Strategic Water Utility Management and Financial Planning Using a ...

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Rehan et al | http://dx.doi.org/10.5942/jawwa.2015.107.0006 Peer-Reviewed

Strategic Water Utility Management and Financial Planning Using a New System Dynamics Tool RASHID REHAN1, ANDRE UNGER2, MARK A. KNIGHT3, AND CARL HAAS3 1National

Institute of Urban Infrastructure Planning, University of Engineering and Technology Peshawar, Pakistan of Earth and Environmental Sciences, University of Waterloo, Ont., Canada 3Department of Civil and Environmental Engineering, University of Waterloo, Ont., Canada 2Department

This study demonstrates how to implement a novel system dynamics (SD) strategic water utility management and financial planning tool. Using data from several local water utilities in Ontario, Canada, the tool is run to simulate 20 years to investigate: (1) long-term fee-hike rates required for system financial sustainability; (2) service and financial performance metrics for pay-as-you-go, borrowing, and capital reserving strategies; and (3) consumer affordability as a result of water use charges. For the

case study, reserving cash and allowing water fees to increase by up to 7% per year are found to be the best financing strategy to eliminate infrastructure backlog/deficit. The study demonstrates the benefits of an SD model for developing and preparing strategic and tactical asset management, water conservation, and financial plans. The SD model parameterization and implementation for the demonstration case study can be helpful to other utilities in adapting the model to their own specific circumstances.

Keywords: water main networks, financial sustainability, system dynamics, infrastructure management, affordability, service performance, customer satisfaction, water consumption During the past decade, significant changes have been made in the regulatory framework governing water utilities in Canada, especially in the province of Ontario. Regulations issued under the authority of the Safe Drinking Water Act (2002) establish licensing requirements for municipal water systems that for the first time require the preparation and submission of financial plans (Ministry of the Environment, 2002). An important guiding principle for preparing financial plans is that water systems are to be financially self-sustainable (Ministry of the Environment, 2007). To realize the mandate of these new regulations, municipal water utilities require comprehensive decision-support tools to help in preparing shortterm (less than 10 years) and long-term (10 to 100 years) asset management plans based on the principle of financial sustainability. Rehan et al (2011) demonstrated that water and wastewater systems are complex, and interrelated systems are best modeled using the system dynamics (SD) approach. Rehan et al (2014a, 2014b) developed and implemented a wastewater utility SD managementdecision support tool that can be used to prepare long-term asset management plans based on the principle of financial sustainability. Rehan et al (2013) developed an SD water utility network management and financial planning model. This water SD model is different from previous SD models (Rehan et al, 2014a, 2014b; Rehan et al, 2011) in that it allows a water utility to investigate cash reserving, pay-as-you-go, and borrowing strategies. Other unique features are controls on the water fee growth as a function of service performance and household financial burden as a result of JOURNAL AWWA

water-fee rate increases. In this study, the Rehan et al (2013) SD water model is populated and implemented in Stella V9.1.4 (by ISEE Systems) using data from a water utility in southern Ontario, Canada. Pertinent information about the case-study city is presented along with a methodology for parameterization of key model variables. The demonstration case-study water network is deemed to be in good condition but contains a small backlog of deteriorated cast-iron pipes. The focus of this study is to address two salient objectives in the context of financial sustainability criteria imposed by legislation: (1) why it may be necessary for a utility to maintain a fee-hike rate in excess of the inflation rate on its expenses and (2) how alternative management strategies, such as pay-as-you-go, borrowing, and capital reserving, can be compared and contrasted. To address these objectives, scenarios are presented as alternative management strategies and are evaluated in terms of measuring the effects on key variables within the infrastructure, finance, and consumer sectors. The merit of each management strategy is ranked in terms of its benefit to the utility and water users. Results of the model application are discussed, and conclusions are drawn.

DEMONSTRATION CASE STUDY For this demonstration case study, data are synthesized from a utility that operates a water distribution network serving a population of 120,000 people (medium-sized city) in southern Ontario. Additional data sources from other utilities are used to

2015 © American Water Works Association

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fill model data gaps. The water utility is managed by the city’s municipal government, which purchases potable water from the upper tier of municipal government that manages water abstraction and treatment. This shared but differentiated arrangement is typical of many municipal governments in Ontario (PWC, 2002). The utility bills residents of the city for water consumption based on metered usage and charges constant volume-based water fees reflecting the costs of water production, treatment, transmission, and distribution. The polyvinyl chloride (PVC) and cast-iron pipes water distribution network is 352 km in length. As shown in Figure 1, PVC pipes constitute 76% of the network length, and about two-thirds of the network is composed of PVC pipes that are less than 25 years old. The next age group (25–49 years) includes both PVC and cast-iron pipes and constitutes about 20% of the network length. Pipes older than 50 years are all made of cast iron with only a small fraction (4%) of the network being in the age group of 75–99 years. Both the water main network length and population are assumed constant during the simulation period. This assumption is deemed valid for the case in which expansion of the network is funded through development charges and does not create a financial burden to the existing customer base.

PARAMETERIZATION OF MODEL VARIABLES This section presents the methodology and estimation procedures required to parameterize and implement the SD model. Typical average water consumption in the local region is 280 liters per capita per day (lpcd) and is adopted as the initial water demand for this study. This study is focused on the long-term effects of the price of water on the consumption behavior of water users. Price elasticity of water demand is assumed to be equal to –0.35, which is the average of the range for residential

FIGURE 1

Length of Pipes—kilometers

300

Profile of the water main distribution network for the case study

Cast Iron PVC

250 200 150 100

Nt = N0egm×t(1)

in which Nt is the number of breaks per unit length per year in a pipe of age t years, N0 is the initial number of breaks per unit length per year, and gm is the growth rate in pipe breaks per annum. Kleiner and Rajani (2000) report that the growth rate in cast-iron pipe breaks gcast iron is 7.8% per year. Insufficient break data for the relatively new PVC pipes (installed post-1960) makes it impossible to estimate gPVC for PVC pipes. Using PVC break data presented in Burn et al (2005), a value for gPVC of 7.0% per year is implemented in the model. The annual cost of maintaining a water distribution network can be divided into fixed maintenance costs and variable maintenance costs. Fixed maintenance costs include administrative overheads, office supplies, salaries, and benefits. These costs are independent of the structural condition of pipes and are assumed to depend on only the length of the distribution network. Summation of pipe lengths Lmt for all materials m and ages t gives the total length of the network TLN (km) as TLN =

