Mandated on-site wastewater reuse in San Francisco: the role of distributive fairness for policy acceptance

Water scarcity poses a critical challenge in many urban settings globally. With increasing population growth and intensified climate change, the strain on water resources becomes increasingly apparent^1,2, necessitating innovative solutions to manage water systems effectively. One such solution gaining traction is on-site wastewater treatment for non-potable reuse^3,4. For this approach, all or part of the wastewater generated within a single building or a small cluster of buildings is treated on-site. The water can then be reused on the buildings’ premises, for example, for irrigation, clothes washing, or toilet flushing. Such on-site wastewater treatment and reuse (hereinafter referred to as on-site reuse) can reduce the demand on a city’s drinking water resources⁵.

To encourage the uptake of systems for on-site wastewater treatment and reuse (on-site systems) in water-stressed regions, some local governments, among them San Francisco in the United States, Bengaluru in India, and Sant Cugat del Vallès in Spain, have enacted policies mandating the installation and use of on-site systems in specific (mostly large) buildings^6,7,8. Yet, for an effective and lasting implementation of such policies and environmental policies in general, they need to be seen as legitimate and accepted by the public⁹. A lack of public acceptance (i.e. a positive valuation of a policy by the general public^10,11,12) can initiate public opposition that may eventually lead to the failure of political measures. In fact, the implementation of several wastewater reuse projects in the United States and Australia failed as a result of local public protests^{13,14,15,16,17} (these projects aimed for treatment in centralised, not on-site treatment plants).

While a lack of public acceptance of environmental policies may have various causes (for overviews, see refs. ^9,18), one core reason has been shown to be a perceived lack of the policies’ distributive fairness (i.e. the fairness of the distribution of costs, risks, and benefits within or between different groups or entities of society^19,20, also often referred to as ‘equity’^21,22)^9,23. A meta-analysis on determinants of policy acceptance and support of climate-change related policies revealed that perceived distributive fairness was the strongest determinant, stronger than, for example, perceived personal fairness, perceived policy effectiveness, or trust in political and implementing institutions⁹. Several experimental studies could further show that a perceived lack of distributive fairness consistently was the strongest barrier of public support for a range of environmental policies, stronger than a perceived lack of procedural fairness or extensive costs²³. Also in the specific case of wastewater reuse, public protests have been associated to distributive fairness. Public opposition against centralised wastewater reuse schemes in San Diego has in part been attributed to a perceived injustice, particularly against low-income groups^13,24. Further, a study by Nancarrow et al.²⁵ showed that a planned scheme for centralised reuse in Australia was less acceptable the more it was perceived as unfair to the local population as well as to a variety of groups of society. These fairness ratings were, however, averaged, forming an overarching measure of perceived fairness for societal groups.

For policies mandating on-site (in contrast to centralised) wastewater reuse, distributive fairness may be particularly relevant²⁶. Such policies usually extend to large buildings only to increase cost-effectiveness and environmental impact, which threatens an equal distribution of the policies’ costs, risks, and benefits among society. Specifically, the costs and risks primarily fall upon the owners or residents of buildings subject to these regulations, while it is likely that the whole local population benefits from the conservation and diversification of public water resources. Alternatively, depending on the quality of the city’s water supply, it is also possible that residents living in buildings with on-site reuse have a better water quality or increased quantity of water than all other citizens. In both cases, the resulting unequal distribution of costs, risks, and benefits sets the fair provision of water services within a society at risk. Specifically, the principle of equality may be at risk as the policy will not equally impact all individuals^27,28. In fact, a recent study with residents of Bengaluru, India, has shown that if people covered by the local policy mandating on-site reuse are perceived to have a worse outcome compared with people not covered, the policy is perceived as less fair and acceptable²⁹.

While research has shown that people aim for distributive fairness of policies, the policy does not necessarily need to have an equal outcome (i.e. an equal distribution of related costs, risks, and benefits) for all individuals in order to be perceived as fair by the public. People give more weight to the outcome for some groups or entities of society than for others. For example, Schuitema et al.³⁰ found that perceived fairness and acceptance of a policy mandating transport pricing was more strongly predicted by considerations of the policy’s outcome for the environment and future generations than by considerations of people’s personal outcome.

