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Modelling future urban and rural water
requirements in a CGE framework
Glyn Wittwer1
Centre of Policy Studies
Monash University
Keywords: structural change, technological growth, environment, water allocation, hydrology
Abstract: Water is increasingly being treated as a tradable commodity. This paper describes
our efforts to include water accounts in a multi-regional CGE model. In order to understand the
conditions under which water trade might occur, we need to add a number of features to the
model and scenarios. These might concern structural change in the economy, differences in
yield functions between different crop types, dynamics and hydrology. Each of these features
requires substantial theoretical and database additions. As a first step, we consider the first of
these, structural change, by projecting the model forwards to the point in the future at which
Australia has 25 million people. We use this application as a platform for discussing further
possible changes to the model.
1
The author is grateful to Ejaz Qureshi for helpful comments.
1
1. Water trading
Water allocation has become an increasingly important issue in rural areas as irrigation regions
are being opened up to water trading. The aim of trading is to allocate water to optimise its use
in the economy. Improvements in allocation have become essential due to a significant
expansion in irrigation areas. Policy makers are seeking to divert water to the environment to
improve both the health of wetlands and sustainability of irrigation. The links between
environmental degradation and consequent losses in productivity are well documented (for
example, Hajkowicz and Young, 2002).
The introduction of water trading may lead to another possibility. The price paid per kilolitre
by irrigators is much lower than that paid by most other users, and we anticipate that the
populations of major urban centres will continue to grow over the next few decades. Therefore,
the potential exists for urban users to purchase water from rural regions.
This sort of scenario has a potentially heated political dimension. Regions heavily reliant on
irrigation agriculture at present may feel either that future growth and prosperity will come
under threat, or that declines in some rural towns may be even more marked than they are at
present. Irrigators may have invested in what, in the short- to medium-term, appears to be sunk
capital. Any attempt to divert their water entitlements to other regions might be perceived as a
potential blow to their profitability. Over time, although larger regional towns have grown,
their populations may have aged more rapidly than the national population. Some smaller
towns have shrunk, while health, education and banking services have diminished. Irrigation
water supply authorities and private irrigation districts are concerned that water trading will
result in reduced economies of scale and thereby place companies and shareholders at risk.
Water reforms may have proceeded slower than otherwise as a consequence of fears about the
future of local regions. As part of the process of policy formulation, it is helpful to have at our
disposal a model that helps predict the economic impacts of water movements between
regions.
The objective of this paper is to model the impacts of water trading on the national and
regional economies of Australia, taking account of estimated structural changes in the next few
decades. We are using a multi-regional CGE model, TERM (The Enormous Regional Model),
which includes water accounts linked to each economic activity. Our approach allows us to
consider a number of elements of structural change over time, each of which potentially may
impact on the demand for water. That is, relatively rapid urban population growth acting alone
may lead to an increase in the urban share of a fixed water allocation if we allow trading. As
we shall see, other elements of structural change work in the opposite direction, increasing the
share of water used by irrigators in the economy.
2. Features of TERM.
TERM is a multi-regional model of the Australian economy, based on a master database
including 167 sectors and 58 regions. Versions of TERM have also been devised for other
nations, including Brazil, Japan and Korea, with a version for China under construction (see
http://www.monash.edu.au/policy/term.htm). We are indebted to Mark Horridge, of the Centre
of Policy Studies, who created TERM. He introduced a number of innovations among some
old ideas to the model.
2
The common proportions assumption
One old idea, the common proportions assumption, allows TERM to solve more rapidly than
some previous CGE models of the “bottoms-up” type, and indeed was introduced to the multicountry GTAP model at an early stage (Hertel, 1997). “Bottoms-up” models treat each region
of the model as a separate economy, including their own input-output-based data and
behavioural equations. Inter-regional trade matrices link each region to others within the model
and to the rest of the world. Such models contrast with the “tops-down” approach, in a which a
single national database and national equations are represented in detail, while regions are
represented as mainly exogenous shares of national activity.
While “tops-down” models provide limited representation of regions, they are much smaller
than “bottoms-up” models containing a similar number of sectors. The advantage of the TERM
approach is that more regions can be represented in a “bottoms-up” manner than previously has
been possible. This is particularly helpful in the context of the present study, in which we wish
to model both irrigation regions and urban centres in different states.
The USE matrix in the TERM database includes details of the sales of all commodities to
industries (i.e., intermediate sales) plus final users (i.e,. households, investment, exports,
government spending). The only information it includes on sourcing is domestic v. import
origin. It includes information on the destination of the sale, but not the regional origin. A
separate matrix, the TRADE matrix, provides details of regional origin and the regional
destination, but not the user of each commodity. In order to match the USE matrix, summed
across all users, to the TRADE matrix plus the TRADMAR matrix (containing details of trade
plus transport markups), summed across origins, the common proportions assumption is used.
That is, all users of a particular commodity in a particular region source that commodity from
regional origins in common proportions. This assumption is commonly used in SAM-based
tables. Consider a case in which we have 40 sectors, 18 regions and 2 margins in the database.
