Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

A New Ecological Footprint Calculation for the Australian Water Industry:
Regionalisation and Inclusion of Downstream Impacts

By

Gregory Peters*, Fabian Sack**, Manfred Lenzen***,Sven Lundie* and Blanca Gallego***Abstract
A new calculation of the Ecological Footprint (EF) of a water provider addresses limitationsin the previous methodology by regionalising a previously national input-output model, anddetermining the area of disturbance caused by environmental toxicants not considered in the

traditional EF model. In a first step, the regional input-output model determines indirect ef
fects of water services activities in the form of point sources of pollutants. Accuracy is improved by hybridisation using "process data" to account for the direct environmental burdenof the water service. The accuracy of the input-output model is improved by reconcilingdata sources; calibrating concordance tables and employing optimisation techniques to deal
with conflicting data sources. The second step involves a nested fate model, which follows
the fate of the point source emissions at several spatial scales. The final output provides anindication of the direct and indirect burden connected with the water business, throughout

its entire upstream supply chains. This proposed EF methodology improves on previous EF
methodologies by avoiding exclusive reliance on national average data, and by including
toxicants in a disturbance-based calculation analogous to the established inclusion of green

house gases in EF, making it more comprehensive. It is hoped that the additional detail andcomprehensiveness will make the new method a more effective environmental reporting andcommunications tool for the Australian water industry. This generic approach to environ
mental reporting may potentially be applied to other economic activities.

1

Introduction

The ecological footprint (EF) was conceived as a simple and elegant method for com
paring the sustainability of resource use among different populations. Consumption by
a population is converted into a single index: the land area that would be needed tosustain that population indefinitely. This area is then compared to the actual area of
productive land that the given population inhabits, and the degree of unsustainability isexpressed as the difference between available and required land. Unsustainable popula

tions are thus simply populations with a higher EF than available land. EFs calculated
Received April 2006,final version received December 2006.
*

University of NSW, Centre for Water and Wastewater Technology**

Sydney Water Corporation***

University of Sydney, School of Physics

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Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

according to this original method became important educational tools in highlighting
the unsustainability of global consumption. EFs have also been used for policy devel
opment and planning, both in government and industry.

1.1

Use of the EF in the Australian Water Industry

As currently performed, EF allows combined assessment of direct land disturbance andgreenhouse gas emissions, incorporating all the contributions to these made by a water
corporation's supply chain. Following an initial pilot of the EF in the UK (Chambers& Lewis, 2001) an improved methodology, using the widely accepted economic analy
sis technique of input-output analysis, has been mailed in the Australian water industry(Lenzen et al., 2003). To date, several Australian Water authorities have used the EF tocommunicate the combined impact of materials and energy consumed by urban water
cycle management activities and each customer's contribution to this impact. The methodology is considered to be mathematically robust, allows some limited benchmarkingof performance, and has outputs that are easily communicated to a broad public audi
ence (eg.: newspaper readers-O'Dwyer, 2006). Financial data provides the information

required to calculate EF. Since financial systems are generally computerised andaudited, data collection for EF use is practical and relatively accurate.

1.2 Methodological Constraints
Since the formulation of the EF, a number of researchers have critiqued the methodol
ogy originally proposed (Levett, 1998; Opschoor, 2000; van den Bergh & Verbruggen,1999; van Kooten & Bulte, 2000). Their comments largely refer to the oversimplifica

tion of the complex task of measuring the sustainability of consumption patterns, leading to comparisons among populations becoming meaningless, or the result for a single
population being significantly underestimated. In addition, the geographically aggre
gated form of the final EF makes it difficult to formulate appropriate policy responses
to the factors driving unsustainable consumption.

An additional constraint on the use of the EF methodology by the water industry
is its inability to include downstream aquatic impacts in the calculation (Lenzen et al.,2003a; Sack et al.9 2003). To be accurately applied to industry sectors such as thewater industry, where the ecological impact of discharges may be as significant as

those of the industry's inputs, the EF must be able to characterise impacts to ecologicalprocesses. Importantly, this must include ecological impacts downstream of the wastewater treatment process, in addition to the ecological impact of processes with the supply chain upstream of the water and wastewater treatment and distribution process.

The methodology described in this article addresses both these limitations by re-gionalising the input-output model that has previously supported national EF calcula
tions, and by determing the area of disturbance caused by some environmental burdens
not considered in the traditional EF model. In a first step, the regional input-output

model determines indirect effects of water services activities in the form of point
sources for a range of pollutants. Accuracy is improved by using "process data" whereavailable to account for the direct environmental burden of the water service provider.
The second step involves a nested fate model, which follows the fate of the pointA New Ecological Footprint Calculation for the Australian Water Industry: Regionalisation and Inclusion of Downstream Impacts

75

source emissions at several spatial scales. The final output of this model can thus pro
vide an indication of the direct and indirect burden connected with the water business,throughout its entire upstream supply chains. The accuracy of the input-output model isimproved by the reconciliation of disparate data sources; calibrating concordance tables
and employing optimisation techniques to deal with non-convergence.