50 0

water demand reported by Boland et al (1984) and Olmstead et al (2007). Price-induced reduction in water demand is assumed to occur over a 10-year demand adjustment period. The limit to which water demand can decline is assumed to be 200 lpcd—a limit set on the basis of data published in Environment Canada (2006). For long-term planning purposes, an argument can be made in favor of assuming a single pipe size and associated cost of installation for all pipes within the network. Accordingly, the unit cost of pipe renewal is Can$450 per meter based on installing 300-mm (6-in.)-diameter PVC pipe using the open-cut method (Unger et al, 2014a). The service life of PVC pipe is assumed to be 100 years, whereas that of cast-iron pipes is assumed to be 75 years. Two important parameters used in this study are the unit cost of pipe maintenance (Can$/m/year) and leakage rate (water leaked per unit length of pipe as a percentage of water supplied). Each of these parameters is a function of the structural condition of a pipe. For example, a pipe that is structurally deficient is likely to break more often and thereby increase maintenance costs. Similarly, water leakage from a pipe is a function of its structural condition (Kleiner, 1997). Unlike sanitary sewer pipes, there is no generally accepted standard structural condition–grading protocol for water main pipes. As a result, the annual number of breaks in a water main pipe generally is considered an indicator of the network structural condition—that is, an increased annual number of breaks means a decrease in structural condition. Shamir and Howard (1979) proposed a simple model that relates a pipe’s condition to its age and material type:

0–24

25–49 50–74 75–99 Pipe Age Groups—years

PVC—polyvinyl chloride

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> 100

 Lmt m t

(2)

For a network composed of TLN km of pipes, the fixed component of unit maintenance cost, UMf (Can$/m/year), is calculated as FCN UMf =  m — TLN × 1,000 km


(3)

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in which FCN is the annual fixed maintenance cost for the whole network (Can$/year). Fixed maintenance costs for the utility are Can$2.763 million per year and are distributed over the 361.5-km-long water main network. Therefore, UMf is calculated as Can$7.64 /m/year. Variable maintenance costs include expenditures on routine maintenance, pipe flushing, and emergency repairs. These costs are assumed to be a function of the pipes’ structural condition and are assumed to increase exponentially with pipe age. For a pipe age t consisting of material m, the variable component of maintenance cost, UMv (Can$/m/year), is given by mt

UMv

mt

= UMv egm×t

(4)

0

in which UMv (Can$/m/year) is the unit variable maintenance 0 cost for a new pipe, and gm is the growth rate in pipe breaks (per year) for material m. Note that UMv is the same for all new pipes 0 regardless of the pipe material. The annual variable maintenance cost for the entire network, VCN (Can$/year), is given by VCN =

m   UMvmt × Lmt × 1,000  km m t

(5)

Annual variable maintenance costs for a utility with the network age profile shown in Figure 1 are Can$1.679 million per year. To estimate UMv0, the average age for each pipe group in Figure 1 is assigned as t for its respective group. Next, gm is taken as 7.8% and 7% per year for cast-iron and PVC pipes, respectively. Finally, UMv0 is determined as Can$0.0321/m/year. Calculated values of UMvmt for both pipe materials and all age groups are summarized in Table 1. Total unit maintenance cost, UMmt (Can$/m/year), for a pipe of material m and age t is simply the sum of the fixed and variable unit maintenance costs: (6)

UMmt = UMf + UMv mt

Calculated values of UMmt and its components for both the pipe materials and all age groups are shown in Table 1. Leakage rate is defined as nonbillable water volume as a fraction of the total water supplied (billed) to customers. The leakage rate refers to the nonrevenue water (NRW) that is composed of

TABLE 1

unbilled authorized consumption and leakage on distribution mains. The leakage rate for a pipe is assumed to increase with age and at the same rate as the number of water main breaks. For a pipe of material m and age t years, the leakage rate LRmt (% of metered consumption) is given as LR

mt

= LR0egm×t

(7)

in which LR0 (% of billed consumption) is the leakage rate for new pipes, and gm is the growth rate of water main breaks (per year). Eq 7 implies that some leakage will occur even when the whole distribution network is composed of new pipes. This is attributed to flushing of hydrants, street washing, and even poor quality of pipe installation. If PFmt is a fraction of the network composed of pipes made of material m and t years of age, then the total volume Vmt (cubic meters per year) of water leaking from all pipes is given by Vmt = LRmt × PFmt × VB(8)

in which VB is the total annual metered consumption of water (cubic meters per year). It is noted that the sum of PFmt for all pipe materials and ages in the network is equal to unity. The total volume of lost water VNB (cubic meters per year) from the whole network is VNB =

  Vmt m t

(9)

Annual metered consumption VB is 11.043 106 m3. Given the age distribution of the network shown in Figure 1, the volume of lost water VNB is 1.198 106 m3 (10.85% of the metered consumption). The leakage rate of new pipes LR0 is estimated to be 0.075% of VB. Finally, values of LRmt are calculated for all pipe groups and presented in Table 1. The price of treated water from the upper tier of municipal government is Can$0.6680/m3. This price includes the cost of abstraction and treatment of the water. The total cost Wpc (Can$/year) the utility pays the upper tier of municipal government for potable water is given as Wpc = 0.6680 (VB + VNB)(10)

Summaries of calculated values of UMvmt for both pipe material and all pipe age groups Pipe Age Groups Parameter

Pipe Material

0–24 years

25–49 years

50–74 years

75–99 years

> 100 years

Fixed unit maintenance cost (UMf)—Can$/m/year

PVC

7.64

7.64

7.64

7.64

7.64

Variable unit maintenance cost (UMv) —Can$/m/year Total unit maintenance cost (UMm)—Can$/m/year Leakage Rate—% of metered consumption

