# Nudges in sustainable water management practices: Implementation, key findings and research agenda

Marie-Estelle Binet, Maria Garcia-Valiñas, Sara Suarez-Fernandez

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Marie-Estelle Binet, Maria Garcia-Valiñas, Sara Suarez-Fernandez. Nudges in sustainable water management practices: Implementation, key findings and research agenda. Journal of Cleaner Production, 2024, 456, pp.142282. 10.1016/j.jclepro.2024.142282. halshs-04567861

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# Nudges in sustainable water management practices:
## Implementation, key findings and research agenda

Marie-Estelle Binet, Maria Garcia-Valiñas, Sara Suarez-Fernandez

2023

## Abstract

Nudging has emerged as an alternative policy for managing water demand in the residential sector. Indeed, numerous field studies have been recently published to assess the impact of nudges on water consumption. In parallel, a substantial body of literature has developed in the field of behavioral economics, including laboratory experiments to evaluate the effects of nudge-type or boost treatments on individual behavior. However, the corresponding results are frequently overlooked in field studies. In this context, focusing on the residential water sector, the aim of this survey paper are, first, to review field experiments and address key issues in behavioral economics; and second, to present results obtained from laboratory experiments that could enhance nudging policies. We conclude by discussing additional unexplored areas and their policy implications.

**Acknowledgments:** The authors would like to thank the European Commission for funding in the frame of the collaborative international consortium financed under the 2018 Joint call of the WaterWorks2017 ERA-NET Cofound. This ERA-NET is an integral part of the activities developed by the Water JPI.

**Funding:** This research was funded by the European Commission in the frame of the collaborative international consortium financed under the 2018 Joint call of the WaterWorks2017 ERA-NET Cofound.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Keywords:** nudging, water demand, experimental economics

**JEL codes:** D01, D12, C91, C93, Q25.

## Highlights

* Highlight 1: Nudging has emerged as an alternative policy to manage water demand.

* Highlight 2: The number of field studies aiming to analyze the impact of nudging in managing residential water consumption has increased since the 2010s.

* Highlight 3: Other alternative behavioral strategies, such as boosting and lab experiment, are useful but are relatively unexplored in the water sector.

* Highlight 4: We provide new research perspectives for water conservation based on other fields of behavioral economics.
# 1 Introduction

Water-use management has been considered a key issue to address for a long period of time. Some progress has been made between 2015 and 2022, with the proportion of the world's population with access to safe drinking water increasing from 69% to 73%. However, according to the United Nations (UN, 2021), approximately 2,000 million people live in areas that suffer from water stress including 703 million without basic water services and 653 million without any hand-washing facilities. Furthermore, droughts are increasingly affected by climate change (UN, 2021). Indeed, climate change and rapid population growth will require radical changes in water consumption and production (desalination). Additionally, weak sanitation infrastructures and economic activities are causing strong deterioration in the quality of water bodies (UN, 2021; Reynaud, 2015). Moreover, there is still a high percentage of households with no access to basic water services (UN, 2021). Facing those challenges is an implicit objective in the 2030 UN Agenda for Sustainable Development, with Goal Number 6 aimed at providing clean water and sanitation for everybody and ensuring sustainable management of water resources. Despite not being the largest water use in volume,<fnref n="1" /> the residential water sector is clearly one of the key water sectors to be managed. In this context, policy aimed at eliminating over-consumption or even reducing certain uses previously considered as essential will become more widespread. Thus, residential water has multiple uses (cleaning, food and gardening) that can be influenced by nudges. In contrast, agricultural water use is regulated in different ways, see Li et al. (2024).

In this context, demand-side policies (DSP) have arisen as a significant tool for managing residential water use at the expense of traditional supply-side policies aimed at expanding water storage infrastructures. In this respect, numerous authors have analyzed the impact of several DSPs in domestic water demand (Arbués et al., 2003; Nauges and Whittington, 2010; Grafton et al., 2011). Traditionally, experiments have emerged naturally, i.e., changes in water tariffs (Olivier, 2010; Nataraj and Hanemann, 2011), rationing by means of water cuts (Roibás et al., 2019) or informational campaigns (Gaudin, 2006). However, since the work of Ferraro et al. (2011), *ad hoc* designed experiments have appeared as an alternative empirical approach to assessing water management public policies in the residential sector.

In this respect, information is crucial when making economic decisions; thus, it should be provided in such a way that it generates more rational behavior. Consequently, nudging (Thaler, 2018) could also be considered a significant tool to manage water resources and a useful way to ensure sustainable household consumption (Lehner et al., 2016). *Nudges*, according to Thaler and Sunstein (2008) (pp.6-8), are “any aspect of the choice architecture that alters people’s behavior (...) without forbidding any options or significantly changing their economic incentives (...)”, significantly altering “the behavior of Humans even though it would be ignored by Econs”. These kinds of policies are far from coercive. Such policies are based on information that is intentionally provided to economic agents to motivate behavioral changes but without prohibiting

<footnote n="1">¹According to OECD (2020), agricultural uses of water accounting around 70% of all freshwater withdrawals in the world</footnote>
any option. Congiu and Moscati (2022) discuss the elements of this definition that are still being contested. We focus in this paper on nudges 'in the broad sense', according Sunstein (2018) or Berthet and Ouvrard (2019) i.e. nudges implemented by researchers or more specifically by water utilities to favor sustainable water consumption, but that also allow households to go their own way. We are more specifically interested in pro-social nudges as defined by Congiu and Moscati (2022) i.e. mainly social norm nudges but also interventions to make water tariff scheme more salient or water-saving tips.

Moreover, other alternative behavioral strategies, such as boosting or lab games, have emerged in the field of environmental economics. Both kind of behavioural strategies can be implemented in field or in the laboratory. However, the latest option has been relatively unexplored in the water sector. We thus provide new research perspectives for water conservation based on these other fields of behavioral economics.

Previous noteworthy papers that review the literature on nudges include Congiu and Moscati (2022), which take a general perspective on the subject. Next, Nauges and Whittington (2019), which focuse exclusively on social comparison strategies and provide a benefit-cost analysis. Our review also follows Koop et al. (2019), who analyze Behavioural Influencing Tactics (BITs) in connection with domestic water use. They focus on behavioural psychological mechanisms at work in individuals subjected to various water conservation policies such as nudges. Our paper completes these reviews to include numerous articles published since 2019, but also extends the analysis to boosts and lab games that are not covered in these papers. Our review adds value to the existing literature as it offers guidance to researchers intending to conduct both field and laboratory experiments using nudge techniques in the water domain. This guidance covers all types of nudging strategies that can be implemented, emphasizing the essential considerations in experiment design and the expected potential results of their work.

The outline of the paper is as follows. In the next section, a review of field behavioral experiments mainly in the residential water sector is presented, focusing on several key nudging issues. The third section discusses new approaches of behavioral influencing strategies, based on boosts and lab experiments, to manage residential water demand. Last section concludes with a summary, discussing unexplored fields and some policy implications.