2 Regionalising a Generalised Australian Input-output Model
The research team has regionalised the generalised input-output model that has previ

ously supported national EF calculations (Lenzen & Murray, 2001; 2003). This region
alisation refers to the economic data published by the Australian Bureau of Statistics(Australian Bureau of Statistics, 2004), as well as accompanying existing physical dataon land disturbance (Graetz et aL, 1995; Lenzen & Murray, 2001) and greenhouse gas

emissions (Australian Greenhouse Office, 1999), as well as additional physical datafrom the National Pollutant Inventory (National Pollutant Inventory, 2005). The final
data set comprises:• input-output data for 8 States1 and 344 economic sectors2;• land use for 49 land types (see Table 1), by State and by economic sector;• greenhouse gas emissions for 6 gases3, by State and by economic sector;• pollutant emissions for 86 contaminants4, 3 compartments (air, water, soil), by
State and by economic sector.

An effort was made to adhere to the input-output table base year of 1998-99, but

this was not possible where data sets were only available for neighbouring period. The

physical data of the generalised input-output model is arranged in form of a matrix Q
that measures the amount of environmental disturbance directly caused by a given eco

nomic sector during its on-site production of output. Rather than describing the entire
physical dataset, we give an example for the land data only.

2.1 Example of Physical Data: the Land Database
From an environmental management perspective, the most comprehensive Australianland-use data set available today is the one generated by the National Land and Water

Resources Audit (NLWRA), in collaboration with several Commonwealth, State andTerritory agencies. It follows the Australian Land Use and Management Classification(ALUMC) (Bureau of Rural Sciences, 2001), which has been designed for users inter-

1

New South Wales, Victoria, Queensland, Western Australia, South Australia, Tasmania, Northern Ter
ritory, Australian Capital Territory.

2 These 344 are a subset of the sectors in the IOPC 8-digit classification, documented by the Austra
lian Bureau of Statistics (2001a). The subset includes the non-confidential IOPC items.

3 Carbon dioxide, methane, nitrous oxide, perfluorocarbons, sulphur hexafluoride, hydrofluorocarbons
4 See www.npi.gov.au/about/list_of_subst.html for a current list of the 90 reportable substances. Notethat for four substances (hexachlorobenzene, 2-methoxyethanol, 2 methoxyethanol acetate and 4,4'Methylene-bis (2-chloroaniline)) no emissions have been reported.

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Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

Table 1: ALUMC land types available in the IRDB data set.
Primary Level

Secondary Level

Tertiary Level

Primary Level

Secondary Level

Tertiary Level

1.CONSERVATION AND

1.1. Nature conservation

1. 1.1. Strict nature reserve

NATURALENVIRON

1.11.2. Wilderness area

MENTS

1. 1.3. National park
1. .4. Natural feature protection

1.1 .5. Habitat/species management area
1. .6. Protected landscape1.] .7. Other conserved area

