Optimizing Municipal Solid Waste Management in Hangzhou: Analyzing Public Willingness to Pay for Circular Economy Strategies

Abstract

Effective municipal solid waste (MSW) management is crucial for urban sustainability, especially in fast-growing cities, like Hangzhou, China. This study examines residents’ willingness to pay (WTP) for the following five key MSW measures: differentiated waste charging, smart recycling points, on-site organic waste recovery, volunteer-based waste sorting supervision, and a community self-governance fund. Based on a survey of 521 residents across 13 districts, we use logistic and interval regression models to identify factors influencing WTP and payment amounts. Key findings include the following: Higher-income and more educated residents prefer cost-efficient, technology-driven solutions, like smart recycling and differentiated charging. Newcomers (≤5 years of residence) show higher WTP and greater sensitivity to environmental information, highlighting the need for targeted outreach. Providing explicit environmental benefits (e.g., waste reduction, increased recycling) significantly boosts WTP rates and payment levels. Community characteristics matter—residents in high-density areas favor waste charging, while those in older neighborhoods support volunteer programs and self-governance funds. Policy implications center on targeted outreach, transparent fee structures, and incentive programs to foster public trust and enhance participation. Although MSW management in Hangzhou remains predominantly government-led, select collaboration with private enterprises (e.g., in specialized recycling services) may offer additional efficiency gains. By aligning these measures with localized preferences and demographic patterns, Hangzhou—and other quickly urbanizing regions—can develop robust and inclusive MSW systems that contribute to broader sustainable development objectives.

1. Introduction

Municipal solid waste (MSW) management has emerged as a critically urgent global challenge. According to the World Bank’s latest report, What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050, the total volume of global waste is projected to increase by 70% between 2016 and 2050, rising from 2.01 billion to 3.4 billion metric tons [1]. Rapid urbanization and population growth continue to drive this increase, particularly in low-income regions, where waste recycling rates remain relatively low [2]. The report underscores that well-structured waste management systems are essential for fostering sustainable, healthy, and inclusive urban communities, and they also form the cornerstone for developing a circular economy. Nevertheless, solid waste management often receives insufficient attention and investment, highlighting the need for collaborative efforts across multiple stakeholders.

In Hangzhou, a major city in China, the annual generation of MSW has remained relatively stable at approximately 4.7 million metric tons, corresponding to a per capita daily output of around 1.1 kg [3]. Under the guidance of the Hangzhou Municipal Domestic Waste Management Regulations and related incentive policies, the city’s safe disposal rate of domestic waste has consistently reached 100% over the past few years, primarily relying on incineration and biological treatment technologies [4]. Beyond end-of-pipe solutions, Hangzhou has adopted a wide range of strategies to promote waste segregation, recycling, and resource recovery. These include differentiated incentives, market-driven operational models, and extensive collaboration with private enterprises [5]. Notably, the establishment of an intelligent waste collection monitoring system—integrating Internet of Things (IoT) devices and big data analytics—has enhanced management efficiency, while laying the groundwork for carbon credit incentives and the promotion of green lifestyles [6].

Despite these advances, Hangzhou’s municipal solid waste initiatives still face considerable challenges related to social acceptance and long-term sustainability. Policymakers and urban managers are particularly concerned with how to effectively motivate residents to participate in waste management and how to evaluate the social value and economic viability of different interventions. Existing literature has explored public willingness to pay (WTP) for individual strategies, such as constructing waste separation facilities, recycling systems, kitchen waste processing, and waste-to-energy incineration [7]. However, few studies have offered a systematic comparison of multiple waste management measures—particularly those that directly affect residents’ daily routines and require their active engagement to be effective.

To address this gap, the present study investigates the WTP of Hangzhou residents for the following five representative waste management measures: (1) a differentiated waste charging system, (2) an intelligent recycling points platform and community co-construction initiative, (3) on-site resource recovery facilities for kitchen waste in community settings, (4) the establishment of volunteer and public supervision systems for waste sorting, and (5) the formation of a community self-governance fund. By systematically comparing five different interventions rather than focusing on a single solution, we provide a holistic perspective on MSW management choices. Drawing on empirical data from 521 residents across 13 communities, our approach also examines how demographic and neighborhood characteristics—including newcomers vs. long-term residents—shape individuals’ WTP. To further explore this, we employ two survey scenarios (with and without explicit environmental benefit information) to capture how additional knowledge influences both the decision to pay and the amount paid. Additionally, we integrate logistic and interval regression methods to separately assess whether respondents are willing to contribute at all and, if so, at what level. Moreover, by incorporating social–psychological factors, such as trust in government, policy awareness, and perceived community benefits, this study demonstrates how targeted education and transparent governance can bolster public support for diverse MSW measures. The findings will inform city planners and policymakers in designing targeted communication strategies and policy interventions aimed at expanding waste reduction, resource recovery, and safe disposal efforts. Ultimately, by continuously improving waste governance frameworks and boosting public participation, Hangzhou can offer a scalable and replicable model for the creation of “zero waste cities” worldwide and the realization of broader sustainable development goals.

The remainder of this paper is organized as follows: Section 2 reviews the relevant literature on public WTP for MSW management and identifies key influencing factors. Section 3 describes the methodology, including the survey design, data collection, and rationale for applying logistic and interval regression. Section 4 presents and discusses the empirical results, highlighting policy implications and potential avenues for refinement. Section 5 concludes with a summary of the main findings, acknowledges study limitations, and offers recommendations for future research.

2. Literature Review

2.1. Public WTP for Low-Carbon Measures

In recent years, a growing body of research has investigated the public’s WTP for MSW management across various cities and countries. These studies typically focus on one or two particular interventions or products/services, such as household waste sorting equipment, door-to-door collection services, upgrades to waste-to-energy facilities, or incentive programs. Public WTP is often quantified in monetary terms (e.g., the extra amount individuals are prepared to pay for improved waste transportation or for higher resource recovery rates) or in terms of specific performance outcomes (e.g., increasing treatment efficiency by 30% or reducing one ton of greenhouse gas emissions) [8,9].

Most studies address only one or two specific MSW management strategies (e.g., incineration or waste sorting) in isolation, rather than comparing multiple interventions simultaneously. As in the domain of “low-carbon measures,” this single-focus approach does not capture how residents balance different costs, benefits, and personal preferences when they face multiple MSW management choices [10,11]. In urban contexts, people often encounter a range of options—such as differentiated waste charging schemes, smart recycling platforms, community-level composting, volunteer oversight programs, or community self-governance funds—each differing in terms of financial investment, implementation timeline, and co-benefits. A side-by-side evaluation of such schemes is critical; it reveals how differences in perceived individual vs. collective benefits, implementation costs, and technical requirements shape residents’ readiness to support each intervention.

Empirical findings consistently show that WTP levels vary markedly by region and specific MSW measures; wealthier or higher-income areas generally exhibit higher WTP figures [12,13]. This pattern may reflect economic flexibility and local environmental priorities, as well as broader fiscal policies. Notably, WTP can also vary within a single city, depending on the distinct income strata, educational backgrounds, or cultural settings of different neighborhoods. Thus, when designing MSW management programs in a city like Hangzhou, policymakers need to tailor their approaches to the unique socio-economic conditions of each community.

