SUSTAINABLE WATER MANAGEMENT–MODELLING ACCEPTABILITY FOR DECISION SUPPORT: A METHODOLOGY

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Sustainable water management – modelling acceptability for decision support: a

methodology

Ward, S., Abdelmeguid, H., Farmani, R., Butler, D. and Memon, F. A.

Abstract

Previous research into the acceptability of S/IWM measures has focused on

characterising acceptability, which can differ if referring to water-saving micro-

components (appliances) or alternative water technologies (e.g. rainwater harvesting).

In contrast, limited research has focused on how to represent and integrate

acceptability within decision support tools (DST). This paper describes the

development of a methodology to represent the socio-cultural acceptability of micro-

components and alternative technologies within the ‘Acceptability Function’ (AF)

model, which links to the ‘Urban Water Optioneering Tool’ (UWOT?) DST.

Previously, acceptability was represented within UWOT? by a qualitative indicator

between 0 (unacceptable) and 5 (acceptable) selected by the DST user. In order to

provide a more comprehensive representation of acceptability, the model

distinguishes between different components of acceptability determined using

methods such as structural equation modelling and regression analysis. For alternative

water technologies components of acceptability include subjective norms (social

pressure to accept), fairness, health risks, system risks, trust and emotions.

Incorporation of these components within the AF model requires a number of

functionalities, such as weightings and scales and the development of a suitable

graphical user interface (GUI). In essence, this paper describes a methodology for

capturing ‘soft’ information in an essentially ‘hard’ modelling environment.

1. Introduction

Finding enough water and sanitation resources to meet people’s needs is still one of

the greatest challenges faced by politicians, engineers and planners. Climate change

will require water (and wastewater) infrastructure to be more resilient and adaptable,

as river and groundwater levels become increasingly harder to predict and demands

placed on them increase (Butler and Ward, In Press). Pressures of water availability

and flood risk, due to population growth and development encroaching into

vulnerable areas, mean conventional systems are being pushed to their limits. The

time has come for a shift in thinking – towards that of sustainable/integrated water

management (S/IWM). S/IWM involves thinking about meeting the water needs of

societies, the environment and the economy and considers water demand management

measures, as well as the potential interactions between water supply, wastewater and

stormwater. Alternative water technologies representing such interactions include

rainwater harvesting (RWH) and greywater reuse (GWR) systems (small scale),

stormwater and effluent reuse (large scale) and sustainable drainage systems (SuDS).

However, the uptake of these technologies is, at present, impeded by a number of

barriers (Ward, 2010).

One of the perceived main barriers to the implementation of alternative water

technologies is their socio-cultural acceptability by a range of stakeholders (Jeffrey

and Jefferson, 2003). These can include water service providers (WSPs), planning and

building control professionals and system/water resource end users. The topic of

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public acceptance of water recycling is subject to a great deal of controversy and

rhetoric and it is widely used as a way to explain the lack of progress on recycling

(Stenekes et al., 2006). Over the last 10 years the volume of research undertaken into

community and individual attitudes towards S/IWM techniques has increased

substantially. However, Stenekes et al (2006) notes that most of the available social

research focuses on effluent reuse. This may be due to the fact it is generally

centralised (rather than decentralised) and consequently is an alternative option the

majority of water service providers are open to consider. WSPs are likely to view

large-scale, centralised recycling systems as less complicated (for both them and their

customers), because of the fragmented and contradictory arrangements relating to

other techniques. Additionally, WSPs may not easily invest in alternatives if they

detract from their market share or asset base.

It has been identified that public opposition has the potential to make or break

sustainable water management projects (Jeffrey and Jefferson, 2003; Friedler et al.,

2006). Consequently, the acceptability of an alternative water supply technology is

vital for its successful implementation. Bruvold et al. (1981) caution that projects

deemed appropriate by engineers and other technical personnel, may not be similarly

accepted by the community who may not share their opinion. Often techniques or

end-uses resulting in financial gain and minimal contact with the resulting water are

favoured (Friedler et al., 2006). However, the issue of acceptability is more complex

and cannot be fully explained by merely gain and contact. Analysing and

characterising acceptability depends very much on the framing of the problem.

