure 20 Impact of varying energy rating for front loading machines (% Difference from base case)

The scenario analysis of both top loading and front loading machines compared against

emerging technologies suggests that environmental impacts are more closely linked to

machine size than energy rating. This is because the majority of the impacts are dependent

-100%

-80%

-60%

-40%

-20%

0%

20%


Minerals Depletion

Energy Use

Base Case (Top Loader 2*) Front Loader 2* Front Loader 2.5* Front Loader Base Case 3* Front Loader 3.5*

Front Loader 4* Front Loader 4.5* Reason Waterless




on detergent manufacture, as the greatest contributor to energy use. Since detergent use is

set based on the detergent manufacturers‘ recommendations per wash, it is not dependent

on wash size. Therefore smaller washing machines (such as the average 2 star top loading

machine in Figure 19) are responsible for higher impacts as the detergent impacts are

concentrated over a smaller mass of clothes.

The change in energy star rating affects the thermal and mechanical energy requirements

only. For the front loading machines the size of machine is more consistent across star

ratings resulting in impacts which are more closely correlated to star rating.




7.2.5 Varying Water Rating

As with energy ratings, scenario analysis was conducted on top and front loading machines

of various WELS ratings, maintaining the same energy star rating as the base case. This

section looks at the impact of varying WELS ratings across top loading machines (and

emerging technologies) first and then considers the impacts of varying energy ratings

across front loading machines (and emerging technologies). These two different scenarios

are then considered in a combined commentary on the impacts of varying WELS ratings in

general.

Box 4: WELS Rating

Water Efficiency Labelling and Standards (WELS) Scheme

WELS is water efficiency labelling scheme which enables households to compare the water

efficiency of different products. The system is based on a six star metric, where an increased star

rating represents an increased water efficiency. The labels also show a water consumption or

water flow figure.

The WELS Scheme applies to plumbing products such as showers, toilet equipment, white goods,

washing machines and dishwashers. The rating is determined by using a formula derived from the

total water consumption of the machine. Other tests performed include soil removal, water

extraction, severity of wash and rinse performance.

The star rating is calculated to the nearest ½ star and a star rating of less than 1 receives 0 stars.

Clothes washing machines are differentiated by their water consumption. This is calculated based

on testing to AS 2040.2 on the higher claimed total water consumption for warm or cold wash.

The average total water consumption for clothes washers is determined by testing on a program

recommended to wash a normally soiled cotton load, at the rated load capacity of the machine.

The WELS rating of a clothes washing machine is then determined using the following formula:

Where:

Star Rating Index = fractional star rating used to determine the number of stars to appear on the label, rounded down to the nearest half star

BWC = Base Water Consumption = 30 × C C = rated load capacity of clothes washer (kg) WC = Water consumption of the model in litres under test conditions WRF = Water reduction factor per additional star (30%) = 0.3

The WELS rating takes into account the size of the washing machine. That is, a large

machine consuming more water per wash will receive a similar star rating to a smaller

machine using less water per wash as shown in Figure 21.

The impact of this is that increases the WELS rating should result in an increase in direct

water consumption per kg of clothes regardless of the size of the machine. This is observed

in the results for water consumption as direct water consumption in the use phase

constitutes the majority of the lifecycle water consumption.




Figure 21: WELS Rating comparison of washing machine size

Scenario analysis relating to a variation in WELS rating was firstly conducted for top loading

machines (with hot and cold connections) of various WELS ratings, maintaining the same

energy star rating (3 star) as the base case. For some combinations of energy star and

WELS rating, there are no machines currently on the market and therefore not all WELS

ratings are represented. These results were compared against emerging technologies to

provide an indication of the type of environmental impacts which can be reduced in the

future through new machine technology.

The machines considered in this scenario analysis are presented in Table 24.

Table 24 Machine Parameters for Top Loader Varying WELS Rating Scenario

Note: The shaded row represents top loader base case scenario

TL/FL

Energy

Star

Rating

WELS

Rating

Nu

mb

er

of

Rate

d

Ma

ch

ine

s o

n M

ark

et

Average Standard

Test Performance

Ave

rag

e R

ate

d

Cap

ac

ity

(kg

pe

r w

ash

)

Cap

ac

ity

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water

(L per

wash)

TL 2 star 1.5 star 6 1.18 141.83 5.83 50% 2.92

TL 2 star 2 star 5 1.09 96.80 5.20 50% 2.60

TL 2 star 2.5 star 2 1.57 118.50 7.03 50% 3.52

TL 2 star 3 star 34 1.59 97.29 7.03 50% 3.52

TL 2 star 4 star 3 1.75 76.33 6.25 50% 3.13

Reason n/a n/a n/a 0.04 9.73 10.00 50% 5.00






Table 25 Impact of varying WELS rating for top loading machines

Scenario

Impact Category

Wa

ter

Us

e

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l W

arm

ing

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

Top Loader (3*

WELS Rating)

30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10-3

1.5* WELS

Rating Top

Loader

51.9 3,012 0.25 1.4 189.19 12.284 4.6 x 10-3

2* WELS

Rating Top

Loader

40.8 3,065 0.25 1.6 189.44 13.760 5.1 x 10-3

2.5* WELS

Rating Top

Loader

36.6 2,561 0.21 1.2 159.12 10.231 3.9 x 10-3

4* WELS

Rating Top

Loader

22.4 2,262 0.19 1.1 138.11 9.332 3.5 x 10-3

Reason 10.0 1,247 0.11 0.4 76.47 5.90 1.5 x 10-3

Waterless 1.5 2,166 0.14 0.1 169.56 4.92 2.7 x 10-4




Figure 22 Impact of varying WELS rating for top loading machines (% Difference from base case)

Scenario analysis was also conducted for front loading machines (with hot and cold

connections) of various WELS ratings, maintaining the same energy star rating (3 star) as

the base case. For some combinations of energy star and WELS rating, there are no

machines currently on the market and therefore not all WELS ratings are represented.

Front loader results were compared against emerging technologies to provide an indication

of the type of environmental impacts which can be reduced in the future through new

machine technology. The machines considered in this scenario analysis are presented in

Table 26.

Table 26 Machine Parameters for Front Loader Varying WELS Rating Scenario

TL/FL

Energy

Star

Rating

WELS

Rating

Nu

mb

er

of

Rate

d

Ma

ch

ine

s o

n M

ark

et

Average Standard

Test Performance

Ave

rag

e R

ate

d

Cap

ac

ity

(kg

pe

r w

ash

)

Cap

ac

ity

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water

(L per

wash)

FL 3 star 4 star 22 0.99 66.23 6.93 50% 3.47

FL 3 star 4.5 star 8 1.03 63.50 7.44 50% 3.72

Reason n/a n/a n/a 0.04 9.73 10.00 50% 5.00




-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%


Minerals Depletion

Energy Use

Base Case (Top Loader 3*) Top Loader 1.5* Top Loader 2* Top Loader 2.5* Top Loader 4* Reason Waterless




Table 27 Impact of varying WELS rating for front loading machines

Scenario

Impact Category

Wa

ter

Us

e

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l W

arm

ing

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

Front Loader

(3* WELS

Rating)

30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10-3

Base Case

Front Loader

(4* WELS

Rating)

21.6 2,496 0.21 1.2 151.96 11.880 3.9 x 10-3

4.5* WELS

Rating Front

Loader

20.8 2,559 0.22 1.2 155.52 11.882 3.9 x 10-3

Reason 10.0 1,247 0.11 0.4 76.47 5.90 1.5 x 10-3

Waterless 1.5 2,166 0.14 0.1 169.56 4.92 2.7 x 10-4

Figure 23 Impact of varying WELS rating for front loading machines (% Difference from base case)

The impacts for both front loading and top loading machines for water use are consistent

with the star rating. This is because water use impacts during the use phase dominate the

lifecycle such that a better water rating results in reduced water consumption as expected.

The front loading machines also have reduced water consumption compared to the top

loading machines analysed. Emerging technologies generally outperform both top loaders

and front loaders, regardless of their WELS rating.

-100%

-80%

-60%

-40%

-20%

0%

20%


Minerals Depletion

Energy Use

Base Case (Top Loader 3*) Front Loader (4*) Front Loader (4.5*) Reason Waterless




For other impact categories, impacts do not correlate with the water rating and are more

closely linked to washing machine size than WELS rating.

7.2.6 Water system type

Scenario analysis was undertaken for the base case (20 C wash temperature) using a

number of different hot water system types to provide the thermal energy for the wash cycle.

Since thermal energy is a relatively small contributor to the base case, the analysis was also

repeated at a 60 C wash temperature where the variation in hot water system type would

have more of an effect. The results of this analysis are presented in Table 28, Figure 24.