Other studies have shown that special attention is also paid to outcomes for members of society that are marginalised and in need. This is in line with the fairness principle of ‘need’^27,31, denoting that an allocation of outcomes based on people’s need is perceived as fair. For instance, the above-mentioned study on transport pricing³⁰ showed that the policy measures were perceived as less fair and less acceptable when low-income groups were burdened. A review on carbon pricing policies³² and a recent study³³ reported lower acceptance the more low-income groups were burdened, and a study by Andor et al.³⁴ indicated that acceptance of a levy on renewable energies for electricity costumers was higher when low-income groups were exempt from it. Further, a study by Pitkänen et al.³⁵ indicated that also other groups of society that are in need are considered for fairness evaluations: Participants perceived a policy on personal carbon trading as unfair if it burdened people with high mobility needs, such as families with children or rural households.

To our knowledge, only one study²⁹ investigated which different groups or entities of society are considered for perceived fairness and acceptance of a policy mandating on-site wastewater reuse. This study, conducted in India, showed that among participants who are covered by the policy, perceived fairness and acceptance of the policy was predicted by considerations of the policy’s outcome for the environment and future generations but not by their personal outcome, which is in line with Schuitema et al.³⁰. However, in contrast to the studies described above^{30,32,33,34,35}, Kollmann et al.²⁹ found that the policy’s outcomes for low-income groups or those vulnerable to water insecurity were not considered when assessing fairness and acceptability. Yet, the outcome for people covered by the policy explained fairness and, only among people not covered by the policy, also acceptance.

It is unclear whether the findings by Kollmann et al.²⁹ and their deviation from previous findings regarding groups in need indicate a general pattern for policies that mandate on-site wastewater reuse or that cover only part of the population or whether they are context-specific to India. There are arguments in favour of both interpretations. What could suggest a general pattern, is that in the context of policies mandating the use of a technology (compared with pricing policies) the outcome for low-income groups may be less relevant, as such policies involve not only monetary but also behavioural costs. Moreover, for policies covering only part of the population, only the outcome for those covered may be considered, while additional disparities caused for people in need may be considered less. What could, however, support a context-specific interpretation, is the fact that India is a lower middle-income country³⁶, while the studies found that vulnerable groups of society being considered for fairness and acceptance ratings^{30,32,33,34,35} were all conducted in European high-income countries. Residents of a lower middle-income country might be less able to ‘afford’ considering the implications for people with low incomes than residents of high-income countries. In addition, also the cultural context in India may have had an influence. The Indian caste system, though abolished, still influences the perception of social inequalities, potentially making them more accepted³⁷. As it is unclear why groups in need were not considered by participants in the study by Kollmann et al.²⁹, it is important to investigate in a context that differs in many aspects with India if and to what extent perceived fairness for different groups of society, including those in need, can explain acceptance of a policy mandating on-site reuse for part of the population.

In addition, it is equally relevant to better understand why a policy is perceived as (un)fair for the specific groups of society in the first place. Understanding which specific implications of a policy lead people to evaluate it as (un)fair for a certain group can help designing future policies in a way that they are perceived as fair and acceptable by the public. Previous research on environmental policies showed that they are considered fairer if they consist of pull mechanisms that incentivise or reward pro-environmental behaviour rather than of push mechanisms disincentivising certain behaviour or imposing costs^30,38. Moreover, policies encouraging upward social mobility³⁹ or redistributing their revenues to environmental projects⁴⁰ are considered fairer. Yet, only a limited number of studies investigated which aspects of a policy design can explain its perceived fairness, and for the context of wastewater reuse, it has, to our knowledge, not been investigated at all. It is, for example, possible that financial implications of a policy are more or less important for fairness ratings than non-financial ones. Further, it is also possible that perceived benefits of a policy are considered more than perceived costs and risks. Kollmann et al.⁴¹ showed that perceived benefits of on-site systems are more important for the acceptance of on-site reuse than their costs and risks. It is possible that this pattern also emerges for the perceived fairness of a policy mandating on-site reuse.

Although (on-site) wastewater reuse has become increasingly important around the world^4,42 and with it policies regulating its implementation⁶, so far only one study²⁹ has investigated whether fairness of such a policy explains policy acceptance and which groups of society are considered for fairness and acceptability assessments. In addition, it has not at all been investigated which specific implications of such a policy explain whether the policy is perceived as fair or unfair for different groups.

Moreover, to our knowledge, neither with regard to such policies nor with regard to environmental policies in general have previous studies investigated whether and how strongly the perceived fairness for several groups of society explains policy acceptance. Previous studies have investigated whether the outcome of a policy (i.e. its costs, risks, and benefits) for different groups of society explain the policy’s perceived overall fairness, but without assessing individual fairness ratings for each group^29,30,35. Studies that have included individual fairness ratings did so either only with regard to people themselves and for others in general, but not for more groups of society⁴³ or created mean scores after the assessment resulting again in a measure of overall fairness²⁵. While an overall fairness measure is relevant for the investigation of many research questions, it is too broad for analysing how the perceived fairness for different groups can individually explain policy acceptance and why a policy is rated as (un)fair for each group.