The USE matrix (commodity x dom/imp source x user x regional destination) will have
dimensions of 40 x 2 x (40+4) x 18. The TRADE matrix (commodity x dom/imp source x
regional origin x regional destination) will have dimensions of 40 x 2 x 18 x 18. The
TRADMAR matrix, in the case of two margins, will be twice as large as the TRADE matrix. A
further matrix describing the supply of margins, SUPPMAR (margin x regional origin x
regional destination x regional margin supplier), has dimensions 2 x 18 x 18 x 18. This
representation of the database results in considerable compression as the number of regions or
margins increases, relative to providing origin and destination information in the same matrix.
Contrast this with the data matrices in MMRF, contain both the origin and destination. This
greatly increases the size of the database, once a given ingaggregation contains more than a
couple of regions, as can be seen in table 1 in a comparison of the number of database cells for
a given dimension of each model. As a consequence, TERM solves much more rapidly than a
version of MMRF covering the same number of sectors and regions. An enhancement in
TERM is that the supplier of margins need not be located in the same region as the destination
(as in MMRF), or either the origin or destination (as in FEDERAL, Madden 1992). That is, a
transport company located in the Wimmera statistical division could transport goods between
the Mallee and Western Districts of Victoria.
3
Table 1. Comparing core database sizes in MMRF and TERM
COM
IND
MAR
REG
MMRF
database
cells
TERM
MMRF
database database
cells
Mb
TERM
database
Mb
Ratio
MMRF/TERM
48
38
48
38
2
2
20
45
4318080
13445730
420320
905220
17.27
53.78
1.68
3.62
10.27
14.85
20
30
140
144
20
30
140
144
2
2
2
5
2
2
2
2
13760
29040
566720
982656
6448
13268
246688
260648
0.06
0.12
2.27
3.93
0.03
0.05
0.99
1.04
2.13
2.19
2.30
3.77
20
30
140
144
20
30
140
144
2
2
2
5
8
8
8
8
148160
313440
6144320
11098368
31648
61808
1027168
1084160
0.59
1.25
24.58
44.39
0.13
0.25
4.11
4.34
4.68
5.07
5.98
10.24
20
30
140
144
20
30
140
144
2
2
2
5
30
30
30
30
1836000
3888000
76356000
140175360
199200
351900
4407600
4637160
7.34
15.55
305.42
560.70
0.80
1.41
17.63
18.55
9.22
11.05
17.32
30.23
The TRADE matrix and the Horridge procedure
We have reasonable information on the commodity demands and supplies in each region of
Australia, based on industry employment data, regional mining data, agricultural data (from
ABS AgStats) and household expenditure surveys. For Australia, hardly any detailed data on
inter-regional state trade are available. Horridge used the gravity formula (trade volumes
follow an inverse power of distance) to construct trade matrices consistent with pre-determined
row and column totals. In defence of this procedure, note that:
• Wherever production (or, more rarely, consumption) of a particular commodity is
concentrated in one or a few regions, the gravity hypothesis is called upon to do very little
work. Because the master database sectoral classification was so detailed, this situation
occurred frequently.
• Outside of the state capitals, most Australian regions are rural, importing services and
manufactured goods from the capital cities, and exporting primary products through a
nearby port. For a given rural region, one big city is nearly always much closer than any
others, and the port of exit for primary products is also well defined. These facts of
Australian geography again reduce the weight borne by the gravity hypothesis.
For a particular commodity the traditional gravity formula may be written:
r≠d
V(r,d) = λ(r).μ(d).V(r,*).V(*,d) /D(r,d)2
where
V(r,d) = value of flow from r to d (corresponding to matrix TRADE in Fig. 1)
V(r,*) = production in r (known)
4
V(*,d) = demand in d (known)
D(r,d) = distance from r to d
The λ(r) and μ(d) are constants chosen to satisfy:
ΣrV(r,d)= V(*,d) and ΣdV(r,d)= V(r,*).
For TERM, the formula above gave rather implausible results, especially for service
commodities. Instead Horridge set:
V(r,d)/V(*,d) ∝ V(r,*)/D(r,d)k
r≠d
where K is a commodity-specific parameter valued between 0.5 and 2, with higher values for
commodities not readily tradable. Diagonal cells of the trade matrices were set according to:
V(d,d)/V(d,*) = locally-supplied demand in d as share of local production
= MIN{ V(d,*)/V(*,d),1} x F
where F is a commodity-specific parameter valued between 0.5 and 1, with a value close to 1 if
the commodity is not readily tradable.
Horridge then scaled the initial estimates of V(r,d) (using a RAS procedure) so that:
ΣrV(r,d)= V(*,d) and ΣdV(r,d)= V(r,*).
Transport costs as a share of trade flows were set to increase with distance:
T(r,d)/V(r,d) ∝ D(r,d)
where T(r,d) corresponds to the matrix TRADMAR in Fig. 1. Again, the constant of
proportionality is chosen to satisfy constraints derived from the initial national IO table.
All these estimates are made with the fully-disaggregated database. In many cases, zero trade
flows can be known a priori. For example, ABS data indicate that rice is grown in only four of
the 58 statistical divisions. At a maximum disaggregation, the load born by gravity
assumptions is minimized.