1.2. Managed resource protection

1.2.5. Traditional indigenous uses.
I.2.U. Unclassified

1.3. Other minimal use

1.3.1. Defence

1.3.3. Remnant native cover
I.3.U. Unclassified

2.PRODUCTION FROM
NATU

RELATIVELYRAL ENVIRONMENTS
3.PRODUCTION FROMDRYLAND AGRICULTURE AND

PLANTATIONS

2.1 Livestock grazing

2.1.0 Livestock grazing

2.2. Production forestry

2.2.0 Production forestry

3.1. Plantation forestry

3.1.0 Plantation forestry

3.2. Farm forestry

3.2.0 Farm forestry

3.3. Grazing modified pastures

3.3.0 Grazing modified pastures

3.4. Cropping

3.4.1. Cereals

3.4.3. Hay and silage

3.4.4. Oilseeds and oleaginous fruit
3.4.5. Sugar

3.4.6. Cotton
3.4.8. Legumes

3.5. Perennial horticulture

3.5.1. Tree fruits
3.5.3. Tree nuts

3.5.4. Vine fruits

3.5.U. Unclassified

3.6. Seasonal horticulture
4.PRODUCTION FROMIRRIGATED AGRICULTURE AND PLANTA

3.6.4. Vegetables and herbs

4.3. Irrigated modified pastures

4.3.0. Irrigated modified pastures

4.4. Irrigated cropping

4.4.1. Irrigated cereals

4.4.3. Irrigated hay and silage

TIONS

4.4.4. Irrigated oilseeds and oleaginous fruit
4.4.5. Irrigated sugar

4.4.6. Irrigated cotton

4.4.8. Irrigated legumes
4.4.U. Unclassified

4.5. Irrigated perennial horticulture

4.5.1. Irrigated tree fruits

4.5.3. Irrigated tree nuts
4.5.4. Irrigated vine fruits

5. INTENSIVE USES

6. WATER

4.6. Irrigated seasonal horticulture

4.6.4. Irrigated vegetables and herbs

5.4. Residential

5.4.1. Urban residential

5.7. Transport and communication
5.10. Intensive uses -Other

5.10.0 Intensive uses -Other

6.1. Lake

6.1.0 Lake

6.2. Reservoir

6.2.0 Reservoir

6.3. River

6.3.0 River

6.5. Marsh/wetland
U.UNCLASSMED

5.7.1. Airports/aerodromes

6.5.0 Marsh/wetland

6.6. Estuary/coastal waters

6.6.0 Estuary/coastal waters

5.10. Intensive uses -Other

5.10.0 Intensive uses -OtherA New Ecological Footprint Calculation for the Australian Water Industry: Regionalisation and Inclusion of Downstream Impacts

77

ested in land management practices and outputs. It contains a three-tiered hierarchicalstructure designed in terms of the degree of intervention or potential impact on thenatural landscape. The surface area of water is also included in the classification (seeTable 1). As this classification has become the standard tool for the reporting of land
use and management by Australian Government agencies, we have adopted it here as

the standard tool for analysing and reporting land use in our EF calculations.
The NLWRA data set is published as the Integrated Regional Database (IRDB),

and provides the hectares of land-use of a given ALUMC type in a given Statistical
Local Area (SLA) of Australia (Australian Bureau of Statistics, 2001c). SLAs generally

correspond to local government areas (LGAs), with a number of exceptions including

urban environments where a large number of people may live in one LGA and the
SLA is a smaller collection of suburbs (Australian Bureau of Statistics, 2001d). How
ever, in order to include this land-use information into our generalised input-output

model we also need to know which economic sectors are the main users of that pieceof land and how they share it. Identification of the main users of a given land type in agiven region has been carried out applying basic knowledge of the industrial activitiesof the various economic sectors in Australia and its relation with the ALUMC land
types. Land shares have been assigned to economic sectors on the basis of their em
ployment, using the Australian Business Register (Australian Bureau of Statistics, 2001b).

As with most data sets, the land-use data from the IRDB is incomplete and there
are many land type-SLA cells for which data is missing. Furthermore, the group ofland types reported in the data set is only a subset of the complete array of land types
in Australia. The list of the ALUMC land types available in the IRDB data set (together with the added unclassified categories) is shown in the Table 1.

2.2 Disaggregating national physical data by State and by detailed industry sector
Before embarking on potentially protracted computations an integration strategy was

designed. Based on available data, this strategy involved considering:• The aspired level of overall sectoral detail;• Proportionality assumptions underlying pro-rata techniques;• The degree of inconsistency in the raw data;• Concordances between different classification systems; and• Balancing methods to be employed.

2.2.1 Choice of level of sectoral detail
The standard classification used in Australia and New Zealand for the collection, com

pilation and publication of various statistics by economic sector is the Australian and
New Zealand Standard Industrial Classification, ANZSIC. In its most disaggregatedform it contains 465 industrial classes labelled by a 4-digit code (ANZSIC4). On theother hand, the industrial classification used in the Australian input-output tables is the
Input-Output Industrial Classification, IOIC. The IOIC consists of about 106 economic
sectors (depending on the publication year) and it is based on ANZSIC, though redefined to eliminate secondary or subsidiary production. Additional commodity informa
tion included in the Australian input-output tables is given using the 8-digit unpub-

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Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

lished version of the published 4-digit Input-Output Product Classification, (IOPC4 and
IOPC8; Australian Bureau of Statistics, 2001a), defined in terms of the characteristic
products of industry sectors. In our economic model, we use a classification consistingof 344 sectors defined as a subset of IOPC8 items and labeled by a 7-digit code (called
ISAPC).

A major task before any disaggregation was to prepare concordance tables be

tween ISAPC and ANZSIC4, ALUMC and the National Greenhouse Gas Inventory(IPCC classification - Intergovernmental Panel on Climate Change). Concordance ta
bles were "calibrated" using data known in both classifications, for example businessturnover / gross output, and employment (Australian Bureau of Statistics, 1998; 2001b).