Table 28 Impact of varying hotwater system type for 20°C wash temperature

Scenario

Impact Category W

ate

r U

se

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l W

arm

ing

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

(Gas Storage

5*)

30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10-3

Gas Inst 3* 30.4 2,482 0.21 1.2 152.60 10.184 3.9 x 10-3

Gas Inst 5* 30.4 2,454 0.21 1.2 150.16 10.184 3.9 x 10-3

Off Peak

Electric Single

Element

30.5 2,935 0.27 1.2 173.72 10.201 4.0 x 10-3

Off Peak

Electric Dual

Element

30.5 2,954 0.27 1.2 174.87 10.202 4.0 x 10-3

Solar Electric

Split System 30.4 2,522 0.22 1.2 149.12 10.190 3.9 x 10

-3

Solar Electric

Thermosyphon

(High

Efficiency)

30.4 2,431 0.21 1.2 143.69 10.188 3.9 x 10-3

Solar Electric

Thermosyphon

(Minimum

Efficiency)

30.4 2,510 0.22 1.2 148.40 10.190 3.9 x 10-3

Gas Storage

3* 30.4 2,542 0.21 1.2 158.36 10.183 3.9 x 10

-3

Solar Gas Split

System 30.4 2,296 0.20 1.2 136.90 10.183 3.9 x 10

-3

Solar Preheat

with Gas

Instantaneous

30.4 2,382 0.20 1.2 144.46 10.183 3.9 x 10-3




Figure 24 Impact of varying hot water system type for 20°C wash temperature

(% Difference from base case)

Since thermal energy phase is a relatively small contributor to the base case (from between

<1% and 10% depending on impact category), the analysis was also repeated at a 60 C

wash temperature. At this temperature, change in hot water system type has a more

profound effect on results as presented in Table 29 and Table 30.

Figure 25 Impact of varying hot water system type for 60°C wash temperature

(% Difference from base case)

-50%

0%

50%

100%

150%

200%

Globa

l War

ming

Eutro

phicat

ion

Land

use

Wat

er U

se

Fossil F

uels D

epletio

n

Miner

als Dep

letio

n

Energ

y Use

Base Case (Gas Storage 5*)

Gas Inst 3*

Gas Inst 5*

Off Peak Electric Single

Element

Off Peak Electric Dual

Element

Solar Electric Split System

Solar Electric

Thermosyphon (High

Efficiency)Solar Electric

Thermosyphon (Minimum

Efficiency)Gas Storage 3*

Solar Gas Split System

Solar Preheat with Gas

Instantaneous

-50%

0%

50%

100%

150%

200%

Globa

l War

ming

Eutro

phicat

ion

Land

use

Wat

er U

se

Fossil F

uels D

epletio

n

Miner

als Dep

letio

n

Energ

y Use

Base Case (Gas Storage 5*)

Gas Inst 3*

Gas Inst 5*

Off Peak Electric Single

Element

Off Peak Electric Dual

Element

Solar Electric Split System

Solar Electric

Thermosyphon (High

Efficiency)Solar Electric

Thermosyphon (Minimum

Efficiency)Gas Storage 3*

Solar Gas Split System

Solar Preheat with Gas

Instantaneous




Table 29 Impact of varying hotwater system type for 60°C wash temperature

Scenario

Impact Category

Wa

ter

Us

e

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l

Wa

rmin

g

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

(Gas Storage

5*)

30.4 4,616 0.36 1.3 325.44 10.196 4.0 x 10-3

Gas Inst 3* 30.5 4,668 0.37 1.3 325.71 10.200 4.0 x 10-3

Gas Inst 5* 30.5 4,437 0.35 1.3 305.56 10.200 4.0 x 10-3

Off Peak

Electric Single

Element

31.2 8,417 0.87 1.3 500.42 10.344 5.5 x 10-3

Off Peak

Electric Dual

Element

31.2 8,577 0.89 1.3 509.93 10.348 5.5 x 10-3

Solar Electric

Split System 30.7 5,003 0.50 1.2 296.94 10.255 4.6 x 10

-3

Solar Electric

Thermosyphon

(High

Efficiency)

30.6 4,249 0.41 1.2 252.03 10.235 4.4 x 10-3

Solar Electric

Thermosyphon

(Minimum

Efficiency)

30.7 4,903 0.49 1.2 290.99 10.253 4.6 x 10-3

Gas Storage

3* 30.4 5,166 0.39 1.3 373.37 10.196 4.0 x 10

-3

Solar Gas Split

System 30.4 3,127 0.27 1.2 195.87 10.196 4.0 x 10

-3

Solar Preheat

with Gas

Instantaneous

30.4 3,845 0.31 1.2 258.41 10.196 4.0 x 10-3

From this analysis it is clear that the importance of selection of hot water system type is

elevated for an increased wash temperature. Of the systems analysed, off peak electric and

3 star gas storage perform worse for all impact categories with the exception of

eutrophication where natural gas production has a greater impact than electricity generation.

The best performing system is the solar gas split system for the majority of impact

categories.

7.2.7 Washing machine temperature scenarios

Relationships were derived for each impact category according to variation in wash

temperature. The relationships are represented by a linear trends line as presented in

Table 30 and Figure 26 to Figure 32.




Table 30 Washing machine temperature relationships

Impact Category Relationship

Water Use (L) W = 0.0016T + 30.3

Energy Use (kJ eq) E = 53.50T + 1405

Global Warming Potential (kg CO2 eq) GW = 0.0037T + 0.13

Eutrophication Potential EP = 0.003T + 1.11

Fossil Fuels Depletion FF = 4.32T + 66.1

Minerals Depletion M = 0.0003T + 10.177

Figure 26 Relationship between Wash Temperature and Water Use

30.36

30.38

30.4

30.42

30.44

30.46

30.48

30.5

30.52

0 10 20 30 40 50 60 70 80 90 100

Wash Temperature (Degrees C)

Wate

r U

se (

L H

2O

per

kg

of

dry

clo

thes)




Figure 27 Relationship between Wash Temperature and Energy Use

Figure 28 Relationship between Wash Temperature and Global Warming Potential

0

1000

2000

3000

4000

5000

6000

7000

0 10 20 30 40 50 60 70 80 90 100


En

erg

y U

se (

kJ e

q p

er

kg

of

dry

clo

thes)

0

0.1

0.2

0.3

0.4

0.5

0.6

0 20 40 60 80 100


Glo

bal

Warm

ing

(kg

CO

2 e

q p

er

kg

of

dry

clo

thes)




Figure 29 Relationship between Wash Temperature and Eutrophication Potential

Figure 30 Relationship between Wash Temperature and Fossil Fuels Depletion

1.15

1.2

1.25

1.3

1.35

1.4

1.45

0 10 20 30 40 50 60 70 80 90 100


Eu

tro

ph

icati

on

(g

PO

4 e

q p

er

kg

of

dry

clo

thes)

0

100

200

300

400

500

600

0 10 20 30 40 50 60 70 80 90 100


Reso

urc

e D

ep

leti

on

(F

ossil

Fu

els

) (k

J s

urp

lus

per

kg

of

dry

clo

thes)




Figure 31 Relationship between Wash Temperature and Minerals Depletion

Figure 32 Relationship between Wash Temperature and Land Use

The gradient of the graphs compared to the y axis intercept indicate the relative effect of

wash temperature such that the eutrophication, water use, land use and minerals depletion

are relatively independent of wash temperature, whereas the other impacts show an

approximate 1.5% to 2.5 % increase per degree rise.

Therefore reducing wash temperature by around 10 C can result in a decrease in global

warming impacts of up to 18%, a decrease in energy use of up to 22% and a decrease in

fossil fuel depletion of 28%.

10.18

10.185

10.19

10.195

10.2

10.205

10.21

0 10 20 30 40 50 60 70 80 90 100


Reso

urc

e D

ep

leti

on

(M

inera

ls)

(kJ s

urp

lus p

er

kg

of

dry

clo

thes)

3.80E-03

3.85E-03

3.90E-03

3.95E-03

4.00E-03

4.05E-03

4.10E-03

4.15E-03

0 10 20 30 40 50 60 70 80 90 100


Lan

d U

se (

m2 p

er

kg

of

dry

clo

thes)




7.2.8 Detergent

Scenario analysis was undertaken for different detergent brands. Due to the difficulty in

obtaining detailed detergent ingredients and the lack of lifecycle inventory data for the highly

specialised chemical components, the detergent analysis is highly uncertain.

Notwithstanding, indicative results as generated by the model are presented in Table 31 and

Figure 33.

Table 31 Impact of varying detergent type

Scenario

Impact Category

Wa

ter

Us

e

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l W

arm

ing

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

(Top Loader

Concentrated

Powder)

30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10-3

Top Loader

Liquid 28.7 2,155 0.19 0.1 134.78 3.424 2.8 x 10

-3

―Eco‖ Powder 29.0 2,159 0.19 0.1 131.25 4.950 6.2 x 10-3

Generic

Powder 35.2 4,040 0.28 0.3 240.12 23.664 1.0 x 10

-2

Figure 33 Detergent type impact scenarios

The ‗generic‘ detergent showed the highest percentage impact across all the categories due

to the reduced concentration and hence higher volumes required. The exception to this was

-150%

-100%

-50%

0%

50%

100%

150%

200%

Global Warming Eutrophication Land use Water Use Fossil Fuels

Depletion

Minerals Depletion Energy Use

Base Case (TL Powd) TL Liq Eco Generic




for eutrophication, where the generic detergent performed better than the top loader powder

for phosphate tests carried out by Choice TM

. Both the top loading liquid and the eco powder

detergents displayed similar impact results, although the eco powder impact percentage

rose an additional 10% within the land use categories.

7.2.9 Fabric softener

Scenario analysis was undertaken for fabric softener use. While the fabric softener data is

subject to the same uncertainties as the detergent scenarios in 7.2.8 above, the results are

more easily interpreted. The results for this analysis can been seen in Table 32 and Figure

34.

