Chu, Wang-- Agent Based Residential Water Use Behaviour Simulation and Policy Implification- A Case Study in Beijing City

7/21/2019 Chu, Wang-- Agent Based Residential Water Use Behaviour Simulation and Policy Implification- A Case Study in Beijing City

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Water Resour Manage (2009) 23:32673295DOI 10.1007/s11269-009-9433-2

Agent-Based Residential Water Use Behavior

Simulation and Policy Implications:A Case-Study in Beijing City

Junying Chu Can Wang Jining Chen Hao Wang

Received: 11 August 2008 / Accepted: 13 April 2009 /

Published online: 5 May 2009 Springer Science+Business Media B.V. 2009

Abstract Residential water use constitutes a major part of urban water demand, andhas be gaining importance in the urban water supply. Considering the complexity ofresidential water use system, an agent-based social simulation, i.e. the ResidentialWater Use Model (RWUM), is developed in this paper to capture the behavioralcharacteristics of residential water usage. By disaggregating total water demandsdown to constituent end-uses, this model can evaluate heterogeneous consumer

responses on water, taking into account the factors of market penetration of water-saving technologies, regulatory policies, economic development, as well as socialconsciousness and preferences. Also, uncertainty analysis technique is innovativelyapplied in this agent-based model for parameter calibration and model robust testing.According to the case study in Beijing, this model can provide insights to water man-agement agency in evaluating different water usage polices, as well as estimations forpotential water saving for future infrastructure development planning.

Keywords Agent-based social simulation Residential water use Water consumption behavior Uncertainty analysis Water policy Water demand

J. Chu C. Wang (B) J. ChenDepartment of Environmental Science and Engineering,

Tsinghua University, 100084, Beijing, Peoples Republic of Chinae-mail: [email protected]

H. WangEngineering Research Center for Water Resources &Ecology Ministry of Water Resource P. R China,No. 20 Chegongzhuang West Road,Haidian District, 100044 Beijing, China


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3268 J. Chu et al.

1 Introduction

Large scale and ever-rising demands on urban residential water have played akey role in determining urban water supply. It has been the predominant factor

in determining the scale of construction and required investment in water-relatedinfrastructure (Chu et al. 2002). As a result, an understanding of the internal andexternal factors affecting residential water usage, for example, decisions regardingthe purchase of water conservation devices and water use frequencies, is vital fora wide range of water management approaches, such as: forecasting future waterneeds, formulating financial management decisions and formulating integrated waterresources management and planning.

Econometric regression has traditionally been applied to the study of urban resi-dential water use, seeking to construct a quantitative relationship between relativefactors and the aggregate cross-section or time series water use data (Howe andLinaweaver1967; Hewitt and Hanemann1995; Espey et al.1997; Herrington1997).More recently, contingent valuation method (CVM) has been used to determine thesocial, technical and behavioral responses to hypothetical water price changes basedon questionnaire surveys. This has been considered a good surrogate for gaugingactual household water use behavior in light of limited data (Thomas and Syme1988). With the development of newer software and hardware, end-use analysishas also applied to the analysis of household water use behavior in certain smallresidential zones (Peter et al. 2003; Mayer et al. 1999). Others, such as statisticalanalysis and cost benefit analysis, can also be used to assess residential water use and

water conservation potential (Sarac et al.2003; SPUC1999).Although the approaches above have played a major role in interpreting the

characteristics of residential water usage, they are significantly limited when thereis a lack of reliable cross-section and time-series data, or when water use behavioris experiencing a period of rapid change. The latter could be due to both a changein the context of social and/or economic spheres, and a proliferation of high-efficientwater usage technologies and wastewater reuse possibilities (Chu et al.2004). Thesefactorial changes made it necessary to look beyond the previous methods and toconsider the effect of technological changes in the use of water in the home. Re-cently, residential water demand have been considered with stochastic characteristics

(Blokker et al.2008; Alcocer-Yamanaka et al.2004,2006; Alvisi et al.2003), whichmake the traditional research method even more weak.

With the explosive growth in computational power over the past several decades,the agent-based social simulation (ABSS) has been proven to be a powerful tech-nique in exploring various complex adaptive systems, as it represents agents andtheir interactions with each other in order to simulate behavior in the real world(Edmonds2000; Terna1998). Given the strong interdisciplinary character, a variantof ABSS models with multiple types of agents have been developed in a large rangeof social and natural fields, such as economic, organizations, finance, social structure,

consumer behavior, and traffic process etc. (Marietto et al.2003). Especially, Agent-based Computational Economics (ACE), i.e. the computational study of economicprocesses modeled as dynamic systems of interacting agents is given more concernand is developing as a new discipline of economics (Tesfatsion and Judd2006; Phan2004; Tesfatsion2002). Especially, ABSS is being increasingly applied in the field ofwater policy analysis, such as: water supply system development (Tillman et al. 2001),


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Agent-Based Residential Water Use Behavior Simulation 3269

water resource allocation and watershed management (Bars et al.2002), agriculturalland use and water resource management (Berger2001), residential water demandunder conditions of climate change (Downing et al. 2000) and agricultural waterpollution control (Hare2000), amongst others.

In China, remarkably, no much effort has ever been made in the application ofABSS in water management area, although ABSS have been explored in disas-ter, emergency and other environmental fields, with the development of complexadaptive system theory and practices (Wei et al. 2002; Zhu and Ye2001). Mostly,individual water use is usually aggregated at the regional or sector level in orderto evaluate policy options in China, with the weakness of lacking consideration ofindividual decision making and interactions, as well as water use fixture technologicaland structure change.

In this study, an agent-based Residential Water Use Model (RWUM) is attemptedto be developed and calibrated against residential water use data in Beijing city overthe last 15 years based on municipal statistical, government planning, social andmarket survey data. Also, uncertainty analysis technique is innovatively applied inRWUM model for parameter calibration and model robust testing, and can providenew insights to ABSS technique. The identified model parameters will then be usedto provide behavioral information regarding the end-use of water in residentialsettings and changing trends in water used for each appliance. The RWUM modelcan be helpful to evaluate the microcosmic responses of regulatory or economicpolicies from the water management agency, and will also be used as a predictivetool to forecast future residential water demand for water infrastructure planning

and development.

2 The Model

2.1 The Model Framework

Considering the complexity of residential water use system, influencing by factors ofregulatory policies, technologies innovation, information, income level and decisionmaking heterogeneity, the RWUM (Residential Water Use Model) is developed

with the application of Agent-based Social Simulation techniques. The RWUMmodel consists of three agents,regulator, water appliance market and householdsas shown in Fig.1.

2.1.1 Regulator

The regulator agent is responsible for establishing the structure and level of waterpricesincluding water resources, water supply and wastewater treatmentbasedon factors such as costs of water supply, historical water pricing and affordability forhouseholds. The water price is a key factor determining individual household water

expenditure. Aside from sending water price signals, the regulator can also providevarious high-efficient water devicesfree-of-chargeto certain households; providedirect subsidies such as rebates to households who purchase certain kinds of waterdevices such as faucet, toilet, washing machine and shower et al; and set efficiencystandards for water devices. The combination of these policy instruments ultimatelyserves to influence the water use behavior of households.