In the present study, we therefore investigated (1) whether and to what extent the perceived fairness of the policy for five different societal groups and one entity (the environment) can explain acceptance of the policy, and (2) whether and to what extent different implications of the policy for each group and the environment explain perceived fairness of the policy for the specific group or entity. In additional exploratory analyses, we investigated whether perceived financial implications are more or less important for fairness ratings than non-financial ones and whether perceived benefits are more or less important than perceived costs and risks.

These relationships were investigated in San Francisco where a policy is in place mandating the installation and use of on-site systems for certain building types and thus only for part of the population. The following five groups of society and one entity were investigated in the study: residents of buildings with mandated on-site systems, owners of buildings with mandated on-site systems, the city of San Francisco and its population, low-income residents of San Francisco, future generations living in San Francisco, and the local and regional environment.

Policy implications explaining perceived fairness for different societal groups and the environment

We first assessed which policy implications explain perceived fairness of the policy for different societal groups or entities. All results of the regression analyses as well as the means and standard deviations are displayed in Tables 1–2. The bivariate correlations of all variables and the multicollinearity diagnostics for the regression analyses are presented in the Supplementary Information (Supplementary Tables 1–9).

Of the eight policy implications for residents, four explained perceived fairness of the policy for this group of society. Two of these implications were on average perceived as negative by participants, namely that residents have to bear monetary costs related to the on-site systems and that residents might face reduced maintenance and water quality (compared with users of the centralised systems) as on-site systems are not managed by the city’s utility. The more these two implications were perceived as negative, the more the policy was perceived as unfair for residents. The other two policy implications explaining perceived fairness for residents were on average perceived as positive. These were that residents are likely to have a positive and sustainable image due to living in a ‘green’ building and that residents will have financial savings due to a lower freshwater consumption and potentially also due to a reduced fee to be paid to the utility for wastewater treatment. The policy was perceived as fairer for residents the more positively these two implications were viewed.

Of the five policy implications for owners of buildings with mandated on-site systems, four explained perceived fairness of the policy for this group. Two of them were on average perceived as negative: The financial costs for installing on-site systems as well as the financial risk in case of technical issues that is carried by the building owners. The policy was perceived as more unfair for building owners the more these two implications were seen as negative for them. The other two implications explaining perceived fairness for building owners were on average perceived as positive: Financial savings for building owners due to a reduced fee to the utility for wastewater treatment and a positive and sustainable image of building owners for building or renting out units in a ‘green’ building. The more positive these two implications were perceived, the more fair was the policy perceived for building owners.

Of the seven policy implications for the city of San Francisco and its population, three explained perceived fairness of the policy for this group of society. One of these implications was on average perceived as negative by participants, namely the possibility that the city’s centralised water and wastewater system becomes more expensive for users of the centralised system run by the utility, as residents of on-site system pay a reduced fee to the utility. The more participants perceived that this implication is negative for the city of San Francisco and its population, the more was the policy perceived as unfair for this group. The other two implications were on average perceived as positive by participants. These were that the policy leads to an increased wastewater treatment capacity of the city by taking load off the centralised system and that new, green jobs are created in San Francisco, as the policy increases the demand of on-site systems and thus builds a local, green economy. The more these implications were viewed as positive for the city of San Francisco and its population, the fairer was the policy perceived for this group.

Of the four policy implications for low-income residents of San Francisco, three explained perceived fairness of the policy for this group of society. One of them was on average perceived as negative, namely that most low-income residents cannot profit from the monetary and non-monetary benefits of on-site reuse as most low-income residents will not live in buildings with on-site systems (due to the exemption of low-income housing developments from the policy). The more this implication was perceived as negative, the more was the policy perceived as unfair for low-income residents of San Francisco. The two other implications explaining perceived fairness for this group were on average perceived as positive by participants. These were that low-income housing continues to be built in San Francisco (due to the exemption, these projects are less expensive than conventional projects) and the implication that rents for low-income residents will likely not increase due to the exemption and that water prices in the city will likely be more stable (as the water reuse saves some freshwater resources of the city, which may lead to more stable water prices, benefitting particularly low-income residents). The more these implications were viewed as positive for low-income residents, the more was the policy perceived as fair for this group.