Stenekes et al. (2006) assert that lack of acceptance is often (wrongly) attributed to

public misunderstandings based on ignorance due to a lack of information. This is

evidenced by Friedler et al. (2006), who highlight that a number of large-scale potable

wastewater reuse (WWR) schemes in the USA, were completed but did not operate,

due to public opposition resulting from insufficient and poorly managed information

dissemination schemes.

However, studies are divided on the value of information in predicting acceptability.

Stenekes et al. (2006) and Nancarrow et al. (2009), identified that knowledge (linked

to information) was not a predictor of acceptability of potable WWR and information

and education campaigns only had limited success in increasing acceptability. In

contrast, Domenech and Sauri (2010) and Hurlimann (2007a, b) for water reuse and

Lienert and Larsen (2009) for urine separation, identified that information positively

influenced acceptability (by reducing perception of risk) and that information or

communication should originate from expert, unbiased, non-sensationalising sources.

Studies are also divided over the importance of demographic, socio-economic and

biogeographical factors. Hurlimann (2007a) found that there was a correlation

between education and gender and perception of risk affecting the acceptability of

recycled water. On the contrary, two studies (Friedler et al., 2006; Lienert and Larsen,

2009) found that socio-economic and biogeographical factors were not good

predictors of acceptability. Stenekes et al. (2006) cautions that information and socio-

demographic ‘problem frames’ oversimplifies acceptance and does not account for

factors attached to the importance and variability of everyday water practices

(contextual circumstances, shared meanings attached to water).

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Hartling (2001) asserts that being transparent, revealing all facts, using non-

professional terminology and encouraging participation in the decision making

process, will lead to greater acceptability of S/IWM schemes. Building on this,

Stenekes et al. (2006) recognise that values rather than facts underpin people’s

acceptance perspectives, as responsibility for a resource (water) required for their

everyday life is designated to an external organisation (WSPs) perceived to be beyond

their influence. Consequently, people’s beliefs about the trust, credibility, stability,

familiarity, transparency, accountability and legitimacy of organisations ‘in charge’ of

the water resource (potable or non-potable) are crucial parts of acceptability (Stenekes

et al., 2006; Menegaki et al, 2007). Furthermore, Nancarrow et al. (2009) identified

that crucial factors affecting acceptability were trust and emotion. Engaging in open

dialogue was more affective in increasing acceptability, as it resulted in increased

trust, which decreased negative perceptions of health and system risks. Emotion is

often overlooked, however, it is becoming increasingly recognised that practice-based

behaviour (such as water use) is deeply rooted in the emotions of everyday activities

(Shove, 2003).

Despite this plethora of studies characterising acceptability, limited research has

considered how to distil findings into an easily useable format for planners, water

managers and engineers. For example, limited focus has been placed on incorporating

complex social phenomena into decision support tools (DST). Mitchell et al. (2007)

highlight that issues not well covered by DSTs include social assessment and that

such issues are becoming increasingly important as S/IWM analysis increases in

complexity.

A summary of S/IWM-related DSTs is given in Table 1 (this is not an exhaustive list),

along with an indication of whether each includes representation of social phenomena

or more specifically, acceptability.

Table 1 A summary of selected decision support tools and their consideration of

social phenomena

DST Social Phenomena

Included?

Acceptability

Included?

Reference

Krakatoa Yes Unknown* Stewardson et al. (1995)

DRHM No No Dixon et al. (1999)

RWIN (KOSIM) No No Hermann & Schmida (1999)

PURRS No No Coombes & Kuczera (2001)

MUSIC No No CRCCH (2005)

Aquacycle No No Mitchell (2005)

RainCycle No No Roebuck & Ashley (2007)

UWOT? Yes Yes Makropoulos et al. (2008) * Krakatoa includes a tutorial on social issues, but is not a publically available tool so inclusivity of

acceptability could not be confirmed

As Table 1 reveals, the ‘Urban Water Optioneering Tool’ (UWOT?), developed at the

University of Exeter, is one of a very limited number of DSTs that incorporates

representation of social aspects of S/IWM measures. However, within UWOT? the

acceptability of a range of S/IWM measures is, at present, represented by a single

qualitative indicator between 0 (unacceptable) and 5 (acceptable) selected by the DST

user. This simplistic representation does not adequately characterise the complex

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array of concepts that constitute the construct termed ‘acceptability’. In order to

incorporate a more comprehensive representation of acceptability within the UWOT?