Researchers commonly employ methods such as open-ended questions, bidding (single- or double-bounded), discrete choice experiments (DCE), or payment scale approaches to estimate MSW-related WTP [14,15,16,17]. These methods can produce different numerical outcomes and are highly sensitive to survey design. If the bidding intervals are poorly set or if respondents lack adequate contextual information, the results may be over- or underestimated. For instance, Frew et al. [18] found that payment scale methods sometimes yield lower WTP estimates than closed-ended questions; other studies note that single- or double-bounded bidding can introduce bias if not meticulously constructed [19]. Therefore, when designing MSW surveys for urban residents, it is crucial to strike a balance between research accuracy and respondent comprehension and to provide clear cost–benefit information.

Given the inherent complexity of urban waste management, several scholars now advocate for comprehensive evaluations that simultaneously cover multiple solutions [20]. This point is especially relevant in rapidly developing regions, where numerous MSW initiatives emerge quickly, yet they have not been systematically assessed across all sectors. Such comparative studies can illuminate how differences in individual and collective benefits, perceived implementation expenses, and technical feasibility affect people’s willingness to invest in different MSW measures. However, published research focusing on multi-option comparisons remains scarce. This underscores the need for more integrative analyses, particularly in swiftly growing cities where government-led pilot programs in MSW management are expanding rapidly.

Interestingly, similar challenges regarding environmental services and affordability arise in water supply contexts, where the contingent valuation (CV) method has also been used extensively. For example, research in Cape Verde, a country with severe water scarcity and high operating costs for desalination, demonstrates that households’ WTP can be sensitive to both resource constraints and financial barriers [21]. In that study, payment-card surveys were administered to gather point and interval data on users’ maximum acceptable rates for an improved water supply. By employing interval data models and comparing them with multinomial logit results, the authors found that socio-economic characteristics (e.g., income level, household size) and the perceived urgency of the service strongly influenced WTP. This reinforces the importance of designing context-specific questionnaires, ensuring robust modeling approaches, and understanding how public acceptance of higher tariffs or fees may hinge on local resource conditions and service quality demands.

2.2. Determinants of Public WTP for MSW Management

A wide array of research on residents’ willingness to pay for sustainability initiatives suggests that multiple categories of factors can influence individuals’ decisions. In the context of MSW management, these determinants generally fall into the following three interrelated groups: sociodemographic factors, internal (cognitive) factors, and external factors. This section reviews these three categories, highlighting their significance in shaping WTP for diverse MSW measures.

2.2.1. Sociodemographic Factors

Variables such as age, educational level, income, occupation, and gender are commonly considered significant predictors of WTP for MSW management, although the direction and magnitude of their effects may vary according to local contexts [21,22]. For instance, numerous studies find that individuals with higher education and income levels tend to show both a stronger likelihood of paying for environmental interventions and a greater amount of financial contribution when they do so [23,24]. These groups often have more disposable resources and may also possess a broader awareness of environmental challenges, leading them to value waste reduction or recycling efforts more highly.

Meanwhile, household-related characteristics—such as household size, homeownership, and marital status—are also frequently associated with environmental responsibility, influencing support for initiatives like enhanced waste sorting infrastructure or organic waste treatment programs [25,26]. However, the extent to which these sociodemographic characteristics drive commitment to various MSW strategies remains insufficiently explored, particularly in rapidly urbanizing contexts, like China’s large cities.

Another aspect that may play a role is political affiliation or ideological leanings; some authors observe that respondents aligned with progressive or pro-environment parties display higher WTP for green policies [27,28]. While relevant in some international settings, empirical work on how political orientation influences WTP for MSW management in Chinese cities remains limited.

In sum, sociodemographic factors—encompassing basic demographic traits and household composition—often establish the baseline propensity for environmental support. Yet, their exact effect on willingness to invest in a range of MSW initiatives (e.g., differential waste charging, community composting, volunteer programs) still requires deeper analysis, particularly in fast-evolving urban contexts.

2.2.2. Internal (Cognitive) Factors

Compared with sociodemographic variables, internal or cognitive factors frequently offer more direct insight into the public’s attitude and WTP for MSW management. Existing literature indicates that individuals who exhibit a higher degree of environmental concern, stronger personal responsibility, and specialized knowledge regarding waste sorting or resource recovery are more inclined to bear the costs associated with MSW governance measures [29,30]. Moreover, trust in governmental institutions or managing authorities has been identified as a critical prerequisite for raising WTP [31], suggesting that public confidence in the competence and transparency of local agencies significantly boosts their willingness to financially support novel or expanded waste management initiatives.

Another closely related factor is the perception of collective benefits, which can heighten individual WTP. For instance, realizing cost savings on household utilities through more efficient waste handling [32] or anticipating increased property values following the adoption of community-wide waste classification [33] can motivate residents to invest in relevant environmental programs. Place attachment—capturing emotional or functional bonds to one’s living area—is similarly influential. Lu et al. [34] observed that subjective life satisfaction (e.g., living space, neighborhood safety, access to green areas) was positively linked to stronger support for advanced waste classification and other ecological actions. Chen et al. [35] likewise reported that people with higher life satisfaction were more willing to pay for emissions reduction or sustainability measures. This aligns with findings on psychological ownership—when residents feel a sense of ownership over a community project or space, they are more inclined to provide financial support or take on volunteer roles [36].

Overall, while sociodemographic factors shape basic tendencies, these internal (cognitive) dimensions—such as environmental awareness, perceived shared gains, institutional trust, and emotional investment in local spaces—often exert a decisive influence over individuals’ willingness to invest in MSW improvements. Investigating these personal and psychological elements in tandem with demographic traits can yield a more holistic understanding of how residents make decisions about waste management options.

2.2.3. External Factors

In contrast to the more commonly examined sociodemographic and internal factors, the role of external drivers in determining WTP for MSW management remains relatively understudied. Nonetheless, multiple studies suggest that social norms and information dissemination can significantly shape an individual’s decision making [37,38,39]. Social norms refer to attitudes and behaviors promoted or expected by one’s reference group; these norms may encourage individuals to conform to collective standards for sorting, recycling, or reducing household waste [40]. Survey findings further emphasize that social interactions—such as the size and closeness of one’s personal network—can bolster people’s engagement and WTP for sustainable behaviors [41].

Additionally, the provision of timely and comprehensible environmental information emerges as another key external factor [42]. In contexts where citizens are unsure about the specifics of MSW policy or technology, clear guidance or educational outreach can substantially improve their recognition and acceptance of proposed measures. Notably, not all audiences respond to information cues the same way. Research indicates that people with higher educational attainment may be less influenced by “anchoring effects,” whereas simplified experimental interventions can significantly raise WTP for emissions reduction among those with lower baseline awareness [43].

Lastly, environmental conditions themselves may serve as contextual triggers for heightened WTP. For example, Diederich and Goeschl [44] found that participants surveyed at higher temperatures reported greater concern about climate change and, thus, showed a higher willingness to pay. Similarly, Guo et al. [45] demonstrated that individuals living in heavily polluted areas expressed stronger support for air-quality improvement plans. Taken together, these insights suggest that external environmental stressors and clearly communicated information can prompt more robust public engagement in, and financial backing for, MSW measures—particularly when local conditions intensify the perceived urgency of action.