Cast iron

7.64

7.64

7.64

7.64

7.64

PVC

0.07

0.43

2.46

14.17

81.54

Cast iron

0.08

0.58

4.04

28.42

199.76

PVC

7.72

8.07

10.11

21.81

89.18

Cast iron

7.73

8.22

11.69

36.06

207.40

PVC

0.13

0.42

1.34

4.29

13.70

Cast iron

0.19

1.34

9.45

66.40

466.72

PVC—polyvinyl chloride, UMv —variable component of maintenance cost mt

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The current water fee the utility charges its customers is Can$1.2159/m3. Consequently, the initial revenue R0 (Can$/year) earned by the utility in year 0 is given by R0 = 1.2129 × VB. This revenue is generated by the utility billing households that are assumed to consist of 3.5 persons on average. The median household income in the case study area is Can$70,000 (US$62,662) per year. Unger et al (2014b) report a 6.4% per annum long-run inflation by analyzing 30 years of payment progress certificates for water main construction projects in southern Ontario between January 1984 and May 2010. The 6.4% inflation rate is used in this study to inflate the unit cost of pipe renewal (Can$/m), the unit cost of pipe maintenance (Can$/m/year), and the unit price of treated water (Can$/m3). The consumer price index (CPI) is used to inflate household income over time. Unger et al (2014b) determined the CPI long-run inflation rate for the period from January 1984 to May 2010 to be 2.39% per annum. All costs are inflated using a continuously compounded interest-rate model. The rate at which the utility earns interest on its cash reserves is deemed to be equal to the risk-free (or nominal) rate rNi. To model rNi , Unger et al (2014b) used the monthly returns on a 10+ years Canadian bond in the interval between January 1984 and May 2010 and employed the Vasicek model in its discrete form (Demers, 2003): drN = N (N – rN ) dt + N dZN i

i

(11)

E[rN ] = rN0e–Nt + N (1 – e–Nt) i

in which, N is the speed of adjustment, θN the reversion level, and N the volatility on the index with estimated values of 0.0039, 6.34% per annum, and 0.2702, respectively. The expected value of the risk-free rate E[rNi] is used to forecast rNi for given timestep ∆ti years at future time t within the simulation period based on its initial value rN0. In the province of Ontario, municipal governments can borrow funds from Infrastructure Ontario at an interest rate that typically is about 1% per annum in addition to the risk-free rate (Infrastructure Ontario, 2011). Consequently, a borrowing rate of 1% (+ rNt) per annum is applied to all outstanding debt that the utility carries. The project depreciation rate  needed to discount all dollar figures to present value is also assumed to be equal to the expected value of the risk-free rate, t  E[rNt ]. As noted earlier, E[rNt ] is adjusted for each time t step and is not a constant value throughout the simulation. Accordingly, to discount cost xt from time t to its present value x0, a series of operations are performed, each using the prevailing discount rate t: Dt

xt – Dt = xt e–t Dt xt – 2Dt = xt – Dt e–t–Dt Dt x0 = x0 +Dt e–0+Dt

(12)

MODEL APPLICATION TO DEMONSTRATION CASE STUDY Water main pipes typically have a service life in the range of 75 to 100 years depending on the pipe material. To draw meaningful insights about the water utility long-term financial sustainability, JOURNAL AWWA

it is critical to simulate the system over a similar planning horizon. The Ministry of the Environment (2007) recommends that longterm strategies based on full cost recovery be compared over planning horizons that encompass the service life of the physical assets. The difficulty becomes one of identifying suitable market transactions and instruments with a matching term to enable future cost projections and inflation predictions given market volatility. Therefore, the analysis period in this study is restricted to 20 years. Rehan et al (2013) presents in detail the following policy levers that are used in this study: • Debt capacity of the utility • Desired cash reserve • Preferred network rehabilitation rate • Maximum acceptable fraction of deteriorated pipes • Desired elimination period for deteriorated pipes’ fraction of the network • Maximum allowable fee-hike rate These policy levers are used in several combinations to explore a variety of network management scenarios. To illustrate the utility of the model, three scenarios are presented to highlight tradeoffs between issuing debt, building cash reserves, and pay-as-you-go strategies. Scenario 1 involves a pay-as-you-go strategy with debt capacity and cash reserve kept at zero. Scenario 2 allows borrowing up to a debt capacity (annual debt-service charges as a fraction of revenues) of 25%. This value represents the maximum level of debt that Ontario municipalities are allowed to acquire (Ontario, 2003). Finally, scenario 3 allows build-up cash reserves of up to 4% of the replacement value of the network. It should be noted that the objectives of this article are not to identify the “best” management strategy within the landscape of likely scenarios or to follow a mechanistic process wherein the policy levers are assigned values to optimize some objective functions. All three scenarios are assigned the same values for policy levers 3, 4, and 5. Specifically, the preferred network rehabilitation rate (policy lever 3) is set at 0.65% of the network per year to reflect current utility practice. This rate means the network is replaced in approximately 150 years. Policy lever 4 is assigned a value of 5%, indicating that the utility will allow up to 5% of its network pipes in the worst structural condition. When the fraction of the worst deteriorated pipes remains below 5% of the network length, the utility maintains the preferred network rehabilitation rate. If the 5% threshold is exceeded, a more aggressive rehabilitation strategy is adopted with the goal of renewing all (not just the fraction exceeding the 5% value) deteriorated pipes in the network. A new rehabilitation rate is determined such that the fraction of deteriorated pipes is eliminated over five years. This value is assigned to policy lever 5. Finally, maximum allowable water fee-hike rates are chosen such that the three scenarios yield similar life-cycle costs. Through trial and error, the annual allowable water fee-hike rates are found to be 8.5%, 7%, and 7.5% for scenarios 1, 2, and 3, respectively. Table 2 summarizes the values assigned to all six policy levers for the three scenarios.

RESULTS AND DISCUSSION Simulation results for the three scenarios are presented in Figures 2–6. These figures illustrate the behavior of key model