## 2 Field experiments and nudges for residential water users: lessons and main results

The empirical literature on behavioral economics in the residential water sector has increased since the seminal work by Ferraro et al. (2011). At that point in time, several studies have been developed to assess the impacts of nudging in managing residential water consumption. In this section, some controversial issues will be discussed, such as nudging contents, impacts and temporal issues, communication channels and design issues.
## 2.1 Nudging alternatives and their impact on behavior

Although Table A1 in the Appendix displays a brief piece of information on field experiments using nudges, this subsection provides an extended discussion on the types and impacts of nudges on residential water demand.

In recent decades, a wide range of information-based programs have been put into practice to motivate people to use water efficiently. These programs include actions and policies in line with water-saving campaigns, as seen in Russell and Fielding (2010) or Stewart et al. (2010). When designing nudges in this field, the first decision that should be made is the kind of information that we would like to provide. In this respect, several approaches have been identified, ranging from simple technical advice to more sophisticated informational strategies. On the one hand, water-saving tips are frequent in both natural and field experiments (Schultz et al., 2014; Strong and Goemans, 2015; Novak et al., 2018; Brent and Wichman, 2020). On the other hand, social or self-comparisons are a common tool (Ferraro et al., 2011; Fielding et al., 2013; Schultz et al., 2014; Seyranian et al., 2015; Strong and Goemans, 2015; Novak et al., 2018; Otaki et al., 2019; Brent et al., 2020). Moreover, other options, such as moral suasion and/or social/personal identity treatments, are used in some of these experiments (Ferraro and Price, 2013; Seyranian et al., 2015; Rajapaksa et al., 2019).

### 2.1.1 The simplest nudge: giving tips

Providing tips about how to save water is an easy and inexpensive measure to implement but sometimes has no effect on saving water (Ferraro et al., 2011; Schultz et al., 2014; Seyranian et al., 2015); such provision can even have the opposite effect (Liu et al., 2016; Fielding et al., 2013). Tom et al. (2011) analyzed two experiments in which water conservation programs provided useful information to behavioral change to conserve water and found that the program with greater detail in regard to measurement and feedback was more effective. In Brent et al. (2020), households were messaged with information on their consumption and tips to save water, which resulted in savings of approximately 4%.

In addition, by comparing different interventions for impulse water conservation in households, Seyranian et al. (2015) found that giving information alone (i.e., water-saving tips) was the least effective approach. Similarly, Ferraro et al. (2011) found technical advice to have a negligible effect, while including normative messages and social comparisons significantly reduced water consumption, with the social comparison intervention being the only intervention to have a lasting impact. The usefulness of providing tips is often questioned and criticized, and the disadvantages of such tips range from the fact that they might be helpful only in places with water shortages (Inman and Jeffrey, 2006; March et al., 2015) or be effective only for short periods (Saurí, 2013; Katz et al., 2016).
2.1.2 Social comparison tools

*Social comparison tools* (SCT) have also been another interesting strategy used to induce water savings in the residential sector. An excellent review can be found in Nauges and Whittington (2019). As Seyranian et al. (2015) explained, two normative feedback messages can be applied, namely, descriptive social norms, which are descriptions of people's behavior, and injunctive norms, which are prescriptions of desirable household behavior, indicating social approval or disapproval. Nauges and Whittington (2019) also captured that distinction with descriptive norms by showing a statement or a graph where authors display their own household's water consumption and the water consumption of other households versus injunctive norms, thereby providing households with feedback on that comparison.

SCT is usually implemented with respect to the average or median water consumption in the household's neighborhood (Ferraro and Price, 2013; Ferraro and Miranda, 2013; Seyranian et al., 2015; Miranda et al., 2020), but it is also possible to find comparisons with respect to the average consumption in the city (Miranda et al., 2020). However, the latest benchmark indicator is less effective than the neighborhood reference (Miranda et al., 2020), meaning that closer comparisons tend to work better. Brent et al. (2017) used households with similar characteristics in terms of family members and the size of irrigable land as benchmarks. Nevertheless, it is worth noting that this information is not frequently available for water suppliers. Fielding et al. (2013) made a comparison based on the percentage of households showing some water-saving habits, with this strategy being quite effective for water conservation. It is crucial to set the more adequate framework identity as a benchmark indicator (Miranda et al., 2020).

Additionally, comparisons could be made in either absolute terms (showing information on total con-
sumption) or relative terms. In this respect, Brent et al. (2020) assessed both strategies, and developed a
social comparison framed as performance toward a conservation goal in contrast to the traditional compari-
son (in gallons or cubic meters). They found that the treatment using social comparisons in relative terms
generated conservation among several types of households, including low-use customers<fnref n="2" />.

In general, SCT generates positive effects in terms of water conservation (Ferraro et al., 2011; Seyranian et al., 2015; Brent et al., 2017; Jaime Torres and Carlsson, 2016; Brick et al., 2017; Otaki et al., 2017; Miranda et al., 2020; Brent et al., 2020). Nauges and Whittington (2019), in a review of SCT on water consumption, found water savings ranging from 0.6% to 5.6%. In particular, Brent and Ward (2019), who compared the effectiveness of different nudges, found social comparison nudges to be particularly strong, especially when household consumption was above average, compared to the monetary savings related to increasing the marginal price structure. Similarly, Brent et al. (2020) found that implementing SCT in a conservation goal framework reduced water consumption by 38%.

As Chabé-Ferret et al. (2019) explained, one advantage of social comparison approaches is being cost-

<footnote n="2">²Note that traditionally, richer households and high water-use customers have been more reactive toward SCT (Seyranian et al., 2015; Nauges and Whittington, 2019).</footnote>
effective, since they can be applied to a large population with small costs. Nonetheless, Nauges and Whittington (2019) showed that a main disadvantage of this type of strategy, among others, is a moral tax induced by social comparison. Likewise, Chabé-Ferret et al. (2019) analyzed the disadvantage of the boomerang effect, in which some of the low-use consumers change their behavior in the opposite direction as the targeted direction. Moreover, as Chabé-Ferret et al. (2019) explained, the effect of social comparison relies, on the one hand, on the fear of receiving a social sanction (Sunstein, 1996) or on conforming to what is seen as effective behavior (Thøgersen, 2014). On the other hand, SCT increases the visibility of others' actions and thus influences behavior through automatic heuristics, which raise interest and show better response in customers (Liu et al., 2015); however, their effect is sometimes difficult to isolate (Sønderlund et al., 2014).

Another option is performing *historical self-comparisons* (SFCs). This option avoids the moral tax or similar costs related to social comparisons since attention is exclusively focused on the household's historical behavior. Recently, Otaki et al. (2019), who studied how different communicating methods in historical self-comparisons of water consumption, found SFCs to be effective in water savings even regardless of the level of water consumption.

### 2.1.3 Other nudges

A very simple but also useful method to influence behavior is to give *feedback* (FB) about the user's water consumption. Just by informing the household about its detailed levels of water use, water savings have been achieved, as seen in Liu et al. (2017) or Chung (2021). Nevertheless, this strategy has often been used as a complement to more sophisticated nudging strategies, as seen in Agarwal et al. (2022), Jessoe et al. (2021), Otaki et al. (2022) or Torres and Carlsson (2018), for example.