The classification of the National Greenhouse Gas Inventory (NGGI), for example,
is even more aggregated than the published IOPC4 input-output system. The integration of the NGGI into an input-output framework would be straightforward if the input-output model was aggregated to NGGI level. This reduction in resolution however
would severely limit the capabilities of the resulting model for impact analysis anddecision-making. Especially, if additional indicators (pollutants, downstream impacts,etc) are to be integrated into a comprehensive EF accounts system, it is desirable tocompile the framework at the maximum level of detail, and aggregate the results after
impact analysis if necessary.

Working at a high level of detail is desirable because it is likely that physical dataon different indicators contains varying detail for different industry sectors. For exam
ple, energy data is likely to be detailed for energy-intensive metal sectors, while em
ployment data is likely to be detailed for service sectors, both of which often do notfeature detailed water use data. A consequence of this strategy is that some disaggregation can be achieved only through prorating according to proxy variables. Such pro
rated data is of course not very accurate. However, sectors affected by prorating arealso not likely to be important in terms of their contribution to overall flows. Hewings
et al., (1988) showed that large uncertainties occurring even in a large number of small
elements of input-output tables hardly affect the magnitude of multipliers. Hence, there
is no penalty in keeping a large number of uncertain but small data, as long as important (large) data items are sufficiently accurate, and as long as uncertain data are either
aggregated or clearly marked in the presentation of subsequent impact analysis results.
A practical example may highlight the importance of working at the highest possible detail: consider the embodied emissions passed on from bauxite mining to alumin
ium smelting. Assume that bauxite mining was aggregated with gold mining opera
tions, which need less water, and that gold processing was part of the non-ferrous
metal sector, together with aluminium processing. In an aggregated framework, consid
erable embodied emissions would be passed on to gold processing, even though gold
mining used only little water per dollar of output. In a disaggregated analysis, theseembodied emissions would only affect aluminium. Even if both sectors were aggre
gated after impact analysis, the results would be different from those of an aggregatedimpact analysis, because of the different downstream sales structures of bauxite andgold mining.

A New Ecological Footprint Calculation for the Australian Water Industry: Regionalisation and Inclusion of Downstream Impacts

79

Table 2: Example integration procedure: National Greenhouse Gas Inventory by
ISAPC
A)

State

Extract number of business locations by State by turnover range by

Output (GSO) data by IS

Estimate

Gross

ANZSIC from the 2001 Business Register (Australian Bureau of Sta

APC:tistics, 2001b),
Estimate not provided ("n.p.") entries using RAS,
Estimate (unknown) turnover by State by ANZSIC from (known)

number of business locations by turnover range, by fitting a piecewise continuous, strictly positive business-number-density functions
consisting of quadratic and cubic spline functions across discrete turnover ranges, and finally integrating these density functions over turnover,Calibrate an ISAPC-ANZSIC concordance matrix using existing turnover / gross output data in ANZSIC and ISAPC for the whole of Aus

tralia, and apply this concordance matrix to State data.
B)

Estimate

employment

Extract employment by State by ANZSIC from the 1998 Business
Register ([Australian Bureau of Statistics, 1998),

by State by ISAPC:

Estimate not provided ("n.p.") entries using RAS,
Calibrate a new ISAPC-ANZSIC concordance matrix using employ

ment data classified in ANZSIC and ISAPC for the whole of Austra
lia, and apply this concordance matrix to State employment data.
C) Estimate material flow

Extract material flow data from the 2004 Commodity Statistics (Aus

data by State by ISAPC:

tralian Bureau of Agricultural and Resource Economics, 2004).

D) Create an initial esti

Use material flow, GSO, and employment data as proxies for proratingmate for greenhouseaggregate greenhouse gas emissions across primary, secondary and tergasemissions by State by IS

tiary ISAPC sectors.

APC:
initialUse the NGGI and additional reports (George Wilkenfeld & Associates

estimate with all available

Pty Ltd and Energy Strategies, 2002) to formulate constraints on theconstraints

detailed ISAPC accounts, and use these constraints as an input for aE)

Reconcilesurvey data:

thisderivedfrom

RAS balancing routine.
RAS-balance the initial estimate based on constraints on total sums
over greenhouse gas emissions by sector, and by State, or based on
sub-totals over certain sectors; terminate RAS when results do notconverge any further, that is, when conflicting constraints cause oscil
lation.

Apply optimisation to find the best compromise between conflicting
constraints, based on uncertainty of the survey data. For example, if

for a certain subtotal, one constraint prescribes a value of 50 t ± 10%,and another constraint prescribes 70 t ± 25%, the optimised constraint
may be 55 t.

2.2.2 Data inconsistencies and balancing methods
Many of the physical data sets available in Australia are conflicting, due to sampling
and/or rounding errors, or due to differences in definitions or base years. For example,

energy data by State, industry sector, fuel type and equipment type (Australian Bureau
of Agricultural and Resource Economics, 1999) may add up to different subtotals, depending on whether sums over States, equipment types, fuel types, or sectors. Data

80

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

often refer to different years. Strictly speaking, a temporal misalignment of surveys
calls for a simultaneous balancing procedure of all data over all periods, imposing cer
tain temporal stability conditions (compare Tarancon & Del Rio, 2005).