The common proportions assumptions need not prevent us from including additional
information in cases in which it is available. The key to including such information in the
master database is to prepare it at a sufficient level of disaggregation for such detail to be
included.
3. Variants of TERM and previous applications
The variable aggregation facility of TERM means that the number of sectors and regions are
tailored to the focus of a particular study. Variants of TERM have been created in response to
demand in various applications. Different versions include:
• Basic comparative static version;
• Water accounts in comparative static model;
• Recursive dynamics; and, in the present application,
• Water accounts with smooth growth assumption dynamics.
5
The drought study
The first study undertaken using TERM was of the regional economic impacts of the 2002-03
drought. Various Canberra-based agencies predicted that the drought would shave little more
than 0.75% off drought at the time. On the other hand, Horridge et al. (2003) predicted a result
(GDP = -1.6%) that accorded closely with the gap between pre-drought forecasts of GDP
growth and the eventual GDP result for the year. In addition, this study predicted the impact at
the state level with reasonable accuracy. In this study, estimation formulae were used to
compute productivity losses due to the drought for each agricultural industry in each region.
The formulae related the productivity losses to rainfall deficits in individual regions using data
from the Bureau of Meteorology. Separate formulae were developed for different types of
crops and for livestock. For example, for winter grains grown in southern Australia, Horridge
et al. (2003) assumed that the productivity loss for the crop in a particular region was a
progressively increasing function of the 3-month rainfall deficit, and was also affected to a
lesser extent by the 6-month deficit: as the severity of the 3-month rainfall deficit increased,
productivity losses were estimated to become increasingly greater at the margin. This approach
is of relevance to water-related studies, as a further step in the use of water accounts in TERM
will include the addition of response functions linking crop yields to water application.
The addition of water accounts
The detail in water accounts published by ABS (2004) is gradually improving. These accounts
include details of users (industries plus households) in each state. One advance over the first
release of the water accounts in 2000 was to include dairy cattle production as a separate entity.
But we are hoping two main deficiencies can be redressed in future database preparation. One
is that a relatively large sector, crops & livestock, includes a combination of broadacre and
irrigation activities. The other is that water data by crop type at the statistical division level are
not readily available. This matters less for rice and cotton in New South Wales than for
cropping in Victoria, as rice and cotton are confined to one or two statistical divisions. In our
first effort, we used ABS AgStats data on irrigation area by statistical division to supplement
regional estimates of water usage. This is quite an unreliable method as the application of
water per unit area may vary widely.
Otherwise, we have assumed that the water intensity of a given activity in each state is the
same across all regions in that state. In the absence of more detailed data, this is an assumption
we will alter in preparing future databases, at least in Victoria: the Mallee region has a warmer
climate and higher rates of evaporation than other regions (see Bureau of Meteorology, 2005).
Therefore, we may have underestimated water usage in the Mallee. Consequently, we may
overestimate the amount of water purchased by the Mallee in various hypothetical water
trading scenarios in which water moves to optimise allocation as we ascribe shocks to the CGE
model. In turn, we may underestimate the quantity of water purchased by other users.
Early efforts at modelling with the comparative static version of TERM with water accounts
typically entailed cutting back regional water entitlements (Wittwer 2003; Peterson et al.,
2005). While this demonstrated that there are small welfare gains to be realised by opening
water up to trade between regions, the finding that there was little impact on output in regions
with net sales of water simply reflected that these were essentially small-change simulations.
6
Nevertheless, these first steps in running a CGE model with water accounts help point out
various limitations with the present CGE approach. In particular, we noted that the CGE
approach does not capture the volume of water trading that may occur. This was particularly so
during the 2002-03 drought. Our out-of-model hypothesis is that most water trading was from
growers of annual crops, who found it more profitable to sell water at high prices than to
produce their usual crops in adverse seasonal conditions. Perennial crop growers, on the other
hand, are concerned that their plantations and vineyards (which entail considerable investment
and many years of potential income-earning) will be permanently damaged if they do not
receive sufficient water in drought years. Therefore, they are likely to purchase a minimum
amount of water with relatively inelastic demand. To capture the driver of water trades from
annual crop growers to perennial crop growers, we would need to include crop response
functions and recognise the minimum annual water requirements of perennials -- as we plan to
do so in future research.
Recursive dynamics
Wittwer et al. (2005) detail an application of a dynamic version of TERM to a hypothetical
weeds eradication program. There are many reasons why a move towards dynamic modelling
is desirable for water. For example, in the drought study (Horridge et al., 2005) one of the
difficult-to-measure impacts was that of destocking. Accelerated sales to abattoirs in response
to drought are a form of disinvestment: that is, they may inflate output statistics in one year,
but reduce the income-earning capacity of a farm in following years. A dynamic model
contains both stocks and flows, and impacts such as destocking or plantation removal can be
ascribed as exogenous reductions in capital stocks.
Similarly, rainfall patterns over a number of years affect the stock of water available for
economic and non-economic uses. Dynamics would be a desirable part of adding hydrological
detail to a CGE model.