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3270 J. Chu et al.

Attributes

Housing category

Monthly income

Neighborhood weight

Appliance ownership and years of use

Behavior

Use frequency of different end uses

Type of water appliance

Selection of wastewater reuse

Households

Applianceselection

Water usefrequency

Water appliance

market

Regulator

Wastewater

reuse

Fig. 1 Conceptual framework of RWUM Model

2.1.2 Water Appliance Market

In addition to physically providing households with their chosen water device, themarket agent collects and synthesizes information for households, such as water use

efficiency, costs, life-span and availability of each type of water appliance. At the endof year, market information on levels of ownership of different types of water devicesis fed back to the regulator. In this study, the following examples of water appliancesare considered: faucets, toilets, clothes washers, showers, baths and dishwashers. It isassumed that all water-efficient fixtures can achieve the efficiency standards set outby the regulator.

2.1.3 Households

Within this model, the household reflects the heterogeneous, decentralized, adaptive,

interactive and bounded rationality nature of social water use behavior. In orderto reveal the influence that the social learning process within a neighborhood hason water use behavior, it is assumed that households are spatially distributed inthe urban boundary in a given formatmost often in random uniform distribution.Household numbers are expanding in the selected city at a rate every year. Wateruse of all households totals up to represent the performance of water usage at themacro level. Each household in the system consists of two parts, i.e. attributes andbehavior, which may not only vary across households in the model but may alsochange within certain households during the simulation. The household will useinformation provided by the regulator and the water appliance market to make (be-havioral) decisions regarding the purchase of new water fixtures, or the acceleratedreplacement of their present devices (decisions are based on a households individualincome levels, water efficiency levels, the life-span of different water devices andwastewater preferences) thus modifying their status attributes. The sum of the wateruse behavior of all households will represent the total water use pattern at the macrolevel.


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2.2 Household Behavior Mechanism

2.3 End-Use Analysis

In the RWUM model, water consumption in each household is disaggregated toeight end-uses, including drinking, cooking, dishwashing, toilet flushing, cleaning,showering & bathing, laundry, and others. The end-uses take place through fivekinds of water device including the toilet, shower & bath, clothes washer, faucet anddishwasher, as shown in Table1.Due to limited information, for fixed volume wateruse devices such as baths, no high efficiency alternatives are assumed in this study.

To keep water balances, we can have formula (1) as shown below. Total waterconsumption is the sum of all water end-uses, and also the sum of water usage of allwater devices for the whole population. Considering current water use practices inBeijing, reused water is assumed to be used only for toilet flushing, and calculationof total reused water consumption is similar as given in formula (2). Expansions ofpopulation are shown in formula (3).

Vdt

PO Pti=1

Vdi,t=

PO Pti=1

8e=1

Vdi,e,t=

PO Pti=1

x

Vdi,x,t (1)

Rut=

PO Pti=1

Rui,t=

PO Pti=1

8e=5

Rui,e,t (2)

POPt= POPt1(+PO Rt) (3)

WhereVdtand Rutrepresents the total use of fresh water and reuse wastewaterin yeartin the given city respectively, m3/a.Vdi,e,tandRui,e,trepresents the amountof fresh water and reuse wastewater for end-use e consumed by householdi in yeart respectively, m3/a. Vdi,tandRui,trepresents the amount of fresh water and reusedwastewater consumed by household i in year trespectively, m3/a.Vdi,x,t representsthe fresh water consumed by water device x for household i in year t, m3/a. x canbe denoted as fau, tl, cw, sw and bath, indicating water devices of faucets, toilets,

clothes washers, showers and baths, respectively. POPt and POPt1 represent thenumber of family households of the city in year tand t1 respectively.PORtmeansthe households increase rate in yeart.

Table 1 Household waterend-uses and related devices

aInclude flower watering, fishtanks et al.

No. End-use Faucets Toilets Showers Baths Clothes

washers

1 Cooking Y

2 Dishwashing Y

3 Drinking Y4 Cleaning Y

5 Toilet flushing Y

6 Showering and Y Y

bathing

7 Laundry Y Y

8 Othersa Y


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Generally, the level of end-use for individual households is affected by three keyfactors: water device ownership, water use frequency and device water use efficiency(Edmonds et al. 2002). Device water use efficiency is a result of comprehensiverelations between household current water use volume, new device purchase decision

or accelerated device replacement. Due to data shortage, leakage is included andreflected by the water use frequency of households to meet different water end useneeds and can not be simulated separately in the current model version. Detailedinformation is given in formula (4)(12).

Vdi,1,t= 365K Ei,1,tJ Ai Fri,f au,tYf (4)

Vdi,2,t= 365K Ei,2,tJ Bi Fri,f au,tYf (5)

Vdi,3,t= 365K Ei,3,tJCi (6)

Vdi,4,t= 365K Ei,4,tFri,f au,tYf(J DAi +J DBiJ DCi) (7)

Vdi,5,t= 365K Ei,5,tFri,tl,tJ Ei(1 K Ri) (8)

Vdi,6,t= 365K Ei,6,t

K Ei,sw,t(1 Eb i)J F AiJ F Bi,tFri,sw,tYs

+ Eb i K Ei,bath,tJ FCiJ F Di

(9)

Vdi,7,t= 365K Ei,7,tJGAi,t

K Ei,cw,tEci Fri,cw,t

+ 1 K Ei,cw,tEciJ GBiJGCiYfFri,f au,t (10)Vdi,8,t= 365K Ei,8,t(J Hi +J I AiJ I Bi) (11)

Rui,t= Rui,5,t= 365KEi,5,tFri,tl,tKTdJ Ei K Ri (12)

Where KEi,e,t( e = 18) represents the ownership status of end-use e for householdi in year t (0/1 value, 1 is yes, 0 is no). JAi, JBi, JDAiand JDBi,represent minuteswhen the faucet is open during cooking, dishwashing, face-washing & tooth-brushing,hand washing, per day, for household i.JFAi and JGBi stands for minutes whenthe faucet is openduring showering and washing clothes by hand, per time or per

load respectively. JEi, JDCi, JFBi, JGAi, JFCiand JIBi represent how many timesof toilet flushing, hand washing, showering, laundry, bathing and the changing ofwater in a fish tank, per day, per household, respectively.JGCirepresents the ratio oflaundry times by machine to laundry by hand for household i.JHi, JIAi, JFDiand JCirepresent the water quantities required by flower watering, fish tanks, bathing anddrinking for household i per time. Fri,x,n,trepresents the device water use technicalefficiency ofntype of water devices x for householdi in yeart.nis an integer biggerthan zero and no more thanNwhich represents the total number of differing types ofwater devicex (i.e. 3 is for faucets and showers, 4 is for clothes washers and toiletsdetailed information is given in Table 3). Y

f and Y

s are throttle factors during

faucet and shower use respectively. Throttle factors are used to reflect the fact thatfaucets and showers are often throttled below their maximum rated flows (Brownand Caldwell Consultants 1984; Vickers2001). KRi represents whether householdis intent to adopt reclaimed water (0/1 value, 1 is yes, 0 is no). Ebi represents thebath usage adjustment ratio for household i who has possessed bath devices. Ecirepresents the usage rate of clothes washer by household i.


