Valuing decentralized wastewater technologies: a stated preference analysis of advanced and cluster septic systems

Abstract

Decentralized wastewater treatment systems are key to safe and reliable water access and climate resilience. They provide people with important infrastructure in areas where centralized sewerage is not feasible. While the technology behind traditional septic systems is mature, it has significant flaws leading to nutrient and pathogen release, especially when exposed to changing environmental conditions. Alternatives to traditional septic systems that are more effective at removing nutrients from wastewater have seen limited uptake. Some of these systems, advanced treatment systems, include an additional treatment step for increased treatment efficiency, so do cluster septic systems, which additionally serve multiple homes at once. Using the contingent valuation method, this paper contributes to existing literature by examining individual homeowner preferences and willingness to invest in an alternative onsite wastewater treatment system (OWTS) in the United States, focusing on advanced septic and cluster septic systems. Study respondents positively value both technologies. Specifically, valuation of advanced septic systems is significantly higher than for cluster septic systems. However, respondents asked to contemplate a new house purchase are willing to pay more than people who are asked to replace their existing system. This difference is statistically significant for advanced, but not for cluster septic systems. Our results can aid the development of policies to reduce nutrient inputs from existing and new OWTS by pointing wastewater professionals and policymakers towards areas where investment is most effective.

1 Introduction

Access to reliable and safe water continues to be a challenge and a goal worth pursuing under the current pressures from growing global population, changing climate, and technological progress. While managing drinking water is a focus of many early development projects targeting water infrastructure, wastewater and its adequate treatment follows closely behind (Rodriguez et al., 2012), and both are crucial to sustainable development and climate resilience (Vinke-De Kruijf et al., 2024). Historically, modern wastewater treatment has followed a centralized approach, collecting the effluent at the origin of production and routing it to a central location for treatment at a wastewater treatment plant. However, this path has been criticized as excessively polluting due to frequent sewage overflows, inadequate treatment, and centralized release of nutrients (Panebianco and Pahl-Wostl, 2006; Saadatinavaz et al., 2024). Furthermore, it is challenging to collect and reclaim different types of polluted effluent (industrial vs. domestic wastewater) in the same facility, as it cannot be treated the same way satisfactorily. Centralized collection and disposal also diverts water from its original space in the local water cycle, which can lead to water replenishment issues (Panebianco and Pahl-Wostl, 2006). On top of these general problems, conventional centralized infrastructure is also economically costly, energy-intensive, and difficult to implement in lower population density rural or peri-urban areas.

Smaller decentralized or onsite wastewater treatment systems (OWTS) offer solutions to some of the criticism of centralized operations. One of these technologies, the traditional septic system, has been in use for half a century. It’s a relatively low cost and mature technology, making it the dominant design for on-site wastewater treatment, though the technology is not without flaws, especially regarding treatment efficiency (Jenssen and Siegrist, 1990; Del Rosario et al., 2014; Cooper et al., 2016). There are numerous alternative OWTS technologies and components (e.g., advanced septic systems, cluster systems, membrane bioreactors, biofilters, solar-assisted systems, smart sensors, etc.) which are superior options from a treatment efficiency standpoint. Even though the potential benefits from these technologies are high, their widespread adoption remains limited. This is likely due to factors aside from the treatment efficiency of the technology: adoption decisions are also financial and behavioral.

From an engineering perspective, technologies are designed to improve factors like usability, durability, and/or efficiency. However, this perspective often fails to see the user as more than just the recipient of technology. Behind the “black box” of the end-user, people are complex decision-making machines who also process the social aspects associated with a technology, such as attitudes, risk perceptions, and social norms. This paper bridges the gap between alternative OWTS design and adoption by applying stated preference methods from economics and other nonmarket valuation fields to understand the decision-making surrounding advanced OWTS. The random utility maximization framework underlying our stated preference approach also allows us to translate preferences into an individual’s willingness to pay (WTP) for a new technology, which gives us a means to put a dollar value to the benefit people perceive from switching to an alternative treatment system. Knowing the perceived value of an OWTS is important because it can inform incentives and policies aimed at reducing environmental loading and lead to more widespread adoption of alternative OWTS technologies.

This paper uses two different hypothetical scenarios, targeting users with different experience levels related to OWTS. For respondents with prior OWTS experience, we introduce a scenario where they must consider replacing their traditional septic system with a new system (NS). In contrast, respondents with little or no individual OWTS experience are introduced to a new house (NH) scenario where they must choose between purchasing one of two nearly identical properties, only differing in the OWTS used. We furthermore examined preferences for two broad alternative OWTS categories, which already exist and could be implemented: advanced septic systems (A-OWTS) and cluster septic systems (C-OWTS). In our survey, respondents make decisions between the current status quo, a traditional septic system, and one of the alternative OWTS.