Of the four policy implications for future generations living in San Francisco, two explained perceived fairness of the policy for this group and were both on average perceived as positive. These implications were that the policy leads to innovation in the wastewater treatment sector which will benefit future generations and that it leads to financial savings for the municipal utility (and thus the city), which may benefit future generations. The more positive these two implications were perceived, the fairer was the policy perceived for future generations living in San Francisco.

The two implications of the policy for the local environment both explained perceived fairness for the environment. One was on average perceived as negative, namely the environmental risk in case of system failures as untreated or partially treated water used for irrigation may contaminate the environment. The more this implication was perceived as negative, the more was the policy perceived as unfair for the environment. The second implication was on average perceived as positive, namely that more water stays in the local and regional ecosystems since less water is taken from ground or surface water reservoirs. The more positive this implication was seen, the fairer was the policy perceived for the environment.

Across all groups and entities, perceived fairness of the policy was explained both by implications perceived on average as positive and those perceived as negative. Of the 30 implications included in the study, 18 were on average rated as positive and 12 as negative, of which 11 and 7, respectively, significantly explained perceived fairness. The Fisher’s exact test (two-tailed, p = 1) indicated that neither the positive nor the negative implications were more likely to explain perceived fairness of the policy.

Further, both implications related to financial aspects of the policy (n = 14) and those related to other aspects (n = 16) explained perceived fairness. Of these, 12 and 6, respectively, significantly explained perceived fairness. A Fisher’s exact test (two-tailed, p = 0.011) indicated that implications of the policy related to financial aspects were more likely to explain perceived fairness of the policy than non-financial ones.

Perceived fairness for different groups and entities explaining policy acceptance

Overall, the policy mandating on-site systems was perceived as moderately acceptable (M = 4.81, SD = 1.26). A considerable amount of variance in acceptance was explained by perceived fairness, but only by fairness for three of the five societal groups included in the study. Most strongly, policy acceptance was explained by the perceived fairness for the city of San Francisco and its population, followed by the perceived fairness for future generations living in San Francisco and by the perceived fairness for owners of buildings with mandated on-site systems. With higher perceived fairness for these groups, the policy was evaluated as more acceptable, while it was perceived as less acceptable if it was perceived as less fair for these groups. Interestingly, the policy was on average perceived as rather fair for all groups and entities except for residents of buildings with mandated on-site systems, for whom the policy was on average perceived as slightly unfair (but close to the ‘neutral’ midpoint of the scale). However, perceived fairness for residents did not explain policy acceptance. For the bivariate correlations of all fairness variables and acceptance, see Table S7 in the Supplementary Information.

With increasing water scarcity around the world, (on-site) wastewater reuse has become increasingly important^2,4 and with it policies that regulate its implementation. Therefore, for a policy mandating on-site reuse in San Francisco, the present study investigated, (1) whether and to what extent different implications of the policy for five groups of society and one entity (the environment) explain perceived fairness of the policy for the specific group or entity, and (2) whether and to what extent the perceived fairness of the policy for each group or entity explains policy acceptance. The following groups and one entity were included in the study: residents of buildings with mandated on-site systems, owners of buildings with mandated on-site systems, the city of San Francisco and its population, low-income residents of San Francisco, future generations living in San Francisco, and the local and regional environment.

Across all groups and the one entity, policy implications that were, on average, considered positive were equally likely to explain the perceived fairness of the policy as those that were considered negative. Thus, perceived costs, risks, and benefits arising from a policy all shape the public’s fairness evaluations. Interestingly, of all implications, those that were related to financial aspects (i.e. financial costs, risks, and savings, the creation of green jobs and cheaper technologies, stable rents and water prices) were more likely to explain perceived fairness than non-financial implications. Specifically, for all groups for which the policy has financial implications (i.e. all included groups but the environment), at least one of them explained perceived fairness. Thus, financial implications were central for fairness evaluations across all societal groups and not only for low-income groups. This is in line with studies on other environmental policies that found financial aspects to be relevant for perceived fairness or acceptance^30,44,45,46. This finding implies that it might be particularly important in policymaking processes to consider the potential financial implications (both costs and benefits) of the policy for different groups of society. Financial burdens for some groups could be counterbalanced, for example by providing subsidies, lowering taxes, or reducing fees for public utility services. Moreover, freshwater could be priced appropriately (i.e. be less subsidised) to increase the financial benefit for those people reusing their wastewater^29,47. Some of these measures are already in place in San Francisco, such as the option of reduced water and wastewater capacity charges for buildings that are covered by the mandate⁴⁸ as well as a charge for the excess use of water beyond a designated threshold to promote the reuse of water⁴⁹. Yet, it is essential that measures are in place ensuring that the financial burden is not shouldered entirely by the residents while the financial benefits remain with the building owners.