DST, this paper describes the development of a methodology and model that defines

and represents the different components of acceptability, grounded in a thorough

review of previous acceptability research. Consequently it is possible to link the

model with UWOT? to incorporate acceptability within S/IWM decision making.

2. Methodology

An in-depth review of literature relating to all aspects of perceptions of, attitudes

towards and acceptability of a number of different S/IWM technologies was

undertaken. As previously identified, although there is a growing body of research on

the acceptability of different S/IWM measures, only a small selection of the possible

technologies are represented. Consequently, the authors acknowledge that the

methodology is naturally biased towards those technologies represented in the

available acceptability research. The literature review findings were triangulated in

order to develop a methodology for more comprehensively representing acceptability

within an acceptability model. The development of the methodology was divided into

three stages:

i. Characterising acceptability – determining components;

ii. Representing acceptability – ratings, scales and weightings;

iii. Interfacing acceptability – incorporating components into a graphical

user interface (GUI).

The following sections describe these stages in detail.

2.1. Characterising Acceptability

A number of studies were identified that had used a range of techniques to

characterise and model ‘acceptability’. These included inferential statistics

(Hurlimann, 2007a, b), structural equation modelling (Porter et al., 2005; Porter et al.,

2007; Nancarrow et al., 2009), regression analysis (Lienert and Larsen, 2009) and

multivariate analysis (Domenech and Sauri, 2010). Acceptability is a complex

construct and can be inferred from a number of other proxy terms, which were also

used to create as comprehensive a picture of ‘acceptability’ as possible. The factors

identified as being proxy terms for ‘acceptability’ of S/IWM measures were:

Support Ease of use Satisfaction

Preference Trust Willingness (to accept)

Tolerate Receptivity Attitude

Belief Performance Behaviour

Care was taken to distinguish between perceptions of alternatives and acceptability of

alternatives. People may have certain perceptions, but these do not necessarily linearly

relate to their acceptability – other factors are important, such as necessity and

accessibility (Dolnicar and Hurlimannm, 2009). Additionally, it was identified that the

meaning of acceptability is different between groups e.g. end users (the public) versus

technology selectors (planners, water managers) and from community to community,

as well as being different for water-saving micro-components and alternative water

sources. This stems from the fact that micro-components predominantly use potable

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water, which is already ‘acceptable’, whereas alternatives may involve the use non-

potable water, which may be less ‘acceptable’. Issues such as trust, fairness and risk

are likely to be less crucial in acceptability of micro-components than alternatives as,

to a certain extent; individuals/communities already ‘trust’ the water used within the

former, but not necessarily the latter. For micro-components, value, quality and

performance may have a greater influence on acceptability (Hills and Birks, 2004).

Consequently, the acceptability of alternative water sources and water-saving micro-

components is considered separately.

2.1.1. Acceptability of Alternative Water Sources

A small core of factors emerged from the literature review as being crucial for

determining the acceptability of alternative water sources. These were most clearly

delineated by Hurlimann (2007a), Porter et al. (2007) and Nancarrow et al. (2009) as:

Trust Fairness Emotion*1

Subjective norms Health risk#1

System risk^1

Other (qualitative and quantitative) studies used terminology that resonated with or

could be deemed a proxy of these factors and these are summarised in Table 2 under

the ‘core’ component headings. Those factors that could not be categorised under one

of the core headings are shown under the ‘other’ heading.