Table 32 Impact of fabric softener use

Scenario

Impact Category W

ate

r U

se

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l W

arm

ing

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

(No Fabric

Softener)

30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10-3

With Fabric

Softener at

Recommende

d Dose

31.2 2,924 0.22 1.2 177.05 10.407 6.6 x 10-3

Figure 34: Fabric softener impact scenario

The relative impact of fabric softener use is illustrated in Figure 34. Across all seven

categories the use of fabric softener reveals significantly higher impact percentage results.

The gap between the base case and fabric softener use is greatest for land use due to the

assumption of a palm oil derived fabric softener.

0%

10%

20%

30%

40%

50%

60%

70%

80%


Depletion


Base Case (No Fabric Softener) With Fabric Softener




7.2.10 Drying

Scenario analysis was carried out for two types of dryers, the condenser dryer and the

electric tumble dryer.

The difference between electric tumble dryers and condenser dryers

Electric tumble dryers are sometimes referred to as ‗evaporative‘ dryers as they heat the clothes

within using an electric resistance element. These dryer types do not consume water.

Condenser dryers do use water. The water is extracted from the clothes and condensed on an

air-cooled heat exchanger. The warm, dry air from the heat exchanger is then collected or sent to

waste.

The results from the different dryers considered during the LCA are included in Table 33

and Figure 35.

Table 33 Impact of drying

Scenario

Impact Category

Wa

ter

Us

e

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l W

arm

ing

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

Dep

leti

on

(kJ

su

rplu

s)

Min

era

ls

Dep

leti

on

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

(Line Drying) 30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10

-3

Electric Rotary

Dryer 31.9 12,196 1.27 1.3 738.87 33.228 6.8 x 10

-3

Condenser

Dryer 43.0 12,152 1.26 1.3 736.32 33.227 6.7 x 10

-3

Figure 35 Drying impact scenario

0%

100%

200%

300%

400%

500%

600%


Depletion


Base Case (Line Dry) Electric Dryer Condenser Dryer




The use of a dryer increases environmental impacts of the clothes washing process across

all impact categories. The largest increase in environmental impacts is global warming

potential (greater than 500%), while energy use and fossil fuel depletion are also significant

( greater than 350%). Figure 35 illustrates that both electric and condenser dryers have

similar environmental impacts across all categories except for water use, where the

condenser dryer utilises considerably more water resources.

7.2.11 Detergent overfilling

Relationships were derived for each impact category according to the percentage overfill of

detergent compared to the detergent manufacturer‘s recommended dose. While underfilling

is also possible, the impacts of this on the quality of wash are unknown and therefore only

overfilling was investigated. The relationships are represented by linear trend lines as

presented in Table 34.

Table 34 Detergent overfill relationships


Water Use (L) W = 2.49Dperc_OF+ 27.9

Energy Use (kJ eq) E = 824Dperc_OF+ 1651

Global Warming Potential (kg CO2 eq) GW = 0.036Dperc_OF+ 0.171

Eutrophication Potential EP = 1.13Dperc_OF+ 0.042

Fossil Fuels Depletion FF = 46.5Dperc_OF+ 106

Minerals Depletion M = 6.98Dperc_OF+ 3.22

The impact of overfilling by even 1% is therefore considerable across every impact category

due to the significance of the manufacture of detergent chemicals. This represents an

important result, which should be communicated to CWW households. The impacts of

detergent on the lifecycle of household washing can be diminished if residents are careful

with their use of detergent and ensure that they only ever use as much as they need and no

more – even if that means using less than the recommended dose.

Notwithstanding, it is also important to note that many of these impacts (which occur as a

result of detergent manufacture) occur outside of CWWs immediate environment and in

many cases occur overseas. Detergent overfill impacts are reduced in future machines,

where fuzzy logic is used to sense the exact amount of detergent required, or where non

detergent cleaning agents are used (such as in the Waterless washing machine where

nylon beads are used to absorb stains in preference to the use of detergent).

7.2.12 Washing machine loading

Relationships were derived for each impact category according to the percentage loading of

the washing machine with 50% loading representing the base case. The relationships

between loading and impacts are directly inversely proportional as shown in Table 35 such

that the impact of reducing loading is equal in terms of percentage change across the

impact categories. Figure 36 through to Figure 42 illustrate the relationship with each

impact category.




Table 35 Washing machine loading


Water Use (L) W = 15.2/WMLperc_cap

Energy Use (kJ eq) E = 1240/WMLperc_cap

Global Warming Potential (kg CO2 eq) GW = 0.104/WMLperc_cap

Eutrophication Potential EP = 0.587/WMLperc_cap

Fossil Fuels Depletion FF = 76.4/WMLperc_cap

Minerals Depletion M = 5.09/WMLperc_cap

The nature of the inversely proportional relationship means that the impacts increase

exponentially as the washing machine loading decreases such that very small loads will

have a disproportionately high impact. As the washing machine is filled closer to capacity,

the impact becomes less significant.

Figure 36 Relationship between machine loading and water use

0

20

40

60

80

100

120

140

160

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

% of Machine Filled (Compared to Quoted Capacity)

Wate

r U

se (

L H

2O

per

kg

of

dry

clo

thes)




Figure 37 Relationship between machine loading and energy use

Figure 38 Relationship between machine loading and global warming potential

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


En

erg

y U

se (

kJ e

q p

er

kg

of

dry

clo

thes)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Glo

bal

Warm

ing

(kg

CO

2 e

q p

er

kg

of

dry

clo

thes)




Figure 39 Relationship between machine loading and eutrophication potential

Figure 40 Relationship between machine loading and fossil fuels depletion

0

1

2

3

4

5

6

7

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Eu

tro

ph

icati

on

(g

PO

4 e

q p

er

kg

of

dry

clo

thes)

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0% 20% 40% 60% 80% 100%

Reso

urc

e D

ep

leti

on

(F

ossil

Fu

el)

(M

J s

urp

lus p

er

kg

of

dry

clo

thes)





Figure 41 Relationship between machine loading and minerals depletion

Figure 42 Relationship between machine loading and land use

0

10

20

30

40

50

60

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Reso

urc

e D

ep

leti

on

(M

inera

ls)

(kJ s

urp

lus p

er

kg

of

dry

clo

thes)

0.000

0.005

0.010

0.015

0.020

0.025

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Lan

d U

se (

m2 p

er

kg

of

dry

clo

thes)




7.2.13 Greywater

The use of a greywater system was analysed during the LCA through modelling of a basic

household greywater system where washing waste water was sent to water the back yard.

The results from this scenario are presented in Table 36 and Figure 43.

Table 36 Impact of greywater reuse for irrigation

Scenario

Impact Category

Wa

ter

Us

e

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l W

arm

ing

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

(wastewater to

sewer)

30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10-3

Wastewater to

irrigation 2.9 2,340 0.19 0.1 144.16 10.166 3.8 x 10

-3

Figure 43 Grey water scenario analysis

The use of a grey water system can have significant positive environmental impacts through

the reduction in water use ( approximately 90%) and the decreased potential for

eutrophication ( greater than 90%) compared to the base case. It has a minor impact on

global warming, fossil fuel depletion and energy use, all of which have a slightly reduced

environmental impact compared to the base case. EPA Victoria supports water

conservation methods and believes that greywater can be reused effectively and safely in

domestic situations by following a few simple tips in EPA publication 884.1 Greywater use

around the home.

-100%

-90%

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%


Depletion


Base Case (to WTP) Grey Water




7.2.14 Machine disposal

Machine disposal was considered during the scenario analysis with modelling of two

scenarios in addition to the base case. No recycling of machine parts and maximum

recycling of machine parts (all metals, plastics and concrete) were the two scenarios and

the results are presented in Table 37 and Figure 44.

Table 37 Impact of disposal of washing machine at end of life

Scenario

Impact Category

Wa

ter

Us

e

(L)

En

erg

y U

se

(kJ

eq

)

Glo

ba

l

Wa

rmin

g

(kg

CO

2-e

)

Eu

tro

ph

ica

tio

n

(g P

O4-e

)

Fo

ss

il F

ue

ls

De

ple

tio

n

(kJ

su

rplu

s)

Min

era

ls

De

ple

tio

n

(kJ

Su

rplu

s)

La

nd

use

(m²)

Base Case

(Recycling

metal

components

only)

30.4 2,476 0.21 1.2 152.56 10.183 3.9 x 10-3

No recycling 30.4 2,516 0.21 1.2 155.40 10.516 3.9 x 10-3

Maximum

recycling 30.4 2,460 0.21 1.2 151.27 10.183 3.9 x 10

-3

Figure 44: The impact of disposal scenarios

The impacts of varying disposal scenarios are displayed in Figure 44.

Recycling is dealt with in the LCA by applying a credit for the avoided production of raw

materials. If no recycling is undertaken then the impacts associated with disposal are also

included in the domestic clothes washing lifecycle.

The results indicate that there is an environmental benefit associated with both recycling

options. This benefit is not as large as may have been expected in comparison with not

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%



Base Case (Recycle metals only) Recycle metals, plastics and concrete No recycling




recycling as disposal of a washing machine is a one off event. Over the lifecycle of

domestic clothes washing there are other events, such as the use of detergent, operation of

the machine and disposal of wastewater that occur multiple times per week. When

considered in the context of the most common 14 year life of a washing machine (Table 1),

these events represent a significant source of impacts when compared with the one off

disposal.

