. This paper reports the results of a study of water usage behaviour at a primary school in Stellenbosch in the period le ading up to the 2018 We stern Cape drought. The aim of the study was to describe a school’s water usage patterns and quantify the usage reduction resulting from technology-driven interventions promoting behavioural change . Three interventions were implemented using smart meter dat a: posters, playing cards and daily presentations. Only 58% of water usage was during school hours, with the daily peak between 10:00 and 11:00. After- hour usage was more variable t han during school hours. The information-s haring interventions reduced the school’s water usage by 44% when compared to the usage of a school in the same town where the interventions were not implemented. Keywords: School water bill, smart meter , technological intervention, be haviour change Introduction Water scarcity has become a global crisis and is getting worse. The main contributors to this problem are rapid population growth, incr eased urbanisation and climate change. In South Africa the water crisis is particularly severe. Increased urbanisation, coupled with ongoing development, is increasing the demand for water beyond the supply capabilities. Colvin an d Muruven (2017) note that 98% of the country’s total water supply is already allocated and deman d has increased significantly, the major 1 Department of Electrical and El ectronic Engineering, Stellenbo sch University, Stellenbosch, 7600, South Africa 2 Department of Electrical and E lectronic Engineering, Stellenbo sch University, Stellenbosch, 7600, South Africa mjbooysen@sun.ac.za sectors responsible for this be ing agriculture (63%), municipal ities (26%) and industry (11%). They note that the recent drought in the country’s Western Cape has had adverse effects on the agricultural sector, widening the trade defic it due to losses in agricultura l exports. Cape Town, where the water shortage problems were particularly evident, was subject to severe water restrictions from 1 June 2017 (B rick et al., 2018, Booysen et a l., 2019). These restrictions, which applied not only to domestic user s but also to public spaces su ch as sports ground, shopping centres and schools, prohibited resident s from using municipal water fo r irrigation, topping up swimming pools, and so on, and set a low daily usage limit per person. T he restrictions were a clear indication of the supply shortage, thus shifting the focus from supply man agement to demand management. Water-saving solutions that focus on demand management have bec ome a priority, but the residential sector, as one of the biggest water users, has been more in the spotlight than schools. However, schools also use a lot of water, and as they bring together use rs from many different sectors, they provide an excellent place to spread the message of water conse rvation to a wide variety of households. Efforts to change water use behaviour at schools co uld thus help reduce water usage more generally. Water utilities run campaigns to inform and educate users on th e importance of water conservation (Anderson and Nagarajan, 2016). In South Africa several social campaigns have been promoting water efficient technologies and behavioural change to improve water usage habits. The impact of most of these campaigns has thus far not been quantifiable, but technology is favoured as a possible solution (Nel et al. 2014). The study described in this paper offers an example of the beha vioural change that a school campaign can bring about. It also shows that the resulting reduction in water usage can be quantified. The ultimate aim is to help water providers improve their water cam paigns and social marketing designs and make their water bills user-friendly. Purpose of the study The study’s aim was to explore water demand management solution s. Our hypothesis was that our sample school would be more inclined to conserve water if it wa s given regular and understandable reports of its water usage. We investigated the school’s curren t water behaviour patterns, implemented three behavioural change interventions to reduce it s water usage, and quantified the results. We compared our sample school with another similar sch ool in the area using the difference- in-difference method. In the course of the study we also investigated various behaviour theories, to see how they might be applie d to water conservation. Two related studies We reviewed two studies involvi ng behavioural interventions. Th e first, Fielding et al. (2012), is of particular relevance to our own study, as it used data from a s mart water meter. It was a randomised controlled trial, conducted in S outh East Queensland, Australia , using three different water usage reduction interventions over five months with 221 participating households. The first was an informed intervention that gave the households general information on how to save wat er in the bathroom, laundry and kitchen and by fixing leaks. The second was a descriptive norm intervention that gave the households water-saving tips from ‘low water use households ’. The third was a water end-use feedback intervention, in which a smart water meter was installed at all the households to collect their water usage data and the households were given tailored informa t i o n i n t h e f o r m o f a p i e c h a r t showing the water usage of each appliance in their home. Water usage data was also collected from a control group of households, used as a baseline, that were no t given the information. Over the study period the reductions, compared with the control group, were si gnificant: 7053 litres less used per household for the informed group, 1794 litres used less per hou sehold for the descriptive norm group, and 5720 