Table 2 Summary of Components of Acceptability for Alternative Water Sources (compiled from Friedler et al., 2006; Stenekes et al., 2006; Hurlimann, 2007a, b; Porter et al.,

2005; Porter et al., 2007; Menegaki et al., 2007; Nancarrow et al., 2009; Dolnicar and

Hurlimann, 2009; Domenech and Sauri, 2010; Ward, 2010; Islam et al., 2010)

Trust Fairness Emotion Subjective Norm Other

Operational

regime

Cost

Financial gain

Belief

Attitude

Environmental

concern

Knowledge

Information

Integrity Price Motivation Awareness

Credibility Necessity Meaning Health Risk Communication

Stability Availability Value Quality Context

Familiarity Water scarcity Children Source Fact

Transparency Reliance on imports Use Prior experience

Commitment

(organisational)

Contact Socio-economics

Demographics

Accountability System Risk Biogeographics

Legitimacy Performance

Dialogue Satisfaction

Support Proximity

Failure

Hurlimann (2007b) explains the interaction of the components along the lines of:

quality leads to value, value leads to satisfaction, risk leads to dissatisfaction, trust

leads to low risk, trust leads to satisfaction, communication leads to trust, quality

1 *proxy terms included: mood; ^proxy terms included: performance, maintenance, comfort, design;

#proxy terms included: hygiene

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leads to fairness, trust leads to fairness, fairness leads to satisfaction, fairness leads to

value, environmental concern leads to value.

2.1.2. Acceptability of Water Saving Micro-Components

As mentioned in Section 2.1, value, quality and performance may have a greater

influence on acceptability of water saving micro-components. The full range of

acceptability components identified from previous research is summarised in Table 3,

using some of the headings derived in Section 2.1.1 with the addition of other

headings, where appropriate. As can be seen from Table 3, issues of trust were not

represented in any of the studies reviewed and aspects of performance dominated the

descriptors identified.

Table 3 Components of Acceptability for Water Saving Micro-Components (compiled from Wynia et al., 1993; Hills and Birks, 2004; Millan et al., 2007; Lienert and

Larsen, 2009)

Performance Emotion Health Risk Subjective Norm Other

Environmental

benefit

Willingness

to have

Hygiene

Cleanliness

Environmental concern Signage

Information

Water saving Popularity

Design Mood System Risk Fairness (Value)

Styling Preference Failure Savings

Ease of use Cost

Convenience

Comfort

Similarly to alternative water sources, differences in the acceptability of the

performance of micro-components in the home or work place was identified, as well

as differences in acceptability between the end user (higher flow rate more

acceptable) and the DST user (may assume a lower flow rate is more acceptable)

(Fidar, 2011). Consequently, how the DST user interprets the factors of acceptability

on behalf of the end user will affect the overall acceptability rating attributed to a

particular technology.

2.2. Representing Acceptability

DSTs can take either a multi-attribute decision making (MADM) or multi-objective

decision making (MODM) approach. The MADM involves a limited number of

alternatives, whereas the MODM approach aims to find the best choice to satisfy a

decision maker’s preference from a continuous list (Pohekar and Ramachandran,

2004). The number of solutions for problems associated with assessment and selection

is usually limited and can therefore be addressed with MADM (Xu and Yang, 2001).

Incorporating acceptability within a DST could be defined as a MADM problem, as

both assessment and selection activities are undertaken: an option is assessed for its

acceptability and then selected based on its satisfaction of the decision maker’s

acceptability preferences.

Additionally, multi-attribute value theory (MAVT) and multi-attribute utility theory

(MAUT) use mathematical functions with which decision-makers are able to

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construct their preferences; the a priori preference weighted sum method being the

most commonly used approach (Fidar, 2011). Preferences may be articulated in terms

of goals or the relative importance of different objectives (Fidar, 2011). Most

preference methods incorporate parameters, such as coefficients, exponents or

constraints that reflect a decision maker‘s preferences. Therefore, using a MADM

approach with an a priori weighted sum method, the core components of acceptability

for alternative water sources and micro-components identified in Sections 2.1.1. and

2.1.2. were used as a set of acceptability attributes for incorporation within the model

to yield an overall ‘acceptability function’ for a number of technology options.

Subsequently, the model is referred to as the Acceptability Function (AF) model.