2.3. Research Gaps and Rationale for the Five Proposed Measures

Although a significant body of literature investigates public WTP for MSW management, much of this research concentrates on individual strategies (e.g., incineration or collection systems) or broadly combines various waste reduction and resource recovery methods, without conducting a systematic comparison of multiple interventions. While several studies quantitatively or qualitatively assess support for certain MSW policies, they rarely evaluate different measures under a unified framework. At the same time, many scholars suggest that, in rapidly urbanizing regions—where local socio-economic conditions and policy directives may diverge substantially from those of established urban areas—there is a need to examine WTP in a context-specific manner [46,47]. Against this backdrop, Hangzhou, one of China’s early adopters of city-wide waste sorting and resource recovery, presents both distinct local features and international comparability. However, comparative research targeting multiple MSW measures in this city remains limited.

To address these gaps, the present study focuses on the following five representative measures closely aligned with Hangzhou’s strategic goals for MSW management:

(1) Differentiated waste charging encourages residents to reduce waste generation and sort waste proactively. (2) Smart recycling points and community co-construction employ Internet-of-Things technology and a points-based reward system to enhance the efficiency and uptake of recyclable materials. (3) On-site resource recovery of kitchen waste implements small-scale composting or bioprocessing facilities at the community level to reduce the burden on end disposal systems. (4) Volunteer-based classification and public oversight system leverages volunteer service and community participation to improve sorting quality and monitoring. (5) Community self-governance fund provides stable financial support for MSW-related infrastructure, education, and incentive programs, while also strengthening resident involvement and autonomy.

These five measures were selected for three main reasons. First, municipal regulations and pilot programs in Hangzhou (e.g., garbage sorting ordinances, “zero waste city” trials) identify each measure as a priority domain for reducing urban waste [48]. Second, these measures collectively cover critical governance areas—residential neighborhoods, public services, and community engagement—thereby offering fertile ground for investigating household-level adoption barriers and drivers. Third, they align with strategies of global relevance; worldwide, differential waste charging, on-site resource recovery, volunteer supervision, and market-based incentives are recognized as crucial approaches to mitigating urban waste challenges [49]. Nonetheless, systematic and comparable WTP analyses of these interventions remain scarce in international scholarship.

Building on this framework, the study pursues the following core objectives: (a) evaluate resident awareness and attitudes toward these five MSW measures, (b) quantify and compare their willingness to pay (WTP) for each, and (c) identify key sociodemographic, cognitive, and external factors driving differences in WTP in Hangzhou.

3. Methods

3.1. The Hangzhou Case Study

Hangzhou is a major city in eastern China, with a population of over 13 million residents. As highlighted in the Introduction, it generates approximately 4.7 million metric tons of municipal solid waste (MSW) each year, with a per capita daily output of around 1.1 kg. In recent years, local authorities have adopted various waste management strategies, including incineration, biological treatment, and an IoT-based waste collection system. Despite these efforts, Hangzhou still faces ongoing challenges related to public engagement, long-term sustainability, and policy acceptance, especially given its rapid growth and diverse socioeconomic landscape.

Although some private enterprises have entered the waste sector (e.g., recycling logistics, local composting initiatives), overall MSW management remains heavily government-led. In many cases, private operators cannot effectively scale up services without municipal guidance and financing, underscoring the importance of stable public oversight in China’s dynamic urban environment. Against this backdrop, Hangzhou presents an ideal case for examining multiple MSW management measures—each requiring varying degrees of financial and behavioral commitments from residents—and for identifying the key factors that drive willingness to pay (WTP).

By analyzing household-level preferences within a predominantly government-led system that nonetheless allows for select private initiatives, this study aims to illuminate how policy frameworks, infrastructural investments, and evolving community needs can shape public support for sustainable waste governance.

3.2. Hypotheses

This study centers on multiple municipal solid waste (MSW) management measures that the Hangzhou government has either implemented or plans to adopt. Based on expert consultation and actual municipal requirements, we selected the following five representative interventions:

• Differentiated Waste Charging

This charges residents at different rates depending on their waste volume or sorting accuracy to incentivize reduction and accurate segregation.

• Smart Recycling Platform and Community Co-Construction

This employs IoT and data analytics to create a points-based recycling system, supplemented by community outreach, to encourage higher participation in recycling and reuse.

• On-Site Resource Recovery for Kitchen Waste

This installs small-scale composting or bioprocessing equipment within neighborhoods, converting organic kitchen waste into reusable materials and reducing disposal loads at end-of-pipe facilities.

• Volunteer and Public Supervision System

This strengthens the oversight of community waste sorting quality and processes through volunteer services, public scrutiny, and transparent information sharing, thereby fostering collaboration among residents.

• Community Self-Governance Fund

This establishes a fund at the community or subdistrict level to finance MSW-related infrastructure maintenance, awareness campaigns, and incentive programs. Residents collectively decide fund allocation and oversee its usage, enhancing local autonomy.

These five measures were chosen for several reasons. Actual financial and behavioral costs: each requires residents to make monetary or behavioral commitments (e.g., additional fees, habit changes, or time investments), making them suitable for evaluating real-world WTP. Public engagement and shared decision making: their viability and sustained implementation hinge on active resident involvement, or even co-decision making processes at the community level. Residents as both beneficiaries and executors: citizens not only benefit directly or indirectly from each measure but also often play a role in maintaining or operating the initiatives. Research and policy foundations: prior studies on urban waste management frequently highlight differential charging, local composting, and tech-driven incentive schemes as effective strategies for achieving reduction, resource recovery, and hazard minimization. Drawing on the relevant literature and prior empirical models, this study incorporates key sociodemographic and cognitive factors that may influence residents’ support for these measures. Following classic WTP frameworks, we posit the following five hypotheses (H1–H5):

H1. 

Residents who have migrated to Hangzhou in the past five years (newcomers), along with those who are younger, more educated, male, or have higher income levels, are more likely to pay for these MSW measures and tend to pay higher amounts.

H2. 

Individuals with stronger social and community attachment (e.g., married, with children, owning homes or vehicles) show a higher probability of paying and contribute more financially for MSW interventions.

H3. 

Those with greater knowledge of or expertise in waste management policies and practices demonstrate higher WTP and tend to provide greater financial contributions.

H4. 

Providing more information on the environmental and social benefits of these measures significantly increases both the likelihood and the amount of payment.

H5. 

Residents living in areas with better-developed infrastructure or community amenities express a higher propensity to pay for these waste management measures and are willing to pay more.

The above hypotheses rest on the following theoretical and empirical foundations:

Sociodemographic factors: previous research consistently shows that younger, wealthier, and more educated individuals typically exhibit greater willingness to bear the costs of environmental measures, likely due to stronger financial capacity and environmental awareness [50]. Sense of belonging: household and social capital factors, including family structure, property ownership, and community ties, significantly enhance people’s readiness to support public initiatives like recycling [50]. Cognitive dimensions: those with more in-depth knowledge of or trust in relevant environmental policies and governmental agencies are generally more inclined to participate and support waste reduction actions [50]. Built environment and community conditions: adequate infrastructure and sound urban planning not only improve the outcomes of environmental projects but also incentivize residents to offer financial support [50].