Smooth-growth assumption dynamics
In developing dynamic CGE models, researchers at the Centre of Policy Studies have focused
on the impact of structural change on the economy over time. For example, technological
change makes a large contribution to growth or decline in many sectors (Dixon and Rimmer,
2002, chapter 2). In addition, while agriculture’s share of GDP has shrunk steadily over the
past 50 years, manufacturing’s share has halved over the past 30 years (ABS, 2005; Maddock
and McLean, 1987). When estimating the future water demands of different industries, we
need to consider structural change that may occur.
Even in a model without recursive dynamics, we are able to ascribe shocks to the model to take
account of growth in various macroeconomic levels. We can also ascribe productivity shocks
and water-saving technological shocks where appropriate. This latter effect is among a number
that will impact on relative water scarcity. Missing from this approach, as used in the
projections detailed in sections 4 and 5, are year-by-year paths of adjustment in the labour
market, and various stocks and flows including linkages between investment, depreciation and
capital stocks.
7
One obvious deficiency of the smooth-growth assumption is that it cannot account for
extraordinary destocking that may occur in response to a drought. In such circumstances, the
time path of rainfall will result in outputs and investment that differ markedly from assuming
“typical year” behaviour.
4. Estimating water allocation for an Australian population of 25 million
Figure 1: Regions of Projecting 25 million study
Figure 1 shows the regions in the study detailed in CSIRO (2006, forthcoming). As is usual in
applications of TERM, both the regions and sectors have been aggregated from the master
database to the focus of this study.
In order to project the TERM database ahead 31 years from 2001 (i.e., the median projection of
ABS is that Australia’s population will reach 25 million in 2032), we need to make a series of
assumptions concerning how the economy will change. We make explicit these assumptions,
and present our results in decomposition form, so that we can observe the contribution made to
the water usage by each group of assumptions.
8
The shocks have been assigned to the following groups:
(1) demand growth;
(2) non-agricultural supply growth and consumer taste swings;
(3) technological change (i.e., changes in primary factor requirements) in agriculture;
(4) reduced water availability;
(5) water-efficiency gains in agriculture, plus reduced pipeline leakage; and
(6) changes in household requirements for water.
Before examining the contribution of each group of shocks to projected changes in water
usage, we will elaborate a little on groups (1), (2) and (3). Group (1) contains shocks to depict
changes in the following:
•
•
•
•
State employment and population;
National consumption function shift to fit consumption target;
Non-water household demand taste shifts; and
Export demand shifts.
Table 2. Regional macroeconomic outcomes, 2032 relative to 2001 (% change)
SydneyNSW
MrmbidgeeNSW
MurrayNSW
WestNSW
RoNSW
MelbourneVIC
MalleeVIC
RestIrigVIC
RoVIC
BrisMoretQLD
BurnDarlQLD
RoQLD
AdelaideSA
RoSA
PerthWA
RoWA
TasNT
ACT
Aggregate
consumption
36.2
34.2
33.8
42.9
34.9
38.2
32.4
34.6
61.4
61.1
65.9
14.1
10.3
16.1
42.2
61.3
34.4
12.4
Real GDP
36.3
34.4
33.9
43.0
35.0
38.3
32.5
34.7
61.6
61.2
66.0
14.2
10.4
16.2
42.3
61.5
34.5
12.5
Aggregate
Employment
12.9
17.8
19.8
26.3
16.3
14.3
13.9
16.2
43.0
40.5
46.9
-4.9
-6.9
-4.2
24.1
32.1
19.5
-3.3
Population
20.2
25.5
27.6
35.6
24.2
22.3
21.5
23.9
53.6
51.5
54.2
5.2
3.8
6.6
34.9
43.3
18.6
7.7
Table 2 shows the changes imposed on macroeconomic variables in each region. Three of the
columns, namely aggregate consumption, aggregate employment and population, are
responsible for the effects shown under heading (1). Since we are interested in comparing
national aggregate consumption (our measure of welfare) and regional employment in different
scenarios, we want neither aggregate consumption nor employment to be exogenous.
To allow various macroeconomic variables to be endogenous in different scenarios, we use
closure reversals to position supply and demand curves. For example, the aggregate
9
consumption forecast implies a certain consumption function shift. In the scenarios, a shock is
given to this shifter rather than aggregate household consumption. We can then compare
macroeconomic household consumption to forecast for each scenario. Similarly, we
accommodate our real GDP target with an all-industry technological shift (the effect of which
appears under heading (2)). This technological shift becomes the shock in each scenario, so
that again, we can compare our real GDP in each scenario to forecast, by ensuring that the
demand and supply curves are the same in each scenario as in the base projection.
In the case of the labour market, we allow real wages to differ between regions in order to meet
regional labour force data projections. In effect, we are not allowing any regional labour
market theory to operate (at least not in the base scenario).
In addition to a macroeconomic technological shock, heading (2) (i.e., non-agricultural supply
growth) also includes projected changes in industry-level technological changes outside of
agriculture. Under heading (3), we get the corresponding technological changes for agriculture.
While the effects under (1) may be understood reasonably well outside the economics
profession, those under heading (2) and (3) are not so. Headings (1), (2) and (3) have a
common impact: each increases demand for water. But the demand increases apply to different
groups of users and these competing demands may confound our expectations concerning
which users will be net buyers of water, as is evident from table 3.