Nevertheless, also six non-financial implications significantly explained perceived fairness. These can be grouped in three thematic pairs. One pair is related to a positive, sustainable image, both for residents and for owners of buildings with on-site systems. The more positive their image was perceived, the fairer was the policy evaluated for them. This matches findings of other studies that found increased acceptance of on-site systems⁴¹ and of other types of technologies^50,51,52,53 if the technology use was associated with a better image or status gain. Additionally, this is in line with the ‘innovation diffusion theory’⁵⁴ and the ‘costly signaling theory’⁵⁵, both postulating that adopting (sustainable) innovations is associated with higher social status.

The second thematic pair is related to the policy’s environmental impacts. Specifically, participants’ fairness evaluations for the environment were explained by the perceived environmental risk in case of system failures and the benefit that more water stays in the local ecosystems. This aligns with Kollmann et al.⁴¹, who reported acceptance of on-site systems to be higher, the more people perceived them as beneficial to the environment. Interestingly, the perceived fairness for future generations was not explained by perceived environmental benefits (i.e. saving water resources for future generations). Instead, only perceived financial benefits related to innovations in the local water and wastewater infrastructure explained fairness. Interestingly, also the third pair of non-financial implications that explained perceived fairness is related to San Francisco’s infrastructure. Specifically, the perceived risk of a reduced maintenance and water quality for residents of buildings with on-site reuse compared with users of the centralised system and the perceived benefit of an increased wastewater treatment capacity.

These insights into non-financial implications explaining perceived fairness of a policy can be used for the design of communication strategies employed during the planning and implementation of policies on wastewater reuse. For example, the sustainable and innovative nature of the technologies, as well as the potential for positive publicity for builders and users could be emphasised. Moreover, risk communication strategies could address the perceived risk of a reduced maintenance and water quality for users of on-site systems by emphasising the safety of the treatment and reuse as well as by informing about monitoring and safety measures in place.

Policy acceptance was strongly explained by perceived fairness for three different groups of society. This strong association is in line with previous studies on policies mandating water reuse^25,29,56,57 and environmental policies in general^9,23 and underlines the central role of fairness evaluations for policy design. Specifically, acceptance was explained by perceived fairness for three of the five societal groups included in the study, namely the city of San Francisco and its population, future generations living in San Francisco, and owners of buildings with mandated on-site systems. Acceptance was not explained by the perceived fairness for residents of buildings with mandated on-site systems, low-income residents of San Francisco, or the local and regional environment.

The finding that the perceived fairness for future generations explains policy acceptance suggests that collective considerations impact people’s fairness evaluations of environmental policies. Similar results were found in a study on a policy mandating on-site reuse in India²⁹ and a study on a transport pricing policy³⁰. However, in both of these studies, fairness for future generations was assessed in combination with fairness for the environment. Thus, it is not possible to determine for which group or entity fairness explained policy acceptance. As in the present study policy acceptance was not explained by perceived fairness for the environment, it could be concluded that perceived fairness for future generations might be more relevant for policy acceptance than perceived fairness for the environment. Yet, more research on this question is needed. Consequently, future studies investigating the relation between perceived fairness and acceptance of environmental policies should consider separate fairness assessment for future generations and the environment.

Fairness for current residents of San Francisco also explained policy acceptance, which further emphasises that people consider collective outcomes. Yet, this finding might also reflect participants’ consideration of their personal outcomes. As none of the participants in the survey were covered by the policy, the population of San Francisco is likely the group of society they most identify with, and which is most closely related to their personal outcome. This might also explain why this group’s fairness is the one most strongly related to policy acceptance, as it may reflect both collective and self-centred considerations, which have both been shown to be central for fairness and acceptance of environmental policies^{29,30,33,43,58}.