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TABLE 2

Summary of values assigned to policy levers for the three scenarios Scenario 1

2

3

Debt capacity—debt-service charges as a percentage of revenue

0

25

0

Desired cash reserve—% of the network’s replacement value

0

0

4

Preferred rehabilitation rate—% of network per year

0.65

0.65

0.65

Maximum acceptable fraction of deteriorated pipes—% of network

5

5

5

Desired elimination period for deteriorated pipes’ fraction of the network—years

5

5

5

Maximum allowable fee-hike rate—%/year

8.50

7.00

7.50

Policy Lever

variables over 20 years. Table 3 compares the three scenarios by summarizing the peak values attained, average values over the entire simulation period, final values at the end of simulation, and the cumulative values over the simulation period. All costs (i.e., operational, capital, asset value) presented are discounted to present value. Figure 2, parts A, B, and C, show the average age of pipes in the network, fraction of deteriorated pipes in the network, and the actual rehabilitation rate of the network, respectively. All of these figures have a single curve that indicates that all three scenarios yield similar findings. In Figure 2, part A, the average age of the network is about 26 years at the start of simulation, then increases steadily after year 6 to a maximum value of 33 years at the end of the 20-year simulation. When averaged over the entire 20-year simulation period, the age of the network is 28 years (Table 3). Figure 2, part B, shows that initially about 5.6% of the network is composed of deteriorated pipes. This fraction is higher than the stipulated acceptable limit (policy lever 4). Therefore, the actual exceeds the preferred rehabilitation rate and is calculated as 1.12% of the network length per annum (Figure 2, part C). This rehabilitation rate is maintained for the first six years of simulation and reduces the fraction of deteriorated pipes to less than 1% during the same period (Figure 2, part B). After year 6, the initial backlog of deteriorated pipes is eliminated, and the rehabilitation of the network is carried out at the preferred rate of 0.65% per year until the end of simulation (Figure 2, part C). This rate is sufficient to maintain the fraction of deteriorated pipes at 0.31% of the network. On average, the fraction of deteriorated pipes is 1.25% of the network per year over the simulation period (Table 3). Although the average age of the network rises between year 6 and year 20, the fraction of deteriorated pipes during the same period stays constant. This counterintuitive situation occurs because the initial backlog of mostly cast-iron deteriorated pipes is replaced with PVC pipes, and the PVC pipes are assumed to have a longer service life than cast-iron pipes in this study. Thus, unlike cast-iron pipes, the PVC pipes do not attain a deteriorated state even when they are comparatively older. JOURNAL AWWA

Figure 2, part D, presents the annual funds-balance position of the utility (on the left y-axis) and the asset value of the network (on the right y-axis). For scenario 1, the funds balance remains at zero throughout the simulation as annual revenues generated by the water fee are equal to annual expenses (pay-as-you-go). Under scenario 2, the utility is allowed to borrow for financing capital works. After year 8, the debt starts accumulating rapidly. By the end of the 20-year simulation, the present value of utility’s debt is approximately Can$6 million. It should be noted that during the first six years, the utility is carrying out network rehabilitation at a higher annual rate with no noticeable debt accumulated. The debt accumulation after year 6 is a function of the 7% annual water fee-hike rate imposed on the utility as part of scenario 2. Under scenario 3, the utility builds up cash reserves equal to 4% of the network’s replacement cost. Initially, the utility has no cash reserve and must generate one via the allowable 7.5% annual water fee hike. By year 20, the utility’s funds balance shows a reserve of Can$9.4 million (Table 3). The dotted curve in Figure 2, part D, shows the asset value of the network over the simulation period. The asset value of a segment of water main pipe is based on the assumption that new pipe (age zero years) has an asset value equal to replacement value. The asset value of older pipes is linearly depreciated with age. Once its service life is reached, the asset is deemed to have no asset value. The network’s asset value Pt (dollars) at any time t is calculated as i

Pt = UCR × e(rc×t) 0

Si – At i    × Li(13) S i

in which UCR0 is the unit cost of pipe renewal (Can$/meter) at time 0, rc is the annual rate of inflation for capital expenditures (percent per annum), Si is the total service life (years) for a group of pipes i, Ait is the prevailing age (years) of pipes i at time t, and Li is the total length (meters) of all pipes in group i. The network’s asset value is identical for all of the scenarios consistent with the information presented in Figure 2, parts A and B. Initially, the network’s value is about Can$130 million and increases almost linearly throughout the 20-year simulation period to reach a final value of approximately Can$203 million, suggesting an improvement in the physical condition of the underlying asset (pipe network). Figure 3 presents annual revenues and the parameters on which the revenues depend. Revenue generated by the utility is presented in Figure 3, part A, along with its annualized rate of change, as demonstrated by the dashed lines enumerated using the right vertical axis. Revenue is the product of the water fee (Figure 3, part B) with the total water consumption otherwise denoted as revenue water (Figure 3, part C). This relationship invokes a feedback loop influenced by the price elasticity of demand. Essentially, consumers choose to conserve water as its price increases in an irreversible manner. All three scenarios require the utility to immediately increase the water fee at the onset of the simulations as a result of the initial backlog of deteriorated cast-iron water mains. Scenario 3 requires the utility to simultaneously begin to build its reserve fund. The practice of conservation is shown by the decline in the per capita water demand shown in Figure 3,


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part D, which almost reaches the minimum demand by the end of the simulation period. Figure 3, part B, shows that the target controls of limiting the annual water fee-hike rate to a maximum value of 8.5% per annum for scenario 1, 7% per annum for scenario 2, and 7.5% per annum for scenario 3 are consistently achieved over the simulation period. The single exception is scenario 2 at the end of the simulation period. This is a result of the utility using borrowed funds to pay for capital works and not operational expenditures. This is a result of insufficient revenue to cover operational expenditures, so the utility must temporarily exceed the cap on its annual water fee-hike rate. It should be noted that because of water conservation, the rate at which the utility’s revenues increase is consistently less than the rate by which it increases the water fee. At some point, the spread

FIGURE 2

between the rates of change in these variables exceeds the longrun rate for CPI of 2.39% per annum. Table 3 reports the final water fee rate at year 20 as Can$1.92/m3, Can$1.80/m3, and Can$1.90/m3 for scenarios 1, 2, and 3, respectively. Similarly, the final water demand is 204 lpcd, 206 lpcd, and 203 lpcd for the three scenarios, respecively. Operational expenses for the three scenarios are categorized as follows: annual maintenance expenditures (Figure 4, part A), water treatment/purchase expenditures (Figure 4, part B), and interest expenditures (Figure 4, part C). Capital expenditures involve replacement of water mains and are shown on Figure 4, part D. The annualized rate of change of these expense categories are presented as dashed lines enumerated using the right vertical axis. Of the three operational expense categories, Table 3 shows

Selected indicators for physical and financial performance of the network

Scenario 1 Scenario 2 Scenario 3

B

A 6

Deteriorated Pipes—% of network

35

30 All three scenarios 25

20

5 4 3 2 1

0 0

5

10 Time—years

15

20

0

5

10 Time—years

15

20

D

C

225

15

1.4 1.2

Funds Balance—constant million $

Actual Rehab Rate—% of network

All three scenarios

1.0 All three scenarios

0.8 0.6 0.4 0.2

10 200 5 175

0 Asset value— all three scenarios

–5

150 –10 125

–15

0.0 0

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5

10 Time—years

15

20

Network Value—constant million $

Average Age of Network—years

40

0


5

10 Time—years

15

20

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that water treatment/purchase expenditures are by far the largest, accounting for 70.19%, 69.90%, and 69.95% of the cumulative costs over the 20-year simulation period for scenarios 1, 2, and 3, respectively. Interest rate expenditures are the least with a cumulative value of 0.52% of all operational costs for scenario 2. Maintenance expenditures are approximately 2.5 times greater than capital expenditures. Both are identical for the three scenarios. This is not surprising considering that they are a function of pipe condition within the network, and the pipe network condition is identical for all scenarios (Figure 2, parts A and B). The annualized rate of growth in maintenance expenditures reaches 7.2% per annum by year 20. This value exceeds the inflation rate of 6.4% per annum because the network is getting comparatively older, with the unit cost of maintenance increasing with the age of the pipes (Table 1). In contrast, water treatment/ purchase expenditures are consistently less than the inflation rate