*Moral suasion* (MS) is another alternative used in this field. In general, this tool involves different alternatives to making households feel responsible for the goal of water conservation. Rajapaksa et al. (2019) showed that this kind of message can strongly promote water conservation behaviors. In this respect, Seyranian et al. (2015) distinguished between personal and social identity, with the difference being the type of language used. These authors suggest inclusive language as the preferred language in communications, emphasizing the normative component of ‘who we are’ and ‘how we act’ as a community.

Less frequent is the strategy based on providing feedback on water tariffs and prices in the so-called *financial nudges* (FN). For instance, in Brent and Ward (2019), households were first surveyed to check the degree of information that they have on water consumption and bills; afterward, they were provided with extended and customized information on water bills, prices, and consumption which resulted in reductions in consumption of roughly 6-8%. Interestingly, the authors discovered that low-water-use households increased their water consumption, since they learned that water was cheaper than what they declared in the survey.

Aimed at achieving significant behavioral changes, more sophisticated techniques could be implemented that combine different alternatives at the same time. In this respect, Novak et al. (2018) designed an app/web
supported system to stimulate water savings that combines smart-meter based consumption visualization, water-saving tips and gamified incentives<fnref n="3" />. Once reinforcements are removed, the desired behavior tends to extinguish (Richter et al., 2015), showing the high potential of combining different procedures to get better results than each measure isolated<fnref n="4" />. The effect of the visualizations and tips in Novak et al. (2018) resulted in reductions in water consumption of 27.5% for the intervention group, while the average reduction was 8% for the control group in a trial of three months. Technological issues are closely linked to the information channels detailed in the section below.

## 2.2 Practical issues in nudges implementation

This subsection addresses three relevant aspects of nudging implementation. First, it delves into temporal considerations, including both the frequency of the intervention and the duration of these actions. Second, it provides some insights into the significance of how nudges' information is given to the individual. Lastly, it explores essential visualization features, as prior research has highlighted the substantial variations in the impact of nudges based on these factors.

### 2.2.1 Nudging and temporal issues: persistence and long-run effects

When looking at experimental design, some differences in terms of nudging length have been observed. Some studies have designed experimental frameworks based on a one-time intervention (Ferraro et al., 2011; Ferraro and Price, 2013; Ferraro and Miranda, 2013; Bernedo et al., 2014; Miranda et al., 2020), while others have extended the interventions over a longer period of time (Fielding et al., 2013; Strong and Goemans, 2015; Novak et al., 2018; Otaki et al., 2019; Brent and Wichman, 2020). In this respect, no clear conclusions have been observed.

On one hand, one-time interventions such as that of Ferraro and Price (2013) reduced water use by about 2.5-5%, but reductions substantially decreased four months after the intervention. On the other hand, repeated interventions such as that of Otaki et al. (2019) find self-comparisons promoted continuous water conservation over a five-months period, but Fielding et al. (2013) find water consumption reductions (11.3 L per person per day) returned to preintervention levels after twelve months, concluding that water savings depend on the continued implementation of strategies.

Moreover, a very interesting research question is the temporal duration of nudging effects, what is called *persistence*. Most studies have found impacts in the short run, particularly when social nudges are implemented (Nauges and Whittington, 2019). However, the long-term effects are not clear. In this regard, Beal et al. (2013) showed that tools oriented toward the curtailment of water consumption would generate more reliable water conservation patterns over the longer term.

<footnote n="3">³Gamification consists of presenting the nudge as a game; it has been used to reinforce desired behaviors by competition with neighbors and by distributing virtual rewards for water saving, water efficiency education, profiling and participating in social networks, as seen in Novak et al. (2018).</footnote>

<footnote n="4">⁴A demo can be seen at this link: https://www.youtube.com/watch?v=YLrTW9MVUjk.</footnote>
Seyranian et al. (2015) detected that information given alone (i.e., water-saving tips) are the least effective approach; in contrast, consumers exposed to social norms, social identity or personal identity, reduce their consumption in both the short and long term. In Davies et al. (2014), the long-term effects of giving information through smart metering were assessed; the authors found that the group of participants in their study consumed more than 6% less water, while households in the control group consumed 1% more than before the experiment, even three years after the experiment.

In contrast, Fielding et al. (2013), who conducted a similar experiment to that of Ferraro (2009) or Ferraro et al. (2011), found that the effects of interventions dissipated in the long term in all cases. Similarly, Bernedo et al. (2014), who developed a nudge that combined several strategies (technical information, moral suasion, and social comparisons), observed declining effects on water consumption over time and registered reductions in their effects by almost 50% 12 months later. Miranda et al. (2020) noticed that some effects of nudging could last up to four months, beyond which they decreased. Otaki et al. (2019), who analyzed the impact of a historical self-comparison nudge, did not find any impact on water consumption in the long run. Strong and Goemans (2015), in regard to their technological nudging, observed that water consumption tends to return to previous levels in the long term, similar to the findings of Fraternali et al. (2019).

Finally, the difference in the magnitude and persistence could be due to various reasons. It could depend on whether consumers were initially consuming high volumes of water and were able to significantly reduce the amount, or if, on the contrary, they were consuming little and already have limited room for further reductions. The results also seem to favor a combination of different nudges, implemented over a long period of time.

### 2.2.2 Tools to send information: the role of new technologies

Typically, normative feedback is given via pamphlets or flyers (Schultz et al., 2007). Most field experiments developing nudges include some treatment where information is sent by e-mail (Ferraro and Price, 2013; Brent et al., 2020), even along with water bills (Rajapaksa et al., 2019; Miranda et al., 2020). However, some of these studies use other types of tools, such as e-mail or mobile phone messaging. These alternatives make it easier to send messages at any time. For instance, Otaki et al. (2019) implemented a field experiment in Tokyo in which water droplets with illustrations were sent every two weeks by e-mail. Similarly, Chabé-Ferret et al. (2019) used SMS-based communications to nudge farmers into water saving in Southwest France. Another popular alternative is the use of smart meters in combination with new technologies. The willingness to reduce consumption is a prerequisite; however, as Davies et al. (2014) pointed out, smart metering has the potential to provide knowledge and awareness about consumption to households. This technology is able to give feedback to customers that will empower them to better understand their water consumption and to adapt it according to monetary or environmental benefits. This technology delivers water users near real-time information in much more sophisticated ways (Schultz et al., 2014). According to Strong
and Goemans (2015), water smart readers are monitoring devices that provide households with real-time information on their consumption<fnref n="5" />. Some governments have raised the potential benefits of smart metering (NERA, 2007; PCE, 2009) since they assume that consumers can better manage their resources by making informed decisions.