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2.3.1 Market Penetration of Water End-Use Technologies

As mentioned before, water device ownership has great influence on the level ofend-use for individual households. The ownership status can be reflected by themarket penetration rate, which means the proportion of households owning a certainwater appliance to all households over time as given in formula(13). In RWUMmodel, the market penetration rate is distributed to all households in a random way.Market penetration rate for toilet, shower, cloth washer and baths are estimated byregression historical statistical data. In detail, the market penetration rate of faucetare given as 1, i.e.KEi,e,t(e= 14, 69) = 1. Market penetration rate of cloth washersare estimated based on data of between 1979 to 1994 year historical statistical datawith high R2 of 0.9152, as given in formula (16). Market penetration rate of showersare estimated based on data of between 1985 to 2002 year historical statistical datawith high R2 of 0.9916, as given in formula (15). Market penetration rate of toilets

are estimated based on data of between 1992 to 2002 year historical statistical datawith high R2 of 0.9448, as given in formula (14). Market penetration rate of baths islinear functions of market penetration rate showers, as given in formula (17).

MPRp,t=

ni=1

KEi,p,t(p=18,bath) (13)

MPR5 = Bt1 + Bt2(t) (M PR5 [0, 100]) (14)

MPR6 = Bs1 + Bs2 Ln (t) (M PR6 [0, 100]) (15)

MPR7 =eBc1+

Bc2t1978 (MPR7 [0, 100]) (16)

M PRbath = Kbath MPR6(MPRbath [0, 100]) (17)

Where MPRcw,t, MPRsw,t, MPRtl,t andMPRb a,t represent market penetrationpercentage rates of clothes washers, showers, toilets and baths among householdsin yeart, respectively. Bc1(Bc2), Bs1(Bs2)andBt1(Bt2)are penetrative co-efficientsfor clothes washers, showers and toilets, respectively. Kbath is ratio of householdswith baths and showers to those only with showers.

2.3.2 Frequency Change of Water End-Use

The frequency of water end-use is affected by a multitude of factors, among whichhousehold income is identified to be the predominant factor (Mayer et al. 1999). Witha rise in household income, water use frequencies will adjust accordingly. Frequencychange has been incorporated in the RWUM model. The speed at which waterend-use frequency increases relative to an increase in income is normally known asincome elasticity, as given in formula (18)(20). Taking into account data limitations,

the most important water end-usesshowering and laundry frequencieschangewith income, while other end-use frequencies remain constant.

JG Ai,t= ElacIncri,tJG Ai,t1 + JG Ai,t1 (18)

J F Bi,t= ElasIncri,tJ F Bi,t1 +J F Bi,t1 (19)

Inci,t= Incri,tInci,t1 + Inci,t1 (20)


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Where Elacand Elasrepresent income elasticity of household laundry and showerwater use, respectively.Inci,tandInci,t1represent income of household i in year tandt1, in Yuans. Incri,trepresents the rate of increase in the income of household i inyeart.

2.3.3 Efficiency of Water End-Uses

In the RWUM model, water consumption for different water devices are given informula (21)(26). Household water use technical efficiency is remarkably affectedby the type of water device they have chosen to use under a given water usefrequency.

Fri,x,t= E f f

Ti,x,t

Ti,x,t n

(21)

Vdi,f au,n,t= e=14,8

Vdi,e,t+ 365KEi,7,t

JG Ai1 KEi,cw,tEci

J GBiJGCiYf Fri,f au,t (22)

Vdi,tl,n,t= Vdi,5,t+Rui,5,t (23)

Vdi,cw,n,t= 365KEi,7,tJG Ai K Ei,cw,tEci Fri,cw,t (24)

Vdi,sw,n,t= 365KEi,6,tK Ei,sw,tJ F AiJ F Bi Fri,sw,tYs(1 Eb i) (25)

Vdi,bath,n,t = 365KEi,6,tK Ei,bath,tJ FCiJ F Di Eb i

Rui,x,n,t=

Rui,5,t(x = tl)0 (x = cw,sw, tl, bath)

(26)

Determinants affecting the type of water device individual households wouldchoose generally include factors such as the persons individual decision on newdevice purchase, or accelerated device replacement, based on his local status, per-sonal character, and availability of water devices and related information, as well ascurrent related government policy, as shown in formula(27). Due to data shortage,households decisions are not affected by the neighborhood in the current modelversion, i.e. neighborhood weight of households in the model is set as the same.

n = Ti,x,t= Decision

DHi,x,t,D Ri,x,t,DTi,x,t,DMi,x,t,Poli,x,t

(27)

Where Ti,x,t represents the type of water device x for household i in year t.Decision represents the decision process function; this will be discussed in detailbelow. DTi,x,t, DHi,x,t, DRi,x,t and DMi,x,t represent the type of water device xchosen by household i with the randomhabitualdeliberative rule and acceleratedreplacement decision rule in yeartrespectively.Poli,x,trepresents the type of waterdevice x chosen by household i in year tunder the influence of government water-related policy.

1. New device purchase decision

The behavioral decision of households pertaining to the replacement of an oldappliance is rather complicated in practice, with the exception of circumstanceswhere the appliance is broken owing to age, consequently having to be replaced.In addition, new householdswhere dwellings are still under constructionneed topurchase new water fixtures, as a matter of course.


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Several empirical studies have shown that theoretical conclusions derived from theassumption of full rationality fail to explain observed outcomes in many situations(Ostrom et al. 1994). People have multiple utilities or values that determine theirbehavior, and some of these utilities are not associated with economic payoffs they

receive (Izquierdo et al. 2003; Janssen and Jager1999). According to results fromsocial survey conducted in Beijing in 2003 by our research group, three possible rulesare incorporated in the RWUM model, including habitual (or repetitive), random(or indiscriminate), and deliberate (or rational) rule for Beijings case.

The habitual or random rule applies to those households who have had a highdegree of satisfaction with previous water device purchases, thus, also having alow level of uncertainty regarding future water device purchases. In contrast, thedeliberative rule applies to those households who may have low satisfaction with acurrent device and as such, they are prompted to actively consider what they wish topurchase and what purchase alternatives are available.

For the whole system, the decision rule applied to households will be distributedrandomly, (an approach which has been adopted in many ABSS models (Gotts et al.2003a), as given in formula (28).