7.3 Machine replacement

The LCA study did not model when a machine should be replaced. Replacement periods

depend on a number of variables such as the age of the machine and its frequency of use.

A study carried out in America (Bole, 2006) did investigate washing machine replacement

and how to optimise environmental efficiencies through the replacement of a new machine.

The study highlighted that the environmental benefits associated with the replacement of an

old machine with a newer, more efficient machine need to be weighed against the financial

and environmental impacts of purchasing and manufacturing the new machine, as well as

disposing of the old machine.

The study found that there was a ―disconnect between optimal replacement interval from an

environmental perspective and the optimal replacement interval from a financial

perspective‖. The results showed that a short replacement cycle (5 years) was good from an

environmental perspective, but from a financial perspective the optimum replacement was to

replace any machine older than 5 years with a newer machine and retain that machine for

its useful life (20 years).

The results from this LCA also suggest that from an environmental perspective, it makes

sense to replace old machines with newer, more efficient models. Table 38 below highlights

the percentage savings available for different machines in comparison to the base case

machine (negative values represent an additional cost). Even replacement with the market

leading machine (which was selected as a front loading machine with a 4 star energy rating

and 4.5 star WELS rating) can produce significant environmental benefits such as water

savings of approximately 64%.

Table 38 Life cycle impacts comparison between machines

Impact category Base Case Current Market

Leader Reason Waterless

Water Use 0.0% 64.4% 67.0% 95.0%

Energy Use 0.0% 36.3% 49.6% 12.5%

Global Warming 0.0% 46.6% 44.7% 31.6%

Eutrophication 0.0% -5.5% 64.3% 91.4%




8 Conclusions and recommendations

8.1 Summary of results and significant issues

The LCA of Clothes Washing Options separated the clothes washing process into three

phases; upstream, use and downstream. Within each phase are a series of individual ‗unit

processes‘ (Section 5). The key findings of the LCA have been provided by both phase and

unit process for each of the different impact categories of interest to EPA Victoria and

CWW.

Findings are presented for the base case, that is the most common clothes washing

scenario for households within the CWW region and then by different scenarios. All results

relate to the lifecycle impacts required to produce 1 kg of clean dry clothes.

Base case

The LCA determined that the use phase of the washing process has the largest proportion

of environmental impacts due to the frequency of operation of the machines and utilisation

of the detergents. The use phase contributes to impacts across:

• water use (92% of the life cycle impact);

• energy use (60% of the life cycle impact);

• global warming potential (73% of the life cycle impact); and

• fossil fuel depletion (62% of the life cycle impact).

Of the 92% life cycle impact, 91% is attributable to the washing machine water

consumption, which represents a significant opportunity area for CWW in forming a

behaviour change program. In regards to global warming, 39% of impacts are associated

with the mechanical energy of the washing machine and 25% with standby power. There is

real potential to reduce the contribution to global warming by influencing household

behaviour regarding standby power.

The LCA determined that the addition of a dryer to the base case scenario lead to significant

increases (between 70% to 500%) to the environmental impact categories of energy use,

global warming potential, fossil fuel depletion land use. Rationalising the use of dryers with

the CWW region presents an opportunity to considerably reduce a number of environmental

impacts. Advising those households who use dryers to turn them off at the power point will

also provide energy savings.

Scenario analysis

Scenarios analysis was undertaken to better understand the sensitivities of the

environmental impacts to certain parameters within the LCA process. The scenarios were

defined through changes in individual unit processes.

Washing machines

When considering the environmental impacts associated with the use of washing machines,

results from the study suggest that in general, energy use impacts are more closely linked to

machine size than energy rating. This is because the majority of impacts are related to

detergent manufacture, which is based on the manufacturers‘ recommendations per wash.

In contrast, a change in energy star rating affects the thermal and mechanical energy

requirements of the machine only. Additionally, the impacts for both front loading and top

loading machines for the water use are consistent with the star rating.

Another key finding relating to machine use is that the Waterless and Reason machines

performed better than the base case in regard to greenhouse gas emissions and water use,

while the current market leading machine provided the least contribution to global warming

and the Waterless machine the most water efficient.




Given the importance of machine size on the impact of domestic clothes washing, it is

recommended that households purchase an appropriate size machine when replacing their

washing machine. Machine sizes are often quoted based on the weight of clothes which

they can wash.

Choosing an appropriately sized machine depends on the individual washing needs of

households (e.g. separation of loads, behaviour related requirements to wash frequently

etc). Households should first try to reduce the number of loads per week to the extent

possible and then adopt the following formula to determine what size machine to buy:

Rated capacity= the rated size capacity of the washing machine

Mass of clothes per week = the total weight of all clothes washed during a week

Loads = the number of loads of washing washed per week

%Capacity = how full the washing machine is, expressed as a percentage

Most households only fill their machines to 50% capacity. For the purposes of the use of the

formula, the percentage capacity should be increased, but remain less than 100% to provide

some contingency for larger loads.

Water system type

The LCA results suggest that the importance of selection of hot water system type is

elevated for an increased wash temperature. Of the systems analysed, off peak electric and

3 star gas storage perform worse for all impact categories with the exception of

eutrophication where natural gas production had a greater impact than electricity

generation.

The solar gas split system appeared to be the best performing system for the majority of

impact categories.

Washing machine operations

The findings from the LCA study imply that reducing wash temperature by around 10 C can

result in a decrease in global warming impacts of up to 18%, a decrease in energy use of up

to 22% and a decrease in fossil fuel depletion of 28%.

With regard to loading, the LCA determined that environmental impacts increase

exponentially as washing machine loading decreases, such that very small loads have a

disproportionately high impact on the environment. As the washing machine is filled closer

to capacity, environmental impacts becomes less significant.

Detergent

‗Generic‘ detergent showed the highest percentage impact across all impact categories

within the LCA results due to the reduced concentration of certain chemicals in the

detergent. This means that higher volumes of the generic detergent are required to produce

the same level of cleaning as the other, more concentrated detergents. The exception to

this was for eutrophication, where the generic detergent performed better than the top

loader powder for phosphate tests carried out by Choice.

Results from the LCA study suggested that the impact of overfilling detergent by even 1%

occurred across every impact category. This is due to the large environmental impacts

associated with the manufacture of detergent chemicals and their impact throughout the

lifecycle of the clothes washing process.




Fabric softener

Across all seven impact categories within the LCA, the use of fabric softener (compared with

its absence) revealed higher impact percentage results, specifically within land use and

cumulative energy demand demonstrating an increase of approximately 20%.

Grey water

The LCA model found that use of a normal household grey water system can have positive

environmental impacts through the reduction in water use and the decreased potential for

eutrophication. Other studies have found that grey water is having a negative

environmental impact on soil structure and accumulation of contaminates however these

impacts were beyond the scope of the study. Further information regarding grey water

systems can be found within the EPA report 884.1 Greywater use around the home.

Machine disposal

Results from the study confirm that the greatest environmental impacts resulting from

machine disposal (particularly minerals depletion) occur when no components of a washing

machine are recycled. The impact on fossil fuel depletion is minimised through recycling of

metals, plastics and concrete.




8.2 Recommendations for LCA model improvement

The availability of data relating to certain domestic clothes washing LCA processes within

Australia was in some cases, very limited. There are several processes for which more

detailed data could improve the accuracy of the model, as set out in. Table 39 Further

discussion on this is contained within the LCA of Clothes Washing Options for City West

Water's Residential Customers - Life Cycle Inventory Report.

Table 39 Recommendations for additional data collection

Process Areas for Data Improvement

Upstream

Washing machine manufacture energy consumption during final stage of manufacture

water consumption during final stage of manufacture

waste and emissions during final stage of manufacture

Dryer manufacture general assembly of components and their material

composition and manufacture method

energy and water consumption during final stage of

manufacture


Detergent and fabric softener

manufacture

exact composition of detergents and fabric softeners

energy consumption during final stage of manufacture

water consumption during final stage of manufacture


Use

Machine replacement period historical Australian data on trends in replacement periods

of washing machines

Downstream

Waste treatment of washing

machine at end of life

energy consumption during disposal / destruction

% machines recycled in Australia

components recycled

Waste treatment of dryer at end of

life

energy consumption during disposal / destruction

% machines recycled in Australia

components recycled




9 References

ACA (Australian Consumers Association), Life Cycle Analysis of Washing Machines,

October 1992.

Arup, LCA of Clothes Washing Options for City West Water's Residential Customers - Goal

and Scope Report, 2009.

Arup, LCA of Clothes Washing Options for City West Water's Residential Customers - Life

Cycle Inventory Report, 2009.

Bole, R, Life-Cycle Optimization of Residential Clothes Washer Replacement. Report No.

CSS06-03, Centre for Sustainable Systems, http://css.snre.umich.edu/make

frame.php?content=4_1_NewPubs, April 2006.

BS EN ISO 14044:2006 Environmental management — Life cycle assessment —

Requirements and guidelines, 31 August 2006.

BS EN ISO 14040:2006 Environmental management — Life cycle assessment — Principles

and framework, 31 August 2006.