As Sønderlund et al. (2014) point out, a smart meter can record consumption in real time or near real time and communicate this information to the utility and the consumer, but it is important to examine the effect of such feedback about water consumption to change household behavior<fnref n="6" />. One advantage of such information is providing households with a clearer (compared to tips) sense of which activities consume more water and, therefore, they can save water more effectively (Fielding et al., 2013). In any case, as Strong and Goemans (2015) pointed out, smart meters by themselves do not give households information on water prices or tariff structures unless they are accompanied by some additional communications (Novak et al., 2018), since households are in general highly uninformed about water tariffs (Carter and Milon, 2005; Binet et al., 2014; Garcia-Valiñas et al., 2021). One suitable communication channel for such information is in-home displays. In-home displays (IHDs) or mobile media apps could be powerful tools to reduce water consumption, reinforcing conservation and saving behaviors (Strengers, 2011; Harou et al., 2014).

Fielding et al. (2013) used smart water metering as a tool for behavioral change and as a resource to test the effectiveness of water use management interventions. They demonstrated that these strategies can promote significant water savings. In this line, Strong and Goemans (2015) used smart metering to assess the effect of providing real-time information on households’ water consumption, finding households better informed about pricing systems and consumption more price sensitive. Additionally, when giving feedback on average daily water consumption, possible boomerang effects could occur, as seen in Schultz et al. (2007), where feedback messages had the undesirable effect of increasing consumption on low consumers, as Seyranian et al. (2015) explains. Either way, real-time quantity information affected water consumption, as consumers became aware of prices and reacted more rationally to them (Strong and Goemans, 2014, 2015; Roseta-Palma et al., 2020).

In any case, technology acceptance is crucial. Schultz et al. (2014) conducted a nudging field experiment in San Diego (US); to do so, the authors used a web-based interface and postal mail as communicating channels, and they observed that the former was less effective in reducing water consumption, since only 18% of households checked the website. Therefore, policy design should also be aimed at reinforcing technology acceptance since it is a prerequisite to making technological nudging effective. Moreover, the cost of different alternatives to send the information should be assessed.

<footnote n="5">⁵Water smart-metering enables the gathering of detailed data registered through real or near to real-time monitoring, high resolution interval metering, automated water meter reading and access to data from the internet, as explained in Makki et al. (2015). Check Makki et al. (2015) for a review on different smart meters.</footnote>

<footnote n="6">⁶Check Sønderlund et al. (2014) for a discussion on water consumption feedback using smart meters.</footnote>
### 2.2.3 Visualization and design

Since framing refers to the way (cost vs. saving, present vs. future, loss vs. gains...) the nudge is presented to the public, visualization refers to the creation of images, diagrams, or animations to communicate a message, and is an important item to consider in nudging. Novak et al. (2018) summarized the guidelines for water consumption visualization to support behavioral change according to the study of Micheel et al. (2015).

An adequate design could make a nudge more effective. Thus, the use of colors, symbols or images/pictures could help to reinforce the power of nudges as a tool to manage residential water demand (Otaki et al., 2019). In this respect, there are several studies that have used some interesting design features that are worth discussing.

Inserting different colors in a nudging design is a technique quite frequently used to give behavioral feedback. Traditionally, green or blue colors are associated with “good” behavior, while red is linked to water waste or overconsumption (Miranda et al., 2020). Otaki et al. (2019) showed that the use of graphical water droplet illustrations in different colors led to an increase in the proportion of households showing a slight water decrease with respect to households in the control group.

A few papers have also included some emoticons when providing feedback to households related to their water consumption. Smiling/sad faces/emojis have been used in water-nudging messages, with heterogeneous results. Otaki et al. (2017) found that high-use consumers decreased their water consumption when they received such emoticons, whereas in low-use consumers, water use decreased when they saw that their water consumption had already decreased. Similarly, Novak et al. (2018) included some 'like' and 'dislike' icons to provide customized feedback in their visualization tool7 found that this tool generated significant water conservation behaviors. Actually, Novak et al. (2018) found positive user feedback on both consumption visualizations and tips, leading people to think about water conservation more often and learn about their own consumption (thus being better informed).

Furthermore, the use of drawings or pictures showing water as a scarce resource is also important. For instance, Tiefenbeck et al. (2018) installed a device in the household's shower to display the water and energy consumption, providing households with information on their real-time consumption. The visualization strategy displayed a polar bear animation with an iceberg that progressively shrunk as the quantity of resources consumed increased. This strategy led to a 22% reduction in energy and water consumption for showering.

Studies on nudges in the residential water sector generally are based on field experiments. However, there are other behavioral alternatives, that can enrich water saving policies.

<footnote n="7">7 Note that the web/app-based visualization tool is more complex and was developed by Novak et al. (2018), including further information (consumption, tips, other drawings, etc.).</footnote>
# 3 Behavioral influencing strategies to manage residential water demand: alternative approaches

In this section, some important alternative issues linked to behavioral influencing strategies is introduced. Indeed, boosting and lab experiments emerge as an unexplored strategy in water economics field. Their differences, advantages and disadvantages will be briefly described before outlining possible applications to the residential water sector<fnref n="8" />.

## 3.1 Boosts as an alternative to nudges to change behaviors

Boosts have the same objective as nudges, i.e., correcting behavioral biases and helping individuals overcome their limitations. However, boosts are presented in the literature as an alternative to nudges. Boosts generally take the form of basic and very general training that allows the development of a set of skills that can be used to solve many different choice problems. Recent papers have discussed the relevance of boosts and the conditions for using them instead of nudges (Grune-Yanoff and Hertwig, 2016; Hertwig, 2017; Grune-Yanoff et al., 2018; Grune-Yanoff, 2018).

One can distinguish different sets of boosts, i.e., risk literacy from uncertainty management and motivational boosts (Krawiec et al., 2021; Huang, 2018). Risk literacy provides training in basic skills, such as frequency calculation and interpretation. Boosts of this type are, for example, used to better understand the statistics of disease survival, as seen in Gigerenzer et al. (2008). Uncertainty management boosts propose to breakdown complex decisions into several simple tasks (or step-by-step rules, (Krawiec et al., 2021)). For instance, Milkman et al. (2014) combined effort (gym use) with a reward (downloading and listening to music at the same time). Next, Drexler et al. (2014) taught entrepreneurs to separate family and business accounts to better manage their business. Finally, motivational boosts aim to develop individuals’ skills, such as their own motivation, effort or attention (Tang and Posner, 2019). These are boosts that are often used to increase school performance (Alan et al., 2019; Aronson et al., 2002; Blackwell and Dweck, 2007; Good et al., 2003). Other types of boosts exist, such as those proposed by Kozyreva and Hertwig (2020) or Krawiec et al. (2021) called inoculation strategies, which consist of teaching individuals to identify relevant information or misinformation in a complex framework.