FIGURE 3

of 6.4% per annum because of conservation. Nonrevenue water (which leaked from the network) is shown in Figure 3, part C, and does not contribute significantly to water treatment/purchase expenditures given the excellent condition of the network, amounting to 2.2% of cumulative water purchase expenditures for all three scenarios (Table 3). The annualized rate of growth in capital expenditures remains constant over the entire simulation period at the inflation rate of 6.4% per annum. This occurs because cast-iron pipe is consistently replaced with PVC pipe, and the actual rate of capital works remains constant with the single exception as it declined from 1.12% of the network length per annum to the preferred rate once the backlog of deteriorated cast-iron pipes is removed. Returning to the objectives of this work, the first objective is to determine why it may be necessary for a utility to maintain a fee-hike rate in excess of inflation rate on its expenses. Two

Annual revenue, water fee, water consumption volume, water purchase volume, and water demand


B

6 10

4 2

5

Solid lines: left axis Dashed lines: right axis

0

0

Water Fee—constant $/m3

8 15

-2 0

5

10 Time—years

15

6 1.5 4 1.0

13

13

280

12

12

11

11 Solid lines: left axis Dashed lines: right axis

10

9

9

8 5

10

15

20

8

Water Demand—lpcd

300

–2 0

5

10 Time—years

15

20

260 240 220 200 180

0

Time—years

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0

D 14

Water Purchase—million m3

Water Consumption—million m3

C

0


0.5

0.0

20

8

2.0

14

10

10

2.5

Rate Revenue—% per year

Revenue—constant million $

10

Rate Water Fee—% per year

A 20

5

10

15

20

Time—years


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potential reasons are leakage and conservation. For this case study, leakage is an inconsequential contribution given the excellent condition of the network. Conservation as a contributor is tested by making price elasticity of demand “zero.” For scenario 1, the resulting annual water fee-hike rate became identical to the inflation rate on capital and operation expenditures of 6.4% per annum over the entire simulation period. The second objective of this work is to address how alternative management strategies, such as pay-as-you-go, borrowing, and capital reserving, can be compared and contrasted. The relative merits of each strategy are discussed with reference to Figure 5, parts A, B, and C, for scenarios 1, 2, and 3, respectively. The shaded regions in Figure 5 represent annual maintenance expenditures, water purchase expenditures, interest expenditures, and

FIGURE 4

capital expenditures, progressively stacked over each other in that order. The black dashed line delimits the sum of operational expenditures, whereas the red line denotes the sum of all expenditures. In contrast, the blue solid line indicates revenues. For scenario 1, revenues are equal to the sum of expenditures over the entire simulation period. For scenario 2, the cap of 7% per annum in the water fee-hike rate causes revenues to decline relative to total expenditures. Once this spread is greater than capital expenditures, this cap must be temporarily exceeded because borrowed capital cannot be used to cover operational expenditures. Because capital expenditures are a small component of the overall expenses when operating a water main network, borrowing provides little opportunity to influence the long-term operation of the network beyond smoothing short-term fluctuations in

Maintenance expenditures, water treatment expenditures, interest expenditures, and capital expenditures


8

8

6

6

4

4

2

2

0

0

5

10

15

–2 20

10

10

8

8

6

6


4

0

2

0

–2 0

5

Time—years

C

15

20

D 12

Interest income

0.5

0

5

10

15 Interest expenditure

–0.5

–1.0

20

Capital Expenditures—constant million $

Interest Expenditures—constant million $

10 Time—years

1.0

0.0

2


10

8

8

6

6

4

4

2 All three scenarios

2

0

0 Time—years

Rate Capital Expenditures—% per year

0

12

Rate Water Treatment Expenditures—% per year


10

Water Treatment Expenditures—constant million $

B 10

Rate Maintenance Expenditures—% per year

Maintenance Expenditures—constant million $

A 12

–2 0

5

10

15

20

Time—years

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FIGURE 5

Comparison of expenditures and revenues

Operational expenditures Revenue Maintenance expenditures Water purchase expenditures

Total expenditures Interest expenditures Capital expenditures

A Scenario 1

Constant million $

20

15

10

5

0

0

2

4

6

8 10 12 Time—years

14

16

18

B Scenario 2

Constant million $

20

15

10

the actual water fee-hike rate. For scenario 3, the cap of 7.5% per annum in the water fee-hike rate allows revenues to exceed expenses until year 6, at which point the backlog of deteriorated cast-iron pipes is rehabilitated and the fund balance reaches a surplus of Can$7.2 million (Figure 2, part D). Thereafter, revenue is more or less equal to expenses, and the surplus is maintained and used to smooth out short-term fluctuations in the water feehike rate. Figure 3, part B, shows the water fee (solid lines) in constant Can$/m on the left axis and the rate water fee (in percentage) on the right axis, for scenarios 1 to 3 over the 20-year simulation period. This figure shows water fee rate increases linearly after year 6 until the maximum annual water fee-hike rate is achieved in year 20 (i.e., scenario 1 = 8.5% per annum, scenario 2 = 7% per annum, scenario 3 = 7.5% per annum). Figure 6 shows the water bill (solid lines) in percent of household income on the left y-axis, and water bill rate (dashed lines) in percent per year on the right y-axis over the 20-year simulation period. This figure shows that the initial value of the water bill is 0.62% of a household’s income and increases to between 0.88— 0.94% of household income in year 20. It should be noted that the percentage of household income is below the 2% financial hardship level noted by the US Environmental Protection Agency (USEPA) (NDWAC, 2003). Figure 6 also shows that the water bill rate will increase over the 20-year simulation period at close to the long-term CPI rate of 2.39% per annum. Note the low percentage increase in water bill as a fraction of household income when compared with CPI. This is because of the price elasticity of demand of –0.35, which models water conservation as a result of rising water fees. Water conservation, shown in Figure 3, part D, shows the initial water usage of 280 lpcd to be reduced to 200 lpcd in year 20. Table 3 presents a summary of the performance of scenarios 1, 2, and 3 based on service levels; asset liabilities; and cumulative total, operational, capital, maintenance, water