Dtyi =

1randi RDT2 RDT


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Where DHi,x,t1and DHi,x,t represent the type of water device x chosen forhouseholdi by the habitual decision rule in year t1 andtis type of water device xchosen by householdi in yeart1.rand(n)represents an integer random generator,evenly distributed between 1 and n.

c) Deliberative or rational rule

Under the classical decision theory in economics, households will purchase anappliance based on least total cost (including capital and operation and management(O&M) cost) principle, while also taking into consideration the current water pricestipulated through government policy, as shown in formula (32)(35).

DRi,x,t=n

where Tci,x,n,t= Mn

nTc

i,x,n,t n N&Avx,n,t= 1

(if Dty

i= 3) (32)

Tci,x,n,t= Drx,n Invx,n REbatex,n,t+ Opei,x,n,t (33)

Drx,n =R 1+RL fx,n

1+RLfx,n 1(34)

Opei,x,n,t= Vdi,x,n,t K Ri Rui,x,n,t Pat+Pwt+ EYe +Rui,x,n,t Prt (35)

Where TCi,x,n(n),t, Invi,x,n,t and Opei,x,n,t represent total, capital and O&M costsof type n of water device x for household i in year t. Drx,n represents the annualamortization co-efficient for n type of water device x. EYe represents energy cost

savings of clothes washer (e = c) or shower (e = s) per m3 of water supply. Lfx,nrepresents the life-span ofn type of water devicex. REbatex,n,trepresents the subsidyprovided by the regulator for n type of water device x in year t. Invx,n representsinitial capital cost of n type of water devicex. Patand Pwtare price of drinking waterand wastewater treatment cost respectively. R means government long-term bondrate. KRi is the wastewater reuse preference coefficient, as given in formular (50).

2. Wastewater reuse and accelerated device replacement decision

For treated wastewater, its quality and service and the consumers attitude, economic

level, education character et all will influence the consumers decision on whether toadopt it. In RWUM model, households have various preferences when the reusedwastewater is available. Households wastewater preferences can be reflected bytheir reservation prices, i.e. the willingness to pay for it. According to the differenceof households reservation prices, three categories of households have been identifiedin the RWUM model, that is: 1) Rejecter(RDi = 0), who will refute to use treatedwastewater for toilet flushing; 2) Crazier(RDi = 2), who are mostly with highenvironmental consciousness, and will adopt reused water once it is available; 3)Adopter(RDi = 1), who will adopt reused water when the ratio of fresh water priceto reused wastewater price is larger than their acceptable aspiration level. The ratiosof Crazier and Rejecter to the total households areRE1andRE2respectively.

Aside from a households decision to replace an aged or broken device, anaccelerated device replacement decision for inefficient old water appliances canalso be made by households. The decision to do so is often influenced by manyfactors, including: i) Household decision characteristics. In the RWUM model, onlyhouseholds adhering to the deliberative decision rule are in a position to collect


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detailed information on related water devices to calculate and compare costs, andfinally, to choose whether or not to make an accelerated decision to replace it. ii)Household economic income level. In accordance with the aspiration level theory(Simon1957), households will behave differently when their aspiration levels are

reached (Gotts et al.2003b). Based on social surveys, the ratio of water expenditureto income can be chosen as the aspiration index (Brook and Smith2001). iii) Costeffectiveness. As a general rule, the decision to replace a device will be acceleratedif the total cost of replacing the device is the cheapest option. This is influenced bya range of factors including residual amortized capital costs of the previous device,O&M costs, and amortized capital costs of new alternatives available in the market.

When the price of water is high, households tend to opt for behavioral changes ortechnological strategies to reduce water expenditures. They do so by either replacingor retrofitting their current water appliances or adopting reused wastewater. Theabove optimization process consists of a combination of the various cost savingstrategies, based on the total cost minimization principle. The retrofit decision isgiven in formula (36)(37).

DMi,x,t=

n

i f T ci,x,n,tApsi&Dtyi = 3

(otherwise)

(36)

Inri,t= Vdi,x,n,t K Ri Rui,x,n,t (Pat+Pwt+ EYe)+Rui,x,n,t Prtinci,t

100% (37)

WhereAspi andInri,trepresent the threshold and real ratio of water expenditureto economic income for household i in year t. Tci,x,n,t and Tc

i,x,,t1 represent the

total costs for n* and type of water device x for householdi.For the devices of clothes washers, showers and faucets, the retrofit decision is

made if the cost of the retrofit is lower than that of before, as given in formula (38)(40). The retrofit cost includes residual amortized capital cost of the old water device,which has not completed its life-span yet.

Tci,x,n,t= Minn

Tci,x,n,t

n Avx,n,t= 1

(x = cw,sw, f au) (38)

Tci,x,n,t= Drx,nInvi,x,n,t+REbatex,n,t

+ Opei,x,n,t (39)

Invi,x,n,t= Invx,n +

Lfx,Yl fi,x,,tt=1

Drx,Invx, REbatex,,t

(1+R)t

(40)

Where Invi,x,n,t

and represent capital, total retrofit cost of n type of water device xof householdi in yeart. Invx, andLfx, represent capital, life-span of type of waterdevice x. Ylfi,x,,trepresents used years of type of water device xfor household i inyeart.

For toilets, the adopter can have three possible choice alternatives, that is, (1)decision one: retrofitting current toilets to high water use efficiency ones using freshwater; (2) decision two: using reused wastewater with traditional toilets; (3) decision


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water devices. In the RWUM model, the assumption is that water devices providedfree-of-charge by the regulator will be positively received, and used for promptreplacement of previous water devices by targeted households, as given in formula(51)(52).

Pini,t=

1

if rand (1)i Pcax,n,t

0

(51)

Poli,x,t=

n

if Pini,t= 1

if Pini,t= 0 (52)

Wheren represents the type of water device provided by the regulator on a free-of-charge basis.Pcax,n,trepresents the proportion of households affected by the policyto total households for n type of water device xin yeart.Pini,trepresents whether

householdi will be affected by the policy in year t, (i.e. 1 is yes and 0 is no).In conclusion, the total decision process algorithm (which determines the type of

water device) of each household can be represented in formula (53).

Ti,x,t=

Poli,x,t DTi,x,tDHi,x,t

DRi,x,t

(if Htyi = 0)orHtyi = 1&Yl fi,x,,t L fx,

or

Htyi = 1&MIi,x = 1

DMi,x,t

(53)

WhereHtyi represents type of household i in yeart, (0/1 value, is an old household,and 0 is a new household). MIi,x represents whether household i needs a new waterdevicex in yeartwith increase of income (0/1 value, 1 is yes and 0 is no).

2.4 Model Implementation

The RWUM model is implemented in Swarm context (Hiebeler 1994; Langtonet al.1995), accompanied with Visual Basic and Matlab programming. Visual Basicprogram is developed to multi-call Swarm program to conduct parameter calibration

under uncertainty; Matlab program is used to process data analysis and scientificgraphing. The implementation interfaces of RWUM under Swarm context are givenin Fig.2.