Conflicting data causes conventional RAS balancing techniques (Gretton & Cotterell, 1979) to oscillate, thus preventing convergence. The more serious the data dis
crepancies, the more imperfect the final RAS result. Such non-convergence can only be

dealt with using optimisation techniques. These techniques have been the subject of recent articles in the expert literature, and shall not be dealt with further here.

The challenges of a practical integration procedure are perhaps best illustrated us
ing a real-world example. Creating greenhouse gas accounts for all Australian States
and Territories at the ISAPC sectoral detail requires the actions given in Table 2.
The procedure sketched above may give a feeling about how involved the recon

ciliation of more than two or three disparate data sources can be. A number of issues

require further attention5.

2.3 Enhancing geographical resolution from States to Statistical Local Areas
After integration of the physical and economic data into one regional input-output
framework, the usual input-output calculus is followed, including calculating:• the direct requirements matrix A = Tx1 from the regional input-output coefficient
table T and diagonalised Gross State Output x ;• the physical coefficients matrix q = Q x"1 ;• the Leontief inverse L = (I - A )"' ;• generalised multipliers q # L, where # denotes element-wise multiplication.
Import and exports from the Australian economy are taken into account-for further details on these procedures see Lenzen (2001). Let element q'j describe the EF impact oftype i (land type, emissions type) caused by industry sector j in State r. Let element
Ljl describe the gross output of industry sector j is State r necessary to satisfy final demand from industry sector k in State s. Then (q#L)'jk = q^ L," is the impact of final demand from industry sector k in State s in terms of the EF of type i caused by industry

sector j in State r. The Australian Business Register (Australian Bureau of Statistics,
1998; 2001b) contains employment e)n size and business counts b)n by industry sector j

in rn Statistical Local Areas (SLAs) in State r. Using this knowledge of which indus
tries actually occur in the 1322 Australian SLAs, the EF can be broken down into SLA

regional detail by assuming that the gross output L," required from industries j in State

5 Firstly, in theory there should be only one concordance matrix between two classification systems,
for example ISAPC and ANZSIC. However, attempts to estimate such a matrix using crosscalibration of two national datasets (turnover and employment) generated two substantially different
concordance matrices, hence either employment and/or turnover in one or both systems must include
significant errors. Secondly, RAS techniques do not work when data conflict. Often, analysts traceinconsistencies manually, and/or make subjective selections based on data quality. Optimisation techniques can assist previously manual adjustments to conflicting survey data. Faced with problems
such as these, an effort could be made to streamline data collection by using only one classification

system. The lack of such harmonisation is ubiquitous, not only in Australia, and has been deploredby analysts for some time (Jones et al, 1994).

A New Ecological Footprint Calculation for the Australian Water Industry: Regionalisation and Inclusion of Downstream Impacts

81

r is distributed homogeneously across all industries of type j in subregions rn of Stater, for example according to their employment size:(q#L)rijk = qijHep"mthis enhancement of the geographical resolution of the regional input-output
model (States and Territories) to Statistical Local Areas is not supported by local inputoutput data, it facilitates an improved approach to modelling the area disturbed as a result of point source emissions of a wide range of pollutants.

3 Modelling Environmental Burdens Due To Emissions
The calculations to improve the regionalisation of the input-output procedure for the
calculation of an EF outlined above may be used to produce an emission matrix that
includes the direct onsite impacts-an improved approach to modelling areas disturbed
as a result of point source emissions, as shown in Figure 1.
Fig. 1: General calculation process for the novel EF"s" or "final
demandmatrix"

270 x 344

(emissions x
industries)"Q" or"L" or"direct requirements"Leontief inverse matrix"

matrix" based on NPI

based on ABS data

270 x 344

square 344"y" or "demand

onsite impact

vector" based

matrix basedon company

on company

accounts

environmental

344 x 1

report270x 1322(emissions x industries)($ turnover in industries)(one per state)(one per state)emission matrix

emission fate andecologicalDisturbance

270x1322

effectfootprint

vectorcharacterisation

disturbanceprocess

area process($ for indirect
impacts)(emissions)(one per state)(emissions, SLAs)

1322x1(per SLA)

business data basedon ABR344 x1322(industries per SLA)This is what might be called a "hybrid approach" to EF calculation - obtaining
improved accuracy by using "process data" where available to account for the directenvironmental burden of a water service provider, and adding this to input-output cal
culations to provide an indication of the burden of upstream units in the supply chainsconnected with the service provider (Peters et al, 2006). Process data is consideredmore accurate for the estimation of environmental burdens caused directly by a service

provider (eg.: Peters and Lundie, 2002; Lundie et ai, 2004), but it is impractical tocollect data for the entire supply chain, hence the interest in using input-output analysis
to cope with the indirect disturbance due to economic activities further up the supply

82

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

Fig. 2 Conceptual representation of a Mackay level III fugacity model in USES
4.0 (adapted from RIVM, VROM, VWS, 1998)

temperate

global scale

chain. Combining the areas disturbed directly by the service provider and the indirect
disturbances gives an idea of the total surface area of the earth, which is disturbed inorder to support the service provider.