(1) Demand growth
General economic growth is likely to increase the share of water demanded by non-agricultural
users at the expense of agriculture. As per capita income grows, households spend more on
relatively income-elastic services sectors than on food. Engel’s law applies. Population growth
favours relatively non-traded sectors rather than relatively traded sectors, including agriculture.
In the initial year of the database (2001), value-added activity in crops & livestock is $6.3
billion, in dairy cattle $0.7 billion, cotton $0.8 billion and rice $140 million. The crops &
livestock sector therefore captures most agricultural activity, with the other three sectors
represented separately due to their high water-intensities in production.
Since the total water allocated in the 1st column of table 3 sums to zero, we can interpret
movements as changes in shares of national water usage (this is the case for all columns other
than the 4th, in which the total is the reduction in assumed water allocation). This explains why
households, despite growth in aggregate consumption, suffer a small loss in share due to
demand growth. Crops and livestock, dairy cattle, and various manufacturing, mining and
service sectors increase their demand for water slightly more rapidly than households.
The demand growth shown is slightly positive for dairy cattle both in terms of share of water
usage (+43 GL, table 3, 1st column) and output (9.8%, table 4, 1st column) because a relatively
high proportion of sales of dairy products is to domestic consumers, whose demand grows over
time with income and population. Crops & livestock is relatively export oriented: the 1st
column in tables 3 and 4 captures, among other demands, export price shifts, which tend to be
negative. However, overall demand growth is sufficient for the share of water usage (99 GL)
10
and output (+8.2%) to be positive. Cotton and rice both suffer in terms of share of water usage
(-433 GL and -196 GL respectively, table 3, 1st column) and output (-11.9% and -11.2%
respectively, table 4, 2nd column).
(2) Non-agricultural supply growth and consumer taste swings
The impacts of changes in the supply side are shown in columns (2) and (3) of tables 3 and 4.
As described in Dixon and Rimmer (2002, chapter 2), much structural change in an economy
over time concerns productivity changes at the industry level and consumer taste changes at the
commodity level. This heading captures such changes with two exceptions: it excludes
agricultural productivity growth and water-saving technological change, as these appear under
separate headings. Estimates of the supply shifts (technological changes) and demand shifts
(taste changes) have been updated using Giesecke (2004).
Households increase usage in the 2nd column rises (+69 GL), with income growth arising from
productivity growth raising demand via the impact of rising income on household
consumption.
Primary-factor productivity increases in industries have the effect of raising the shadow price
of water. Hence water becomes more valuable through the non-water productivity effect
captured in the 2nd column of tables 3 and 4, so that non-agricultural industries (including
downstream processors of agricultural products) increase their share of water usage at the
expense of other water users. In the case of cotton and rice, productivity growth in downstream
sectors has a weakly positive impact on their water shares (+14 GL for cotton, +30 GL for rice,
table 3, 2nd column).
(3) Agricultural technological change
As already mentioned, despite agriculture contributing a shrinking proportion of GDP,
agricultural production has grown relatively rapidly over time, with much of the growth
explainable by productivity growth. Agricultural technological change, as with non-agricultural
technological change, raises the shadow price of water (3rd column, table 1.3). The magnitudes
are large enough to be quite critical in project ting future rural-urban water needs. This
productivity growth favours relatively less water-intensive users at the expense of more waterintensive users. This occurs to the extent that the impact of productivity growth on some
irrigation sectors is negative (-32.7% for cotton and -23.5% for rice, table 4, 2nd column), as
primary factors plus water are diverted to crops & livestock. An increase in the shadow price of
water draws water away from households (-91 GL). “Other” industries use more water, and
increase output through this effect. Other industries include downstream processing industries
that benefit from lower input costs arising from increased productivity in agriculture.
In this base scenario in which we do not allow water trades between regions, the shadow price
of water rises by $0.52/litre in the Murrumbidgee region, and $0.51/kl in the Murray region of
NSW (table 1.3, 3rd column). These two regions account for most rice grown in Australia. This
indicates that water will move to other activities, even on the same farm, in response to
productivity growth in agriculture, as water becomes more valuable in less water-intensive
activities with such growth.
11
(4) Reduced water availability
We assume a 15% drop in water supply by 2032 relative to 2001 in all regions outside Western
Australia, Tasmania and Northern Territory. The 4th column shows that reduced water
availability through climate change or increased allocations to the environment is strongly
negative for agriculture and negative for all other users.
(5) Agricultural water-efficiency gains and leakage reductions
The 5th column of tables 3 and 4 captures the effects of two forms of water savings: reduced
requirements in agriculture per unit output, plus reduced pipeline leakage. We impose a 34%
reduction on irrigation water requirements per unit of output, and a 22% reduction on pipeline
leakage in the 31 year period. This reduces the share of water used in most agriculture, but
results in the share of water used in the irrigation sectors and households growing. Irrigators, as
the most water-intensive users among industries, gain the most from water-efficiency gains
which have the effect of increasing the effective water endowment. For example, the effect on
rice output is +61.2%, compared with only +21.9% for crops and livestock (table 4, 5th
column). “Other” industries, includes the water & drains services sector, to which water
leakage is allocated as usage. Therefore, reduced leakage reduces the water used by the water
& drains sector, explaining much of the drop in “Other” industries water usage (-811 GL, table
3, 5th column).