The time in RWUM model is divided into years. The simulation period is longover a 20 year horizon (i.e. from the year 1985 to 2030), so the discrete replacementof water device purchase and replacement decisions of households can be reflectedto the whole lifespan. The implement process is as follows: In the initial year, agentsof households, regulator and water appliance market are created and initialized.The ownership and technical properties of water use devices and water use habitsamong households are done such that those match a real distribution in Beijing inthat year. Water price of the initial year is set by the regulator agent. Householdsuse water according their water use devices and habits, and the output is theamount of water total households use. In the cycle year, new household agents arecreated and initialized with population increase in the city. The water price of eachyear is decided by the regulator agent. Water appliance market agent provides theinformation on water devices in the market for households to choose. Households


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3280 J. Chu et al.

Fig. 2 Model implementation interface of RWUM model

use water according to their device characteristics, income and price level and otherinformation to make decisions on water device purchase, accelerated replace, wateruse frequency change or wastewater adoption and form total household water use inthe new year.

3 Data Source and Model Validation

Due to the complexity of residential water use system, two categories of data arerequired for the model of RWUM, i.e. data from municipal statistics, censuses,development plans and various surveys belong to type I; data from model parametercalibration process are type II. Detailed parameter and input data list in the RWUMmodel are given in Table2.

3.1 Data from Statistics, Planning and Surveys

With regard to the Beijing city, the input data which come from municipal statistics,censuses, development plans and various surveys is given in Tables 3and4. Obvi-ously, Table3gives the social and economic data and data relating to water devicescan be found in Table4.


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Table 2 Parameter and input data list in the model RWUM

Item Meaning Unit Explanation

POPa0 Initial households number Household Type I

PORat Household increase rate / Type I

Inca

i,0 Income Yuan/household/month Type I

Incrai Income increase rate / Type I

RE1 Ratio of crazier for reused / Type I

wastewater

RE2 Ratio of rejecter for reused / Type I

wastewater

RE Mean of adopter for reused / Type I

wastewater

RES Standard deviation of adopter / Type I

for reuse wastewater

EYc Energy cost savings of clothes Yuan/m3 Type I

washer, per m3 of water supplyEYs Energy cost savings of shower,

per m3 of water supply Yuan/m3 Type I

Bs1 Co-efficients for showers

penetration / Type I

Bs2 Co-efficients for showers


Bc1 Co-efficients for clothes

washers penetration / Type I

Bc2 Co-efficients for clothes

washers penetration / Type I

Bt1 Co-efficients for toilets


Bt2 Co-efficients for toilets


Ys throttle factors during

shower use / Type I

Yf throttle factors during

faucet use / Type I

R Government long-term

bond rate Type I

Ebi The bath usage adjustment ratiofor householdiwho has

possessed bath devices / Type I

Eci The usage rate of clothes

washer by householdi / Type I

Pcax,n,t The proportion of households

affected by the policy to total

households for n type of water

devicexin yeart / Type I

RDR The ratio of random decision

households to the total

population for the whole system / Type II

RDH The ratio of habitual decision

households to the total population

for the whole system / Type II

Kbath Ratio of households with baths and

showers to those only with showers / Type II


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3282 J. Chu et al.

Table 2 continued

Item Meaning Unit Explanation

Elas Income elasticity of household

shower water use / Type II

Elac Income elasticity of household

laundry water use / Type II

JIBi Times of changing of water in a

fish tank per day per household Times/day/household Type II

JDBi Minutes when the faucet is open

hand washing Minutes/day/household Type II

JDCi Times of hand washing per day

per household Times/day/household Type II

JDAi Minutes when the faucet is open

during face-washing and

tooth-brushing Minutes/day/household Type II

JFBi,0 Times of showering per day perhousehold Times/day/household Type II

JFAi Minutes when the faucet is open

during showering Minutes/ time or load Type II

JGAi,0 Times of laundry per day per

household Times/day/household Type II

JEi Times of toilet flushing per day

per household Times/day/household Type II

JGBi Minutes when the faucet is open

during washing clothes by hand Minutes/time or load Type II

Aspi Threshold ratio of water

expenditure to economic income

for householdiin yeart Percent Type II

JGCi Ratio of laundry times by machine

to laundry by hand for householdi / Type II

JIAi Water quantities required by fish

fish tanks for householdiper time m3/household/time Type II

JFDi Water quantities required by bathing

bathing for householdiper time m3/household/time Type II

JAi Minutes when the faucet is open

during cooking Minutes/day/household Type II

JBi Minutes when the faucet is openduring dishwashing Minutes/day/household Type II

JCi Water quantities required by

drinking for householdiper time m3/household/time Type II

JFCi Times of bathing per day per

household Times/day/household Type II

JHi Water quantities required by flower

watering for householdiper time m3/household/time Type II

3.2 Parameter Calibration and Sensitivity Analysis

Considering there is inherent data shortage in the model, we attempt to use uncer-tainty analysis technique in the RWUM model to calibrate and evaluate the modelsparameters under different initial assumptions. The algorithm of Hornberger, Spear,and Young (i.e. HSY algorithm) is adopted in the model (Beck 1987; Chen and


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Table 3 Social and economic input data in RWUM

Item Data Item Data Item Data Item Data

POPa0 280 Ebi 0.50.77b RE1 0.10

d Bc1 5.1002f

PORat Eci 0.830.91b RE2 0.267

d Bc2 7.7298f

Inca

i,0 264.8 Pcatl,n,1991 0.0416

c EYc 16.02e Bt1 7.09

f

Incrai Pcatl,n,1992 0.0305c EYs 6.68

e Bt2 3.7f

Pcatl,n,1999 0.001c Bs1 152.71

f Ys 0.66g

Pcasw,n,1991 0.0286c Bs2 81.92

f Yf 0.66g

Pcafau,n,2000 0.166c R 0.06h

aData sourced from Beijing Municipal Bureau of Statistics (2004) and Beijing MunicipalGovernment (2001). The total figure is given by 10,000 households per yearb Refers to social survey result conducted by Zhang(2003a,b)c Based on data from research (He and Li2002)d Field work data (Zhang2003b)e The calculations are undertaken by adopting mathematic methods through research (Gleick

et al. 2003) in local natural gas and electricity price data from Beijing Municipal Commission ofDevelopment and Reformf Estimations based on serial historical data from Beijing Municipal Bureau of Statistics througheconometric regression techniquesg According to research (Vickers 2001), the parameter range is 0.540.68; this paper borrows the datafrom research (Brown and Caldwell Consultants1984) with 0.66h Refers to long term bond rate of government at 0.06 (Gleick et al.2003)

Beck1999). The process of parameter calibration and sensitivity analysis are givenas follows: firstly, the objective function is assumed as standard deviation (STD) lessthan 10% of the observation water use value from 1985 to 1994 in Beijing. Secondly,