To our knowledge, this study is the first to elicit WTP across multiple price points using a Contingent Valuation (CV) payment card method to compare different alternative OWTS technologies and their desirability for potential consumers. Our study ties technological advances to practical applications by connecting individual human behavior at the user level to technology adoption and water security. Knowing individual demand for improved OWTS and the related WTP allows engineers, septic system professionals, and policymakers to provide people with the services that they want and for which they are willing to pay.

2 Background

In the United States, an estimated 20%–25% of the population relies on OWTS treatment systems (US EPA, 2025). While the numbers differ from state to state, based on demographic, environmental, and economic context, this form of wastewater treatment system is popular in less densely populated rural areas as well as quickly developing exurban and suburban spaces (US EPA, 2025). As part of a larger project, this study has an emphasis on North Carolina (NC), where the fraction of households relying on septic systems is closer to 50% overall (∼2,000,000 systems) (Pradhan et al., 2007), and more than 60% in the less densely populated Eastern part of the state (North Carolina National Estuarine Research Reserve NCNERR, 2001).

While OWTS have some benefits over centralized systems for wastewater treatment, there are also concerns, especially regarding treatment efficiency. The predominant technology for OWTS in NC and the US more broadly is the traditional septic system (for a technology description see Table 1), which has not changed dramatically since the 1960s (US EPA, 1980). Traditional septic systems are prone to failure from inadequate maintenance and rising groundwater tables in coastal areas impacted by sea level rise (NC State Extension et al., 2016; Humphrey et al., 2017; 2021). Such failures can lead to nutrient overabundance, which poses significant risks to aquatic life and human health, especially for at-risk populations (Cooper et al., 2016)., and contribute substantially to nutrient pollution in waterways (Humphrey et al., 2010).

In North Carolina, OWTS do not require regular tests or assessments after the initial permitting for new housing developments (except for larger residential or industrial systems), leading to poorly understood risk in changing environments.

Advanced (A) and cluster (C) OWTS are two important alternatives to traditional systems (Table 1). Both of these technologies are currently available and have a degree of onsite implementation in North Carolina (albeit in low numbers) (Vorhees et al., 2022; Erban et al., 2024; US EPA, 2024). A-OWTS can be more cost effective in the long term¹ and convenient to operate compared to traditional septic systems. Using alternative systems can create more inviting neighborhoods, because they often require less space for the drain field and have a lower impact on the landscape (Joubert et al., 2004). Advanced systems are also a good alternative to replace failing traditional systems in places where installing a new conventional system would not pass the current building code, such as in densely built town centers (Joubert et al., 2004).

In contrast to traditional and A-OWTS, C-OWTS cover multiple households at once, reaping the economies of scale to achieve improved treatment at a lower price point for the individual homeowner. To adopt a C-OWTS, various homeowners must collaboratively decide to implement this technology. Benefits of adopting are, amongst others, the reduction of space designated to wastewater treatment (for the individual home), increased removal of pollutants from effluent (i.e., remove about 80%–95% of the harmful components in wastewater), reduction in design flow, and the pooling of maintenance responsibilities. On the negative side, there are larger upfront investment costs for the overall system, though these may not be significantly more than other systems when the cost is shared amongst households, as well as legal costs and challenges to set up a shared system, and reduced agency of the individual homeowner.

To assess the benefits of amenities such as ecosystem services, economists rely on two different methodological vehicles: revealed and stated preferences. Revealed preferences infer amenity values based on observed behaviors and decision making. Revealed preference methods include hedonic price and travel cost valuation methods. However, there are many amenities for which it is difficult or impossible to infer values based on behaviors, typically because there does not exist a market through which we can identify value for the amenity, such as with pollution reduction or, in the case of this study, products that are nonexistent and rare in practice (Mangham et al., 2009). Using a revealed preference method like hedonic pricing models to value alternative wastewater treatment technologies would require significant market penetration of these technologies; the fact that A- and C-OWTS technologies are novel, largely unknown in our study region, and infrequently utilized at the household scale makes it implausible to identify their valuation among consumers based on housing purchase decisions.