By testing these hypotheses, this study aims to elucidate how various sociodemographic and psychological variables drive Hangzhou residents’ willingness to pay for diverse MSW solutions—ranging from differential charging to smart recycling platforms, kitchen waste processing, volunteer oversight, and community funds—and to offer evidence-based insights for shaping more targeted and effective urban waste management policies.

3.3. Survey Design and Administration

This study aims to estimate Hangzhou residents’ willingness to pay (WTP) for multiple municipal solid waste (MSW) interventions and test the hypotheses outlined in Section 3.1. To achieve this, we developed a four-part questionnaire and employed a mixed-mode (online and offline) data collection strategy covering 13 districts of Hangzhou. All monetary values in this research were originally in Chinese yuan (CNY) and converted to U.S. dollars (USD) at an exchange rate of 1 USD = 7.02 CNY (as of October 2024).

3.3.1. Questionnaire Design

To ensure clarity and validity, the research team adapted established scales from prior studies on environmental behavior [50] and consulted local experts in waste management and community affairs. The final questionnaire is divided into four sections.

Section 1:

Socioeconomic and Demographic Information

Collects fundamental demographic and economic data (e.g., age, gender, education, income, employment, marital/family status, housing, and vehicle ownership). These variables help us explore how different social groups respond to various MSW measures.

Section 2:

Attitudes Toward Waste Issues and Government Trust

Evaluates respondents’ subjective views on the severity of urban waste problems and the role of governmental agencies in waste management. Measures trust in local government bodies responsible for waste-related policies and funding.

Section 3:

WTP for Different MSW Management Measures

Uses hypothetical scenarios to gauge initial willingness to pay for five interventions: differential waste charging, smart recycling platforms, on-site kitchen waste processing, volunteer oversight, and community self-governance funds. Participants first indicate their WTP and reasons for willingness/unwillingness. Then, they are provided with supplemental information on each measure’s potential environmental and social benefits (e.g., “This initiative can reduce XX tons of landfill/incineration annually”), after which they state their revised WTP, if any.

Section 4:

Awareness of Current MSW Policies in Hangzhou

Begins with an open-ended question asking respondents to list any municipal waste policies they are aware of. Then follows a series of closed-ended questions, presenting several policies (some implemented, some not) and asking respondents to classify each as “Yes, I know it’s implemented”, “No, I know it’s not implemented”, or “I don’t know”. This section allows us to assess participants’ breadth and depth of knowledge regarding existing local waste governance measures.

To minimize social desirability bias and protect privacy, the questionnaire explicitly states that “there are no right or wrong answers”, and all responses will be handled anonymously. Only non-identifiable demographic information, such as age range (rather than exact age), is collected. The final instrument achieved a Cronbach’s alpha of 0.831, indicating robust internal consistency [51].

3.3.2. Sampling and Distribution Process

(a)

Timeframe and Geographic Coverage

Data collection was conducted from 10 September to 20 December 2024, across 13 administrative districts in Hangzhou (Show in Figure 1). Given that Tonglu, Chun’an, and Jiande were recently merged into Hangzhou and have relatively low population densities, these areas were aggregated into a single district entity to streamline the sampling procedure. In each district, one or two residential communities were randomly chosen to ensure diversity. We calculated questionnaire quotas proportional to the population of each district (see

Table 1. Number of surveys distributed across Hangzhou.

District	Population	Number of Questionnaires (Planned)	Number of Neighborhoods	Number of Questionnaires Collected
Shangcheng	139.0	56	2	56
Gongshu	118.8	48	1	50
Xihu	117.1	47	1	49
Binjiang	54.3	22	1	23
Xiaoshan	214.0	86	2	90
Yuhang	140.5	55	1	59
Linping	112.7	45	1	47
Qiantang	80.2	32	1	34
Fuyang	85.7	34	1	36
Linan	65.2	26	1	27
Tonglu, Chun’an and Jiande	122.3	49	1	50
	Total	500	13	521

). Data were later weighted if necessary.

(b)

Mixed-Mode (Online and Offline) Approach

Online distribution: A unique survey QR code was created and publicized via neighborhood bulletin boards, WeChat groups, and community social media within the selected sites. To assist respondents unfamiliar with digital tools, trained staff members offered guidance in scanning the code and completing the questionnaire. IP and device checks were used to detect and prevent multiple submissions from the same source.

Offline distribution: In each chosen community, a small field team set up a station to recruit residents passing by or attending community events. Paper-based questionnaires were provided to those preferring a non-digital approach, particularly older adults or those with limited education. Trained interviewers were present to clarify instructions and answer queries. Each participant received a small gift (~2 RMB value) upon completion, as a token of appreciation. Combining online and offline methods broadened demographic coverage and addressed potential barriers to participation. This hybrid strategy helped maintain a high response rate and improved data reliability through immediate clarification of questions.

(c)

Quality Control

During distribution, researchers randomly audited questionnaire completeness and flagged anomalies or duplicated entries. For online submissions, back-end data were monitored (e.g., completion time, IP, and browser info) to detect automated or batch responses. A total of X completed surveys were collected, with an effective response rate of Y%. After excluding invalid responses, the final sample size met the analytical requirements, ensuring representativeness for subsequent statistical analyses and hypothesis tests. By implementing these procedures, we obtained a robust dataset capturing diverse perspectives on MSW management in Hangzhou, setting the stage for the empirical examination of residents’ willingness to pay and the potential drivers behind it.

To ensure robust representation, Equation (1) (see below) was used to calculate the required sample size, adopting a 5% margin of error based on Hangzhou’s latest census (over 13 million residents). This calculation indicated that at least 399 valid responses were needed. Accounting for potential incompleteness or invalid entries, the study targeted 500 distributed questionnaires. Ultimately, 521 valid surveys were collected after data screening, thus meeting the initial requirement. In this equation, n is the sample size, N is the population size in the surveyed area, and e is the margin of error.

$$n={\displaystyle \frac{N}{(1+N{e}^2)}}$$

(1)

Table 1 compares the planned and actual numbers of questionnaires gathered in each district. Because Tonglu, Chun’an, and Jiande were recently merged into Hangzhou and have smaller overall populations, they were treated as one statistical unit. In each district, one or two residential communities were randomly selected to capture diverse urban environments (e.g., older neighborhoods vs. newer developments).

During the survey process, participants were initially given an informed consent form that outlined the study’s objectives, emphasized the voluntary nature of participation, and ensured anonymity. Researchers supervised the completion of the questionnaires both online and offline, offering clarification for any confusing items (e.g., “differentiated waste charging” or “community waste management systems”). This supervision was essential to maintain data quality. Afterward, the researchers reviewed the completed questionnaires for accuracy and completeness, and participants were given a small token of appreciation for their involvement.