5

FIGURE 6 0

2

4

6

8 10 12 Time—years

14

16

18 Scenario 1 Scenario 2 Scenario 3

C Scenario 3

10

1.0 Water Bill—% household income

Constant million $

20

15

10

5


0

2

4

6

8

10

Time—years

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12

14

16

8

0.8

6 0.6 4 0.4 2

CPI = 2.39% 0.2

0.0 0

Financial impact (affordability) for consumers

0

0

5

18

10 Time—years

15

20

Water Bill Rate—% per year

0

–2

CPI—consumer price index


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TABLE 3

Comparison of the three scenarios Scenario

Service Level

Water

fee—Can$/m3

Maximum

1.80

1.90

1.92

1.80

1.90

9.13

7.50

Final

7.57

8.85

7.50

Average

6.35

6.00

6.30

Final

204

206

203

Average

256

257

253

Maximum

0.95

0.90

0.94

Final

0.95

0.90

0.94

Unbilled—106 m3

5.57

5.58

5.53

—% of total purchased

2.43

2.42

2.43

224.22

224.92

221.80

97.57

97.58

97.57

229.79

230.50

227.33

Maximum

5.64

5.64

5.64

Final

0.31

0.31

0.31

Average

1.25

1.25

1.25

Maximum

33

33

33

Final

33

33

33

Average

28

28

28

—million Can$

283.71

285.93

281.52

—% of total expenditures

89.63

89.71

89.56

—million Can$

32.81

32.81

32.81

—% of total expenditures

10.37

10.29

10.44

Total expenditures

—million Can$

316.52

318.74

314.33

Water purchase expenditures

—million Can$

199.13

199.86

196.94

—% of OpEx

70.19

69.90

69.95

Maintenance expenditures

—million Can$

84.59

84.59

84.59

—% of OpEx

29.81

29.58

30.05

—million Can$

0.00

1.48

0.00

—% of OpEx

0.00

0.52

0.00

—million Can$

4.43

4.44

4.39

—% of water purchase exenditure

2.22

2.22

2.23

194.70

195.43

192.55

—% of water purchase exenditure

97.78

97.78

97.77

Final funds balance

—million Can$

–0.01

–6.11

9.39

Final network value

—million Can$

203.35

203.35

203.35

Water consumption

Billed—106

m3

—% of total purchased Total purchased—106 m3 Deteriorated pipes fraction—% of network

Average network age—years

Operational expenditures

Capital expenditures

Interest expenditures

Leaked water cost

Billed water cost

Assets/Liabilities

1.92

8.50

Water bill burden—bill as % of household income

Cumulative Water Purchase Expenditures

3

Final

Water demand—lpcd

Cumulative Operational Expenditure

2

Maximum

Fee-hike rate—%/year

Cumulative Expenditure

1

—million Can$

OpEx—operational expenditures

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purchase, leaked water, and billed water expenditures. These metrics are aggregated over the 20-year simulation period and are interpreted as life cycle costs. Table 3 shows that the total volume of water consumed during the 20 years is largest under scenario 2 with 230.5 106 m3, and this is slightly greater than scenario 1 with 229.8 106 m3. Scenario 3 water consumption is the lowest with 227.3 106 m3. For all three scenarios, the percentage of volume of unbilled or leaked water is 2.43%. This is inferred to be because of the high percentage of the network consisting of relatively new PVC pipes. Total cumulative expenditures are the highest under scenario 2 (Can$318.74 million), followed by scenario 1 (Can$316.52 million) and scenario 3 (Can$314.33 million). Under each scenario, approximately 90% of the total cost is operational expenditures, and 10% is capital expenditures. Approximately 70% of the operational expenditures cover the cost of water purchase with the rest being maintenance expenditures (~ 30%). All three scenarios also have a very similar average age and fraction of deteriorated pipes. This means all three scenarios will have similar network service performance (e.g., pipe breaks, service disruptions, and discolored water events). Scenario 3 is considered to be the preferred strategy for the following reasons. First, this scenario has the least cumulative total expenditures in the simulation period. Second, although the asset value is the same for all scenarios, under scenario 3 the utility ends up with a cash reserve of Can$9.4 million compared with almost zero under scenario 1 and an outstanding debt of Can$6.1 million under scenario 2. Third, the maximum fee-hike rate that the consumers experience under scenario 3 is 7.5% per annum, whereas for scenarios 1 and 2 it reaches 8.5% and 9.1% per annum, respectively. Fourth, the total volume of water consumed under scenario 3 is the least among the three scenarios. This is preferable when considering conservation of water resources. Scenario 2 yields the lowest water fee over the 20-year simulation period. This is achieved by the utility maintaining a low 7% per annum cap on the allowable fee-hike rate over much of the simulation period. As discussed earlier, this cap forces the utility to borrow money by year 8 to fund capital works. In this scenario, the 7% fee-hike cap (Table 2) is increased to 8.85% per annum at the end of the simulation period to cover operational expenses. Table 3 shows cumulative interest expenses at Can$1.48 million, which is 0.52% of operational expenditures. The Can$1.48 million in borrowing cost is deemed to be small when compared with operational and capital expenditures, which are orders of magnitude larger. In scenario 2, it should be noted that interest costs are not offset by cost savings resulting from improved system performance, such as a decrease in leaked water. The previous sections presented scenarios 1, 2, and 3 as independent but comparable end-members typifying pay-as-you-go, borrowing, or cash-reserving approaches for the utility to achieve financial sustainability. These three scenarios can be viewed as end members; however, a continuum of scenarios does exist that will achieve the same objectives. Considering this, three of the five policy levers are varied (Table 4) to determine the effect of debt capacity, cash reserve, and maximum allowable fee-hike rate on the networks’ long-term (20-year) financial sustainability. The JOURNAL AWWA

TABLE 4

Policy levers for three selected scenarios Scenario Set A

Scenario Set B

0–25

0

0

0–5

0–10

0–10

Maximum acceptable fraction of deteriorated pipes—% of network

10

10

Desired elimination period for deteriorated pipes’ fraction of the network—years

10

10

0.65

0.65

Policy Lever Debt capacity—debt-service charges as a % of revenue Cash reserves—% of network replacement value Maximum allowable fee-hike rate—%/year