In addition, the findings indicate that people consider the impact a policy has on those most strongly affected by it. Specifically, the fairness for owners of the buildings with mandated on-site systems explained policy acceptance. Interestingly, however, the fairness for residents of these buildings did not explain policy acceptance, even though this group is also strongly affected. This contradicts the finding of Kollmann et al.²⁹ that perceived fairness and acceptance of a policy mandating on-site reuse in India was explained by the outcome for residents. Potentially, having to use an on-site system is perceived as a bigger burden on residents in India, where the systems often do not function well, and the residents are at higher risk of an impaired water quality, while users of such systems in San Francisco are much less likely to face such issues. Indeed, while both positive and negative policy implications were perceived for residents, the policy was perceived neither as unfair nor as fair for residents (i.e. the average fairness perception was close to the scale midpoint).This could explain why, in San Francisco, fairness for residents did not explain policy acceptance.

Lastly, we did not find perceived fairness for low-income residents of San Francisco to explain policy acceptance. This is particularly interesting as environmental justice (i.e. the equitable distribution of costs, risks, and benefits of environmental decisions among members of society, regardless of their race, ethnicity, gender, or socio-economic status) is a key argument in the debate on water resource allocation in California and is considered pivotal for the successful implementation of water innovations^21,59 and for sustainable urban planning in general^60,61,62. It is especially relevant given that California legislated the human right to water in 2012, acknowledging the fundamental right of all residents to have access to safe, clean, and affordable water⁶³. Notably, the design of the policy mandating water reuse in San Francisco bears both benefits and costs for the city’s low-income residents as low-income housing projects are exempted. For that reason it might be possible that participants did not form a strong opinion regarding the policy’s impact on low-income residents, which might explain why it did not explain policy acceptance. In fact, the policy was, on average, perceived as moderately fair for low-income residents, and three out of four implications for this group were perceived as moderately positive, which might explain why fairness for low-income residents was not associated with policy acceptance. The finding also sheds light on the question raised in the introduction whether there might be a general pattern that marginalised groups of society might not be considered in the context of policies mandating on-site reuse (or technologies in general) or policies that cover only part of the population. While the present finding deviates from those of other studies on environmental policies^{30,32,33,34,35}, it is in line with the one other study investigating a policy mandating on-site reuse for part of the population²⁹. In that study, the outcome for low-income residents did also not explain perceived fairness or acceptance of the policy. Together, the two findings support the conclusion that for policies mandating on-site reuse (or technologies in general) or that cover only part of the population, low-income groups may be considered less than in the context of other environmental policies. This could either be because, compared with pricing policies, the implications are not ‘just’ financial but also behavioural. Alternatively, for policies that cover only part of the population, people may focus on the outcome for those covered, while additional implications for vulnerable groups may be overlooked. To investigate this further, future studies could investigate whether this pattern consists for policies covering only part of the population but in a different context, unrelated to on-site systems or other technologies.

Taken together, the findings suggest that individuals evaluate the acceptability of a policy based on its perceived fairness for the collective (i.e. population of San Francisco and future generations) as well as for those directly and strongly affected (i.e. building owners). However, considerations of fairness do not necessarily extend to all affected or particularly vulnerable groups of society. Potentially, perceived fairness and policy acceptance are shaped less by a group’s marginalised or affected status per se and more by the specific impact of the policy on that group. Given that participants received information about specific implications of the policy for the different groups, it is likely that they integrated this information into their fairness judgements. Consequently, participants may have developed stronger fairness perceptions for some groups over others, which could explain why perceived fairness for certain marginalised or affected groups, such as low-income residents and residents of buildings with on-site reuse, did not explain policy acceptance.

To the best of our knowledge, the present study is the first that investigated whether and how strongly the perceived fairness for different groups of society and the environment explains acceptance of a policy mandating on-site reuse and that investigates which policy implications explain whether the policy is perceived as (un)fair to the individual groups or the environment. It is also one of only few studies that investigated perceived fairness of a policy designed to support climate change adaptation instead of mitigation. Nevertheless, a few limitations need to be mentioned. First, while the range of policy implications included in the study covered an extensive range, it was likely not exhaustive. Moreover, it should also be borne in mind that the occurrence of some of the implications is uncertain, as they will, if at all, only manifest themselves over the next few years or decades. This has been communicated to participants, but we do not know whether and how they integrated the uncertainty in their evaluations. Second, the range of societal groups or entities selected for inclusion in this study, though more comprehensive than in previous studies, may be incomplete. These groups and entities were chosen based on previous literature^29,30,64 and based on the conducted stakeholder interviews. Yet, it is possible that groups that were not considered in this study could explain part of the remaining variance in perceived fairness. Lastly, some limitations concerning the sample should be noted. The collected sample size was only sufficient to detect effects of at least small to medium size. Thus, it is possible that small effects were not detected. Furthermore, our study comprised only residents of San Francisco not covered by the policy. We know from Kollmann et al.²⁹ that fairness and acceptance ratings can differ between people covered by the policy and those not covered. Thus, we cannot draw confident conclusions about the perceptions of residents of San Francisco covered by the policy. Moreover, the majority of study participants were highly educated and had a high income, while marginalised groups, such as residents with a lower income or education, were underrepresented. This might have been exacerbated by the exclusion of participants who failed to answer the knowledge questions and attention checks correctly. This restricts the validity of the conclusions that can be drawn from the study regarding the policy perceptions of these groups. Therefore, future studies should investigate the research questions among residents covered by the policy and, in particular, among those covered and with a low-income.