In contrast, Contingent Valuation (CV) is a survey-based stated preference method well suited to these conditions, enabling us to evaluate homeowner preferences for alternative wastewater treatment technologies. CV has a long tradition in environmental economics, and has been used to estimate benefits and costs of safe drinking water (Witt, 2019), evaluate (green) infrastructure for flood reduction and water quality improvement (Reynaud et al., 2017), wetland protection (Ghanian et al., 2022), and water reuse (Chopra and Das, 2019) to name just a few. Previous literature about individual on-site wastewater treatment (Naman and Gibson, 2015; Fizer et al., 2018; Vorhees et al., 2022) indicates that there are a variety of different attributes important to the respondent. Our application focuses on estimating the value of two specific technologies without attempting to identify the marginal value of specific attributes of a technology (for example, expected reductions in nitrate levels, annual probability of system failure, etc.).

Understanding the economic preferences of individuals for traditional septic systems versus advanced or cluster-based OWTS alternatives is an important task for formulating effective nutrient reduction interventions or policies that incentivize behavior as many sophisticated technologies exist today (Wallin et al., 2013). In this study, we estimate the stated preference of individuals to adopt advanced or C-OWTS over traditional systems using the CV methodology as these technologies are relevant to, but rarely implemented in, the rural and sprawling peri-urban settings in the US. Ultimately, homeowner perceptions of independent OWTS (traditional, advanced, and cluster) may have a significant influence on models for public infrastructure provision, i.e., OWTS versus centralized sanitary sewer systems, in regions with significant exurban development and rural sprawl.

3 Materials and methods

3.1 Survey design and content

In CV methodology, respondents are asked to state their preferences contingent on one or several possible future scenarios, typically by comparing a hypothetical alternative to a current status-quo situation. Our status-quo involves the use of traditional septic systems in comparison with an alternative system that delivers preferred environmental benefits at a typically higher cost. Therefore, our study design uses a Willingness to Pay (WTP) framework rather than a Willingness to Accept framework, which is used to estimate the amount someone would need to be compensated to accept a reduction in some amenity relative to the status-quo.

There are several different formats available to obtain respondents’ preferences in CV. The four most common are binary choice, double-bounded binary choice, payment card, and open-ended question. We follow a payment card approach in our experiment. This methodology was introduced to CV by Mitchell and Carson (1989) and presents respondents with a series of different hypothetical payment options. We divide the range of possible installation costs ($0-$17,000), determined through market research² that verified that range was realistic for all three technologies, into five equidistant steps. For each price, respondents indicate whether they would prefer the traditional septic or the alternative technology at the stated additional installation premium. As such, WTP estimates generated from this procedure reflect not total willingness to pay for an alternative septic system, but the premium that people are willing to pay for an alternative system in addition to the cost to install a traditional septic system. The interval-type data generated from this approach can then be used to fit a parametric distribution to attain the population WTP (Czajkowski et al., 2024). Besides our ability to directly determine WTP values from the data, the payment card approach has several advantages over other methods to elicit stated preferences: respondents are more likely to state values they are more confident about (Ready et al., 2001), the estimated values are more robust than using dichotomous choice (Ready et al., 2001), and it is less vulnerable to starting point bias (Mitchell and Carson, 1989). However, there are other biases that might influence our WTP estimates, such as the end point bias (Hu, 2006), and a range and centering bias (Mitchell and Carson, 1989).

In addition to assessing the valuation of various OWTS, we also explore how different hypothetical scenarios might impact WTP for different technologies. Based on feedback from initial pilot surveys and consultations with experts, we elected to use one status quo technology (traditional septic system), two different alternative technologies (alternative septic system and cluster or community septic system) and, based on the respondents’ familiarity with OWTS, two different investment scenarios: new system, where respondents decide between two options for replacing their current system, for people who currently are or previously were using OWTS to treat their wastewater; and new house, where respondents decide between two new properties to purchase, a scenario suitable for all homeowners, even those with little or no prior knowledge of OWTS.

The survey was reviewed and approved by the East Carolina University Institutional Review Board (IRB) on 11/25/2024 (UMCIRB 24-001830). After answering a few preliminary questions, respondents were presented with an introductory paragraph defining some key concepts related to OWTS (wastewater, determining which wastewater treatment system technology the respondent currently uses, on-site vs. centralized wastewater treatment). After explaining how respondents can determine which technology they currently use in their home, we asked them to identify and name their current treatment system. If the respondent selected that they did not currently use septic or advanced septic, they were prompted to indicate if they ever lived in a home that did use one of these technologies. These two questions were used to filter participants into the two groups: 1. people without prior OWTS experience, who were shown the New House scenario only, and 2. people who either currently lived in a home that uses OWTS or had previously lived in such a residence. This second group was randomly assigned to either the New House or the New System scenario.