In addition to this inclusion of local environmental effects we wish to further improve the EF method, by enhancing its environmental scope. Previous calculations of
the EF of Australian water service providers have only considered land directly or indi
rectly demanded by resource-producing activities or disturbed by climate change effects
due to greenhouse gas emissions. Here we propose a novel extension of the impact assessment process consistent with previous methodology to include toxic effects due toemissions. This will be used to characterise the emission both upstream and down

stream of the water service provider's processes. We also develop a more complete cal
culation of direct disturbances.

3.1

Characterisation of emissions

In order to include the toxic effects of emissions to air, land and water, a modelling ap

proach must be sophisticated enough to cope with the variable residence time of con
taminants in these environmental compartments, their transport between them and theirultimate potential to cause environmental damage. To be of practical use as an environ
mental management and reporting tool within industry, it must be simple enough tooperate on an ordinary personal computer and provide instant results. This rules out thepossibility of the construction of dynamic models given the large number of contami

nants (270) which would require simultaneous fate simulation involving mass transfer,reaction, sorption, degradation and other environmental processes in a large number ofA New Ecological Footprint Calculation for the Australian Water Industry: Regionalisation and Inclusion of Downstream Impacts

83

locations (1322). A simpler approach had to be adopted based on the idea of steadystate multi-compartmental modelling to compute contaminant characterisation factorsthat could be loaded into an EF calculator.

Fortunately the need for such an approach has previously been recognised by en

vironmental life cycle assessment practitioners and governments concerned with prior

ity setting for environmental health, and the concept has been extensively developed by
European and American researchers (eg : Huijbregts et al, 2000; Bare et al, 2003).

The leading European model, Uniform System for the Evaluation of Substances(USES) was originally developed by a whole-of-government effort in the Netherlands(RIVM et al, 1998), and continues to be updated and improved (RIVM et al, 2002).

It has also been adapted for Australian environmental conditions by Huijbregts et al,(2003).

Consistent with its Canadian and European predecessors (Mackay, 1991; RIVM etal, 1998) this involves the construction of a nested fate model, which follows the fateof an emission at several spatial scales. As shown conceptually in Figure 2, the model
considers transfer functions between different environmental compartments and differ

ent frames of spatial reference. More recently the usefulness of this approach has beenfurther improved by the calculation of characterisation factors based on emissions inseveral different Australian situations: urban air; rural air; agricultural soil; industrial

soil; and freshwater and marine environments (Lundie et al, 2005). By using GIS soft
ware we are able to associate appropriately location-specific characterisation factors ofthese types to individual SLAs. This is similar to the approach taken to regional impact
characterisation in Japan in recent work by Nansai et al, (2005).

A marginal change in the steady-state concentration of a contaminant emitted ineach of the relevant environmental compartments is considered by the calculation ofcompartment specific fate factors of the form:r,_ 9Cj,swhere FUs is the compartment-specific fate factor that accounts for the transport effi
ciency of substance s from compartment / to and persistence in compartment j (year.

m~3), dCj,s is the marginal change in the steady state dissolved concentration of sub
stance s in compartment j (kg.m3), and dMirS is the marginal change in emission ofsubstance s to compartment i (kg.year1). It has been previously shown by van deMeent and Huijbregts (2005) that chemical effect factors may be calculated on the ba
sis of toxicity data using the formula:p_ dmsPAFj

1

where EjlS is the effect factor of substance s for compartment j (mMcg"1), dmsPAFj isthe marginal change in the potentially affected fraction of species due to exposure to a

mixture of chemicals in compartment j , and HC50s is the hazardous concentration ofsubstance s where 50 percent of the species is exposed above an acute or chronic toxic

value (kg.m"3). On this basis, where CFix, is the characterisation factor for contaminants emitted in compartment i on compartment j, it is possible to calculate:84

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

CFj,i,s — Fj,i,s ' Ej,sFinally, where f} is the area fraction of compartment j in environment e, compartment-

specific factors are aggregated into an environment-specific characterisation factor:

CFes, = S CFj,i, 'fjeejeeThis approach differs from many previous approaches to characterisation factor calculation, which did not explicitly consider fate modelling or were expressed as an effect ratio to an equivalent substance, analogous to the carbon-dioxide equivalents in common
use for characterisation of different types of greenhouse gas emissions. The current ap
proach to characterisation factor calculation is described in greater detail by Lundie etal (2007). The benefits of this approach for EF calculation become apparent in theease with which a disturbance area can be calculated.