(6) Reduced household water requirements
We do not have evidence in the historical simulations of Dixon and Rimmer (2002) and
Giesecke (2004) that the volume of water requirements used by households is falling. This is
because their estimates of an inward shift in demand for water are most likely based on the
services component of water supply rather than water volume. Nevertheless, washing machines
and dishwashers have become more water-efficient over time. In Sydney (for example), per
capita water consumption has fallen considerably. Average water usage per capita at 412 litres
per person per day in 2001-2002 was 23 per cent less than in 1980/81 when usage levels
peaked at 530 litres per person per day. Sydney Water’s customer research has found that
customers support water conservation. An example of this awareness and interest is the
introduction of regular reporting on the evening TV news of the water levels in Sydney’s major
dams (Barrett, 2004). In the 31 year period to 2032, we impose a 22% downward shift on
household water requirements.
The 6th column of tables 3 and 4 shows the impact of projected savings in household water
usage. More water becomes available for non-household users as 362 GL is diverted from
households (table 3, 5th column), resulting in output increases in agriculture (table 4, 6th
column).
12
Table 3. Change in water usage, Base scenario, 2032 relative to 2001, GL
CropsLivestk
DairyCattle
Cotton
Rice
Hou
Other
Total
Demand
growth
(1)
99
43
-433
-196
-8
495
0
Non-agri Agricultural
Water
Non-h’hold Household
supply
tech.
growth
change
availability water savings water savings
(4)
(5)
(6)
(2)
(3)
-623
1586
-815
-774
151
-73
-363
-701
219
112
14
-904
-596
785
77
30
-380
-448
337
56
69
-91
-142
244
-362
583
153
-479
-811
-34
0
0
-3182
0
0
Total
-384
-714
-1081
-611
-310
-81
-3182
Table 4. Output, national, Base scenario, 2032 relative to 2001, %
CropsLivestk
DairyCattle
Cotton
Rice
Demand
growth
(1)
8.2
9.8
-11.9
-11.2
Non-agri Agricultural
Water
Non-h’hold Household
supply
tech.
availability water savings water savings
growth
change
(2)
(3)
(4)
(5)
(6)
-16.5
47.9
-9.1
21.9
1.7
-5.7
15.3
-16.8
40.9
2.6
0.5
-32.7
-17.6
63.2
2.2
1.4
-23.5
-26.1
61.2
3.4
Total
54.4
50.1
3.9
4.5
Table 5. Impact of each effect on shadow price of water, Base scenario, 2032 relative to
2001, $/kl
SydneyNSW
MrmbidgeeNSW
MurrayNSW
WestNSW
RoNSW
MelbourneVIC
MalleeVIC
RestIrigVIC
RoVIC
BrisMoretQLD
BurnDarlQLD
RoQLD
AdelaideSA
RoSA
PerthWA
RoWA
TasNT
ACT
Demand
growth
(1)
3.77
0.61
0.61
0.75
1.15
2.73
1.18
1.25
1.27
2.93
0.74
1.06
1.61
0.81
4.34
3.13
1.93
3.81
Non-agri
supply
growth
(2)
3.81
-0.17
-0.17
-0.55
0.14
2.39
-0.08
-0.02
-0.01
2.31
-0.11
-0.13
0.51
-0.07
4.61
1.44
0.16
2.55
Agricultural
tech.
change
(3)
-0.59
0.52
0.51
0.71
0.33
-0.22
0.84
0.62
0.58
-0.17
0.52
0.80
0.66
0.54
-0.17
1.76
1.11
-0.69
Water
availability
(4)
2.52
0.41
0.41
0.22
1.21
2.23
0.78
0.74
0.72
1.91
0.39
0.65
1.27
0.52
0.40
0.78
0.59
1.16
Non-h’hold
water
savings
(5)
-0.77
-1.12
-1.13
-1.43
-1.27
-1.39
-1.75
-1.50
-1.49
-1.07
-0.93
-1.49
-1.24
-1.09
-1.18
-3.45
-2.09
-0.22
Household
water
savings
(6)
-2.47
-0.05
-0.05
-0.05
-0.16
-1.15
-0.14
-0.15
-0.15
-1.28
-0.07
-0.09
-0.82
-0.09
-1.97
-0.53
-0.37
-3.80
Total
6.20
0.17
0.17
-0.34
1.44
4.41
0.85
0.98
1.20
8.51
0.80
0.30
0.11
0.46
9.47
4.23
1.49
1.95
13
Table 6. Decomposition of national results, Base scenario, 2032 relative to 2001, %
Aggregate
consumption
Real GDP
Aggregate
Employment
Population
Demand
growth
(1)
Non-agri
supply
growth
(2)
Agricultural
tech.
change
(3)
Water
availability
(4)
Non-h’hold
water savings
(5)
Household
water savings
(6)
Total
14.7
15.0
21.3
21.2
1.1
1.1
-0.5
-0.5
0.6
0.7
0.1
0.1
38.4
38.0
16.4
27.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16.4
27.1
Table 5 shows the impact of population growth and associated economic changes over time on
the change in the shadow price of water, in the absence of inter-regional trading. Highlights
include:
•
Each rapidly growing urban centre suffers major price hikes for water. In Sydney, the
price increase is $6.20/kl, in Melbourne $4.41/kl, in Brisbane & Gold Coast $8.51/kl
and Perth, $9.47/kl.