3.4. Estimating WTP for MSW Management Using the Contingent Valuation Method (CVM)

In this study, we employed the payment scale (PS) method to estimate the WTP for various MSW management initiatives. Unlike open-ended questions, which can be difficult for respondents due to the absence of cues on possible amounts, and close-ended questions, which do not fully capture individual responses and rely on assumptions, the PS method offers a more straightforward and efficient means of eliciting WTP. While bidding or bargaining methods may be less time-efficient due to the continuous need for trained surveyors to negotiate offers with participants, previous studies [52] have demonstrated that PS and multi-bounded discrete choice models tend to provide more conservative and reliable WTP estimates.

We explicitly chose logistic and interval regression models for data analysis due to their effectiveness in managing binary choices (i.e., willing or unwilling to pay) and interval data from payment scales. Although discrete choice experiments (DCE) are another common valuation method offering high analytical depth, we did not adopt this method, because it generally demands greater cognitive effort from respondents, potentially leading to fatigue and lower response accuracy, particularly among diverse demographic groups. In contrast, logistic and interval regression are well-established in the literature for their simplicity, interpretability, and proven robustness in contingent valuation contexts, aligning closely with our study objectives. For our study, a conservative estimate was preferred to ensure robustness. Consequently, the PS method was selected for this research. We selected this approach primarily for its simplicity and its ability to yield interval-based payment data, which aligns well with interval regression analysis. However, we acknowledge inherent methodological limitations associated with contingent valuation methods (CVM). Specifically, hypothetical bias may arise, where respondents tend to overstate their actual willingness to pay due to the hypothetical nature of the questions. Social desirability bias is another potential source of bias, as respondents might express greater environmental commitment or generosity than they would exhibit in real-life scenarios. To mitigate these biases, we maintained strict anonymity throughout the survey, clarified explicitly that there were no “right or wrong” answers, and avoided framing survey questions in a way that would lead participants toward socially desirable responses.

Despite these mitigation measures, some residual bias may still remain, underscoring the need for cautious interpretation of the estimated WTP values. Future studies might validate these findings by complementing CVM with revealed preference approaches or field experiments involving real economic transactions.

The WTP value is then calculated as the weighted average of responses across various bid intervals [53]:

$$\mathrm{WTPmean}= {\displaystyle {\sum }_{i=1}^n{f}_i{\alpha }_{iL}}$$

(2)

where WTPmean is the mean WTP value, $f_i$ refers to the frequency of WTP at bid interval $i$, and ${\alpha }_{iL}$ refers to the lower bound of bid $i$.

4. Analysis of the Results

The analysis in this study is divided into two sections. The first part provides a descriptive analysis of respondents’ WTP for five low-carbon measures in various scenarios, as well as the reasons behind their willingness or reluctance to pay. The second part involves the development of statistical models using explanatory factors to analyze respondents’ WTP and the corresponding payment amounts. The following subsections outline these results.

4.1. Descriptive Analysis of Survey Results

This subsection presents a summary of the responses to the four sections of the survey.

4.1.1. Social, Economic, and Demographic Characteristics of Respondents

The survey included a diverse group of participants from various social, economic, and demographic backgrounds.

Table 2. Social, economic, and demographic distribution of survey respondents.

Variable	Survey	Census (2023)
Gender	Male: 274 (52.9%) Female: 247 (47.1%)	Male: 52.1% Female: 47.9%
Age group	18–29: 143 (28.1%) 30–39: 171 (32.6%) 40–49: 107 (20.4%) 50–59: 63 (12.0%) ≥60: 37 (6.9%)	0–14: 12.7% 15–59: 67.7% ≥60: 19.6%
Education level	Junior high school or below: 58 (11.1%) High school or equivalent: 112 (21.5%) College (associate’s) degree: 155 (29.8%) Bachelor’s degree: 176 (33.8%) Postgraduate or higher: 20 (3.8%)	Elementary school (12.47%) Junior high school (33.83%) High school or equivalent (22.43%) Bachelor, associate degree, or higher (31.26%)
Occupation type	Self-employed or small business owner: 63 (12.1%) Corporate sector (state-owned or private): 267 (51.3%) Public institution (science, education, healthcare, etc.): 22 (4.2%) Unemployed (including unemployed, retired, etc.): 96 (18.9%) Student: 20 (3.8%) Government, military: 5 (1.0%) Freelancers and others: 48 (9.2%)	-
Income level (measured in CNY)	3000 and below: 63 (12.1%) 3001–4500: 14 (2.7%) 4501–6000: 70 (13.4%) 6001–8000: 91 (17.5%) 8001–10,000: 92 (17.7%) 10,001–15,000: 90 (17.3%) 15,001–20,000: 70 (13.4%) 20,001–30,000: 16 (3.1%) 30,001 and above: 15 (2.9%)	-
Marital status	Single (including divorced and widow): 177 (34.0%) Married: 344 (66.0%)	-
Parental status	Yes: 335 (64.1%) No: 186 (35.7%)	-
House ownership	Yes: 250 (48.0%) No: 271 (52.0%)	-

shows the distribution of these characteristics. The gender ratio among respondents was 1.121 (274 males to 247 females), slightly higher than the overall gender ratio of 1.087 in Hangzhou. The median age of respondents was 36 years, which is slightly lower than the city’s median age of 38.7 years, likely due to the inclusion of communities with a younger population.

In terms of education, the sample showed a higher level of educational attainment compared to the general census data. This may be because the census data also include people under 18 who are still in school. Regarding monthly income, the median income range for respondents was CNY 8001–10,000, which is above the reported median of CNY 6150 in Hangzhou [54]. Overall, while the sample is generally reflective of Hangzhou’s population, it does display some bias, particularly in age, education, and income levels.

4.1.2. Respondents’ Awareness and Perceptions of MSW Management

To understand respondents’ subjective perceptions and attitudes toward municipal solid waste (MSW) management, this study designed several assessment questions covering topics such as waste reduction, recycling, government effectiveness in waste management, and public trust. Specifically, the survey included eight statements, and respondents were asked to rate their agreement on a 5-point Likert scale from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”).

The results show that most respondents have a high level of awareness regarding the severity of waste management issues and their environmental impacts (mean = 4.01, SD = 0.73). They generally agree that the rapid growth of household waste is mainly caused by human activities, such as population growth and changes in consumption habits (mean = 4.11, SD = 0.77). Respondents also recognize that promoting waste sorting and resource recovery is an effective way to address the “waste siege” problem (mean = 3.95, SD = 0.75). In contrast, they have somewhat less knowledge about specific waste management measures and policies (mean = 3.67, SD = 0.85), although they report receiving a relatively high amount of promotional or advertising information related to waste sorting or recycling in their daily lives (mean = 3.73, SD = 0.86).

Regarding government trust, respondents generally have a positive attitude about whether the government can effectively allocate relevant funds and advance urban waste management (mean = 3.90, SD = 0.78), and they also rate the transparency of government waste management policies somewhat positively (mean = 3.73, SD = 0.80). This indicates that most citizens recognize the government’s role in waste management but still need more information to build higher confidence in the specific implementation and effectiveness of these measures.