3.2 Including direct local environmental burdens
To include the direct onsite impacts associated with a water service provider's opera
tions within a regionalised EF we take the hybrid approach. We propose that land di
rectly occupied by water service providers is carried through the matrix algebra un
modified as usual, but we also allow the water business to use data which may exist on
the area of water bodies directly disturbed by it. For example, Sydney Water has per

formed extensive research to determine the scale of the typical "mixing zone" offshore
where the concentration of scheduled substances is exceeded prior to dilution (Sydney
Water, 1998). The area of the ocean burdened in this way is entered into the model andcarried through in the same way as the directly burdened land area.
The application of biosolids as a soil additive has beneficial effects for soil organic matter and other factors in productivity, but it also results in very small quantities

of unwanted contaminants being deposited on agricultural land. The application of bio
solids is therefore characterised using the factors described in 3.1 to provide a fractional disturbance intensity of the areas over which the biosolids are deposited. This

will require some data collected by water authorities on biosolids application to be en
tered into the model. These disturbance areas are added to the land disturbance due tothe direct land use of upstream businesses as part of a total land disturbance calculation.

3.3 Disturbance area calculation
Annual emissions in an SLA estimated in kg/year by the hybrid input-output methodol

ogy are characterised by multiplication by the appropriate characterisation factor described in Section 3.1. Having characterised an emission of a particular substance, EF

calculation requires the results to be expressed in a real terms. Unlike previous ap
proaches to emission characterisation based on the use of a reference substance such asdichlorobenzene (Guinee et al., 2002), characterisation using the new Australian factors
produces a dimensionless result. This result reflects the intensity of the impact causedby emissions from that SLA. A dimensionless result is consistent with the approachtaken by Lenzen and Murray (2001) which allows for the intensity of land disturbances

A New Ecological Footprint Calculation for the Australian Water Industry: Regionalisation and Inclusion of Downstream Impacts

85

caused by different degrees of change in landuse to be characterised. The intensity of

disturbances caused by several emissions can be aggregated into a total disturbance per
SLA. Partially disturbed areas can be aggregated on the basis of the aggregated distur

bance intensity and the area of the disturbed environment to produce a total AustralianEF, as shown in the equation:

Dtotal — Ddirect + 222 CFe,s,iMs,

where D^m is obtained from the onsite impacts matrix (see Figure 1), msA is the annual
emission of contaminant s to compartment / and Ae is the area of the effected environ
ment.

Another point of consistency between this process and previous developments indisturbance-based EF methodology is that, similar to the approach of Lenzen and Mur
ray (2001) to greenhouse gas emissions, the responsibility for damages outside a geo
graphic area caused by emissions from that area, remain associated with the emittingarea. Therefore, in addition to the national EF calculation, the novel EF methodology
enables the user to compare the EF of an SLA with the actual SLA area.

4 Example calculation
As an imaginary example, let us assume that annual emissions of benzene to air, waterand soil from various industries located on the urbanised coastal fringe of Australia

were 1200000, 50000 and 110 kg respectively. The relevant characterisation factors
CFesi for benzene (= s) as presented in Lundie et ai, (2007) are shown in Table 3.
Table 3: Example characterisation factors (kg/yr)
Impacted environmental compartments 'e'

Emissioncompartments *i'

Freshwater

Marineurban air2.3xl(T14

1.6xlO"163.8xl0"15

marine water

2.6x1 (T15

7.4xl(T15

4.0xl0"16

industrial land

3.9xl0"12l.lxlO"164.8xlO"12terrestrial

If we were considering emissions in an inland location we would chose the characterisation factors for freshwater instead of marine water, as the aquatic emission com
partment would be different. Similarly, rural air and agricultural land might be more
appropriate — the actual location of the source of the emission would determine this.

To calculate the disturbance area in each of the impacted environmental compartments

we multiply the given emissions by the characterisation factors and the areas of the impact compartments (Ae) which are taken as 6xlO6, 2.5xlO8 and 77xlO8 ha for freshwa
ter, marine and terrestrial ecosystems, respectively. This produces the results shown in
Table 4.