2.2.1. Representing Acceptability Components

Balancing the representation of ‘expert’ and community knowledge within a DST

requires a dialogue to be established and maintained between stakeholders, so that

public knowledge can find a place in water service planning. Therefore a DST should

not fully replace the thought-processes of the user, rather it should assist them in

utilising local information to make judgements in the decision making process.

In order to represent this interaction within the AF model, the DST user inputs certain

acceptability component values, based on their knowledge of community attitudes for

their area. DST users are thereby incentivised to maintain an interest in the

views/perceptions of their area. Judgements on the value to input could be estimated

from newspaper articles related to (supporting or rejecting) water schemes/initiatives

or popular pole surveys that are conducted from time to time. Therefore although the

representation of acceptance within the DST is largely numerical, the DST user still

has to ‘know’ their community and make inferences/judgements about their values.

Such knowledge can only be gained through consultation with the water end-user, in

order to make informed judgements on the rating used for each of the acceptability

components. Consequently, each acceptability component is represented in the AF

model in the form of a scale associated with a question aimed at determining the value

of the core acceptability components. For alternative water sources these are:

TRUST:

For trust this would not necessarily need to be determined for each alternative water

technology, but for another acceptability component it might.

EMOTION:

-1 +1 0

Weak Strong Neutral

-1 +1 0

No Trust Full Trust Neutral

Q) How would you rate trust towards water management organisations in your area?

Q) How strong are feelings towards water issues in your area?

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FAIRNESS:

SUBJECTIVE NORM:

The latter two components are technology specific and would need to be rated for

each technology option being considered by planners or water managers.

2.2.2. Representing the Proportionality of Acceptability Components

Friedler et al. (2006) used weighted grades to investigate the public acceptance of 21

WWR options. A weighted grade above 56% was deemed to indicate acceptance,

below 44% was deemed to indicate rejection (opposition) and between 44 and 56%

was deemed as neither accepting nor rejecting WWR. Furthermore, in constructing an

acceptability attitudinal model (Figure 1) using structural equation modelling and

intended behaviour as a proxy of acceptability, Porter et al. (2007) and Nancarrow et

al. (2009) estimated the proportionality of representation of the components, which

are summarised in Table 4.

Consequently, they observed that:

Figure 1 Acceptability Attitudinal Model (modified from Nancarrow et al., 2009)

A = S + F + T + E

Where: T = HR + SR

HR = T + SR

SR = T + E

Acceptability (A) Health Risk (HR)

Subjective

Norm (S)

Fairness (F)

Trust (T)

Emotion (E)

System Risk (SR)

-1 +1 0

None A lot Neutral

-1 +1 0

Not Fair Fair Neutral

Q) How fair do you think use of X technology would be for your area?

Q) How much pressure/necessity is there for using X technology in your area?

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Table 4 Proportionality of the Acceptability Model Components

Model 1

(Porter et

al., 2007)

Model 2

(Nancarrow

et al., 2009)

Average Normalised to

100%

Subjective Norm (S) 30 22 26 25

Fairness (F) 18 26 22 21

Trust (T) 20 28 24 23

Emotion (E) 37 27 32 31

Total 104 100

Health Risk (HR) % T 49 SR 34 T 13 SR 86 T 31 SR 60

System Risk (SR) % T 58 E 22 T 37 E 55 T 47.5 E 38.5

Using these proportionalities in conjunction with the scale ratings for each

acceptability component, an overall ‘acceptability function’ is determined as being a

number between -1 and +1, with a number tending towards +1 indicating a positive

acceptability (support for the selected option).

2.3. Interfacing Acceptability

Utilising the scales determined in section 2.2.1. and the proportions identified in

section 2.2.2. permitted the estimation of quantitative values for each of the

acceptability components and consequently estimation of the acceptability function.

In order to automate the estimation of the acceptability function, a simple spreadsheet-

based tool was developed to act as an interface between the DST user and the

UWOT? DST. The GUI of the tool is illustrated in Figure 2.

Figure 2 GUI of the simple 'Acceptability' tool

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2.3.1. Summary of the Acceptability Modelling Methodology

A flow chart summarising the components of the acceptability function modelling

methodology is illustrated in Figure 3.