Boosts and nudges do not affect individual behavior in the same way. Grune-Yanoff and Hertwig (2016) have identified situations where the use of boosts is recommended. First, unlike nudges, boosts are effective in the presence of heterogeneous preferences, either time-varying preferences or contracting individual objectives (Hertwig, 2017). Indeed, boosts provide new skills to individuals but leave them free to make their own decisions and promote their autonomy, leading to the acquisition of new or improved

<footnote n="8">⁸However, it is necessary to be careful when designing and conducting these behavioral strategies, since they should comply with some ethical requirements and personal data protection regulation (i.e. informed consent of participants).</footnote>
individual skills and allowing individuals to exercise personal agency (Hertwig, 2017). Boosts thus have potentially more lasting or persistent effects than nudges. Second, according to Grune-Yanoff (2018), nudges and boosts differ in their ethical implications. Indeed, boosts may have less negative welfare effects as they develop autonomy and are less intrusive compared to nudges, which sometimes generate the feeling of being manipulated or loss of autonomy. Conversely, boosts require the participation of individuals and are therefore more transparent than nudges. Boosts thus protect more against manipulation or malevolent governments than nudges (Hertwig, 2017). However, boosts are not always operational when faced with skills that are difficult to develop through basic training or to teach or when faced with individuals who are not motivated.

To our knowledge, there are few experiments that have examined boosts as they relate to ecological issues. DellaValle and Sareen (2020) discussed the relevance of boosts to address fuel poverty. According to these authors, boosts in the form of basic education can enable individuals to identify fuel poverty situations and to find relevant solutions. In a recent paper, Lazaric and Toumi (2022) proposed to assess the implementation of boosts associated with electricity consumption reduction targets. These boosts consisted of very specific technical tips (such as unplugging electrical outlets at night). The final results showed the efficiency of the boosts combined with a low ambitious consumption reduction target (15% consumption reduction). Water consumption is also a privileged field of application for boosts but remains unexplored to the best of our knowledge.

## 3.2 New research perspectives for water conservation using boosts

Firstly, comparing the impact of boosts and nudges can enrich existing field studies about persistence. Most studies have found that nudging leads to significant water consumption reductions in the residential sector, but positive effects dissipate over time. But, this is still an interesting topic that needs to be extended. In our opinion, this issue is clearly connected with boosting. Next, field studies by Ferraro and Price (2013) proposed the use of small technical instructions that explain small water-saving actions. This treatment was not found to be significant, probably because it was not sufficient enough to train individuals in water-saving actions. Full training sessions, in the form of boosts, could be provided at home to teach basic water-saving actions (e.g., turning off the water during certain stages of showering or dishwashing or not watering the garden when it has rained during the week). In this respect, the training framework could also be oriented toward how to estimate the level of one's approximate daily water consumption. This would allow one to evaluate the amount of the water bill and to trigger more virtuous consumption behaviors to reduce consumption when the threshold of the highest bracket is almost reached by the household.

Secondly, boosts can also be used for new research purposes. For example, boosts could be used to improve financial information leading to a better understanding of the complex pricing of tap water (tariff literacy), or to make small everyday actions that reduce water waste more operational (uncertainty management) and to increase the motivation of agents by boosting their ecological concern, particularly in
relation to the issues of water resource scarcity (motivational boost).

Finally, boosts such as nudges can be rolled out in both laboratory and field experiments. However, there are few papers based on laboratory experiments in the area of water (i.e., mentioned as a key word). And, except papers by Binet et al. (2022) or Mayol and Staropoli (2021), devoted to water tariff scheme, most papers, which can be relevant, do not relate to residential water consumption.

## 3.3 New research perspectives for water conservation using lab experiments

Laboratory experiments are complementary to field studies. More specifically, there is a very large body of literature that analyzes the impact of social norms on individual behavior in lab games and addresses the conditions that are favorable to the emergence of new norms.

### 3.3.1 Interest and limitations of laboratory experiments to analyze the impact of nudges on consumer behavior

Laboratory experiments have several advantages compared to field studies. First, they allow the testing of the impact of treatments in a controlled environment and can also include monetary incentives that help participants change their behavior almost instantly. They also allow the researchers to ask the participants about their beliefs and even modify them. Such experiments are therefore a useful and low-cost first step before a field study, where behaviors are influenced by multiple factors and often characterized by inertia.

In addition, many lab experiments can be built on induced values (Smith, 1976; Lusk and Shogren, 2007). In such a setting, we are able to define the individual optimal consumption derived from the induced utility function and budgetary constraints and thus measure the individual's deviation from her optimum, which is impossible to define in practice. Contrary to field studies, laboratory experiments are therefore perfectly suited to evaluate the impact of nudges on consumption choice and to distinguish deviation below from above the optimum, thus addressing overconsumption and rebound effects. Indeed, a recent laboratory experiment by Binet et al. (2022) was conducted based on induced values, suggesting promising research and policy implications. Binet et al. (2022) designed an incentivized lab experiment in which participants selected a consumption level that could be compared to their optimal value. They evaluated the impact of a clarification nudge about the pricing structure on the consumption choices of participants whose monetary gains are maximized at the optimum. These preliminary results show that the impact of the nudge varies according to the level of consumption chosen, compared to the optimal value. If these results are confirmed by future research, we will be able to provide public decision-makers with “tailor-made” nudge campaigns depending on their objectives. Our hypothesis is that nudges encouraging rational choices, facilitating convergence towards the optimal solution, differ from those solely aiming to reduce over-consumption, without convergence.

Another interest of lab experiments lies in the absence of evocative framing for the choice framework. As noted by Alekseev et al. (2017), evocative framing, which relies on a real-life situation but can also
evoke strong emotions, should be used carefully since it can interact with the participants' observable char-
acteristics. However, according to Levitt and List (2007), there are limitations to the interpretation and
generalization of the lab results. Indeed, the literature has shown that being observed exaggerates the im-
portance of prosocial behaviors compared to unsupervised environments. Existing studies also indicate that
participants in lab experiments, who are often students, are unrepresentative of the general population be-
cause they are younger, more educated, and more empowered to understand the tasks they are given. And,
the range of choices that an economic agent faces is very limited in the laboratory setting compared to the
field.

However, as water is a common good, common pool resources (CPR) or public goods (PG) games are
a natural framework which can be used to analyze the impact of social norms on water consumption<fnref n="9" />.

3.3.2 Lab games and social norms

The impact of social norms can be studied in laboratory games such as common pool resources, public goods or dictator games. Generally, lab experiments include more than 200 participants (often students) in groups of 4 or 8 people, making it possible to identify the impact of the choice of other participants on individual behavior from one period to another.

Many studies since (Walker et al., 1990) have observed behaviors predicted by self-interest, which corresponds to over-exploitation issues. In this context, the literature has shown the importance of social interactions among the participants of the group in the laboratory to get closer to the optimum (contribute all endowment to a public good game). Indeed, the studies by (Ostrom et al., 1994) or (Poteete et al., 2009) showed the importance of communication among participants to reach the optimal level of extraction in a repeated CPR game. Communication helps decrease the level of extraction and converge toward the cooperative optimal solution. Public goods (PG) games, in which participants are asked to contribute to a collective account and have incentives to free ride, are also relevant to analyzing the impact of social norms on water overconsumption, see Chaudhuri (2011) for a review of the literature. PG games are generally carried out over several periods. During the first period, participants individually choose the amount of their contribution to the public good (referred to as a project in practice) or to keep the full endowment for herself. The total amount collected is multiplied by a coefficient measuring the marginal payoff of a contribution to the PG, and then shared among all participants, regardless of their initial contribution. In the second period, the contributions of other participants are revealed. The results support compliance with social norms. Indeed, there is a tendency to increase one's contribution when one realizes that it was below the average of the others in the same group.