Preferred rehabilitation rate—% of network per year

following analysis is intended to demonstrate the power of the SD model. It is not intended to suggest that any point in this continuum of scenarios is optimal for financial sustainability as certain combinations of the policy levers may yield impractical or alternatively desirable outcomes. In scenario set A, the maximum allowable fee-hike rate is combined with the maximum debt capacity of utility to explore a variety of borrowing strategies. It should be noted that scenario set A includes previously discussed scenario 1. In scenario set B, the maximum allowable fee-hike rate is combined with the cashreserve fraction to explore a variety of capital-reserving strategies. This scenario set includes previously discussed scenario 3. In both scenario sets A and B, the maximum allowable fee-hike rate is varied from 0 to 10% per annum in increments of 0.5%. In scenario set A, for each value of the maximum fee-hike rate the cash reserve is set to 0%, whereas the maximum debt capacity is varied from 0 to 25% in increments of 2.5%, thereby yielding 231 individual scenarios. Similarly, in scenario set B for each value of the maximum fee-hike rate, the maximum debt capacity is set to 0% and the cash reserve is varied from 0 to 5% in increments of 0.5%, thus yielding 231 individual scenarios for this set. In addition, 20-year life-cycle contours for metrics cumulative total expenditures and peak deteriorated fraction are shown in Figure 7. Similar contours for final funds balance and network asset value are shown in Figure 8. In Figures 7 and 8, scenario 1 straddles the x-axis in all plots. The optimal pay-as-you-go scenario is deemed to be the one that simultaneously minimizes cumulative expenditures and the peak-deteriorated fraction of pipes in the network, maximizes the network asset value, and yields a zero funds balance. Using this criterion, the optimal payas-you-go scenario is deemed to the left of the scenario 1 location with an allowable fee-hike rate of 7% per annum. The optimal borrowing strategy is deemed to achieve the same objectives as pay-as-you-go with the addition of minimizing the amount of debt incurred (i.e., maximize the funds balance). Figure 7, parts A and C, and Figure 8, parts A and C, show that all four metrics have a relatively broad flat optimal region to the right of scenario 2 as the allowable fee-hike rate increases beyond 7% per annum. It also indicates that the operational region the utility might


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FIGURE 7

Total expenditures and peak deteriorated fraction indicators for scenarios landscape

Cumulative Total Expenditures—millions of $

310

315

320

325

330

335

A

340

B

25

5

Desired Reserve Fraction—% of network value

Maximum Debt Capacity—% of revenue

Scenario 2

20

15

10

5

4 Scenario 3 3

2

1

Scenario 1 0

0

2

4

6

8

Scenario 1 0

10

0

2

4

6

8

10

Peak Deteriorated Fractions—% of network

5

5.5

6

6.5

7

7.5

8

8.5

C

9

9.5

10

D

25

5



Scenario 2 20

15

10

5

4 Scenario 3

3

2

1

Scenario 1 0

0

2

4

6

8

Scenario 1 10

0

0

Allowable Fee-Hike Rate—% per year

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2

4

6

8

10


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FIGURE 8

Funds balance and network asset value indicators for scenarios landscape

Final Funds Balance—millions of $

–10

–5

0

5

A

10

B

25

5



Scenario 2 20

15

10

5

4 Scenario 3 3

2

1

Scenario 1 0

0

2

4

6

Scenario 1

8

0

10

0

2

4

6

8

10

Network Asset Value—millions of $

182

184

186

188

190

192

194

196

C

198

200

202

204

D

25

5



Scenario 2 20

15

10

5

4 Scenario 3

3

2

1

Scenario 1 0

0

2

4

6

8

Scenario 1 10

0

0


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2

4

6

8

10


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consider is much greater than for scenario 1 (pay-as-you-go). As mentioned earlier, borrowed funds can be used only to pay for capital works, which is approximately 10% of the total expenditures. Because operational costs dominate in this demonstration case study, the ability to borrow funds to complete capital works does little for optimizing utility finances. The optimal capital-reserving strategy is also set to achieve pay-as-you-go objectives, as well as to build a positive funds balance that complies with the desired reserve fraction policy lever. Figures 7, parts B and D, and Figure 8, parts B and D, show scenario 3 in the upper-right quadrant. Figure 7, parts B and D, and Figure 8, part D, show that the desired reserve fraction is not very sensitive to cumulative total expenditures, peak deteriorated fraction, or network asset value. Figure 8, part B, shows that the final funds balance is very sensitive to the desired reserve fraction and allowable water fee hike, and ultimately generates the optimal region for the utility to operate (i.e., the region on this figure in which the final funds balance is greater than zero). Again, for this case study, the optimal region appears as a vertical extension of the optimal scenario 1 (pay-as-you-go) position across the entire range of desired reserve fraction. Surprisingly, this region is slightly suboptimal in that Figure 7, part B, shows an elevated cumulative total expenditure relative to neighboring regions at a lower allowable fee-hike rate. At the highest permissible value of allowable fee-hike rate and desired reserve fraction, the utility simultaneously minimizes its cumulative expenditures and maximizes its final funds balance. Scenario 3 is located just outside this region. Because the majority of the pipes in the network are in excellent condition at the start and end of the simulation, extensive funding to renew deteriorating and aging pipes is not required in this case study. Thus, in this case the conclusion that reserving funds (scenario 3) is the best strategy is deemed reasonable as large capital expenditures to renew the pipe network are not required. However, many utilities may not have infrastructure assets in such a desirable condition. Further analysis beyond the 20-year simulation is required to determine when reserving funds should begin. For networks that have a large percentage of pipes in the network that will be reaching or approaching the end of their service life during the 20-year simulation period, large-capital work funds will be required to renew the network and to reduce operational expenditures. In such cases, borrowing funds and not reserving may be a better management strategy.

CONCLUSIONS As a result of this study, the following conclusions are drawn: • Interrelationships and feedback loops among model variables have significant effects on key performance indicators. • The choice of inflation rates has a major influence on the water utility financial sustainability in the long term. • Cumulative total expenditures over the 20-year simulation period present an incomplete picture of the full effects of any management strategy and should not be used as a sole criterion for selecting a management strategy. • The implementation of the system dynamic water utility tool can assist with long-term management and financial planning JOURNAL AWWA

and the establishment of defensible strategic and tactical organization objectives.