litres per household for the water end-use feedback gr oup. The descriptive norm group’s reduction was smallest because it had more high water users tha n the other two groups. Towards the end of the study a sizeable number of the smart water meters of all groups were damaged by flooding. The quality of the data worsened over time as more water meters failed. The final estimates of water reduction were hence not as reliable as at first and the study had to stop earlier than planned. It was also noted that the reductions were not long term – usage by th ese groups returned to pre-intervention levels six to ten months a fter the interventions. Although these behavioural change interventions successfully re duced water usage, the study had some limitations. The most interesting of these, from the point of view of the present study, was the lag between water use and feedback in the water end-use feedbac k group. The smart meters used did not have real-time feedback capabilities and many of them were no longer functional towards the end of the study. Users in this gr oup only received feedback monthl y. Another limitation was that the participants who agreed to take part in the study may already h ave been engaged in the issues of water conservation previously, which made it unclear how much of thei r behavioural change was due to the interventions. The second case study, (Zoratto et al., 2015), a randomised con trolled trial conducted in Belen, Costa Rica, in 2015, investigated the effectiveness of three simple w ater usage reduction interventions. The study hypothesised that interventio ns that allowed users to ben chmark their usage against their peers would be useful in reducing water usage. Three interventions we re designed using insights from a target population focus group: a neighbourhood comparison that compared the household’s water usage to the average house hold in their neighbourhood, a city comparison that compared the household’s usage to the average household in the city of Belen , and a plan making intervention that drew the household’s attention to their usage and helped them q uantify their daily water use reductions, set goals and find ways to achieve these goals. Sig nificant reductions were observed compared with the previous year. The neighbourhood comparison a nd the plan making interventions had larger reductions than the city comparison intervention. Th e average monthly water usage reduction over two months was 0.98 to 1.47 kL per household for the neighbourhood comparison intervention and 0.9 to 1.49 kL for the plan making interventio n (between 3.7 % and 5.6 % of the control group’s consump tion for the same period). The plan making intervention suffered from a limitation caused by the water bills, on which it relied. The bills were often delayed, making it difficult for the house holds to track their reduction goals in a timely manner, and the information in the bills was not necessa rily a true reflection of the household’s water usage as it of ten contained errors caused by manually rec ording the water usage. Behaviour theories Here we summarise three theories of human behaviour: planned be haviour, social practice and diffusion of innovation, as background to our study. The popula r and widely applied theory of planned behaviour has been used to predict and expl ain 20 to 30% of variance in b ehaviour brought about through interventions. It can be used to identify influen ces that could be targeted to bring about change, but it is not practical ly useful for designing an inter vention that will re sult in a specific behaviour change (Morris et al. 2012). Social practice theory, which sees people’s choices and attitudes as often secondary to contextual factors, making them ‘carriers’ rather than initiators of behaviours, has been used to understand sustainable behaviours in the fields of energy, transport and waste. In focusing on social practice rather than individual b ehaviour, it helps to explain why certain behaviours are adopted or disreg arded and what role technology plays in changing or evolving behaviours. Diffusion of innovation theory focuses on innovation as an agent of behaviour change. I t states that behaviour will change rapidly if people perceive an innovation as better than the existing options yet still consistent wit h current values, experiences a nd needs. The theory takes into account mass media and interpersonal channels for creating awareness an d persuading people to change their behaviour. The theory notes that the process requires time. It starts with identification of the problem or need, followed by five steps : knowledge, persuasion, decisio n, implementation and finally confirmation. For further information on the use of these theor ies, see Mosler (2012) and Morris et al. (2012) . For our study, we found all three of these theories useful for ideas on how to use information communication and technology (ICT) to affect and quantify behav iour change. Difference in differences method The difference in difference (DiD) method is used to estimate c ausal effect. It is often used in behavioural economics and the social sciences to estimate the e ffects of interventions and policy changes. It is most suitable for studies where it is not possib le to control for confounding variables, but pre-treatment information is known. It compares four groups of objects, three of which are not affected by treatment: before treatment for the control and tre atment groups, and after or during treatment for the control and treatment groups. The study is di vided into pre- and post-treatment periods. The research population is divided into a control and a treatment group, with the intervention being applied only to the tre atment group (Lechner 2011). Methods We installed