Technologies were randomly assigned, with roughly 50% of respondents making decisions between the status quo (traditional septic system) and an advanced septic system, and the other half choosing between the status quo and a cluster septic system. Respondents with past septic system experience answered additional questions about the age and maintenance schedule of their systems, as well as if they ever had issues that led them to consider switching to a different system. All respondents answered questions about their perceptions of wastewater treatment before being presented with the CV choice. Specifically, we asked respondents how frequently they thought about wastewater, if water pollution was an issue in their community, and who would be responsible for ensuring adequate wastewater treatment. All questions, scripts, and scenarios presented to respondents are included in the Supplementary Material. Respondents were only given information on the alternative septic technology that was included in their CV design. This means that respondents who were randomly presented with CV questions comparing traditional septic and advanced septic were presented with information on traditional septic and advanced septic technologies but were not shown the information on cluster septic systems. Similarly, respondents randomly selected to receive cluster septic system CV questions were shown information on cluster septic technology, but not advanced septic technology.

The payment card instrument was introduced with a cheap talk script (Cummings and Taylor, 1999) where we reminded participants to be cognizant of hypothetical bias and do their best to answer in a way that accurately reflects their true preferences and not how they think the researchers want them to respond. Each instructional script (see Supplementary Material) was presented to participants in a series of 1.5–2 min videos, which they could watch in addition to reading the script. Using video instructions to supplement lengthy text has been shown to improve the quality of CV responses (Lim et al., 2020), while keeping the questionnaire leaner by reducing the need to attach visual aids (Carson, 2012) and reducing hypothetical bias (Penn and Hu, 2021). After respondents viewed the relevant information, they answered the CV question in payment card format using 5 equidistant price steps from $0 to $17,000: “if the following installment prices/increase in monthly mortgage payments would be applicable for each of the pairs, which house (scenario 1)/system (scenario 2) would you like to buy?” (see Figure 1).

FIGURE 1

While cheap talk scripts have been shown to reduce hypothetical bias, CV responses have been shown to often face hypothetical bias issues even with such scripts (Penn and Hu, 2018). We follow the literature to further mitigate hypothetical bias through certainty recoding (Champ et al., 2009; Penn and Hu, 2023). When respondents selected the alternative septic option for any of the payment values, they were given a follow-up question about how certain they are, ranging from 0 – not certain at all to 10 – certain. These values were later used to adjust responses according to findings in the literature that respondents who express preference for the amenity under hypothetical conditions but acknowledge significant uncertainty in their response tend to select the status-quo option when incentives are present. Our approach recodes all responses selecting the alternative septic option to the status quo option if respondents express a level of certainty below 7 on the 0–10 scale. While the cutoff point for certainty recoding is ad hoc, using a value of seven as the cutoff point is both the modal and average selection in the certainty recoding literature according to a recent meta-analysis (Penn and Hu, 2023). We provide the results of sensitivity analysis where we alter the threshold at which recoding occurs in the Supplementary Material.

3.2 Survey administration

The survey was administered to a U.S. sample with oversampling of residents of North Carolina. We obtained a total of 2,068 valid responses, with 960 of these coming from North Carolina residents.³ While we acknowledge that our sample is heavily weighted towards North Carolina residents relative to the rest of the U.S., models that allow for differences in OWTS preferences between North Carolina residents and the rest of our sample find no statistically significant differences, so for our study we pool the data from North Carolina and the rest of the U.S. The questionnaire was programmed in Qualtrics and distributed through the third-party crowdsourcing company Academic Prolific (Peer, 2024). All participants over the age of 18 were allowed to participate according to a quota sampling approach mirroring the demographic distribution of gender, age, and ethnicity in the U.S. based on Census estimates from 2021 and received payment for their participation at $10/h, which translated to $4.33 per valid submission. All participants also signed a consent form explaining their participation was voluntary. After initial pilots, the final questionnaire was made available to participants on 14 May 2025, and data collection closed on 17 July 2025. On average, participants spent about 31 min answering the survey.

3.3 Contingent valuation analysis

CV and other stated choice models rest on the random utility maximization framework (Manski, 1977), which assumes that even though respondents know their utility (and resulting WTP), the investigator does not, and therefore from an investigators’ perspective, the indirect utility function U for individual i can be expressed by two separable components (Equation 1):where U_ij is the latent utility individual i receives from alternative j, where the alternatives are status quo (traditional septic system) and alternative treatment technology (either advanced septic or cluster septic). This latent utility consists of an observable, systematic component V_ij and an unobservable stochastic component ε_ij, which are assumed to be independently and identically distributed (i.i.d.) and follow a Type 1 (Gumbell) extreme value distribution. The values X_ij represent a vector of observed attributes including the cost premium related to each option and an alternative-specific constant for the alternative septic option. There are four alternative-specific constants in the model, reflecting the two different hypothetical scenarios of a new house (NH) vs. a new system (NS) and the two different alternative technologies of advanced septic system (AS) and cluster septic system (CS), making combinations NH_AS, NH_CS, NS_AS and NS_CS. β_i is a vector of marginal utility parameters associated with those attributes.