Taken together, for the policy mandating on-site reuse in San Francisco, perceived fairness for the five groups of society and the environment was explained by several policy implications for the groups and the environment. This included both implications that were on average considered positive and implications that were on average considered negative by participants as well as implications related to financial aspects of the policy and implications related to non-financial aspects. This implies that all these different types of implications should be considered by policymakers when evaluating the fairness implications of policies related to on-site reuse. Moreover, policy acceptance was explained by the perceived fairness of the policy for the city of San Francisco and its population, future generations living in San Francisco, and owners of buildings with mandated on-site systems. This suggests that, overall, fairness for the collective as well as for those most affected is important to people when assessing policy acceptance. However, not necessarily the fairness for all people affected or for particularly vulnerable groups of society is considered.

Study location and policy design

San Francisco faces increasing water scarcity due to population growth, more stringent regulations about in-stream flows, and changing climatic conditions, such as increasing temperatures and reduced snowpack in the Sierra Nevada. In response to these issues, San Francisco mandated the installation and use of an on-site system for new construction projects in 2015⁶⁵. The latest version of the policy applies to construction projects of ≥100,000 gross ft² and mandates residential buildings to install an on-site system for the collection and treatment of the building’s greywater (i.e. wastewater from sinks, showers, washing machines, dishwashers etc. but not from toilets) as well as their condensate (e.g. from air conditioners). The recycled water has to be reused for the following non-potable purposes within the building or its premises: clothes washing, toilet flushing, and irrigation. Moreover, the full demand of water for these purposes must be met by the recycled water.

Notably, low-income housing projects are exempted from the mandate. As a consequence, low-income construction projects are less expensive compared with conventional projects that fall under the mandate, as no on-site system has to be installed. The exemption aims at encouraging developers to build low-income housing. For a very limited number of low-income housing projects that voluntarily install on-site systems, San Francisco’s utility offers a funding scheme to offset costs.

Procedure and sample

Data were assessed through an online questionnaire, which was programmed with Unipark and distributed to the general public of San Francisco via the survey panel company Bilendi between April and June 2023. Participation took around 25 min and was financially compensated. All participants gave informed written consent prior to participation. The study protocol was approved by the institutional review boards of Eawag and the University of California, Berkeley [2021-08-14578] and was pre-registered on the Open Science Framework (OSF) on 04/27/23 before data collection started (osf.io/5c3z9). Prior to the data collection, semi-structured, virtual interviews were conducted between March and September 2022 with 12 San Francisco-based key stakeholders, including representatives from the water utility, the water quality control board, public advocacy groups, and the plumbers union as well as with developers of on-site systems, architects, and property managers. These interviews informed the design and content of the survey.

Residents of San Francisco above the age of 18 were eligible for participation. Of the 1020 people who started the survey, 523 participants were screened out before the main part of the questionnaire because of incorrect answers to multiple-choice questions on the content of an information text on on-site systems and the policy in San Francisco (see section Questionnaire and measures). Of the remaining participants, 116 did not complete the questionnaire. During data cleaning, 205 participants were removed from the sample because of either of the following reasons: repeated participation (n = 28), speeding (defined as being faster than one third of the sample median; n = 56), failed attention checks (n = 55), straightlining (n = 27), or random answers to open questions (n = 39). Of the final sample (N = 176), 51.1% identified as female, 46.0% as male, and 0.6% (one person) as non-binary. On other person preferred to self-describe their gender, and 1.7% did not indicate their gender. The participants’ age ranged from 18 to 80 years (M = 48.29; SD = 17.59). All but three participants (98.3%) had completed high school, with 78.5% having completed tertiary education (Bachelor’s degree or higher). About half of the participants (54.8%) indicated having a yearly income of over $100,000. None of the participants reported living in a building with an on-site system. Compared with those who dropped out or were excluded during data cleaning, participants included in the analyses were significantly older (t(212.61) = 6.43, p < 0.001), were more likely to be female (χ²(4) = 10.21, p = 0.037), and had a lower education level (χ²(4) = 13.58, p = 0.009) and lower income (χ²(4) = 24.78, p < 0.001).