In regions containing the largest users of water, namely rice, cotton and dairy cattle, the impact
of agricultural productivity growth on the shadow price of water is large. For example, in
WestNSW, this affect alone raises the price by $0.71/kl.
The link between productivity growth and the rising price of water is simple enough to explain.
As labour and capital in a given industry become more productive, the marginal output of
relatively fixed factors, namely water, will increase. Therefore, the price of water should rise.
There must be some uncertainty as to where water will move following the opening up of
water trading. The present set of assumptions appears to favour movement of water to urban
areas. However, only a fraction of this potential movement arises from increased household
demands. In Sydney, for example, demand growth alone induces a $3.77/kl hike in the price of
water, while non-agricultural productivity and import supply growth induces a further increase
of $3.81/kl (table 1.3). Similarly, the extent to which water conserving measures are taken by
households, has a marked effect on the price, with our present assumption concerning such
savings reducing Sydney’s shadow price in this scenario by $2.47/kl.
Perhaps the main insight from table 6 is that economic growth is partly attributable to
population and employment growth, as shown in column (1), and partly to growth in capital
stocks and technology change, the effects of which are included in column (2). Changes in
water availability have relatively limited impacts at the national level.
14
5. Other scenarios
Other scenarios are detailed in CSIRO (2006, forthcoming). Rather than present the results in
the same detail here, we will summarise.
Scenario 2 concerns projecting to 25 million, while allowing inter-regional trade of water. In
some urban regions, water price increases are greatly alleviated when we allow such trade. In
Brisbane & Gold Coast, instead of the price hike being +$8.51/kl, it is only +$1.23/kl, via
water trading with Darling Downs/Wide Bay-Burnett. Significant reductions are also apparent
in Melbourne, the price hike falling from +$4.41/kl to +$0.37/kl, through water trading with
the Murray-Darling Basin regions. In Sydney, water trading with the remaining coastal regions
of NSW reduces the hike from +$6.20/kl to +$1.48/kl. Less dramatic is the fall in Perth, from
$9.47/kl to $4.80/kl. A tentative conclusion from this scenario is that water trading alone could
satisfy Melbourne’s future water needs, but significant upward price pressures on water remain
in Perth, and, to a lesser extent, Sydney and Brisbane & Gold Coast.
Water trading also provided benefits at the macroeconomic level, with national aggregate
consumption being 1.0% higher in scenario 2 than the base scenario without inter-regional
water trading. After trading, in these projections, the output losses in some major irrigation
regions are quite substantial. Real GDP falls in the Murrumbidgee (-4.6%) and Murray (-5.3%)
statistical divisions of NSW, and West NSW(-11.0%) (combining Far West, North West and
Northern NSW statistical divisions) relative to the base 25 million scenario. Either rice and
cotton currently dominate water usage in these regions. Another region with a substantial
economic decline relative to no water trading is Wide Bay-Burnett/Darling Downs in
Queensland (-4.3%), as a consequence of water being acquired by Brisbane & Gold Coast. The
regional declines are significant, though potentially not as small as if nothing were done to
address environmental issues.
Since desalination costs have fallen well below $2.00/kl, there may be a role for desalination to
play in some urban centres. In addition, desalination may be a relatively low cost means of
filling water supply needs in relatively remote coastal regions. Eyre Peninsula in South
Australia is one example. Such relatively local possibilities for desalination are at a finer level
of disaggregation in the regional dimension than we model in this study.
Scenario 3 includes desalination plants in Sydney, Brisbane-Moreton and Perth, together with
inter-regional water trading. In each of three urban regions, the plant has an annual capacity of
80 GL per annum with average costs of $1.50/kl. If projected water demands fall well below
the additional capacity, average costs of supply will rise. The CSIRO study presents the results
of a lower desalination price ($1.00/kl) combined with larger capacity (120 GL per annum) in a
separate simulation.
The desalination plants providing water at $1.50/kl lower the shadow price increase for water
in Sydney (from $1.48/kl in scenario 2 to $1.24/kl), Brisbane & Gold Coast ($1.23/kl to
$1.03/kl) and Perth ($4.80/kl to $3.11). Larger capacity desalination with lower unit costs, as in
scenario 3a, lowers shadow price hikes further, to only $1.16/kl in Sydney, $0.96/kl in
Brisbane & Gold Coast and $2.56/kl in Perth.
15
The macroeconomic effects of lower average desalination costs are minor. There is debate
concerning whether desalination plants are appropriate for meeting future urban water needs.
Desalination remains relatively energy-intensive. Alternative means of meeting water
requirements include storm water capture, water recycling and further conservation measures.
The costliness and practicality of these measures warrant further investigation.