To further verify the objective extent of respondents’ understanding of waste management initiatives, we followed the approach of Rhodes et al. [55] by using both open-ended and closed-ended questions to assess their specific knowledge of urban waste management policies and measures. The survey included a text box where respondents were asked to write down any waste management policies or measures they knew of in Hangzhou. The results show that 311 respondents skipped this question, stating “I don’t know” or “I am not aware”; among the remaining 210 respondents, the most frequently mentioned measures included waste sorting (such as mandatory sorting and centralized disposal), waste-to-energy projects, and community recycling and resource recovery. A few respondents also mentioned more specialized concepts, such as “zero waste city” and “on-site processing facilities”.

To visualize these open-ended responses, the study analyzed the key terms provided by the 210 valid respondents and created a word cloud (Figure 2). The word cloud shows that terms such as “waste sorting”, “waste-to-energy”, and “community recycling facilities” appeared most frequently, indicating that the public is relatively familiar with these “visible” waste management methods or campaigns, while they are less knowledgeable about emerging or specialized measures, like “biological treatment of kitchen waste” or “self-management funds”. Overall, respondents strongly agree on the importance and necessity of waste management at a macro level, but there is still a significant information gap regarding specific implementation measures and operational models, highlighting the need for further public education and outreach.

After this open-ended question, we used five close-ended questions to understand the respondents’ knowledge of five low-carbon policies. We gave a brief introduction to each policy and then asked the respondents whether they knew if the policy was currently being implemented in Hangzhou. The answer distribution for these five policies is shown in Figure 3. As can be seen from the order in the figure, most respondents were not familiar with the listed policies, except for the policy combination related to differentiated garbage fees. Among the remaining four policies, the “smart recycling points platform” had the highest recognition rate (128 votes), followed by the “waste sorting volunteer and public supervision system” (108 votes) and the “community self-management fund” (81 votes).

We defined respondents’ self-assessment of waste management knowledge as “perceived awareness” and measured “actual knowledge” through the completeness and accuracy of six open-ended and closed-ended questions. Since no respondent answered all questions correctly, the scale score ranged from 0 to 6. Figure 4 presents the distribution of these two indicators, with darker colors indicating higher frequencies. The results show that most respondents subjectively believed they were quite knowledgeable about waste management, but objectively, they were only able to correctly answer one to two questions related to policies implemented in Hangzhou. Further Spearman correlation analysis (r(521) = 0.233, p < 0.001) revealed a significant positive correlation between the two, although the overall association was weak. This indicates that there is still a considerable gap between the public’s confidence in their own level of understanding and their actual knowledge.

4.1.3. Respondents’ Willingness to Pay for Different MSW Management Measures

We assessed respondents’ WTP for five representative MSW management measures. For each measure, two scenarios were presented: one with and one without additional information on the environmental and social benefits of the measure.

Table 3. WTP of respondents for MSW management with/without environmental benefit information.

Measurement	WTP Ratio	Average WTP Amount
Differential Waste Charging (No Information)	55.1%	CNY 18.5 per household per month
Differential Waste Charging (With Information)	63.5%	CNY 21.2 per household per month
Implementation of Smart Recycling Points Platform and Community Engagement (No Information)	52.4%	CNY 68.2 per household per year
Implementation of Smart Recycling Points Platform and Community Engagement (With Information)	59.7%	CNY 82.3 per household per year
On-Site Organic Waste Resource Recovery in Communities (No Information)	48.3%	CNY 132.1 per household per year
On-Site Organic Waste Resource Recovery in Communities (With Information)	54.2%	CNY 153.4 per household per year
Establishment of Waste Sorting Volunteer and Public Supervision System (No Information)	46.9%	CNY 6.2 per household per month
Establishment of Waste Sorting Volunteer and Public Supervision System (With Information)	52.5%	CNY 7.4 per household per month
Establishment of Community Self-Management Fund (No Information)	50.7%	CNY 185.9 per household per year
Establishment of Community Self-Management Fund (With Information)	57.8%	CNY 216.4 per household per year

shows the WTP ratios and amounts for each MSW management measure. The WTP results for each of the five measures are discussed in detail in the following subsections.

• WTP for Differential Waste Charging

This study evaluated respondents’ willingness to pay (WTP) for differential waste charging, offering a more realistic scenario in the field of waste management. Differential waste charging is a fee structure based on waste generation volume or waste sorting quality, designed to encourage residents to reduce waste output and improve sorting accuracy. In the survey, respondents were asked if they would be willing to pay an additional amount on top of the current waste management fee in exchange for more refined waste sorting and processing services. A tiered pricing model was used, allowing respondents to compare different charging options directly.

The results showed that 55.1% of respondents (287 out of 521) were willing to accept differential waste charges, with an average payment of CNY 18.5 per household per month. The primary reasons for willingness to pay included reducing overall waste, increasing recycling rates, and lowering environmental pollution. Some respondents believed that a reasonable waste charging mechanism could incentivize waste reduction and encourage more active participation in waste sorting.

Among the 234 respondents unwilling to accept the differential charges, 126 felt that the current waste management fee was sufficient, and additional charges were not reasonable; 74 respondents argued that the government should bear the cost of waste management rather than individual residents; 34 respondents expressed doubts about the fairness of the differential charging model, stating that it might be difficult to supervise, and some people might evade charges. Additionally, some respondents expressed distrust in the implementation of waste sorting, fearing that waste might still be mixed during transportation or final disposal, thus undermining the environmental benefits of differential charging. Feedback included statements like, “If all the waste is eventually mixed, why should I pay?” and “Will the waste management company really dispose of it according to the sorting method? This system needs more transparency”.

Further analysis explored respondents’ WTP for a more detailed differential charging model. Some respondents indicated that if the fee system were more transparent and directly linked to actual sorting behaviors (such as charging by bag or weight), they would be willing to pay a higher fee. For example, 62.5% of respondents, after learning about a more detailed waste sorting incentive mechanism, were willing to pay an additional CNY 21.2 per household per month to support the implementation of an intelligent waste sorting system and stricter sorting supervision. At the same time, some respondents expressed concerns about the long-term economic feasibility of the waste charging model. Some believed that if fees increased annually, it might reduce acceptance, while others suggested a points-based reward system where residents earn points for correctly sorting waste, which could offset part of the waste management fee rather than directly increasing costs.

• WTP for Smart Recycling Points Platform and Community Engagement

This study assessed respondents’ WTP for a smart recycling points platform, which combines Internet of Things (IoT) technology and community-based models to incentivize residents to properly dispose of recyclable waste through a points system. Respondents were asked to choose between the “current waste sorting model” and “additional payment to support the smart recycling platform”. The survey was designed to assess respondents’ willingness to pay independently of government subsidies or mandatory requirements.

The results indicated that 52.4% of respondents were willing to support the smart recycling points platform, with an average payment of CNY 68.2 per household per year. Respondents cited the direct incentive to promote waste sorting through the points system as the primary reason for their willingness to pay. Additionally, the platform’s potential to increase community recycling rates and reduce environmental pollution was frequently mentioned. Some respondents also highlighted the convenience brought by the smart recycling system, such as the reduction in manual sorting complexity through QR code scanning.