Figure 3 Flow chart summarising the acceptability modelling methodology

3. Testing the Acceptability Methodology

In order to illustrate the calculation of the acceptability function from values for the

acceptability components, this section applies the methodology to a comparison of the

acceptability of rainwater harvesting (RWH) and greywater reuse (GWR) systems for

a hypothetical geographical location. Table 5 summarises some hypothetical example

responses (of a Local Authority spatial planner) to the scales for the four questions

relating to each acceptability component determined in section 2.2.1.

Table 5 Example responses to the four acceptability component questions

Question/Acceptability Component Example Response

RWH GWR

Trust (Q1) 0.8 0.8

Emotion (Q2) 0.8 0.4

Fairness (Q3) 0.8 0.4

Subjective Norm (Q4) 0.6 0.2

Next, each of the responses is multiplied by the corresponding component

proportionality outlined in Table 4 and the acceptability function (Af) calculated,

which is summarised in

Characterise Acceptability

Identify Components

Develop Response Scales

Estimate Proportionality

Develop Weights

Implement within DST

GUI

Characterising

Representing

Interfacing

Acceptability Function

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Table 6. As can be seen in Table 6, the Af of RWH is greater than GWR, indicating

that, for the hypothetical geographical location, RWH is more acceptable than GWR,

based on the judgements made by the DST user.

Table 6 Hypothetical example of calculating the acceptability function (Af) for

RWH and GWR

RWH GWR

SR = (0.8 x 0.475)+(0.8 x 0.385)

= 0.38+0.308 = 0.688

SR = (0.8 x 0.475)+(0.4 x 0.385)

= 0.38+0.154 = 0.534

HR = (0.8 x 0.31)+(0.6 x 0.688)

= 0.248+0.688 = 0.408

HR = (0.8 x 0.31)+(0.6 x 0.534)

= 0.248+0.320 = 0.568

T = 0.68+0.408 = 1.088 T = 0.568+0.534 = 1.102

T = 1.088 x 0.23 = 0.25 T = 1.102 x 0.23 = 0.254

E = 0.8 x 0.31 = 0.248 E = 0.4 x 0.31 = 0.124

F = 0.8 x 0.21 = 0.168 F = 0.4 x 0.21 = 0.084

S = 0.6 x 0.25 = 0.15 S = 0.2 x 0.25 = 0.05

Af = 0.25+0.248+0.168+0.15 Af = 0.254+0.124+0.084+0.05

Af 0.8 0.5

4. Conclusion

Finding enough water and sanitation resources to meet people’s needs is still one of

the greatest challenges faced by politicians, engineers and planners. Consequently,

new approaches to water management recommend the utilisation of sustainable and

integrated techniques, such as rainwater harvesting and greywater reuse. However, the

uptake of these technologies is, at present, impeded by a number of barriers. One of

the main perceived barriers is their socio-cultural acceptability by a range of

stakeholders. Despite the plethora of studies characterising acceptability, limited

research has considered how to distil findings into an easily useable format for

planners, water managers and engineers.

By undertaking a thorough literature review of acceptability research, the main

components of acceptability for alternative water systems and water saving

microcomponents have been established. Acceptability components for alternative

water systems include subjective norms, trust, emotion, system risk, health risk and

fairness. Using these components as a framework, a methodology and model to

estimate the ‘acceptability function’ of alternative water systems and water saving

microcomponents has been developed. A simple spreadsheet model has been

constructed, allowing the interface of the model with the Urban Water Optioneering

Tool (UWOT?), a decision support tool developed at the University of Exeter. Further

research is required to examine the efficacy of the developed methodology and model

in the ‘real world’ i.e. in use with Local Authority planners and water managers.

Acknowledgements

This work was carried out as part of the ‘Regional Visions of Integrated Sustainable

Infrastructure optimised for Neighbourhoods’ (ReVISIONS) project

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(http://www.regionalvisions.ac.uk) funded under the UK Engineering and Physical

Science Research Council’s ‘Sustainable Urban Environment’ Programme.

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