We observe each respondent’s preferred choice, which is the option with the highest indirect utility. Given our assumption that the random component of utility follows a Type 1 extreme value distribution, the probability of individual i selecting alternative j can be stated as a function of the observable attributes X_ij (Equation 2):

Since our model has two alternatives (J = 2), and the status quo has all zeros for X_ij values, we can simplify the probability of choosing the alternative to Equation 3:

We estimate these probabilities using a mixed logit model, also called random parameter logit (Train, 2012). Mixed logit models relax the assumption of preference homogeneity, instead assuming that respondents have different tastes for different attributes in the model. Rather than estimating a single preference parameter for an attribute (price, for instance), mixed logit models allow for a distribution of preferences for this attribute in the sample and estimates the first two moments of this preference distribution. Each individual i has their own taste parameter , drawn from a distribution (e.g., normal; log-normal). In this formulation, the probability of respondent i selecting option j is given by Equation 4:f(β_i|θ) is the probability density function of β_i, parameters θ represent the mean and variance of the probability density. This integral is solved by simulation using the mixlogit command in STATA 19.0 (RRID:SCR_012763). We cluster standard errors by respondent, to account for the payment card design in which each respondent made 5 choices, which leads to 5 observations per respondent.

To obtain the optimal combination of desired parameters and distributions, we ran different models and compared each model using the Bayesian Information Criterion (BIC), as it punishes increasing the number of parameters more than the Akaike Information Criterion (Brewer et al., 2016). This approach follows Czajkowski et al. (2024), who encourage use of a range of different specifications to model the observed data to find the optimal model. We compared a combination of models that include certainty recoded vs. non-recoded choices, models that treat the cost parameter as fixed (meaning we estimate a single preference parameter for cost rather than a distribution) vs. a lognormal distribution, and models that use monthly cost of a system (translated into an increase in monthly mortgage payments) vs. models that use the total premium for the system. In our case, the model with the lowest BIC under the desired parameters used certainty recoding, a log-normal distribution for cost and the full cost measure (different models and decision criteria can be found in Supplementary Table A4). Except for the price, we model all other parameters using a normal preference distribution.

Using our selected model, we generate two main sets of welfare estimates. First, we estimate WTP for the average respondent using our mean preference parameter estimates and the compensating variation formula in contingent valuation (Equation 5) (Haab and McConnell, 2002):

Where WTP_X is willingness to pay for the alternative OWTS in system-scenario X, β_X is the mean coefficient for alternative-specific constant for the alternative OWTS in system-scenario X, and β_Price is the mean coefficient for price. Additionally, using the probability formula in Equation 4, we construct demand curves for each technology-scenario pairing by estimating the probability of selecting the alternative OWTS option at a variety of price points.

4 Results

Table 2 displays summary statistics for our sample. The sample is fairly representative of the U.S. population in terms of gender, race, and ethnicity (Supplementary Table A5)⁴. The proportion of respondents living in sub-urban (48%) and rural (21%) settings are higher than national rates but are more apt for our OWTS application, as urban dwellers tend to be connected to sewer systems and do not utilize OWTS. Twenty-four percent of survey respondents currently live in a residence that uses conventional OWTS to treat their wastewater, which is representative of the total U.S. population using this technology. Alternative technologies are not as widely adopted, with only 1% of respondents using advanced septic systems and 6% using cluster or community septic systems. About 9% of respondents are not sure about their current wastewater treatment technology. Additionally, when asked about their experience with septic systems, 60% of respondents are either currently living in a residence on OWTS or have in the past lived in a home that used OWTS as wastewater treatment infrastructure. Since only respondents with septic system experience could be presented with the NS scenario, we arrived at 31% of people who were shown this condition, with the other 69% presented with the NH scenario. Technology is roughly evenly spread, with 48% of respondents who made their decisions between traditional septic systems and C-OWTS systems, and 52% between A-OWTS and traditional septic systems.