However, laboratory studies also provide results that could be beneficial for implementing social norm nudges in the residential water sector. Indeed, compliance with social norms is not universal as Fischbacher

<footnote n="9">⁹Standard CPR games in the lab have been developed by Walker et al. (2000); see Anderies et al. (2011) or Ostrom et al. (1994) for a review of the literature.</footnote>
and Gächter (2000) showed in a public good game that there are several participant profiles. They identify each profile by asking participants about their level of conditional contribution relative to the increasing average values of the other members of the group. In their lab experiment, approximately 50% of the subjects were conditional cooperators, i.e., people who are willing to contribute more to a public good the more others contribute. But one-third of the participants remained free riders who contributed 0 regardless of the contribution level of others and did not conform to the descriptive norm. This result also carries implications for future field research and the implementation of social norm-type nudges by water utilities. An initial step would involve identifying free riders as done in the lab. Subsequent field research could explore treatments, aside from social norms, that are likely to reduce free rider consumption. Such research would enable the proposal of “tailor-made” policies to local decision-makers, aiding in their pursuit of sustainable residential water management objectives while considering consumer heterogeneity.

More recently, Kandul and Lanz (2021) also proposed analyzing the impact of out-group behavior on individual choices in a public good game. The authors showed that participants adjust their contribution depending on the out-group information transmitted. For example, participants in one group do change their contribution if they realize that participants in other groups contributed more on average than they did. Finally, Fehr and Gächter (2000) showed that the contributions are increased due to sanctions. The results of these studies can additionally enrich field studies. For example, the in-group versus out-group distinction should lead to distinguishing several social groups in the field, i.e., one close and one more distant. Field studies should also identify conditional cooperators from others when analyzing the impact of nudges on consumption.

Finally, another crucial contribution of lab games is to analyze the role of normative and descriptive beliefs and expectations in social norm adoption.

### 3.3.3 Individual beliefs and social norms

Lab experiments show that individual beliefs and expectations influence the probability of adopting a norm. However, these issues have been neglected in field studies. Indeed, the set of publications by Bicchieri (2017), Bicchieri and Xiao (2009) and Bicchieri (2006) allows us to better understand the mechanisms of the adoption or rejection of a social norm. Laboratory experiments carried out in this field consist of observing the participants behaviors and asking them about their normative and descriptive beliefs. According to Bicchieri (2017), a descriptive belief or expectation is “what we expect others to do”. Thus, individuals who think that others are going to save water will tend to conform with such behavior. A personal normative belief is what “I think should be done”. Normative social expectation is “what we believe others think we ought to do” or what other people approve of (Bicchieri and Xiao, 2009). If one is convinced that society expects one to save water, such a belief will increase your compliance with this standard. These papers show that conditional norm followers, who represent the majority of individuals, conform to social norms if their
normative and descriptive expectations are in the same direction. Bicchieri and Xiao (2009) also focused on the case of divergence among normative and descriptive expectations and revealed that participants follow descriptive but not normative beliefs in that case. They concluded that norm compliance is therefore dominated by descriptive beliefs.

Definitively, the findings of the abovementioned lab experiments should be taken into account to enrich field studies that generally do not deal with beliefs and expectations in terms of social norms. Thus, field research could build on these laboratory findings by identifying conditional cooperators and studying the degree of their social norm compliance based on their empirical and normative beliefs. More precisely, field studies could include questions about consumers' beliefs and their evolving nature, followed by the identification of treatments capable of enhancing compliance with a social norm.

# 4 Policy implications and future research

Nudging and other behavioral strategies have emerged as alternative policy tools for managing water demand in the residential sector. However, policy-makers should consider several information processing pathways to be more successful. First, the starting point could be developing informational campaigns to provide customers with the knowledge to understand the importance of water and to increase their ability to achieve water savings. The messages could be designed as social norms and presented in a manner that would favor people acting in an attempt to conform to these norms. Likewise, the information should be properly framed, highlighting the best option in the present, in a suggestive and intrinsically motivating way; it should preferably be personalized rather than in the form of general messages. Last, campaigns could evoke emotions and use priming by showing images or videos so that the desired behavior appears to the consumers almost automatically and without effort. Taking all these aspects into account, behavioral strategies could perform efficiently in achieving water conservation behaviors.

Both laboratory and field experiments have provided plenty of information to optimally design these behavioral strategies. However, there are still some gaps to be developed in future research. For instance, lab experiments show that individual beliefs and expectations influence the probability of adopting a norm. However, these issues have been neglected in field studies. Thus, field research could build on these laboratory findings by identifying conditional cooperators and studying the degree of their social norm compliance based on their empirical and normative beliefs.

Most studies have found that nudging leads to significant water consumption reductions in the residential sector. Congiu and Moscati (2022) pointed out that in principle, nudges may work in the long run, with their positive effect persisting over time. However, the current literature review has shown that some papers have found that positive effects dissipate over time. No clear profiles have been detected; thus, this is still an interesting topic that needs to be extended. In our opinion, this issue is clearly connected with boosting,
which is a relatively unexplored issue in the residential water sector. Full training or boosts' sessions could be provided at home to teach basic water-saving actions, to improve financial information leading to a better understanding of the complex pricing of tap water (tariff literacy), to make small everyday actions that reduce water waste more operational (uncertainty management) and to increase the motivation of agents to réduce water consumption (motivational boost). Finally, the persistence issue, i.e. the temporal duration of boosts effects should be addressed.

Empirical evidence has shown that households are misinformed about water tariffs and consumption (Binet et al., 2014; Brent and Ward, 2019; Garcia-Valiñas et al., 2021). Nudges and boosts could be used to improve information and awareness levels, guiding households to adopt environmentally-friendly behaviors. Thus, water suppliers could easily include simple messages in the water bill, providing information on tariff schemes and historical or social consumption. This approach could be complemented with the use of technological tools such as websites and apps, which enable the implementation of more sophisticated boost strategies.

Another important issue that has been neglected by previous literature is that most of these studies have focused on comparing water use before and after the nudge but have not assessed the effect of the treatments in a water demand framework, thereby not providing price elasticity estimates. Two significant exceptions are Strong and Goemans (2015) and Brent and Wichman (2020). Strong and Goemans (2015) found that having a smart water device makes households more responsive to price changes and less responsive to changes in income. Brent and Wichman (2020) measured the extent to which the nudge affected price sensitivity and found limited evidence of an economically meaningful relationship between prices and nudges.

Newty, there is still room to analyze the effects of nudges on social welfare. The closest approach is the study by Nauges and Whittington (2019), who provide estimates on the cost and benefits of social norms information treatments, comparing this tool with the use of prices. They found that the net benefits of increasing water tariffs will be higher than the net benefits from the use of social comparison tools. Note that this paper is focusing exclusively on social norms treatments (there are other informational approaches that have been implemented) and based on general assumptions and aggregated data. As far as we know, no paper based on microdata sets from field or lab, and demand function estimation has assessed the impact of nudges on social welfare. This is an unexplored and promising field that will also keep researchers' attention in the next years.