Choice, Laundry detergents: Test results for 45 laundry powder concentrates, May 2007.

City West Water, Environmental Sustainability Plan 2 July 2008 – June 2011, 2008,

City West Water, Sustainability Policy, July 2009.

Commonwealth of Australia, Water Efficiency and Labelling (WELS) Rating Scheme,

http://www.waterrating.gov.au, September 2009.

DEWHA, Energy rating website, www.energyrating.gov.au, September 2009.

EPA, 884.1 Greywater use around the home, http://epanote2.epa.vic.gov.au/EPA/

Publications.NSF/2f1c2625731746aa4a256ce90001cbb5/4fb5d827f4615eaaca25746d0004

df7e/$FILE/884.1.pdf, June 2008.

NWC Research, Water Appliance Stock & User Patterns Survey A Research Report for City

West Water, May 2008.

Systain Consulting, Carbon footprint of selected textiles, extract use phase, June 2009.

http://www.waterrating.gov.au/

http://www.energyrating.gov.au/

Appendix A

Sensitivity Analysis for Option 2

EPA Victoria and City West Water LCA of Clothes Washing Options for City West Water's Residential CustomersLife Cycle Assessment - Final Technical Report


Page A1 ArupIssue 24 May 2010

A1 Sensitivity Analysis for Washing Machine Selection

The sensitivity analysis presents the alternative results for the scenario analysis of washing

machine types. The alternative results use a constant wash load for all machines (3.5kg)

with varying machine loading such that the smaller machines are filled closer to capacity

than the larger machines.

A1.1 Varying Loading Type

The base case (top loading machine) was compared against front loading machines. The

various parameters of these two machines are outlined in Table 40.

Table 40 Machine Parameters for Top Loader/Front Loader Scenario

TL/FL

Energy

Star

Rating

WELS

Rating Number of Machines

on M

arket

Average Standard

Test Performance

Average Rated

Capacity

(kg per wash)

Capacity

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water

(L per

wash)

TL 2 star 3 star 34 1.59 97.29 7.03 50% 3.52

FL 3 star 4 star 22 0.99 66.23 6.93 51% 3.52

Figure 45 Impact of varying loading type (Constant Wash Size)

The results are consistent with the main body of the report due to the similar sized

machines.

-40%

-30%

-20%

-10%

0%

10%

20%

30%



Base Case (Top Loader 2*) Front Loader Base Case 3*




A1.2 Varying Energy Rating

A1.2.1 Top Loading Machines

Scenario analysis was conducted for top loading machines (with hot and cold connections)

of various energy ratings, maintaining the same WELS rating (3 star) as the base case. For

some combinations of energy star and WELS rating, there are no machines currently on the

market and therefore not all energy star ratings are represented. The machines considered

in this scenario analysis are presented in Table 41.

Table 41 Machine Parameters for Top Loader Varying Energy Rating Scenario

TL/FL

Energy

Star

Rating

WELS

Rating

Number of Rated

Machines on Market

Average Standard

Test Performance

Average Rated

Capacity

(kg per wash)

Capacity

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water

(L per

wash)

TL 1 star 3 star 2 2.53 113.00 8.50 41% 3.52

TL 1.5 star 3 star 5 2.11 111.80 8.20 43% 3.52

TL 2 star 3 star 34 1.59 97.29 7.03 50% 3.52

TL 2.5 star 3 star 2 0.98 90.50 6.25 56% 3.52

TL 3 star 3 star 1 1.31 104.00 8.00 44% 3.52

TL 3.5 star 3 star 1 0.78 92.00 7.00 50% 3.52

Note: The shaded row represents the top loader base case scenario.

Figure 46 Impact of varying energy rating for top loading machines (Constant Wash Size)

In contrast to the results reported in the main body of the report, it can be seen that the

machines that are smaller than the base case (filled closer to capacity) have fewer impacts

per kg of clothes washing than larger machines. The influence of the energy rating is

-10%

-5%

0%

5%

10%

15%

20%

Global Warming Eutrophication Land use Water Use Fossil fuels Minerals Cumulative Energy Demand

Base Case (Top Loader 2*) Top Loader 1* Top Loader 1.5* Top Loader 2.5* Top Loader 3* Top Loader 3.5*




secondary to this factor which can be seen for the machine (3.5 star) which is a similar size

to the base case. This implies that loading of the machine is the greater determinant of

energy use than star rating.

A1.2.2 Front Loading Machines

Front loading machines were also included in the analysis based on the most common

machine rating in the front loader market (3 star energy rating and 4 star WELS rating).

Scenario analysis was conducted for front loading machines (with hot and cold connections)

of various energy ratings, maintaining the same WELS rating (4 star) as the front loading


currently on the market. Therefore not all energy star ratings are represented. The

machines considered in this scenario analysis are presented in Table 42.

Table 42 Machine Parameters for Front Loader Varying Energy Rating Scenario

TL/FL

Energy

Star

Rating

WELS

Rating

Number of Rated

Machines on Market

Average Standard

Test Performance

Average Rated

Capacity

(kg per wash)

Capacity

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water

(L per

wash)

FL 2 star 4 star 5 1.51 58.40 6.80 52% 3.52

FL 2.5 star 4 star 2 1.30 51.00 7.00 50% 3.52

FL 3 star 4 star 22 0.99 66.23 6.93 51% 3.52

FL 3.5 star 4 star 6 0.76 66.17 6.92 51% 3.52

FL 4 star 4 star 10 0.72 75.40 7.45 47% 3.52

FL 4.5 star 4 star 9 0.68 80.11 8.11 43% 3.52


Figure 47 Impact of varying energy rating for front loading machines

(Constant Wash Size)

-50%

-40%

-30%

-20%

-10%

0%

10%

20%


Base Case (Top Loader 2*) Front Loader 2* Front Loader 2.5* Front Loader Base Case 3*

Front Loader 3.5* Front Loader 4* Front Loader 4.5*




For front loading machines, the machines are all closer in size such that the capacity has a

reduced effect. The results generally follow the results in the main body of the report where

size was kept constant and the energy rating generally dominates the impact. The one

exception this is water use which is generally greater for more energy efficient machines.

A1.3 Varying WELS Rating

A1.3.1 Top Loading Machines

Scenario analysis was conducted for top loading machines (with hot and cold connections)

of various WELS ratings, maintaining the same energy star rating (3 star) as the base case.

For some combinations of energy star and WELS rating, there are no machines currently on

the market and therefore not all WELS ratings are represented. The machines considered in

this scenario analysis are presented in Table 43.

Table 43 Machine Parameters for Top Loader Varying WELS Rating Scenario

TL/FL

Energy

Star

Rating

WELS

Rating

Number of Rated

Machines on Market

Average Standard

Test Performance

Average Rated

Capacity

(kg per wash)

Capacity

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water

(L per

wash)

TL 2 star 1.5 star 6 1.18 141.83 5.83 60% 3.52

TL 2 star 2 star 5 1.09 96.80 5.20 68% 3.52

TL 2 star 2.5 star 2 1.57 118.50 7.00 50% 3.52

TL 2 star 3 star 34 1.59 97.29 7.03 50% 3.52

TL 2 star 4 star 3 1.75 76.33 6.25 56% 3.52

Note: The shaded row represents top loader base case scenario

Figure 48 Impact of varying WELS rating for top loading machines (Constant Wash Size)

In contrast to the results reported in the main body of the report, it can be seen that the

machines that are smaller than the base case (filled closer to capacity) generally have fewer

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%


Base Case (Top Loader 3*) Top Loader 1.5* Top Loader 2* Top Loader 2.5* Top Loader 4*




impacts per kg of clothes washing than larger machines. The influence of the water rating

appears to only be dominant for the water use impact category.

A1.3.2 Front Loading Machines

Scenario analysis was also conducted for front loading machines (with hot and cold

connections) of various WELS ratings, maintaining the same energy star rating (3 star) as

the base case. For some combinations of energy star and WELS rating, there are no

machines currently on the market and therefore not all WELS ratings are represented. The

machines considered in this scenario analysis are presented in Table 44.

Table 44 Machine Parameters for Front Loader Varying WELS Rating Scenario

TL/FL

Energy

Star

Rating

WELS

Rating

Number of Rated

Machines on Market

Average Standard

Test Performance

Average Rated

Capacity

(kg per wash)

Capacity

(%)

Wash

Mass

(kg per

wash)

Energy

(kWhr

per

wash)

Water

(L per

wash)

FL 3 star 4 star 22 0.99 66.23 6.93 51% 3.52

FL 3 star 4.5 star 8 1.03 63.50 7.44 47% 3.52


Figure 49 Impact of varying WELS rating for front loading machines (Constant Wash Size)

For front loading machines, the machines are all closer in size such that the capacity has a

reduced effect. The results generally follow the results in the main body of the report where

size was kept constant and the WELS rating generally dominates the water use impact.

-40%

-30%

-20%

-10%

0%

10%

20%


Base Case (Top Loader 3*) Front Loader (4*) Front Loader (4.5*)

Appendix B

Uncertainty Analysis



Page B1 ArupIssue 24 May 2010

B1 Uncertainty Analysis

B1.1 Comparison with other LCAs

Data inputs and differences in model assumptions and system boundaries assigned in LCA

studies can lead to vast differences in output results, making comparison of LCA results

across different studies difficult. There have however been a number of LCAs undertaken

which investigate washing machines and washing habits. The proceeding information

seeks to provide a comparison between the existing literature and the results from this

study.