Mixed logit results are presented in Table 3. We find a negative price effect, meaning that when the price premium for an alternative system increases, the utility of that alternative and de facto probability a respondent chooses the alternative over the status quo (traditional septic system) decreases. In other words, as the price for a system goes up, respondents are less likely to choose that option. We find anecdotal evidence to this effect as well, with only 11.5% of respondents selecting the traditional septic option at every price point and only 13% of respondents selecting the alternative septic option at every price point, leaving the vast majority of respondents exhibiting a degree of price sensitivity. Mean coefficients for each technology alternative-specific constants are positive, which indicates that, controlling for price, the average respondent views switching to a different treatment system as an amenity, i.e., a desirable upgrade over traditional septic systems. Standard deviation estimates are large and statistically significant, which indicates substantial preference heterogeneity for all technologies and scenarios, or that respondents are highly diverse in their willingness to invest in each alternative technology.

WTP for an advanced septic system is highest when it is installed as a component of a new home. Respondents are on average willing to pay a premium of $7,409.99 for such a system. The difference between this value and the mean WTP for the same technology when it is installed as a replacement to an existing traditional system (i.e., NS scenario) is statistically significantly–here the respondents are only willing to pay $5,419.82.⁵ The roughly $2000 difference is not surprising as it is consistent with the concept of mental accounting (Thaler, 1999), which finds that people think about investments in mental budgets. Spending money on a new OWTS lumped in with a large investment (like a new house) feels less “painful”, than having to spend a larger sum of money on an individual piece of technology like an OWTS.⁶

WTP estimates for advanced septic systems in both the NS and NH scenarios are also significantly higher than WTP estimates for cluster septic systems⁷, which are $3,887.66 in the NH scenario, and $3,676.45 in the NS scenario. For cluster septic systems there is a lower valuation difference from scenario framing (∼$110), which results in a lack of statistical significance⁸ between the NH and NS WTP estimates for this technology.

Using the probability formula in Equation 3, we can construct demand curves for each technologyscenario pairing by estimating the probability of selecting the alternative OWTS option at a variety of price points. When translated into the probability for adoption (Figure 2), we can see that different technologies also exhibit different elasticities, especially at low and high prices: the probability of adoption for advanced septic systems in a new house only slightly changes between a price of $0 and $1,500 (3 percentage point (pp) decrease). In contrast, the probability of adoption of cluster septic systems in a new house drops by almost 10% when the price increases from $0 to $1,500.

FIGURE 2

Conversely for prices above $9,500, less than 10% of respondents would be willing to invest in a cluster septic system, even in the slightly higher NH scenario. Advanced septic systems are still attractive to over 30% of the respondents at that price point; only at a $13,000 premium would 90% of respondents prefer the traditional septic system over the advanced septic system (about $11,000 in the NS scenario). These facts add detail to the general finding that preferences are significantly stronger for the advanced septic system compared to both cluster septic and traditional septic systems.

While advanced septic OWTS were generally preferred to cluster septic systems in our results, standard deviation estimates demonstrate a large spread in preferences for both systems, indicating that there is no “one size fits all” technology. While one homeowner may have high WTP for advanced septic systems, they might have a zero or below zero WTP for a cluster system, and the opposite may be true for a different homeowner. Even at no cost (or the price of $0), around 5% of respondents are not willing to adopt an advanced septic system over a traditional septic system (Figure 2) when buying a new house (NH scenario). This number is higher for the system replacement (NS) scenario where about 10% of the people would not switch from the status quo to the improved technology even if switching was free. Comparable numbers are 18% in the NH and 19% in the NS scenario for cluster septic systems, showing an even greater resistance to the adoption of cluster systems versus advanced systems.

5 Discussion

OWTS technology adoption is heavily influenced by people’s perception of these technologies and their willingness to invest in them. Our results show that switching to more efficient OWTS is generally considered to be an attractive feature and not a burden. This interpretation is supported by the significantly positive average WTP for all technologies and scenarios presented in our study (Table 3). Since people are willing to pay a premium for an improvement in their wastewater treatment, developers, septic system installers, engineers, private consultants, and government officials who are responsible for installing or retrofitting OWTS may benefit from offering alternatives to traditional septic system, even though they have a larger upfront price. Compared to the rate of technology adoption observed in real-world scenarios (Vorhees et al., 2022; Erban et al., 2024; US EPA, 2024), we find higher acceptance and readiness to invest in alternative OWTS in our survey.

The extent to which adoption rates of alternative septic systems in our survey exceed those observed in actual technology adoption may be explained by several phenomena inherent to stated preference surveys, including potential hypothetical bias and providing information to respondents that the vast majority of people do not have access to when making real-world decisions. Since we are using approaches that in other contexts have been shown to reduce or eliminate hypothetical bias (cheap talk script and certainty recoding), it is reasonable to suspect that our WTP estimates reflect the true underlying WTP and our estimated adoption rates are close to what one would expect to see in practice if comparable information and access to alternative OWTS technology were widespread. This suggests that with additional information about different OWTS alternatives, people are willing to make these alternative septic technology decisions.