Among the 47.6% of respondents unwilling to pay, 112 felt that waste recycling should be a government responsibility supported by public finance, and 79 stated that the points reward system was insufficient to change their waste sorting habits. Fewer respondents mentioned the high construction cost of smart recycling equipment, which might lead to difficulties in long-term maintenance (41 responses) or a lack of confidence in the system’s actual operational effectiveness (55 responses).

A deeper analysis of the reasons for non-payment revealed that some respondents were concerned about the lack of adequate recycling points, making it difficult for residents to use the system (22 mentions). Others were skeptical about the transparency of the points redemption system or its limited redemption options (19 mentions). Some residents, particularly the elderly, expressed discomfort with the technology involved, such as using QR codes and the points system (eight mentions). A few respondents also worried about the potential for misuse or damage to the equipment, which could affect the system’s long-term operation.

Further analysis revealed that after receiving additional information on the environmental benefits of the smart recycling platform (e.g., reducing landfill waste, increasing recycling rates), the willingness to pay increased slightly, with 59.7% of respondents willing to pay, and the average payment increasing to CNY 82.3 per household per year.

• WTP for On-Site Organic Waste Resource Recovery in Communities

To reduce the burden of the long-distance transportation and final treatment of kitchen waste, the Hangzhou government has promoted the deployment of small-scale organic waste resource recovery facilities (such as biodegradation or composting systems) within communities. This study designed relevant survey questions to evaluate residents’ WTP for such measures and their underlying motivations and concerns.

The survey results revealed that 48.3% of respondents were willing to pay for on-site organic waste resource recovery facilities, with an average payment of CNY 132.1 per household per year. The main reasons for willingness to pay were lower waste transportation and disposal costs (187 respondents); higher organic waste reuse rates (149 respondents), such as using compost for community greening or urban agriculture; reducing the odor of kitchen waste (92 respondents), thus improving the living environment in the community; and enhancing community sustainability (67 respondents). Notably, many respondents indicated that if the resource recovery system could be integrated with smart waste sorting or reward mechanisms, their WTP and payment amounts could increase further.

Among the 51.7% of respondents unwilling to pay, 163 argued that the government or property management should bear the cost of kitchen waste processing, 69 worried about odors or sanitation issues with the facilities, and 48 felt that the limited space in the community would be affected by the installation of such facilities. Another 36 respondents questioned the long-term feasibility of these facilities. In-depth interviews revealed concerns about the high maintenance costs, leading to possible abandonment (23 mentions), or a lack of clarity about the economic value and practical use of the compost products (18 mentions). A few respondents also expressed worries about mismanagement adding to the community’s burden (12 mentions).

After further explaining the potential benefits of this measure for carbon reduction and environmental protection, the proportion of respondents willing to pay increased to 54.2%, with the average payment rising to CNY 153.4 per household per year.

• WTP for Waste Sorting Volunteer and Public Supervision System

In this study, we also assessed the willingness to pay (WTP) for the establishment of a community volunteer and public supervision system for waste sorting. The system includes volunteer teams, process supervision, and the implementation of reward and punishment mechanisms. Initially, only 46.9% of respondents (n = 244) supported this system, with an average WTP of CNY 6.2 per household per month. However, when respondents were informed about the positive impact of the system on the community’s environment, such as reducing waste mis-sorting, increasing recycling rates, and fostering community cooperation, the willingness to pay increased to 52.5%, with the average WTP rising to CNY 7.4 per household per month.

The reasons for willingness to pay were mainly centered on the effectiveness of the volunteer system in promoting sorting accuracy and reducing pollution, with 194 respondents believing that such a system would help reduce the error rate in waste sorting and alleviate the burden on waste processing facilities, benefiting environmental protection. Furthermore, 183 respondents felt that the system would enhance community governance and the quality of public services, enabling residents to receive better waste sorting guidance, while promoting positive neighborly interactions. Another 130 respondents believed that the volunteer and supervision system would contribute to improving community cohesion by encouraging collective participation in environmental management and raising awareness of responsibility, thus improving the overall living environment.

On the other hand, the main reasons for unwillingness to pay included the belief that the government or property management should bear the costs, with 211 respondents feeling that waste management services should be funded by public finances or property management companies. Additionally, 47 respondents expressed that their waste sorting habits were already well-established, so they did not see the need to pay for such a system. Some respondents also mentioned economic concerns, with 40 people stating that their family income was too low to afford additional fees. A few respondents questioned the effectiveness of the system, raising doubts about whether it would actually improve waste sorting or if the supervision would be effective.

Interestingly, there were a small number of respondents who, despite not actively participating in waste sorting, were still willing to support the system to maintain a good community environment or to assist others in need. This highlights a degree of altruism, even among those who do not directly benefit from the system.

• WTP for Establishment of Community Self-Management Fund

For the community self-management fund, which was also part of the survey, 50.7% of respondents were initially willing to contribute, with an average WTP of CNY 185.9 per household per year. The main motivations included improving the overall living environment, such as upgrading community public spaces and enhancing waste sorting facilities, which residents believed would improve the comfort of living. In addition, 218 respondents mentioned that the fund could support low-carbon projects, reduce the urban heat island effect, improve air quality, and enhance community greening, which was perceived as an environmental benefit. Furthermore, 88 respondents saw the potential for indirect economic returns, believing that the improved environment would lead to increased property values.

After further explanation of the fund’s potential role in promoting waste sorting, reducing treatment costs, and lowering carbon emissions, the proportion of respondents willing to pay increased to 57.8%, with the average WTP rising to CNY 216.4 per household per year. This suggests that, when residents were informed about the fund’s specific benefits for MSW management, they were more likely to perceive the contribution as worthwhile.

However, despite the increase in willingness to pay, around 49.3% of respondents were still unwilling to contribute. Among them, 211 respondents argued that the costs should be borne by the government or developers, while 39 respondents felt that the current state of community facilities was adequate. Economic constraints were also cited by 32 respondents, who stated that their limited income made it difficult to pay for the fund. Some respondents also expressed skepticism about the fund’s effectiveness in achieving tangible improvements, questioning whether the investment would lead to measurable benefits, and some feared that the fund could be mismanaged.

This shows that, while residents may see the value in contributing to community-based waste management initiatives, concerns about economic burden, fairness, and transparency remain significant barriers to widespread participation.

4.2. Statistical Analysis of Survey Results to Test Hypotheses

In this study, we used a series of statistical models and analyses to test the hypotheses about Hangzhou residents’ willingness to pay (WTP) for five MSW management measures. The following sections describe the methods, key results, and an initial discussion relating them to the earlier survey findings.

4.2.1. Factors Affecting Respondents’ WTP for Five MSW Management Measures

(1)

Model Construction and Multicollinearity Test

To analyze the factors affecting respondents’ willingness to pay for differentiated waste charging, the introduction of intelligent recycling platforms, community-based food waste resource treatment, the establishment of volunteer and public supervision systems, and the creation of a community self-management fund, we constructed binary logistic regression models (Logit), with respondents being “willing to pay” or “unwilling to pay” as the dependent variable. The independent variables included respondents’ socio-economic and demographic characteristics (such as age, education, income, marital status, and whether they had children), as well as their awareness and attitudes toward waste management and environmental protection. Additionally, to examine the potential influence of the living environment, we included the building and spatial characteristics of the 13 communities involved in the survey (such as construction year, floor area ratio, and green coverage) in the model.