The existing studies used for comparison focus on three main impact categories: energy

use, water use and greenhouse gas emissions. This means that comparison against the

other impact categories considered in this study, such as eutrophication or minerals

depletion, has not been possible.

B1.1.1 Bole, 2006

An American study - Bole (2006) found that the use phase of the clothes washing process

accounted for more than 95% of carbon emissions as well as the vast majority of energy

and water use. A comparison between the results from this study and the Bole study is

shown below in Table 45.

Table 45 Comparison to results from Bole 2006

Energy Use

(MJ)

Water Use

(kL)

Emissions

(tCO2e)

Vic EPA Bole,

2006

Vic EPA Bole,

2006

Vic EPA Bole,

2006 Base Case

w/ dryer

Base Case

w/ dryer

Base Case

w/ dryer

Upstream 9.7 16.1 3.5 26.6 30.5 1.1 0.4 0.9 0.2

Use 15.8 113.1 240.2 292.4 305.0 1,086.2 1.6 12.3 13.5

Downstream 0.5 -1.3 0.0 0.0 -0.9 0.0 -0.1 0.1 0.0

Total 25.9 127.8 243.6 318.4 334.4 1,087.3 2.0 13.1 13.7

There are a number of differences in the assumptions and inputs in this study in comparison

to the Bole Study including:

• The useful life of the machine was 20 years compared to 14 years in this study;

• The emissions intensity of electricity was 0.74 kgCO2e/kWh which is significantly

lower than in this study (1.35 kgCO2e/kWh);

• The top loader machine in the Bole study consumed 1.64 kWh per wash,

significantly higher than the base case of this study (0.292 kWh per wash);

• The Bole study did not incorporate standby power or detergent in its estimates;

• Dryer use was assumed for all washes where the user had a dryer (assumed to be

86%);

• The emissions figure is based on a top loading machine washing 391 loads per year

and the functional unit used was a volume based metric (i.e. cubic feet), as opposed

to a weight based metric (i.e. kilograms) which was chosen for this Australian

study;

• The Bole study assumed a much lower recycling rate to this study;




These differences, coupled with the fact that American appliance efficiencies were used,

account for most of the discrepancy between Bole’s results and those achieved during this

study. Discrepancies in upstream impacts are harder to explain. Bole points out that many

of the impacts from the fabrication of components and assembly have not been included in

the study as there was a lack of information from suppliers to the washing machine

manufacturer involved in the study.

B1.1.2 ACA Study

The Australian Consumer’s Association (ACA) published an LCA report in 1992, (ACA,

1992) which investigated the environmental impacts of domestic clothes washing. As part

of this report, they found that the manufacture of a washing machine contributed no more

than between 0.5 and 2% to the total life cycle impact results, no matter which impact

category was analysed. In this study, the impact ranged from between less than 1% for

categories such as water use, up to 5% for greenhouse gas emissions. Table 46 shows a

comparison between this study and the results from the ACA study for a 5kg top loading

machine set on warm wash with a gas storage hot water system.

Table 46 Comparison with Australian Consumer's Association study

Energy Use

(MJ)

Water Use

(kL)

Emissions

(tCO2e)

Vic EPA

Base Case

ACA, 1992 Vic EPA

Base Case ACA, 1992

Vic EPA

Base Case ACA, 1992

Upstream 9.7 31.4 26.6 113.0 0.4 4.5

Use 15.8 31.2 292.4 529.0 1.6 4.0

Downstream 0.5 15.0 0.0 0.0 -0.1 3.0

Total 25.9 77.7 318.4 642.0 2.0 11.5

The major differences between this study and the 1992 study from ACA are in the upstream,

use and downstream impacts. The following points give some explanation of the differences

between the studies:

• The increase in water and energy efficiency in top loading washing machines over

the past two decades;

• A change in the composition of detergents and washing agents over the past two

decades;

• The LCAs that had been published at the time were considered to give “relatively

sketchy treatment to the extraction of raw materials”, meaning that conservative

assumptions were made where there was unknown data;

• The ACA study assumed a much lower recycling rate to this study.

The other differences can be attributed primarily to the differences in assumptions such as

machine capacity, loads per year and the efficiency of machines used during the early

1990’s.

B1.1.3 Systain Study

Carbon footprinting undertaken in Germany by Systain Consulting in 2009 provides

emissions results for the washing of different garment types. This study reported carbon

emissions of 3.3kg CO2-e for the use phase of their project which involved the washing of a

garment 55 times and the occasional use of a dryer and iron. Without ironing or drying




(33.4% of emissions), this equates to 0.18kg CO2-e per kg of washed clothing, which is very

close to the 0.15kg CO2-e per kg estimated by this study’s base case.

The Systain study utilised different system boundary than those selected for this project and

as such, their results are reported differently to those in this study. The findings articulate a

similar pattern by which the carbon footprint is affected by variations in washing temperature

and machine efficiency. It found the energy consumption and thus the carbon footprint to be

reciprocally proportional to load capacity (% full). The study also emphasised the need to fill

washing machines and dryers to capacity each time to reduce global warming emissions.

Another point of interest was the Systain study conclusion that the average efficiency

condenser dryers consumed more energy than average electric dryers and that when a

dryer was used permanently, it made up the largest part of the carbon footprint in the use

phase, producing 6.58kg of CO2-e emissions.

B1.2 Monte Carlo Analysis Methodology

For all raw data collected, uncertainty was estimated using the pedigree matrix to define a

standard deviation and distribution. The Pedigree matrix, originally developed by Weidema,

1996, assesses each data input against six criteria plus a so-called Basic uncertainty factor.

The 95% confidence interval or the squared geometric standard deviation is calculated

using the following formula:

( ) ( ) ( ) ( ) ( ) ( ) ( )22

6

2

5

2

4

2

3

2

2

2

1

2

95

ln(ln(ln(ln(ln(ln(ln(exp b

gg

UUUUUUU

SD

++++++=

=σ

The factors U1 till U6 referring to the scores for:

• Reliability (U1)

• Completeness (U2)

• Temporal Correlation (U3)

• Geographical Correlation (U4)

• Furtherer Technological (U5)

• Sample Size (U6)

The factor Ub refers to the basic uncertainty factor and is emission specific.

The pedigree matrix is presented below.

This process resulted in uncertainty data entered for in excess of 70% of processes and

100% of processes for data collected for the purposes of this LCA. Some upstream

processes adopted from the Ecoinvent and Australian LCA databases do not contain values

for uncertainty.

Uncertainty analysis was then carried out using a Monte Carlo Analysis over 1,000 runs to

approximate the uncertainty level in the data. Uncertainty analysis was undertaken for the

base case and the base case with drying as well as the various components of the lifecycle

to determine which processes contributed the most to the overall uncertainty.




B1.3 Results of Monte Carlo Analysis

B1.3.1 Overall Uncertainty

The results for the base and base case with drying for a 95% confidence level are presented

in Table 47.

Table 47 Uncertainty Analysis

Impact category Unit Mean Standard

Deviation

CV (Coefficient

of Variation)

Base Case

Water Use L H2O 31 4.64 15.0%

Cumulative

Energy Demand kJ eq 2,530 387 15.3%

Global Warming kg CO2 eq 0.211 0.031 14.7%

Eutrophication g PO4 eq 1.19 0.172 14.4%

Fossil fuels kJ surplus 156 24.1 15.5%

Minerals kJ surplus 10.4 2.86 27.4%

Land use m2

3.91E-03 9.57E-04 24.5%

Base Case with Drying

Water Use L H2O 32.7 4.63 14.2%

Cumulative

Energy Demand kJ eq 12,300 1070 8.7%

Global Warming kg CO2 eq 1.28 0.115 9.0%

Eutrophication g PO4 eq 1.38 0.177 12.8%

Fossil fuels kJ surplus 746 65.3 8.8%

Minerals kJ surplus 34.6 9.81 28.4%

Land use m2

6.90E-03 1.12E-03 16.2%

B1.3.2 Contribution of Lifecycle Components to Overall Uncertainty

The different components of the lifecycle’s contribution to the total uncertainty depends on

both:

• the uncertainty of the specific component (represented by the coefficient of

variation); and

• the absolute contribution of that component to the overall impact (% of total).

The combination of these two factors indicates the component’s contribution to the overall

uncertainty. That is, components with inherent high uncertainty will not necessarily have the

greatest contribution to the impact category unless they have a significant impact.

The results of this analysis are presented in Table 48 to Table 54.