Finally, our paper explores various research avenues to deepen our understanding of behavioral strategies in the field of residential water management. Firstly, we propose constructing boosts based on the existing literature and the specific characteristics of water usage. These boosts might then be compared to a set of nudges, enabling us to identify the most effective and persistent treatment, contingent upon the context and the type of consumer. Additionally, the impact of different combinations of these instruments could be tested. Next, we also advocate for deploying in-field the designs implemented in the laboratory experiments described
in this paper. This research strategy would not only enrich the field analyses but also assess the external validity of the lab results to determine their relevance. Thirdly, in future field experiments employing social norm nudges, utilizing the experimental protocols implemented in the lab to identify participant profiles (conditional cooperators or free riders) would enable the proposal of more targeted nudging campaigns. Finally, field surveys should inquire participants' beliefs and analyze their impact on the adoption of a social norm, as well as on residential water consumption behavior. All in all, this literature review proposes a research agenda that will facilitate advancements in academic research and the implementation of more effective and persistent nudging policies.
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<table><caption>Table 1: Nudging strategies review (I)</caption><thead><tr><th>Authors</th><th>Experimental setting</th><th>Intervention mode</th><th>HHs and treatments</th><th>Methods</th><th>Behavioral strategies</th><th>Consumption reductions</th><th>Long-run analysis</th></tr></thead><tbody><tr><td>Willis et al. (2010)</td><td>Australia, Gold Coast; Winter 2009</td><td>IHD</td><td>151 HHs; Continuous</td><td>Sample t-tests</td><td>FB / other</td><td>3%</td><td>-</td></tr><tr><td>Tom et al. (2011)</td><td>United States, California; WWHC March 2000 - 2008 DLS January 2001</td><td>Postal mail / phone</td><td>200 HHs; One-time</td><td>Descriptive statistics</td><td>FB / tips</td><td>-</td><td>-</td></tr><tr><td>Ferraro and Price (2013)</td><td>Atlanta, Georgia; May 2007 - April 2007</td><td>Postal mail</td><td>106,668 HHs; Two-times</td><td>ANOVA / ATE</td><td>tips / SCT</td><td>4.8%</td><td>Dissipated</td></tr><tr><td>Ferraro and Miranda (2013)</td><td>Atlanta, Georgia; May 2007 - April 2007</td><td>Postal mail</td><td>106,668 HHs; Two-times</td><td>CATE / Quantile TE</td><td>tips / SCT / MS</td><td>-</td><td>-</td></tr><tr><td>Fielding et al. (2013)</td><td>Australia, South East Queensland; September 2010 - January 2011</td><td>Postal mail</td><td>221 HHs; Four-times</td><td>Random intercept model</td><td>FB / tips / SCT</td><td>11.3 LPD</td><td>Dissipated</td></tr><tr><td>Bernedo et al. (2014)</td><td>Atlanta; May 2007</td><td>Postal mail</td><td>83,300 HHs; One-time</td><td>ATE</td><td>tips / SCT / SFC / MS</td><td>2.7-4.8%</td><td>Persistent</td></tr><tr><td>Smith and Visser (2014)</td><td>South-Africa, Cape Town; November 2012 - December 2012</td><td>Postal mail</td><td>398,423 HHs; One-time</td><td>OLS / ATE</td><td>FB / tips / SCT / SFC</td><td>1%</td><td>-</td></tr><tr><td>Schultz et al. (2014)</td><td>United States, San Diego; July 2009 - August 2010</td><td>Postal mail / web</td><td>301 HHs; One-time</td><td>ANOVA</td><td>FB / SCT / SFC</td><td>16-26%</td><td>-</td></tr><tr><td>Datta et al. (2015)</td><td>Costa Rica, Belen; July 2014</td><td>Postal mail</td><td>5,626 HHs; One-time</td><td>Descriptive statistics / DID</td><td>SCT / GS</td><td>3.4-5.6%</td><td>-</td></tr><tr><td>Brent et al. (2015)</td><td>United States, California; September 2011 - ongoing</td><td>Postal mail / email</td><td>7,361 HHs; One-time</td><td>ATE / DID</td><td>tips / SCT</td><td>4.9-5.1%</td><td>-</td></tr><tr><td>Seyranian et al. (2015)</td><td>United States, Los Angeles; July - August</td><td>Postal mail</td><td>374 HHs; One-time</td><td>OLS / ANOVA</td><td>tips / SCT / SFC / other</td><td>Boomerang effect</td><td>Dissipated</td></tr><tr><td>Liu et al. (2016)</td><td>Australia, New South Wales; May 2013 - September 2013</td><td>Postal mail</td><td>68 HHs; Two-times</td><td>ANOVA</td><td>FB</td><td>8%</td><td>-</td></tr><tr><td>Jaime Torres and Carlsson (2016)</td><td>Colombia, Antioquia; January 2013 - December 2013</td><td>Postal mail</td><td>1,857 HHs; Twelve-times</td><td>ATE</td><td>FB / tips / SCT</td><td>6.8%</td><td>Persistent</td></tr></tbody></table>