smart water meters at the school, to monitor the w ater usage in real time, and used the data to describe the usage patte rns and to quantify the usage r eductions. The behavioural study involved three interventions, each designed for a particular po pulation of the school: the management, the teachers and the pupils. All the interventions, however, we re designed with the same purpose: to affect behaviour by educating the target population about the i mportance of water and encouraging behaviours that would re duce water wastage. Smart water meter The smart water meter used for the study was designed and built by a spin-off company from the Stellenbosch University3 (Roux 2018). It consists of a standard water meter and a unit, which with the aid of a sensor transmits pulses to a micro-controller. The controller counts the pulses and transmits the cumulative volumes wirelessly to a server for pre sentation and analysis. The volume of water used is then calculated from the data sent to the server. One pulse is equivalent to 10 litres. The data can be viewed on a website, in a graphic form that can be downloaded. Setting and study period The study was conducted in Stellenbosch, in South Africa’s West ern Cape Province. The town has 27 primary and secondary schools in total, some with hostels an d some without. Our sample school had 89 staff members and over 500 pupils. There are four staff houses on the school premises, each with three to four occupants. The school has a large swimming p ool and a large field. Borehole water is used for the pool and the field, but the rest of the school’ s water needs are met with municipal water. The study period was divided into three stages: pre-int ervention, to collect baseline data; intervention; and post-intervention, to analyse the data. The smart meters were installe d on 19 May 2017. Data were colle cted from 20 May 2017 to 17 June 2017 (the intervention period). M onthly municipal bills were us ed for the previous date ranges back to December of 2016, and the same m unicipal meter was used for to generate the pulses. The results excluded data from the middle of April to the middle of May 201 7 as this was the school holiday. The school’s existing water usag e data for three weeks prior to the start of the study was used to established baseline data. The t ypical usage patterns were extr acted for the weekdays during the calendar month of June. 3 Bridgiot: www.bridgiot.co.za Data The water usage data is sent to the server every minute with a time stamp. These data made it possible to calculate the water usage patterns: start and end time, volu me, flow rate, and duration of each water event. We could then calculate the daily flow, the number of ev ents with a volume above a specific amount, the cumulative daily volume and so on. School hours are between 07:00 and 14:00 daily. During this tim e, most occupants of the staff houses are at the school, so usage from these houses was negligible. F or most of the school hours, pupils and teachers spend their time inside classrooms. During this time, the main contributors to water use are urinals, toilets and water used in the kitchen. During break ti mes the classrooms are empty, and this hour accounts for the largest wat er use. Extracurricular activi ties are between 14: 00 and 17:00 every day. This period has minimal water usage as most pupils and tea chers will have left or are taking part in extramural activities. This time also includes builders’ usa ge as the builders only left at 16:00. (For the duration of the study, new cl assrooms were being added to t he school.) The key to calculating losses accurately was being able to isol ate the school’s actual use from the school’s total daily usage. Actual usage means water used by th e school alone, excluding the staff houses’ usage and losses, for exa mple from leaks or wastage. Th e school’s total usage is made up of four parts: actual use, losses , staff house use and builders’ u se. The water use between 00:00 and 05:00, durin g which time no wat er should flow, was considered to be losses. These losses would include continuously running or p eriodically flushing urinals. With water restrictions in place in Stellenbosch at the time of the study, watering gardens with potable water was prohibited, so there were no automatic sprinkler syst ems using municipal water. We calculated the hourly losses be tween 00:00 and 05:00 and extrap olated them to the entire day, to estimate the daily losses. We compared the data from our sample school with data for the s ame period from a secondary school from across the road in Stellenbosch where no intervention was done. Behavioural interventions After the smart meters had been installed, the staff attended a presentation in which we explained the study’s purpose and raised awareness of the need for water cons ervation. We showed them the school’s water usage figures for the previous three days, point ed out the heavy losses, explained ways to save water, and informed them about the three behavioural in terventions that would follow. These took three forms: posters , playing cards and daily reports . The posters were aimed at the pupils – the hypothesis being that if they were given information about the school’s water usage, they would be more inclined to save water. The poster was kept as simple as p ossible, showing only the volume for each day, compared with the previous week (Figure 1). It was p ut up in all the boys’ and girls’ washrooms every morning at nine o’clock, an hour before the ten o’clock break. The playing cards were the Top Trumps kind (Figure 2). Each student was given 15 cards, each card showing a daily usage, water losses due to leaks and the total volume for the d ay. The values on the cards were actual water