A sensitivity power analysis⁶⁶ was conducted to test for the smallest effect size detectable in the most demanding analysis conducted, namely a linear multiple regression with eight predictors given a power of 0.80 and an α = 0.05⁶⁷. The omnibus F-test indicated a smallest detectable effect size of f² = 0.09, corresponding to a small to medium effect⁶⁸.

Questionnaire and measures

After giving their consent to participate and indicating their sociodemographic data, participants read an informational text about on-site systems and the policy in San Francisco, followed by two multiple-choice questions on the content of the text (see ‘Supplementary Note 1’ for text and questions). As the following part of the questionnaire required a basic understanding of on-site reuse and the policy, only participants who answered the questions correctly could proceed with the questionnaire. They were given three trials to give the correct answers with the option to read the informational text in between. Those participants who could proceed were then presented with items assessing the valence of different policy implications for the five different groups of society and the environment as well as the perceived fairness of the policy for these groups and the environment. The items were assessed separately for each group or entity and in random order. Finally, participants’ acceptance of the policy was assessed. See ‘Supplementary Note 2’ for all items.

The policy implications and the groups and entities of society included in the study were selected on the basis of existing literature^29,30,64 and guidelines^8,65 as well as the qualitative interviews with local key stakeholders, to ensure that the implications and groups were both comprehensive and relevant to the local context. For each group or entity of society, between two and nine implications were included in the questionnaire and presented in random order. For example, one implications for residents was ‘Residents have to bear the recurring costs of operation, monitoring, and maintenance of the systems. Moreover, it is likely that the initial costs of installation are passed on to the residents by the builders’. Participants were asked to rate each implication with regard to how positive or negative they are for the respective societal group or entity on a 7-point rating scale ranging from 1 (very negative) via 4 (neither negative nor positive) to 7 (very positive).

To reduce complexity of the data and to avoid multicollinearity in the analyses, item mean scores were created for items that had a similar content, correlated highly and had acceptable internal consistency assessed with Spearman-Brown coefficients⁶⁹. Specifically, means were calculated for the following items: implications for residents of buildings with mandated on-site systems with regard to (1) reliable and (2) unrestricted water supply in case of natural disasters or droughts (ρ = 0.67), implications for low-income residents of San Francisco with regard to the lack of (1) monetary and (2) (non)-monetary benefits of on-site reuse (ρ = 0.81) and, for the same group, with regard to the benefit of (1) stable rents and (2) water prices (ρ = 0.72). For the regression analyses, the means of these items were used.

Directly after rating the policy implications for one of the societal groups or the environment, participants were instructed to rate the fairness of the policy for the respective group or entity when considering all of the stated implications for the specific group or entity. For example, the item for residents was ‘For residents of buildings with on-site reuse, the policy is overall…’, rated on a scale ranging from 1 (very unfair) via 4 (neither unfair nor fair) to 7 (very fair). The design of the item was adapted from previous studies on fairness and acceptance of environmental policies^30,70.

Policy acceptance was assessed with five semantic differential scales that were subsequently combined into one mean scale (α = 0.94). The participants were asked to rate the items ‘Overall, the policy is…’ on five scales ranging from 1 (…very unacceptable / negative / unnecessary / intolerable / useless) to 7 (…very acceptable / positive / necessary / tolerable / useful). The scale also included a midpoint, for example: 4 (…neither unacceptable nor acceptable). The scale was adapted from previous studies on acceptance of environmental technologies and policies^29,71.

Statistical analyses

To analyse which policy implications explain perceived fairness of the policy for different groups or entities, six multiple linear regression analyses were conducted for each of the five groups and one entity considered. Additionally, and across all implications and fairness ratings, two exploratory Fisher’s Exact Tests were conducted to analyse if perceived fairness is more likely to be explained by (1) either financial or non-financial implications and (2) either implications on average considered positive or those considered negative. Another multiple linear regression analysis was conducted to examine whether and to what extent perceived fairness of the policy for the five different societal groups and the environment explains policy acceptance. Owing to the non-normal distribution of the residuals, the analyses were conducted using bootstrap estimation with 10,000 replications. Significance was determined based on the bootstrapped 95% confidence intervals⁷². All analyses were conducted using IBM SPSS Statistics (Version 29)⁷³.