**Abbreviations:** HHs = households, LPD = liters per day, IHD = In home display or mobile media apps, ANOVA = Analysis Of Variance, ATE = Average Treatment Effects, LATE = Local Average Treatment Effects, ATT = Average Treatment on the Treated, CATE = Conditional Average Treatment Effects, DID = Differences in Differences, OLS = Ordinary Least Squares, SEM = Structural Equation Model, GLMM = Generalized Linear Mixed Models, SCT = Social Comparison Tools, SFC = Self-Comparison, PG = Public Good, GT = Goal Setting, MS = Moral Suasion, FB = Feedback.
<table><caption>Table 2: Nudging strategies review (II)</caption><thead><tr><th>Authors</th><th>Experimental setting</th><th>Intervention mode</th><th>HHs and treatments</th><th>Methods</th><th>Behavioral strategies</th><th>Consumption reductions</th><th>Long-run analysis</th></tr></thead><tbody><tr><td>Nayar and Kanaka (2017)</td><td>India, Chennai; -</td><td>Postal mail / phone</td><td>74 HHs; One-time</td><td>Descriptive statistics</td><td>tips / SCT</td><td>5.0%</td><td>-</td></tr><tr><td>Brick et al. (2017)</td><td>Cape Town; November 2015 - April 2016</td><td>Postal mail</td><td>366,048 HHs; Six-times</td><td>ATE</td><td>tips / SCT / FB / PG</td><td>0.6%-1.3%</td><td>-</td></tr><tr><td>Quesnel and Ajami (2017)</td><td>United States, California; 2005 - 2015</td><td>Media coverage</td><td>1,200 HHs; Continuous</td><td>Regression</td><td>other</td><td>11-18%</td><td>-</td></tr><tr><td>Liu et al. (2017)</td><td>Australia, New South Wales; May 2013 - September 2013</td><td>Postal mail / web</td><td>188 HHs; Two-times / continuous</td><td>Descriptive statistics</td><td>FB</td><td>4.2-8.0%</td><td>-</td></tr><tr><td>Otaki et al. (2017)</td><td>Japan, Tokyo; September 2017 - February 2018</td><td>Fax</td><td>246 HHs; Twelve-times</td><td>Variability rate</td><td>FB / SCT</td><td>Reduced</td><td>-</td></tr><tr><td>Wichman (2017)</td><td>United States, North Carolina; February 2009 - June 2014</td><td>Postal mail</td><td>59,000 bills; One-time / continuous</td><td>ATE</td><td>other</td><td>Increase +3.55%</td><td>-</td></tr><tr><td>Bhanot (2017)</td><td>United States, California; November 2012, January 2013, March 2013 and May 2013</td><td>Postal mail / email</td><td>3,896 HHs; Four-times</td><td>ATE</td><td>SCT / other</td><td>Increase +8.22-17.85 GPD</td><td>Persistent</td></tr><tr><td>Katz et al. (2018)</td><td>Israel; May 2013 - June 2013</td><td>Postal mail</td><td>1,399 HHs; Three-times</td><td>DID</td><td>SCT</td><td>6.1-7.6%</td><td>-</td></tr><tr><td>Torres and Carlsson (2018)</td><td>Colombia, Antioquia; January 2013 - January 2014</td><td>Postal mail</td><td>1,311 HHs; One-time</td><td>ATE</td><td>FB / SCT</td><td>6.8%</td><td>Persistent</td></tr><tr><td>Novak et al. (2018)</td><td>Switzerland; July 2015 - February 2016</td><td>IHD / web</td><td>35 HHs; Continuous</td><td>Descriptive statistics</td><td>FB / tips / SCT / GT / other</td><td>27.5%</td><td>-</td></tr><tr><td>Brent and Ward (2019)</td><td>Australia; Summer 2015</td><td>Postal mail</td><td>1,630 HHs; One-time</td><td>LATE, ATT, DID</td><td>FN</td><td>6-8%</td><td>-</td></tr><tr><td>Miranda et al. (2020)</td><td>Costa Rica, Belén; July 2014</td><td>Postal mail</td><td>5,626 HHs; One-time</td><td>DID</td><td>tips / SCT / GS</td><td>4.8-4.9%</td><td>-</td></tr><tr><td>Rajapaksa et al. (2019)</td><td>Australia, Brisbane; 2010 and 2011</td><td>Postal mail</td><td>753 HHs; One-time</td><td>SEM</td><td>tips / MS</td><td>Reduced</td><td>-</td></tr><tr><td>Otaki et al. (2019)</td><td>Japan, Tokyo; September 2017 - February 2018</td><td>Email</td><td>479 HHs; Twelve-times</td><td>Sample t-tests</td><td>FB / SCT / SFC</td><td>Reduced</td><td>Dissipated</td></tr></tbody></table>

**Abbreviations:** HHs = households, LPD = liters per day, IHD = In home display or mobile media apps, ANOVA = Analysis Of Variance, ATE = Average Treatment Effects, LATE = Local Average Treatment Effects, ATT = Average Treatment on the Treated, CATE = Conditional Average Treatment Effects, DID = Differences in Differences, OLS = Ordinary Least Squares, SEM = Structural Equation Model, GLMM = Generalized Linear Mixed Models, SCT = Social Comparison Tools, SFC = Self-Comparison, PG = Public Good, GT = Goal Setting, MS = Moral Suasion, FB = Feedback.
<table><caption>Table 3: Nudging strategies review (III)</caption><thead><tr><th>Authors</th><th>Experimental setting</th><th>Intervention mode</th><th>HHs and treatments</th><th>Methods</th><th>Behavioral strategies</th><th>Consumption reductions</th><th>Long-run analysis</th></tr></thead><tbody><tr><td>Goette et al. (2019)</td><td>Singapore; December 2016 - January 2017</td><td>Leaflets</td><td>899 HHs; Six-times</td><td>ATE</td><td>tips / SCT / MS / FN</td><td>3.9-4.1 LPD</td><td>-</td></tr><tr><td>Brent et al. (2020)</td><td>United States, Nevada; July 2015 - August 2015</td><td>Postal mail</td><td>43,052 HHs; Two-times</td><td>ATE</td><td>FB / tips / SCT</td><td>1.5%</td><td>-</td></tr><tr><td>Brent and Wichman (2020)</td><td>United States, California; November - December 2014, March - April 2015</td><td>IHD / web</td><td>45,223 HHs; One-time</td><td>DID</td><td>FB / tips / SCT</td><td>3.5-4.5%</td><td>-</td></tr><tr><td>Jessoe et al. (2021)</td><td>United States, California; March 2015 - May 2016</td><td>Postal mail / email</td><td>18,500 HHs; Six-times</td><td>ATE</td><td>FB / tips / SCT</td><td>4-5%</td><td>Dissipated</td></tr><tr><td>Bhanot (2021)</td><td>United States, San Francisco; December 2014 - January 2015</td><td>Email</td><td>45,866 HHs; Six/seven-times</td><td>ATE</td><td>FB / SCT</td><td>2.5-3.5%</td><td>-</td></tr><tr><td>Chung (2021)</td><td>United States, Minnesota; 2015 - 2018</td><td>Web</td><td>6,332 HHs; Continuous</td><td>DID / ATE</td><td>FB</td><td>2.1-4.4%</td><td>-</td></tr><tr><td>Daminato et al. (2021)</td><td>Spain, Tenerife; 2010 - 2019</td><td>Web</td><td>51,674 HHs; Continuous</td><td>Regression / DID</td><td>FB / other</td><td>2%</td><td>-</td></tr><tr><td>Kazukauskas et al. (2021)</td><td>Sweden; March 2016 - March 2017</td><td>IHD</td><td>525 HHs; Continuous</td><td>ATE</td><td>FB / SCT</td><td>0%</td><td>-</td></tr><tr><td>Agarwal et al. (2022)</td><td>Singapore; August 2016 - December 2016</td><td>Postal mail</td><td>1.5 million HHs; One-time</td><td>DID</td><td>FB / SCT / other</td><td>3.5%</td><td>Persistent</td></tr><tr><td>Otaki et al. (2022)</td><td>Japan, Tokyo; March 2019 - September 2019</td><td>Email</td><td>783 HHs; Fifteen-times</td><td>GLMM / ANOVA</td><td>FB / SCT / PG</td><td>4.6%</td><td>Persistent</td></tr></tbody></table>

**Abbreviations:** HHs = households, LPD = liters per day, IHD = In home display or mobile media apps, ANOVA = Analysis Of Variance, ATE = Average Treatment Effects, LATE = Local Average Treatment Effects, ATT = Average Treatment on the Treated, CATE = Conditional Average Treatment Effects, DID = Differences in Differences, OLS = Ordinary Least Squares, SEM = Structural Equation Model, GLMM = Generalized Linear Mixed Models, SCT = Social Comparison Tools, SFC = Self-Comparison, PG = Public Good, GT = Goal Setting, MS = Moral Suasion, FB = Feedback.