Table 4 Water device related data in RWUM

x No. Technical effi ciency type Frai,x,nt Invbx,n Lf

cx,n Av

dx,nt

fau 1 Traditional 0.015 5 3 1985

2 MOC standard 0.009 20 5 1985

3 High-efficiency 0.006 45 12 2000

tl 1 Traditional 0.013 200 10 1985

2 Recent 0.009 500 20 1985

3 MOC standard 0.006 700 20 2000

4 High-efficiency 0.0038 900 20 2000

sw 1 Traditional 0.013 25 10 1985

2 MOC standard 0.009 75 10 1995

3 High-efficiency 0.006 150 10 2000

cwe 1 Old traditional 0.150 200 5 1985

2 Traditional 0.120 400 10 1985

3 MOC standard 0.080 1000 10 1985

4 High-efficiency 0.045 2800 25 2000a m3 per minute for faucet and shower, m3 per flush for toilet. The data is estimated according to

market surveys and Ministry of Construction P. R China (MOC) (2002b) standard (CJ1642002)b Data sourced from market surveys and government market supervision reports such as (CPIC2004)

c Faucet life-span is approximately 10 years internationally (Koomey et al.1994) and 312 years inChina (Koomey et al.1994; Gleick et al.2003); life-span is approximately 2025 years internationally(Mou 2003; Water Resources Engineering Inc. 2002) and 1020 years in China for toilets, andapproximately 515 years for showers (Gleick et al.2003). The life-span for clothes washers is 1214 years on average, internationally (Mayer et al. 1999; Koomey et al. 1994), and 1525 years fordrum-types and 510 years for impeller types in the China marketd Available year is given by market surveys and expert judgmente They are assumed to be 5 kg on average, and the same below


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3284 J. Chu et al.

random data are sampled by Monte Carlo technique for 20000 times among the initialrange of the model parameters, and are separated by accepted parameters or rejectedparameters. Information on initial ranges of the RWUM model parameters to becalibrated is given in Table5.Considering the random system structure character,

simulations are repeated 100 times for the specified sample value. Thirdly, thesensitivity of the parameter is evaluated according to the difference of parameter dis-tribution by KolmogorovSmirnov (K-S)test. The identification capacity is expressedas the ratio of parameters STD to its Mean. TheD-value which means the maximumdistance between cumulative distribution function of samplings accepted parametersand rejected parameters reflects the parameters sensitive character. The samplevalue are chosen as that minimizing the weighted quadratic deviations between themodel results and real-world observations of aggregated residential water use arechosen as the parameter value.

Through the HSY algorithm, information of distribution characteristics and sensi-tivity ranks on identified parameters are given in Table 6. Results obtained show thatamong all the model parameters, Elas and Elac are the most sensitive parametersat 95th-percentile confidence level. As the parameters with high sensitivity are alsoeasily identified and have low uncertainty, the model produces consistent internalresults. The model is also in accordance with practices in the past decadetakingbathing, laundry water use as key aspects in determining macro residential water useperformance (i.e. quantity and structure).

Table 5 Initial ranges of parameters to be calibrated in the RWUM Model

Para. Min Max Survey Para. Mean Std Survey Para. Mean Std Survey

data data data

RDR 0 1 JDBi 0.2 0.2 0.10.5b Aspi 1.5 0.5

RDH 0 1 JDCi 15 3 1218b JGCi 1 0.1

Kbath 0 0.8 JDAi 2.4 1.2 2.13.3b JIAi 0.05 0.03 0.020.08

b

Elas 0 0.25 0.171a JFBi,0 0.9 0.6 0.723

c JFDi 0.15 0.05 0.1300.2b

Elac 0 0.25 0.162a JFAi 13 8 618

d JAi 5 2 35g

JIBi 0.03 0.13 0.15b JGAi,0 0.4 0.1 0.91.11e JBi 5 2 36gJEi 13.5 4.5 915

f JCi 0.0072 0.0018 0.00540.009h

JGBi 7.5 5 312b JFCi 0.29 0.14 0.150.42i

JHi 0.015 0.006 0.0060.024j

a Elasticities are based on Gleick et al.(2003)b Data is estimated according to market survey and expert judgmentc 0.700.75 times per day, per capita in abroad (Dziegielewski2000; Mayer et al.1999), and 2.53 and0.81.1 times per day, per household, respectively in summer and winter in China (Zhang2003a)d 5.68.9 min per time internationally (Mayer 2000; Vickers 2001; Peter et al. 2003; Mayer et al. 2003),1821 min per time in summer in China (Zhang2003a)e 0.30.37 times per day, per capita internationally (Mayer et al.1999,2000) and 0.91.11 times per

day, per household in China (Zhang2003a)f 3.215.53 times per day, per capita internationally (Mayer et al. 2000, 2003; Dziegielewski 2000) andfour times per day per capita in China (Zhang2003a)g According to Koomey et al. (1994)h 1.85.0 l per capita per day internationally (Inocencio et al. 1999), 1.83 l per capita per dayaccording to MOC standard in China (MOC2002a)i 0.050.14 times per day, per capita internationally (Dziegielewski2000; Mayer et al.2003)j 2, 3 and 8 l per time, per capita (MOC2002a)


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Table 6 Characteristics of parameters identified by HSY algorithm in RWUM model

Sensitivity typed Name Mean STDa STDaMean D-valueb Identification Sensitivity

rankingc rankingd

Key parameters Elas 0.168 0.010 0.062 0.140 4 1

Elac 0.112 0.009 0.077 0.136 5 2Relative sensitive RDR 0.316 0.054 0.170 0.122 9 3

RDH 0.584 0.021 0.036 0.115 3 4

JFBi,0 0.801 0.083 0.103 0.113 7 5

JFAi 17.967 3.192 0.178 0.112 10 6

Aspi 1.492 0.297 0.199 0.109 12 7

JGAi,0 0.350 0.064 0.183 0.105 11 8

Less sensitive Kbath 0.159 0.02 0.452 0.103 21 9

JEi 15.243 0.270 0.018 0.082 2 10

JFCi 0.251 0.111 0.440 0.071 18 11

JDAi 2.394 1.002 0.419 0.058 17 12

JIAi 0.050 0.024 0.483 0.045 22 13

JBi 3.110 1.730 0.556 0.041 23 14

JDBi 0.204 0.090 0.440 0.041 19 15

JDCi 15.039 2.559 0.170 0.038 8 16

JIBi 0.131 0.026 0.200 0.038 13 17

JHi 0.015 0.006 0.333 0.036 16 18

JGBi 8.196 1.717 0.209 0.032 15 19

JAi 3.813 1.720 0.451 0.031 20 20

JGCi 0.990 0.081 0.082 0.023 6 21

JCi 0.006 0.003 0.208 0.020 14 22

JFDi 0.149 0.002 0.014 0.015 1 23a standard deviationb maximum distance between cumulative distribution function of samplings accepted parametersand unaccepted parametersc Identification is expressed as the ratio of parameters STD to its Meand Sensitive and relative sensitive is at 95th-percentile and 80th-percentile confidence levels,respectively

3.3 Model Validation

The uncertainty simulation results of per capita residential water use per day (litersper capita per day, LCD) form RWUM model between 1995 and 2000 year and meanand its 20% deviation of the real world observation data are plot as asterisk and linerespectively in Fig.3. It shows that the RWUM model can endogenously drive keyparameters values into the ranges supporting the behavior replication of the targetsystem. Linear regression of observed data on simulated results of water use percapita per day in Beijing shows that the slop is 0.887, with STD smaller than 20%of mean values and high R2 equals 0.818, as given in formula (54). The simulatedwater use structure of aggregated households is also consistent with survey results

from Beijing in the year 1999, which can easily be seen in Fig. 4. All of these testsdemonstrate that the model is robust and credible.