To maximize nutrient reduction potential in an area OWTS professionals should present homeowners with a range of available and permitted OWTS options so that individuals can make the choice that fits their preference. Given substantial preference heterogeneity, it’s likely that a menu approach to OWTS will lead to greater adoption across the watershed and greater water quality improvements.

Designing a system to be used in a new development can be cheaper than retrofitting a new system in an existing space that was initially allocated to a different septic technology, as this requires additional permits and evaluations (Joubert et al., 2004). Our results show that people are willing to pay a larger premium for an advanced septic system when it is part of a new house purchase compared to retrofitting an existing system. Additionally, new home purchasers in our sample exhibit a higher WTP for alternative septic compared with those in a retrofit scenario, suggesting early adoption is more likely in new home construction, making it a tangible immediate next step especially in light of limited resources to educate and nudge adoption of such systems. To reap all potential benefits from switching to an advanced OWTS technology, policymakers will likely need to implement policies that reduce homeowner burdens, such as subsidies or tax benefits, to solve the mismatch between a lower WTP and higher costs in case of OWTS retrofitting.

For cluster septic systems, we did not find significant differences in WTP between the system as part of a larger purchase (new house) and the retrofit (new system). This might be because respondents think about this technology as a collective investment by several other homeowners and therefore perceive the burden of cost differently. In addition to the financial costs, some individuals may view cluster systems as possessing nonfinancial costs associated with the necessary collaboration with neighbors inherent in these technologies. While this is plausible, given the nature of our survey we cannot definitively identify why scenario differences impact WTP for one technology but not for the other. A fruitful area of future research would be to further disentangle preferences for OWTS technologies to test differences in views on collective action are correlated with differences in preferences for individual vs. collective OWTS technologies.

From an engineering perspective (judging by nitrogen removal), cluster and advanced septic systems achieve similar treatment efficiencies (Joubert et al., 2004). However, since advanced septic systems are implemented by individual households, the cost per homeowner tends to be higher than with cluster septic systems. Additionally, cluster systems require less space per connected residence, and the space used for the drain field is collective property that does not have to be set aside as part of a single home. Despite the potential benefits, our results show that on average people are more interested in private, individual solutions as opposed to collective solutions: respondents have a higher WTP for advanced septic systems than for comparable cluster septic systems.

Several limitations and possible extensions of this work are noteworthy. In stated preference methods, there is widespread evidence that people overstate their true WTP in hypothetical questions, which makes WTP elicited in surveys typically higher than the behavior we might observe in real world transactions. Sugden (2001) explicitly mentions that people are not insincere or casual about their answers, they just might not be fully aware of their aversion to giving up money. While this is the general trend, much research has been conducted on methods to mitigate or even eliminate hypothetical bias in stated preference elicitation. While we cannot be certain of the role hypothetical bias might play in our estimates, we utilize approaches that have been shown to reduce or even eliminate hypothetical bias. Additionally, our finding shows a strictly positive WTP, which confirms respondents’ interests in the presented characteristics (Yokessa and Marette, 2019). Even in the presence of hypothetical bias, our findings help us understand the relative preferences of different OWTS technologies. These relative preferences between hypothetical amenities are likely insensitive to hypothetical bias, which mainly refers to distorted preferences between a hypothetical amenity and the status-quo alternative of traditional septic systems in this application. A valuable extension of this work would be to supplement our findings with more incentive-compatible studies on alternative OWTS through revealed preference work.

WTP might also be biased through framing, especially as respondents are less informed about the alternative OWTS technologies under consideration. Following previous research on framing in the choice experiment literature, we present the attribute levels in relative terms and are presenting the options using objective, scientifically accurate language to limit framing bias (Kragt and Bennett, 2012). Since we are following CV methodology, we are not comparing the value of each individual technology attribute, which makes our analysis more robust to attribute framing and lowers cognitive burden, increasing validity (Koemle and Yu, 2020). With that said, we acknowledge that there is the potential for any payment card design to frame respondent choices through the price levels presented. Additionally, respondents might also be driven to provide socially desirable answers, which can lead to an overstatement of their WTP. By administering a follow-up question querying their choice certainty, we are providing a way to disclose the underlying preferences and adjust the previous answer.