ABS population projections carry considerable weight when we consider future urban water
needs. The very high employment and population growth rates projected for Perth and
Brisbane & Gold Coast drive rapid increases in demand for water in these metropolitan
regions, and consequently large increase in water prices. Scenario 4 is essentially a repeat of
scenario 3, but this time, the inter-regional growth rate differentials are reduced by reducing
real wage growth differentials between regions. But this effect raises rather than reduces
employment and population growth in Sydney and Melbourne, because their baseline growth
rates are smaller than those applying nationally. It also substantially increases Adelaide’s
employment change relative to scenario 3.
In scenario 4, there is a moderate alleviation of the shadow water price increase in Brisbane &
Gold Coast due to slower population growth. In Perth, the effect is somewhat more substantial,
as it applies to a higher shadow price hike.
An important issue we can consider in the context of scenario 4 is the risk of investing in
desalination in a region in which population growth turns out to be smaller than projected. In
the case of Brisbane & Gold Coast, water trading plays a bigger role in alleviating water
scarcities than desalination. However, the water price increase is still large enough with water
trading that desalination appears to have a role to play in meeting future water needs. In the
case of Perth, the water price hikes are so large in all scenarios that the issue appears not to be
whether to have desalination plants to supplement existing water supplies, but rather the
capacity such plants should have. This assumes that no other water sources are proximal to
Perth: a recent finding that the capacity of an aquifer near Perth is sufficiently large to cater for
Perth’s future water needs requires further analysis.
6. Planned modelling modifications: adding hydrological detail
The projections summarised in this paper illustrate the potential role that water trading could
play in meeting future water requirements in the economy. Projecting TERM ahead 31 years
takes us further than comparative static scenarios and provides a number of insights not
apparent from comparative statics. Notable among these is that with productivity growth in
agriculture over time, the shadow price of water in irrigation regions will rise. In addition,
productivity growth in other sectors also raises the shadow price. This implies therefore that
households will not automatically “outbid” other users if rural-urban water trading proceeds.2
Another insight is that in one urban region (Melbourne), water trading may be sufficient to
meet future water needs. The same result is not as clear in Brisbane & Gold Coast or Sydney,
2
The term “outbid” is somewhat misleading. As with all commodities in TERM, we assume that water is
imperfectly substitutable. An intra-regional Armington parameter applies to households. In industries, the
response is limited by the cost share of water. The smaller the cost share, the more inelastic is the demand for
water.
16
while it appears highly probable that Perth will require additional water sources as the city
grows.
With water trading, the potential exists for incomes earned in irrigation regions to fall
markedly as agricultural production declines. Some income would be recouped from water
sales, but in the event of the holder of a permanent water right selling that right and leaving the
region, the income from the sale would not remain within the region. The inference is that
water trading may not necessarily have a benign impact on regional incomes.
There are a number of areas in which modelling could be improved. The first of these concerns
hydrology. We have assumed quite simply in our projections that the water available for
economic use declines by 15% in most regions in the 31 year interval. A model that depicts
random year-by-year variations in water catchment (perhaps based on historical data) would
provide additional insights. For example, Qureshi et al. (2005) examine environmental flows.
An implication of their paper is that environmental flows discharged in years when water is
relatively abundant due to widespread above-average rainfall will improve the health of
ecosystems while minimizing the impact on regional economies. On the other hand, a severe
drought might present water management issues that are not covered in forward projections of
TERM based on the smooth-growth assumption. It appears desirable to develop a hydrological
module in tandem with a fully dynamic multiregional CGE model. A dynamic version of
TERM already exists, as noted earlier, but it does not include water accounts.
Other issues will arise inevitably as we add hydrology to a CGE model. One stumbling block
so far has been the perception that we ought to be attempting to replicate the behaviour of
regional water managers, rather than using a model to explore optimal water allocation. Other
stumbling blocks concern regional and time dimensions. ABS data particularly for agriculture
are available only at the statistical division level, yet hydrological detail tends to be available in
various combinations of Statistical Local Areas (SLAs), the next level of regional
disaggregation down from statistical divisions. This is an issue that will need to be resolved in
the course of the project.
There are two types of hydrological models, as explained in Dixon et al. (2005). One type
deals with regional water allocation. Typically, such models operate on a monthly basis,
allocating water throughout the year, whereas dynamic CGE models typically run on an annual
basis, presenting another dimensional inconsistency that we will need to deal with. The other
type deals with the relationship between water use and crop yield. This second type
reintroduces an idea used in the first application of TERM to drought (Horridge et al., 2003), in
which rainfall deficits were linked to declines in crop output.
A more ambitious vision for this project is that we start linking environmental outcomes to the
CGE model. In one sense, this is appealing as results of particular policy measures could be
presented as both environmental and economic outcomes. In another sense, it may be simpler
to keep the results of CGE models separate from environmental outcomes. For example,
Qureshi et al. (2005) tell an important story without quantifying environmental benefits. It may
be that it is sufficient to communicate the economic implications arising from a particular
volume of water being allocated to the environment in a particular year. This would leave the
judgment of the environmental impacts to those with expertise in the area.
17
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19