usage readings obtained du ring the intervention period. T he card game worked as follows: two players each presented a card and the player whose total volume was lower won both cards. The aim was to collect as many cards as possible. This game engaged the pupils and got them used to the range of values of the daily usage, which would help them make sense of the information on the posters. The playing cards inte rvention thus goes hand-in-hand with the poster intervention. The daily report was in the form of a simple bar graph showing the school’s dai ly water usage, divided into actual use and losses. The graph also showed the cost of each d ay’s usage in rands, thus adding a financial incentive to change water use behaviour. The report w as shown in the staffroom during break time using a data projector. Results and Discussion Figure 3 shows the distributi on of the school’s average hourly water usage between 07:00 and 17:00 for weekdays (Mondays to Fridays) for the calendar month of Jun e 2017. The distribution of the medians is a positively skewed, with the mass of the distributi on being concentrated on the left, with a peak at 1.4 kL/hr between 09:00 and 10:00. This peak is after the start of classes and before the morning tea break, when children and teachers are occupied in c lasses. This seems to suggest that the peak is a result of water use in the kitchen, which would inclu de washing the cups and plates after the pre-class staff meeting and filling the urns in preparation for the tea break; general cleaning, such as mopping the passages and clean ing the toilets; gardening; an d water used for construction. It transpired that despite the restrictions, minimal gardening act ivities were still using municipal water. Due to the construction of at the school, the usage pattern may not be representative for all schools. However, in a subsequent study that included 355 schools, we ob served that some level of construction is a normal occurrence at schools. Although the median peak was fairly early, more variance was ev ident between 10:00 and 11:00 and 15:00 and 16:00. The high variance of these two timeslots was p robably because of the building work. Builders use water at irregular times, for example for mixing c ement, and do not work when it is raining, which leads to dis continuous usage patterns. Figure 4 shows the distribution of the average daily water usag e by the various contributors to the schools total usage for the month of June 2017. The total use d uring school hours (7.5 kL/day) makes up only 58% of the total usage (12.9 kL/day). The losses period has a high variance, demonstrating the large effect of small behaviour changes, such as closing a tap rather than l eaving it running overnight. The after-hours and hou ses’ contributions are very s imilar, with similar variances. Most of the large water usage happe ns around 10:00, the period befor e, during and after the tea break. During break, all the teachers and pupils are not in the classr ooms and most of the water activities involve eating and drinking and using the facilities. Our focus was on reducing losses to reduce water use. On a few occasions, the staff closed all the taps througho ut the school at the end of the day to locate and resolve leaks, for example a tap left open or stuck toilet flusher would manifest as losses, potentially masking real pipe leaks. These periods thus had the lowest values and contributed to the high variance seen in the losses periods. The other time period s had much lower variance, which is what one would expect with consis tent water use behaviour. Figure 5 shows weekly water usa ge data for the three weeks befo re the study, intervention, the three weeks of the intervention, and th e week after the intervention, week four. The weeks with the lowest usage were weeks three and four, when the intervention had sett led. Weeks three and four used 9 and 8.6 kL less than the baseline week respectively. The daily tota l decreased from 20 to 14.8 kL for second week of the intervention. This value decreased further t o 11.4 kL in the last week of the intervention. Table 1 shows the difference-in-difference results. In comparis on to the baseline period and baseline school, the interventions successfully reduced water use. The e ffect of the interventions, compared to a control school was a 44% reduction in water use. These results show that behaviour al change by intervention is e ffective over short to medium periods but that long-term effectiveness may require continued interven tion or different strategies. Deteriorating results post-intervention were also experienced b y other studies that used randomised controlled trials, such as Fiel ding et al. (2012) and Ferraro & Price (2013). Conclusion With freshwater sources becoming scarcer because of over popula tion, urbanisation and pollution, managing water demand has become crucial. Schools are a major c ontributor to municipal water use, hence managing this demand could significantly reduce total usa ge. However, schools are rarely the focus of conservation campaigns. This study investigated the no rmal water behaviour of a school and showed that behavioura l interventions are effective in inspirin g water conservation behaviour. Behavioural economics were applied to quantify the effect of in terventions on behavioural change. By applying these interventions , the study was able to reduce a primary school’s daily water use by 44%, when using a control school as baseline. The largest part of the reduction resulted from a change in managing losses. The study is the first to provide insight i nto the water behaviour patterns of a primary school, highlighting the times of use and categorising usage and evaluating the impact of technological interventions.