Our study explores two possible technologies available in the wastewater treatment space. These technologies serve as examples of a host of alternative OWTS and are not the only ones worthy of study, especially given our findings of preference heterogeneity and the conclusions of other authors researching diversity of environmental contexts (Joubert et al., 2004; Rich, 2008; Liu et al., 2020; US EPA, 2024). Each OWTS has the potential to differ along a variety of attributes, from treatment efficacy and required drainage field size to appearance, expected lifespan of the system and annual maintenance costs. Given the contingent valuation structure of our work, we are unable to disentangle how individual attributes of alternative OWTS impact homeowner utility. A valuable future extension of this work would be to adapt this OWTS choice into a discrete choice experiment design that examines how homeowners value individual attributes of these systems.

A potential limitation of any similar examination of cluster septic systems stems from the observation that these systems can only be implemented if a group of homeowners decides to do so. Our analysis by necessity treats this as an individual choice, even though in reality such a choice belongs in a larger web of decisions made by multiple group members. An extension of this work could examine how preferences for cluster septic systems may vary by such relevant characteristics as individual perceptions of their neighbors’ views of cluster septic systems, perceived connection to their community, and other relevant interpersonal factors. Another limitation of this work is our inability to identify and remove protest responses from our data. We elected not to include follow up questions to identify protest responses because our expectation is that relatively few respondents will be protestors. Protest responses are most numerous when the good is public in nature and the payment vehicle is controversial (i.e., tax increases), both of which are not the case in our study. With that said, we acknowledge that the potential presence of protest responses would bias our WTP estimates downward. Future work in this area could include questions to identify protest responses in an OWTS choice context.

Compared with centralized wastewater treatment, i.e., sanitary sewer systems, OWTS offer a host of benefits, especially in less densely populated, rapidly developing peri-urban and rural areas. In these settings, traditional septic systems are the status quo, but they face limitations, particularly under changing environments that increase risk of failure. Alternative OWTS technologies, such as advanced and cluster septic systems, have been developed and tested to deal with these limitations. However, their adoption rate in the United States remains low (Rudman et al., 2023). Understanding how WTP for alternative OWTS varies among homeowners is therefore important for guiding policies to support the adoption of technologies that reduce the environmental impacts of development.

By administering a stated preference survey to 2,068 homeowners in the U.S. with a concentration in NC specifically, we investigate how economic factors influence decision-making surrounding the adoption of alternative OWTS. Random utility maximization framework underlying stated preference approaches is used to model individual OWTS choices. Using a mixed logit model, which allows for preference heterogeneity based on unobservables, we estimate the probability of switching to an alternative OWTS in our sample at a variety of price points.

We find that respondents view both alternative technologies as amenities, which makes them preferable to the traditional septic system as wastewater treatment options before accounting for price differences. At the same time, a large spread in WTP indicates a high diversity in preferences for different technologies, suggesting the need for policymakers to look beyond “one-size fits all” solutions to support broad adoption of environmentally beneficial technology options. Furthermore, we observe that framing influences how people value OWTS alternatives: people purchasing a new home (NH scenario) are willing to pay significantly more for the same technology than those who are replacing an existing traditional septic system (NS scenario), suggesting that differences in mental budgets (Thaler, 1999) manifest in context dependent WTP.

Importantly we also observe that many people are willing to pay a premium for individual solutions (advanced septic systems) compared to multi-household systems (cluster septic systems). This finding points to the idea that there is a “cost of cooperation” for the adoption of group-based pro-environmental behaviors that needs to be explored in more detail in future work. The finding is consistent with past work investigating how social trust and collective action barriers shape choices related to collective systems (Arora et al., 2012; 2016; Naman and Gibson, 2015; Rudman et al., 2023; Hardisty et al., 2025). To make systems requiring the buy-in of more than one homeowner viable, trust-building and other measures to ensure cooperation might be necessary.

Overall, our findings suggest that the positive perception of alternative treatment systems should be used to promote the adoption of more effective OWTS technologies. Discrepancies between our results and the current observable uptake of OWTS technologies points to individual homeowners’ WTP for superior technology, but only if they can form their decisions with information about different affordances of the technologies available and access thereof. Yet wastewater treatment is not something people routinely think about. Furthermore, regulatory barriers to technology introduction and training enough professionals for repairs and maintenance of alternative OWTS can also lead to distortions between availability and need. Wastewater professionals therefore have a unique role in providing homeowners and policymakers with adequate and diverse information on available technologies to achieve the optimal match between technology and homeowner. By shining a light on the behavioral aspects influencing the adoption of different OWTS technologies, studies like ours show that interdisciplinary collaboration is important: technical innovation and behavioral insight are both necessary for the transition to decentralized, climate-resilient infrastructure.

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