Y* = 0.887Y+ 5.427 (54)

WhereY* and Yis the empirical observed and model simulated data of water useper capita per day respectively, LCD.


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3286 J. Chu et al.

Fig. 3 Model calibration andvalidation based on historicaldata

Year

Calibration Validation

4 Results and Discussions

4.1 Technological and Economic Characteristic of Water Devices

4.1.1 Different Dynamic Diffusion Pattern of Water Devices

According to estimations based on serial historical data from Beijing MunicipalBureau of Statistics through econometric regression techniques, it shows that thereare various diffusion paths for different water devices among households in Beijing.Diffusion of clothes washers through households occurs predominantly during theyears of 1985 to 1993, with diffusion of showers and baths occurring predominantlyfrom the years 1991 to 1998, and toilets from the years 1985 to 2000, as shown inFig.5.There is a trend in the diffusion rate (i.e. diffusion speed with time) of waterdevices decreasing after a period of increase, as can be seen from Fig. 6. It can also be

Fig. 4 Water use structure bydifferent devices in year 1999

0

20

40

60

80

100

RWUM results Survey data

%

Shower&Bath

Toilet

Clothes washer

Faucet


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Fig. 5 Diffusion paths ofdifferent water devices sinceyear 1985

0

20

40

60

80

100

1985 1988 1991 1994 1997 2000

Year

Possessionrates

(%)

Toilet

Shower

Clothes washer

FaucetBath

inferred that there is a correlational relationship between the diffusion rates of waterdevices and the increase rates of per capita annual disposable incomewith showersand baths the most sensitive, and toilets and clothes washers the next most sensitivewater devices in this relationship.

4.1.2 Incremental Costs of Water Device Replacement

Market survey data shows that water use efficiency is the key factor in affecting waterdevice capital costs, i.e. a higher efficiency device generally requires households toinvest more. Figure7illustrates the relationship between water use efficiency andwater device levelized costs calculated (i.e. the discounted cost per unit of watersaved over the lifetime of the device). Seemingly, the replacement of faucets andshowers can achieve a dramatic shift in water use efficiency at a very low cost, at0.46 and 0.65 Yuan per year, per liter, respectively (i.e. slopes of the related curvesin Fig.7). The replacement to high-efficiency toilets yields high water use efficiencyat a medium cost, at 1.47 Yuan per year, per liter. However, replacement to high-

Fig. 6 Diffusion rates ofdifferent water devices sinceyear 1985.Points on each linein this chart, from right to left,indicate 1-N type of each waterdevice as given above, i.e. aless efficient device is replacedwith one with higher efficiency

0

20

40

60

80

100

1985 1988 1991 1994 1997 2000

Year

Diffusionrates(%)

Toilet

Shower

Clothes washer

Faucet

Bath


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3288 J. Chu et al.

Fig. 7 Relationship betweenwater use efficiency and waterdevice levelized cost.Points oneach linein this chart, fromright to left, indicate 1-N typeof each water device as givenabove, i.e. a less efficientdevice is replaced with onewith higher efficiency

0

50

100

150

200

250

0 20 40 60LCD

Cost(Yuan/Year

)

Faucet

Toilet

Shower

Clothes washer

efficiency clothes washers renders low-efficiency improvements at a relatively highcost, at 12.99 Yuan per year, per liter.

Results also show that, considering energy saving co-benefit (reducing water-heating expenses), some water devices (e.g. showers and clothes washers) havenegative incremental costs, suggesting their replacements are in fact cost-effective.For example, incremental costs of replacement to a high-efficiency shower are 6.09

and 15.02 Yuan/m3 using natural gas and electricity as the water-heating energy,respectively, as shown in Fig.8. Similarly, incremental cost of replacement to a high-efficiency clothes washer is 4.16 Yuan/m3 using electricity as the water-heatingenergy. Since electricity is more expensive than natural gas, energy savings fromelectricity are more significant than those derived from natural gas. The negativecost of conserved water means that the co-benefits of water conservation pay forthe investment in water use efficiency improvementsand saves on the consumersexpenditures as well (Vickers2001).

Fig. 8 Incremental cost ofdevice replacement. Iscenariowith current devices replacedto those up to nationalstandard (CJ1642002);IIscenario with water devices upto national standard(CJ1642002) to highefficiency ones available in themarket;IIIscenario withcurrent devices replaced to

high efficiency ones availablein the market

-24

-16

-8

0

8

16

Bas

eline

Na

tural

gas

Electr

icity

Bas

eline

Electricity

Shower Clothes

washer

Increamentalcost(Yuan/m3)

I

II

III


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5 Water End Use Structure and Policy Responses

5.1 Water End Use Structure

Detailed Water use structure data can not be separated by the statistical data directly.Through the RWUM model simulation with the initial conditions of the year 1985, wecan get more information on structure change of water usage in Beijing from 1985 to2000 year. Results indicate that there is a dramatic structure change from the historicpattern of continued per capita growth. It shows that historically, faucet and clotheswashing water usage constituted the major part of household water use; but morerecently, household water use has been dominated by bathing and toilet flushingusage, in part due to demographic, economic and technology changes. Figure9giveshow residential per capita water usage evolved over the past 15 years. For example,faucet water usage to satisfy basic water demand has decreased to 21% in 2001 from95.2% in 1985. Laundry water usage has increased to 1015% of total water use.Toilet and bathing water usage has increased to 31 and 32% in 2001.

5.1.1 Lock-in Effect of Water Use Efficiency

The long life-span of water devices makes early replacement uneconomical forhouseholds, and causes a lock-in effect (Leydesdorff 2002) of water use efficiency.With other conditions remaining constant, two scenarios are assumed in the model,i.e. one is the baseline and the regulation scenario. In the baseline scenario, allkinds of water devices are permitted to provide in the market for households topurchase. In the regulation scenario, we assume that inefficient water devices havenot been permitted entering in the water devices market since 2001. Simulationresults show that under the baseline scenario, water use per capita will first increasewith increasing socio-economic level before the year 2008 until 165 LCD, anddecrease gradually with high-efficient water use devices entering the market and

Fig. 9 Evolution of water usestructure from 1985 to 2000

0

20

40

60

80

100

120

140

1985 1988 1991 1994 1997 2000

Year

wat

erusepercapitaperday

(LCD)

Shower& Bath

Toilet

Clothes washer

Faucet


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3290 J. Chu et al.

finally level-out to 157 LCD around the year 2015, as shown in Fig. 10.Under theregulation scenario, water use per capita will decrease gradually to 110 LCD aroundthe year 2015. Both of the scenarios show that there being around 15 years of delayfrom the point when inefficient water devices were phased out from the market

reflects the intensity of the lock-in effect of water use efficiency.