Table 48 Lifecycle Components Uncertainty – Water Use (L H2O)

Lifecycle Component Mean

Standard Deviation

CV (Coefficient of

Variation)

Contribution to

overall uncertainty

(%)

Base

Case

Base

Case

with

Drying

Detergent Manufacture 2.57 0.583 22.7% 11.6% 10.8%

Washing Machine Manufacture 0.0452 0.0145 32.1% 0.3% 0.3%

Stand-by Power 0.0655 0.016 24.4% 0.3% 0.3%

Washing Machine Thermal Energy 0.00204 0.00301 147.0% 0.1% 0.1%

Washing Machine Mechanical Energy 0.102 0.0207 20.2% 0.4% 0.4%

Washing Machine Water Consumption 28.1 4.38 15.6% 87.1% 81.2%

Wastewater Treatment 0.0153 0.00315 20.6% 0.1% 0.1%

Washing Machine Disposal -0.021 0.00808 -38.6% 0.2% 0.2%

Dryer Manufacture 0.386 0.129 33.5% n/a 2.4%

Dryer Energy 1.24 0.227 18.3% n/a 4.2%

Dryer Disposal -0.021 0.00815 -38.9% n/a 0.2%

Table 49 Lifecycle Components Uncertainty – Energy Use (kJ eq)


Standard

Deviation

CV (Coefficient of

Variation)

Contribution to

overall uncertainty

Base

Case

Base

Case

with

Drying

Detergent Manufacture 850 196 23.1% 35.6% 850

Washing Machine Manufacture 94.3 29.6 31.4% 5.4% 94.3

Stand-by Power 486 93.9 19.3% 17.0% 486

Washing Machine Thermal Energy 250 83.5 33.3% 15.1% 250

Washing Machine Mechanical Energy 771 111 14.4% 20.1% 771

Washing Machine Water Consumption 42.1 8.02 19.1% 1.5% 42.1

Wastewater Treatment 96.4 16.1 16.7% 2.9% 96.4

Washing Machine Disposal -40.9 14.1 -34.4% 2.5% -40.9

Dryer Manufacture 626 198 31.6% n/a 626

Dryer Energy 9300 1070 11.5% n/a 9300

Dryer Disposal -41.2 14.2 -34.5% n/a -41.2




Table 50 Lifecycle Components Uncertainty – Global Warming (kg CO2 eq)


Standard

Deviation

CV (Coefficient of

Variation)

Contribution to

overall uncertainty

Base

Case

Base

Case

with

Drying



Stand-by Power 0.0535 0.0104 19.4% 24.5% 6.4%





Washing Machine Disposal -8.2E-06 3.76E-06 -45.9% 0.0% 0.0%


Dryer Energy 1.02 0.118 11.6% n/a 73.1%


Table 51 Lifecycle Components Uncertainty – Eutrophication (kg PO4 eq)


Standard

Deviation

CV (Coefficient of

Variation)

Contribution to

overall uncertainty

Base

Case

Base

Case

with

Drying



Stand-by Power 0.00809 0.00163 20.1% 0.9% 0.8%







Dryer Energy 0.155 0.0196 12.7% n/a 9.2%





Table 52 Lifecycle Components Uncertainty – Fossil Fuels Depletion (kJ Surplus)


Standard

Deviation

CV (Coefficient of

Variation)

Contribution to

overall uncertainty

Base

Case

Base

Case

with

Drying

Detergent Manufacture 48 11.2 23.3% 31.7% 9.8%


Stand-by Power 29 5.61 19.4% 16.0% 4.9%







Dryer Energy 554 63.8 11.5% n/a 55.6%


Table 53 Lifecycle Components Uncertainty – Minerals Depletion (kJ Surplus)


Standard

Deviation

CV (Coefficient of

Variation)

Contribution to

overall uncertainty

Base

Case

Base

Case

with

Drying



Stand-by Power 0.0129 0.0098 75.7% 0.2% 0.1%






Dryer Manufacture 25 9.25 37.1% n/a 68.0%

Dryer Energy 0.234 0.157 67.0% n/a 1.2%





Table 54 Lifecycle Components Uncertainty – Land Use (m2)


Standard

Deviation

CV (Coefficient of

Variation)

Contribution to

overall uncertainty

Base

Case

Base

Case

with

Drying

Detergent Manufacture 3.47E-03 9.49E-04 27.4% 88.3% 58.8%

Washing Machine Manufacture 1.15E-04 4.09E-05 35.5% 3.8% 2.5%

Stand-by Power 1.28E-04 2.86E-05 22.3% 2.6% 1.8%

Washing Machine Thermal Energy 4.04E-06 5.95E-06 147.0% 0.6% 0.4%

Washing Machine Mechanical Energy 2.04E-04 3.85E-05 18.9% 3.6% 2.4%

Washing Machine Water Consumption 1.07E-05 2.22E-06 20.7% 0.2% 0.1%

Wastewater Treatment 3.08E-05 6.64E-06 21.6% 0.6% 0.4%

Washing Machine Disposal -8.19E-06 3.76E-06 -45.9% 0.3% 0.2%

Dryer Manufacture 5.11E-04 1.72E-04 33.7% n/a 10.7%

Dryer Energy 2.44E-03 3.65E-04 14.9% n/a 22.5%

Dryer Disposal -8.4E-06 3.73E-06 -44.5% n/a 0.2%

B1.4 Summary of Results

Where comparisons are able to be made, the results of this LCA are generally consistent

with other LCA studies. Inconsistencies are able to be attributed to different assumptions

and/or system boundaries. The results are highly sensitive to the size of the machine and

wash load which differ across the studies.

The results of the Monte Carlo analysis suggest a large degree of uncertainty across all the

impact categories with minerals depletion and land use the most uncertain. The base case

with drying is less uncertain that without drying as there is less degree of error associated

with the dryer energy which is dominant.




Detailed Results: Base Case




Detailed Results: Base Case with Drying




Detailed Results: Detergent Manufacture




Detailed Results: Washing Machine Manufacture




Detailed Results: Stand-by Power




Detailed Results: Washing Machine Thermal Energy




Detailed Results: Washing Machine Mechanical

Energy




Detailed Results: Washing Machine Water

Consumption




Detailed Results: Wastewater Treatment




Detailed Results: Washing Machine Disposal



Page B100

ArupIssue 24 May 2010

Detailed Results: Dryer Manufacture



Page B109


Detailed Results: Dryer Energy



Page B118


Detailed Results: Dryer Disposal

Appendix C

CWW and EPA Sustainability Covenant

EPA Sustainability Covenant

This voluntary SUSTAINABILITY COVENANT is a statutory agreement under section 49AA of the Environment Protection Act 1970 (“the Act”) made on the 22nd day of January, 2009.

Between:

Environment Protection Authority (EPA Victoria) of 40 City Road Southbank in the State of Victoria;

-and-

City West Water Limited (City West Water) (ABN – 70 066 902 467) of 247-251 St Albans Road, Sunshine in the State of Victoria;

In which the parties agree to work together to:

• developandimplementasustainabilityprogramthat:

enhances the resource efficiency; and »

reducesenvironmentalimpact »associated with domestic clothes washing.

• assistCityWestWatertocontinueto achieve net zero greenhouse gas emissionsthroughtheimplementationof EPA Victoria’s Carbon Management Principlesandtoshareinformationontheimplementationofgreenhousegasmanagementprograms.

• developastreamlinedlicencethat:

deliversredtapereductionforCityWest »Water;

providesthecommunitywith »transparencyandaccountability;and

supportsCityWestWatertowork »towards delivering a sustainable water business.

City West Water - EPA Sustainability Covenant

EPAVictoriaisoftheopinionthatthisSustainability Covenant is likely to be effective in increasing the resource use efficiency and reducing the ecological impactofthewaterindustry.

EXECUTED as a deed.

THE COMMON SEAL of the ENVIRONMENT PROTECTION AUTHORITY is duly affixed by the Chairman on the th day of 2009

MICHAEL JOHN BOURKE

Chairman Environment Protection Authority Victoria

THE COMMON SEAL of CITY WEST WATER LIMITED was affixed to this document in accordancewithitsconstitutioninthepresenceof:

ANNE BARKER Managing Director CityWestWaterLimited

STEPHEN ROBERTSON CompanySecretary CityWestWaterLimited

About This Sustainability CovenantThisSustainabilityCovenant(Covenant)isapublic commitmentbyCityWestWaterandEPAVictoriatoworktogether to achieve resource use efficiencies and to reduce operationalecologicalimpacts.ItrepresentsthenextstepinanongoingrelationshipofworkingtogethertohelptheVictoriancommunitylivesustainably.SomeofthespecificactionsrequiredtoachievethisCovenant’spurposewillrequirefurtherexploration,giventheinnovativenatureoftheprograms.Itisexpectedthattheoptionsinvestigatedandtrialledwillformanimportantcomponentoftheinformationsharedbetweenthe organisations and will contribute to making the Victorian community more sustainable.

Washingofclothesisgenerallyundertakenusing domestic washing machines, detergentsandwater.Thisoperationgivesrisetoarangeofenvironmentalimpactsincluding:

• Depletionofwaterresources-approximately15%ofhouseholdwateruse relates to clothes washing;

• Greenhousegasemissionsassociatedwith use of electricity;

• Dischargeofwastedetergentstothesewerage system - this discharge includes salts, which in combination with salt discharges from industry can reduce the ability to recycle water withoutenergyintensiveprocessessuch as reverse osmosis.

CityWestWaterandEPAVictoriawillpartnertodevelopandimplementasustainabilityprogramthat:

• enhancestheresourceefficiency;and

• reducesenvironmentalimpacts,associated with domestic clothes washing.

Theprogramwillbedevelopedinthreephases:

• Phase1–Conceptualdesign.