ACKNOWLEDGMENT The authors gratefully acknowledge the financial support provided by the Natural Sciences and Engineering Research Council of Canada, the Ontario Graduate Scholarship, the University of Waterloo, and the Centre for Advancement of Trenchless Technologies located at the University of Waterloo. The authors also acknowledge the City of Niagara Falls, the City of Waterloo, and the City of Cambridge for their kind financial support, provision of data, and sharing of valuable insights on water utility management.

ABOUT THE AUTHORS Rashid Rehan is an assistant professor at the National Institute of Urban Infrastructure Planning, University of Engineering and Technology, Peshawar, Pakistan. He received his PhD from the University of Waterloo, Ont., Canada. He received a masters of engineering science degree in civil engineering from the University of Western Ontario, London, Ont., Canada, and a bachelor of science degree in civil engineering from the University of Engineering & Technology, Peshawar, Pakistan. Rehan has four years of university teaching experience; eight years of research experience in condition assessment of water and wastewater infrastructure assets, deterioration modeling, infrastructure asset management, utility financing, and service delivery; and seven years of professional experience in the water supply and wastewater engineering industry. Andre Unger is an associate professor in the Department of Earth and Environmental Sciences, University of Waterloo. Mark Knight (to whom correspondence may be addressed) is an associate professor, Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue, Waterloo, Ont., Canada N2L 3G1; [email protected]. Carl Haas is a professor in the Department of Civil and Environmental Engineering, University of Waterloo.

PEER REVIEW Date of submission: 06/20/2014 Date of acceptance: 09/24/2014

REFERENCES Burn, S.; Davis, P.; Schiller, T.; Tiganis, B.; Tjandraatmadja, G.; Cardy, M.; Gould, S.; Sadler, P.; & Whittle, A.J., 2005. Long-Term Performance Prediction for PVC Pipes. Awwa Research Foundation, Denver. Boland, J.J.; Dziegielewski, B.; Baumann, D.D.; & Optiz, E.M., 1984. Influence of Price and Rate Structures on Municipal and Industrial Water Use. Contract Report 84-C-2. US Corps of Engineers Institute for Water Resources, Fort Belvoir, Va. Demers, F., 2003. The Canadian Phillips Curve and Regime Shifting. Bank of Canada working paper 2003-32. Bank of Canada, Ont. www.bankofcanada. ca/2003/10/working-paper-2003-32/ (accessed Nov. 19, 2012).


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Environment Canada, 2006. Municipal Water and Wastewater Survey, Summary Data 2006. Environment Canada, Gatineau, Que. www.ec.gc.ca/Water-apps/ MWWS/en/publications.cfm (accessed May 13, 2010). Infrastructure Ontario, 2011. Lending Rates. Infrastructure Ontario, Government of Ontario crown corporation. www.infrastructureontario.ca/home.aspx (accessed June 5, 2011). Kleiner, Y., 1997. Water Distribution Network Rehabilitation: Selection and Scheduling of Pipe Rehabilitation Alternatives. PhD thesis, Department of Civil Engineering, University of Toronto, Ont. Kleiner, Y. & Rajani, B.B., 2000. Considering Time-Dependent Factors in the Statistical Prediction of Water Main Breaks. AWWA 2000 Infrastructure Conference, Baltimore. NDWAC (National Drinking Water Advisory Council), 2003. Recommendations of the NDWAC to U.S. EPA on Its National Small Systems Affordability Criteria. US Environmental Protection Agency, Washington. www.epa.gov/safewater/ ndwac/pdfs/report_ndwac_affordabilitywg_final_08-0803.pdf (accessed June 4, 2014). Ministry of the Environment, 2002. Safe Drinking Water Act, 2002. Drinking Water Legislation. www.e-laws.gov.on.ca/html/statutes/english/elaws_ statutes_02s32_e.htm (accessed July 1, 2011). Ministry of the Environment, 2007. Toward Financially Sustainable Drinking-Water and Wastewater Systems. Financial Plans Guideline EBR Registry Number: 010-0490. Olmstead, S.M.; Hanemann, W.M.; & Stavins, R.N., 2007. Water Demand Under Alternative Price Structures. Journal of Environmental Economics and Management, 54:2:181. Ontario, 2003. Ontario Regulation 403/02 —Debt and Financial Obligation Limits. The Ontario Gazette, 136:1:775.

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PWC (PricewaterhouseCoopers), 2002. Organization of Municipal Water and Wastewater Systems in Ontario. PWC and Ontario SuperBuild Corp. https://ospace.scholarsportal.info/handle/1873/5202 (accessed July 11, 2008). Rehan, R.; Knight, M.A.; Haas, C.T.; & Unger, A.J.A., 2011. Application of System Dynamics for Developing Financially Self-Sustaining Management Policies for Water and Wastewater Systems. Water Research, 45:16:4737. Rehan, R.; Knight, M.A.; Unger, A.J.A.; & Haas, C.T., 2013. Development of a System Dynamics Model for Financially Sustainable Management of Municipal Water Main Networks. Water Research, 47:20:7184. Rehan, R.; Knight, M.A.; Unger, A.J.A.; & Haas, C.T., 2014a. Financially Sustainable Management Strategies for Urban Wastewater Collection Infrastructure— Development of a System Dynamics Model. Tunnelling and Underground Space Technology, 39:116. Rehan, R.; Unger, A.J.A.; Knight, M.A.; & Haas, C.T., 2014b. Financially Sustainable Management Strategies for Urban Wastewater Collection Infrastructure— Implementation of a System Dynamics Model. Tunnelling and Underground Space Technology, 39:102. Shamir, U. & Howard, C., 1979. An Analytical Approach to Scheduling Pipe Replacement. Journal AWWA, 71:5:248. Unger, A.J.A.; Rehan, R.; Younis, R.; Nazir, A.; Knight, M.A.; & Darrell, R., 2014a. Development of a Unit Cost Database for Water Main and Sanitary Sewer Capital Works. Centre for Advancement of Trenchless Technologies, University of Waterloo. Waterloo, Ont., Canada. Unger, A.J.A.; Younis, R.; Rehan, R.; Yu, S.; Budimir, F.; & Knight, M.A., 2014b. Forecasting the Unit Cost of Water main and Sanitary Sewer Capital Works. Technical Report, Centre for Advancement of Trenchless Technologies, University of Waterloo. Waterloo, Ont., Canada.


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