5.1.2 Price Responsiveness of Fresh Water Demand

Results also show that when the price of water is high enough until water expenditureof households reach 1.492% (Aspi) of their disposable income, the households wateruse behaviors will be different to reduce water bills by either replacing or retrofittingtheir current water appliances. The aspiration level estimation in this case is inaccordance with the results from other related research (Dong and Dong 2000).

An important factor in managing water effectively is to have knowledge of its price

elasticity of demand, which measures the sensitivity of water demand to changesin the water price. In the RWUM model, a scenario analysis technique is usedto quantify the demand elasticity of water through price changes. The baselinerepresents the fresh water price series based on future water supply cost estimationsfrom the projection plans of the Price Bureau of Beijing Municipality. In scenarioC1, an increase of 1.2 Yuan per m3, which is calculated based on the South to NorthWater Transfer Project, is shown compared to the baseline level (Pan and Zhang2001). In scenario C2, the fresh water price will increase at a rate of 10% every2 years until 2020, when the water price will increase at a rate linked with the risein incomes. In scenario C3, the water price will increase to the households incomethresholds (Aspi), and then will rise at a rate of 10% every 2 years.

Results of the RWUM model shows that price elasticity of fresh water demandranges between 0.35 and 0.02 for scenario C1 and C2, and between 0.1 and 0

Fig. 10 Lock-in effect ofhigh-efficient water devicesreplacement

0

50

100

150

200

250

300

2000 2010 2020 2030

Basline

Regulation

LCD

Year


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for scenario C3, as shown in Fig.11. As the fresh water price goes up swiftly in C3,fresh water demand can not change proportionally due to the lock-in effect of wateruse efficiency as mentioned previously. Substantial water price changes will result inonly small changes in fresh water use volumes, i.e. inelastic demand.

Price elasticity of demand for water will change depending upon different waterprice ranges, which is in accordance to findings in other research (Martin and Thomas1986). When the fresh water price is at low level, a small price change does not affectfresh water use, i.e. price elasticity of demand for fresh water is about zero. Whenthe price rises to a certain level (about 5 Yuan per m3 in this case), price elasticityis negative, resulting in an increase in absolute value, with increasing numbers ofhouseholds selecting alternative, high-efficiency fixtures. When fresh water price ishigh enough, (about 8 Yuan per m3 in this case, with most households having alreadyadopted high-efficiency water devices) the price effect on fresh water use decreasesagain with decreasing elasticity.

5.1.3 Income Elasticity of Different Water End-Use Frequency

Results of the RWUM model by the HSY process reveals that the frequenciesof households using the shower and clothes washer will increase with householdsincome. This relationship has a positive income elasticity of 0.168 and 0.112 respec-tively, which approximates recent survey results conducted in USA (Mayer et al.1999).

5.1.4 Water Device Decision Rules Category

The decision rule that households choose can greatly affect the type of water devicepurchased, and further determine the total quantity of water use and its structureof usage. The RWUM model can identify the ratio of households holding differentdecisions through the HSY parameter calibration process. Results show that over

Fig. 11 Price elasticities offresh water demand underdifferent scenarios

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0 10 20 30

C1

C2

C3

Water price (Yuan/m3)

A

bsolutevalueofelasticit


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3292 J. Chu et al.

the past 10 years in Beijing, households making choices based on the deliberativerule accounts for 31.6%, while households making choices based on the randomrule accounts for 10% of total households. Finally, households making choicesbased on the habitual rule accounts for a majority at 58.9% of total households.

It is evident from the results that more than half of all households do not adoptmore economical and efficient water devices at present, ostensibly due to a lack ofinformation and knowledge on high-efficiency water devicesor having purchasedecisions informed through personal preferences. It means that the government canuse public awareness campaigns to promote water conservation. It can be imaginethat the ratio of households with deliberative decision rule can be improved andmore water-efficient devices are adopted, which will restrict the increase of wateruse per capita per day in the future.

6 Conclusions and Outlook

A good understanding of the drivers of residential water use behavior is essential ifwater managers wish to craft effective demand management policies and infrastruc-ture planning strategies. In this paper, the RWUM model has been developed toquantify dynamic patterns of residential water use behavior, by disaggregating house-hold water usage as a whole and exploring end-use levels. This model incorporatesthe availability of high-efficiency water devices in the market, resultant from techno-

logical advancements. The model can show heterogeneous, decentralized, adaptive,interactive and boundedly rational social water use behaviors. Such a model wouldallow both to study and to train policy makers decision making regarding strategiesaimed at shaping residential consumer water use behaviors.

Several interesting findings can be drawn from this study. In current Beijing,household water use predominantly consists of bathing and toilet flushing water use,though faucet and clothes washing water use had been the predominant use in thepast. The government can set the standard of different water use devices to controlthe waste of water use in residential area. Considering the toilet flushing taking alarge part of residential water use, government policies to strengthen grey water

reuse can reduce fresh residential water to some extent, such as setting low reusedwater price, raising public awareness of reused water, as well as regulation on greywater reuse treatment facilities for some communities and buildings.

The replacement of faucets and showers can readily make improvements of wateruse efficiency at a very low cost, while the replacement to high-efficiency toilets canmake improvements in water use efficiency at a medium cost. However, replacementto high-efficiency clothes washers shows low efficiency improvements in water useefficiency at a relatively high cost. Considering the energy saving co-benefit, showersand clothes washers have negative incremental costs, indicating replacements tohigh-efficiency models are in fact cost-effective. Therefore, the government can usefinancial rebate or economic education to residential water use conserver to promotewater conservation, and those managements have been practices in many citiesworldwide now.

Over the past 10 years in Beijing, households making replacement decisionsthrough the deliberative rule accounts for 31.6% of total households, and the habitualand random rules accounts for 68.9% of total households. These figures indicate


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that households do not readily adopt more economical and efficient water devices.It means that the government can use pubic awareness campaigns to change thehouseholds decision rules to accelerate traditional water device replacement toincrease the efficiency of residential water use.

Drivers and behaviors of residential water use are quite interesting and compli-cated issues and deserve more research further. This present study has just attemptedto provide a new research framework for chinas residential water demand researchand scratched some initial findings under this framework, much work remainsto be explored, such as taking into account more factors influencing residentialbehaviors in the model, such as householders level of education, seasonal variations,space relations, neighborhood influence, age structure, price expectations, strategicbehavior and social network and interactions, as well as more comprehensive waterdemand policy design and adaptive assessment.

Acknowledgements This work was supported by National Natural Science Foundation of China(70603018) and Hydrological Simulation & Regulation of Watersheds (50721006).

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Chu, Wang-- Agent Based Residential Water Use Behaviour Simulation and Policy Implification- A Case Study in Beijing City