Thisphasewillinvolve:

Brainstorming a range of innovative »approachestoimprovetheoverallresource efficiency and reduce theoverallenvironmentalimpactof this lifecycle. This may include consideration of washing machine exchangeprograms,centralisedcleaningservices,productleasingetc;

Preliminary lifecycle assessment of all »identifiedoptions;and

Adoptionofatriplebottomline »frameworkforselectionofapreferredoption.

Strategy and actionsThisCovenantwillfocusonthreeprograms.

Program 1 – Sustainable clothes washing

• Phase2–Detaileddesign.

Thisphasewillinvolve:

Developingadetailedprogram »structure, costings and objectives for thepreferredoption;

A detailed lifecycle assessment »includingacomparisontothecurrentstock of domestic washing machines;

Identificationofadditionalprogram »partners;and

Preparationofabusinesscaseand »implementationplanforapprovalbytheparties.

• Phase3–Implementation.

Thisphasewillonlybeimplementedifthenewprogramisshowntohavesignificantsustainability benefits over and above the existing domestic washing machine stock.

Thetimelineforcompletionofeach phaseis:

• Phase1–3monthsfromthedayonwhich this Covenant is signed.

• Phase2–12monthsfromthedayonwhich this Covenant is signed.

• Phase3–Implementedduringthefinal2yearsofthis3yearCovenant.

The management and reduction of greenhouse gas emissions has emerged as an issue of significance for organisations worldwide. A key aim of this Covenant is forCityWestWaterandEPAVictoriatowork together on strategies to address this business and environmental issue. The activities undertaken under this Covenant willbuildtheparties’capacitiestoworkwith their stakeholders and the broader community on greenhouse gas emissions reduction strategies.

Under its Environmental Sustainability Plan 2(ESP)CityWestWaterhascommittedto achieving net zero greenhouse gas emissions. This Covenant formalises EPA Victoria’ssupportforCityWestWaterinachieving this goal.

The strategy committed to under the ESP and this Covenant is the achievement of zero net greenhouse gas emissions from operationsunderCityWestWater’scontrol.ThiswillbeachievedbyimplementingEPAVictoria’scarbonmanagementprinciplesincluding:

• Measure –CityWestWaterwillcontinuetoprepareanannualinventoryconsistent with the International GreenhouseGasProtocol;

• Set objectives–CityWestWaterwillseek to achieve net zero greenhouse gasemissionsfortheperiodoftheCovenant;

• Avoidance –wherepracticable,CityWestWaterwillavoidindirectanddirectgreenhouse gas emissions associated with its business;

• Reduction–wheregreenhousegasemissionscannotbeavoided,CityWestWaterwillinvestigateandimplementpracticestoenhanceenergyandresource use efficiency;

• Switch–CityWestWaterwillinvestigateopportunitiestoswitchtorenewableenergyforexistingoperations;

• Sequester –OpportunitiestosequestercarbonontheCityWestWaterheadoffice site will be investigated;

• Assess –CityWestWaterwillundertakean assessment of residual greenhouse gas emissions that cannot be avoided; and

• Offset-whereallotheroptionshavebeenexhausted,CityWestWaterwill offset residual greenhouse gas emissions. EPA Victoria’s guidance providedatwww.carbonoffsetguide.com.auwillbeusedbyCityWestWatertoensurecredibilityofpurchasedoffsets.

FurtherdetailsofCityWestWater’snetzerogreenhousegasapproachareoutlinedin its ESP which has been endorsed by EPAVictoriaandtheCityWestWaterCommunity Liaison Committee.

Program 2 – Net zero greenhouse gas emissions

Corporatelicensingisaworld-firstinitiativepioneeredbyEPAVictoria.Thisinitiativeseekstocutredtapeby:

streamliningexistingreportingstructures »toasingleannualperformancestatement; and

creatingopportunitiesforcompaniesto »investinprojectsthatcreatethebiggestenvironmental and economic returns.

Acorporatelicenceretainsandsimplifiesallcompliancerequirementsintoasingle,easier to understand document.

CityWestWaterholdsalicenceforitsAltonaTreatmentPlant.CityWestWaterand EPA Victoria will work together to developastreamlinedlicencethat:

deliversredtapereductionforCityWest »Water;

providesthecommunitywith »transparencyandaccountability;and

supportsCityWestWatertowork »towards delivering a sustainable water business.

Thisprogramwillbeprogressedinthefirstyear of this Covenant.

Program 3 – Corporate licensing

Principles

CityWestWaterandEPAVictoriawillshareinformationtohelpfacilitateCityWestWater’ssustainabilityprograms.

CityWestWaterandEPAVictoriawilljointlyinvestigateoptionstoassistCityWestWatertoachieveitsvisionofbeingatrulysustainable water business.

Reporting

PublicreportingwillbeconductedthroughCityWestWater’sannualSustainabilityReportandinaccordancewithcommitments under the ESP.

Life Span of the Sustainability Covenant

ThisSustainabilityCovenantwilloperateuntil30June2011.

CityWestWaterprovidesdrinkingwater,sewerage, trade waste and recycled waterservicestoapproximately276,000residentialand31,300industrialandcommercial customers in Melbourne’s Central Business District and inner and western suburbs.

CityWestWaterisoneofthethreeretailwatercompaniesservicingmetropolitanMelbourne. It is wholly owned by the VictorianGovernment.CityWestWater’sboundaries contain the local government areas of Brimbank, Hobsons Bay, Maribyrnong, Melbourne (north of the Yarra River),MooneeValley,Wyndham,YarraandpartsofMeltonandHume.

RelativetotheothermetropolitanMelbournewaterretailers,CityWestWaterhasasmallercustomerbaseandgeographicarea (which includes Melbourne’s Central BusinessDistrict),withagreaterproportionof non-residential customers. Many of the non-residential customers are large industrialoperationsinthebrewing,chemical manufacturing, oil refining, textile and automotive manufacturing industries.

CityWestWater’svisionistobeatrulysustainablewaterbusiness.ForCityWestWater,sustainabilitymeansbalancingsocial, environmental and economic responsibilities.Inalocalcontext,thepotentialimpactofclimatechangecouldresult in less water being available from Melbourne’sexistingwatersuppliesasa

About the parties

City West Water

resultoftheimpactsofclimatechange.CityWestWaterrecognisesthatitcannotsolveclimate change itself due to the size and scaleofthisissue.Hence,CityWestWatercommits to constructively working with a rangeofpartnersincludingitsresidential

and commercial customers, EPA Victoria, Sustainability Victoria, DepartmentofSustainabilityandEnvironment and the local, national and international water industry to reduce the impactofclimatechange.

EPA Victoria is a statutory body that was established under an Act of the Victorian Parliamentinresponsetocommunityconcernaboutpollution.TheEnvironmentProtectionAct1970replacedstatutoryprovisionsscatteredthroughoutmorethan25 existing Acts, bringing together under one umbrella (symbolised by the EPA Victorialogo),controlofpollutiononland,in water and air, and industrial noise. In particular,itputstheresponsibilityforsoundenvironmental management on all Victorians –businesses,communitiesandindividuals–whereitbelongs.Theemphasisisshiftingto market mechanisms, collaboration and co-regulation, rather than just the traditional ‘commandandcontrol’approachtoachieveenvironmentalperformance.

EPA Victoria’s Vision is ‘The Victorian community living sustainably.’ A community livingsustainablyknowstheimpactsofthedecisions it makes and the actions it takes ontheenvironmentand:

• Efficiently uses and renews resources;

• Understands how what is good for the

environment is good for the economy and society;

• Lives in a healthy environment that providescleanair,waterandland;and

• Meets the needs of today without compromisingtheabilityoffuturegenerations to meet their needs.

EPAVictoria’spurposeistoprotect,careforandimproveourenvironment.EPA Victoria’s values are collaboration, innovation,integrityandrespect.EPAVictoria’s objectives are to increase resource efficiency,reduceemissionsimpact,tackleclimatechange,enhanceourreputationandbenefit the economy.

EPAVictoriacontinuestostrivetoimprovetheenvironmentalperformanceofitsownoperationsandactivities,throughtheimplementationofitsEnvironmentManagement System. EPA Victoria is now aiming to go ‘beyond carbon neutral’. To achieve this they will avoid and reduce our energy use and associated greenhouse gasemissionswhereverpossible.

EPA Victoria

In accordance with section 49AC(b) of theAct,thepartieswillensurethatthiscovenantisreadilyaccessibletothepublicandthatitispublishedontheInternet.

In accordance with section 49AC(c) of the Act,thepartiesauthorisethecopyingofalloranypartofthisCovenantbyanypersonwhowishestodoso.Thepartiesalsoauthorisetheusebysuchapersonofanycopiesmadebythatperson.

More Information

City West WaterLockedBag350SunshineVictoria3020Tel:(03)93138422www.citywestwater.com.au

EPA Victoria40 City RdSouthbankGPOBox4395QQ MelbourneVIC3001Ph:0396952722www.epa.vic.gov.au

Contact Information

Appendix D

CWW LCA Process Flow Maps



Page D1 ArupIssue 24 May 2010

LCA map parts 1 and 3, 2 and 4.

Part 1 (washing machine manufacture) & Part 3 (detergent manufacture)




Part 2 (wastewater treatment)




Part 4 (water supply)

epa victoria and city west water · the detailed life cycle assessment (lca) represents a key...

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