In this example the total disturbance area is 4.3 ha. The emissions to each of thecompartments listed makes some contribution to the total figure but it is dominated by

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Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

Table 4: Example disturbance areas (ha)Impact environmental compartments 'e'Freshwater
Emissioncompartments T

Marineterrestrial

urban air

1.6X10"1

4.8xl(T2

3.5

marine water

7.8xlO"4

9.3 xl(T2

1.5 xl(T2industrial land

2.6x10"3

3.0XKT6

4.1 xlCT1

UxlO1

i.4 xicr1

4.0

Total areas

the relatively large emissions to air and the relatively greater effect of benzene on theterrestrial environment. This is just an example calculation intended to illustrate how

this extension of disturbance-based ecological footprint methodology works. The key

issue illustrated is that in contemporary ecological footprint calculations, this compo
nent of the total footprint is ignored-set at zero. For example, while the use of pollu

tion control equipment to minimise such benzene emissions may have ecotoxicologicalbenefits, existing methodologies are insensitive to them, and the consumption of energy
by the equipment with the consequent greenhouse gas production will always cause afinite, positive ecological footprint to be calculated, irrespective of the reduced toxic
emissions. Future papers will examine specific case studies in depth.

5 Implementing the novel EF across the Australian Water Industry
The aim of this research has been to develop a novel EF concept to be applied at thecompany level to any Australian urban water services provider. The research addresses

major methodological limitations of the existing national EF concept by using a hybridof regionalised input-output analysis and a nested fate model. This proposed EF methodology improves on previous EF methodologies by avoiding exclusive reliance on na
tional average environmental and economic data, and by allowing toxicants to be included in a disturbance-based calculation analogous to the established inclusion ofgreenhouse gases in EF. By making it more comprehensive and quantitative, the new

method should be a more effective environmental reporting and communications tool
for the Australian water industry and allow the water industry to benchmark across sec
tors. Furthermore, being a generic approach to environmental reporting, it may poten

tially be applied by any economic entity that can present accurate financial and process
data. To demonstrate the effectiveness of this method will in practice require testing byvarious water authorities in Australia.

5.1 Data Requirements

We have attempted to strike a workable compromise between making excessive use ofassumptions about a water service provider's operations and a requirement for exces
sive data acquisition and pre-processing by the water service provider in order to use
this technique. This section briefly lists the information that the model will requirefrom the water service provider.

A New Ecological Footprint Calculation for the Australian Water Industry: Regionalisation and Inclusion of Downstream Impacts

87

The calculation of the "upstream emissions" - those that result from an organisa

tion's purchase of goods and services, will require the water service provider to supply
the following data:• An annual expenditure account ($);
• An annual revenue account ($).
The calculation of the "downstream emissions", physical emissions from thepremises of the authority in question, will require the following annual environmental
reporting data:• Greenhouse gas emissions - carbon dioxide and (unflared) methane (standardised
according to the Water Services Association Australia (WSAA) Greencount calcu
lator);• The provider's last National Pollutant Inventory (NPI) submission or equivalentdata;• The provider's biosolids product rate, typical contaminant specifications and ageographical descriptor (postcode or LGA) for where the biosolids were applied.
The water service provider will also need to estimate the land directly disturbed

by its operations. This is done by estimating the area land managed by the authority,and the degree to which its operations have changed it from its natural state - for ex

ample, catchment lands are considered undisturbed by the water authority, whereas a
block predominantly covered with concrete by construction of a sewage treatment plant
would be 100% disturbed. A standardised approach to this estimation is already emerg
ing though the practical application the EF within the Australian water industry.

5.2 Conclusions
We have developed a new method for calculating EFs. This method considers the dis
turbance area caused by human activities at a finer level of geographical disaggregation
than previously - we have driven the area considered from the nation down to 1322
smaller regions ("Statistical Local Areas"). The method also addresses a previous
weakness in disturbance-based EF calculation - its blindness to ecotoxic emissions. As

the paper illustrates, these may contribute a finite area to future EF calculations, andavoid the current theoretical problem whereby pollution control activities increase thecalculated EF without taking into account the benefits of reduced ecotoxicity burdens

on the environment.
To ensure the most effective transfer of this research to industry practice a numberof areas need to be addressed. While the development of a more rigorous EF method
ology should result in wider acceptance by stakeholders and the community, the scal
ability of the methodology for application by both large and small urban water service

providers operating in different geographical and regulatory environments needs to betested and stakeholder requirements of the novel EF methodology need to assessed. In
particular, the manner in which the regionalised and compartmentalised data is best
presented, needs to be established.
A program of practical research is planned to demonstrate that the novel hybrid
EF methodology can deliver in these areas. By a combination of data trials, softwaredevelopment and social research, the research program will ensure that the methodol-

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

ogy is neither too scholastic to be employed by water industry practitioners or commu
nicated to water industry stakeholders nor too pragmatic to undermine its academic or
regulatory credibility.

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