Before building the models, we performed a multicollinearity test for all variables. The results showed that “having children” and “marital status” were highly correlated (r(522) = 0.888, p < 0.001), so “having children” was excluded from the subsequent models. The variance inflation factor (VIF) for the remaining independent variables ranged from 1.0 to 2.2, indicating a low risk of multicollinearity.

(2)

Stepwise Binary Logistic Regression Results

We chose stepwise binomial logistic regression to model the respondents’ binary responses regarding whether to pay or not to pay for different MSW management measures. The model used a stepped approach to iteratively select variables, the inclusion of which can significantly improve the predictive performance of the model. In this way, the model itself works as a feature selector and identifies influential factors. We set the threshold of significance level for a variable to enter the model to be 0.25, and to stay in the model to be 0.15. We used SAS Studio (SAS OnDemand 3.1.0), a professional statistical software, for the modeling.

Table 4. Binomial logistic regression models for respondents’ WTP on low-carbon measures.

Variables	Differential Waste Charging	Implementation of Smart Recycling Points Platform and Community Engagement	On-Site Organic Waste Resource Recovery in Communities	Establishment of Waste Sorting Volunteer and Public Supervision System	Establishment of Community Self-Management Fund
Personal level variables
Age	-(-)	-(-)	-(-)	-(-)	-(-)
Gender	-(-)	-(-)	-(-)	-(-)	-(-)
HouseOwnership	-(-)	-(-)	-(-)	-(-)	-(-)
5YearResidency	-(-)	-(-)	-(-)	-(-)	−0.3978 (0.1266)
EducationLevel	-(-)	-(-)	0.2767 *** (0.0017)	-(-)	-(-)
IncomeLevel	0.2700 *** (0.0027)	-(-)	-(-)	-(-)	-(-)
MaritalStatus	0.6455 ** (0.0285)	-(-)	-(-)	−0.2943 (0.1232)	-(-)
OcupationType_self-employed or small business owner	-(-)	-(-)	-(-)	-(-)	-(-)
OcupationType_public institution	-(-)	-(-)	-(-)	-(-)	-(-)
OcupationType_corporate sector	-(-)	-(-)	-(-)	-(-)	-(-)
OcupationType_student	-(-)	-(-)	-(-)	-(-)	-(-)
OcupationType_govenment, military	-(-)	-(-)	-(-)	-(-)	-(-)
OcupationType_free lancer and other	-(-)	-(-)	-(-)	-(-)	-(-)
LowCarbonAwareness	0.3351 *** (0.0037)	0.2725 *** (<0.0001)	0.2706 *** (<0.0001)	0.2613 *** (<0.0001)	0.3338 *** (<0.0001)
Neighborhood level variables
FloorAreaRatio	-(-)	0.0950 * (0.0666)	-(-)	-(-)	-(-)
BuiltYear	-(-)	-(-)	-(-)	-(-)	-(-)
GreeningRate	-(-)	2.7417 (0.1432)	-(-)	4.4140 ** (0.0130)	-(-)
RatioGreenLandinBuff	-(-)	-(-)	-(-)	-(-)	-(-)
Model Goodness of Fitness
R-square	0.0520	0.1181	0.0676	0.0599	0.0607
Adjusted R-square	0.1062	0.1590	0.0910	0.0798	0.0810
Chi-square statistic	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001

Note: * p < 0.1; ** p < 0.05; *** p < 0.01.

shows the modeling results. All the models were found to be statistically significant with p-values < 0.0001 for the corresponding Chi-square tests.

Waste Management Awareness as the Strongest Predictor

By measuring respondents’ knowledge and understanding of Hangzhou’s current waste classification and management policies through a combination of open-ended and closed-ended questions, we derived an “actual awareness” index. This index showed a significant positive effect in all logistic regression models for the five waste management measures, indicating that respondents who had a better understanding of waste classification and its benefits were more likely to pay for these measures.

Socio-Economic Differences

Differentiated waste charging: higher-income and married individuals were more likely to pay for differentiated waste charges, possibly due to a better understanding of the incentives behind waste charging mechanisms and greater financial capacity.

Intelligent recycling platforms: respondents with higher environmental awareness or waste sorting knowledge were more likely to support the intelligent recycling platform.

Community-based food waste resource treatment: “new residents” who had moved to Hangzhou in the past five years showed a higher willingness to pay, likely due to dissatisfaction with the existing waste collection system or a greater desire to improve their living community’s environment.

Establishing a volunteer and public supervision system: respondents with a higher trust in the government or prior volunteer experience were more likely to support this measure; however, this effect was primarily represented by “awareness” and “marital status” in the quantitative models.

Establishing a community self-management fund: married individuals and higher-income groups were more likely to recognize the benefits of this fund, with some considering it a way to “co-build the community and share the benefits”.

Impact of the Built Environment

Respondents living in high-density or newer communities were generally more willing to pay for the introduction of new facilities (such as food waste treatment systems or volunteer sites). Conversely, residents in areas with lower green coverage were more inclined to support measures like “increasing public space for waste sorting” or “building resource treatment facilities”, likely due to a stronger demand for visible environmental improvements.

(3)

Interval Regression Model: Factors Affecting Payment Amounts

In addition to the binary responses indicating willingness or unwillingness to pay for the different MSW management measures, interval regression models were constructed to examine factors influencing the payment amounts for these measures. Specifically, for respondents selecting the level of payment, denoted as Xt, we consider their actual payment amount should fall within the range of (Xt-1 + 1, Xt], which is interval-censored and makes interval regression a natural fit for this analysis. The modeling results are presented in

Table 5. Interval regression models for respondents’ payment amounts on low-carbon measures.

Variables	Differential Waste Charging	Implementation of Smart Recycling Points Platform and Community Engagement	On-Site Organic Waste Resource Recovery in Communities	Establishment of Waste Sorting Volunteer and Public Supervision System	Establishment of Community Self-Management Fund
Personal level variables
Age	-(-)	-(-)	-(-)	-(-)	−7.4834 *** (0.0083)
Gender	-(-)	51.328 ** (0.0358)	-(-)	-(-)	102.8520 * (0.0637)
HouseOwnership	-(-)	56.657 ** (0.0372)	−190.3404 ** (0.0286)	-(-)	-(-)
5YearResidency	31.5669 ** (0.0194)	-(-)	177.2902 (0.1533)	-(-)	109.2902 (0.1533)
EducationLevel	33.6571 * (0.0909)	-(-)	173.2065 *** (<0.0001)	−54.0075 * (0.0559)	−96.4493 *** (0.0029)
IncomeLevel	37.4048 (0.1253)	15.834 * (0.0834)	-(-)	75.5706 *** (0.0001)	112.5593 *** (<0.0001)
MaritalStatus	-(-)	-(-)	-(-)	-(-)	-(-)
OcupationType_self-employed or small business owner	-(-)	-(-)	-(-)	-(-)	−259.260 ** (0.0229)
OcupationType_public institution	-(-)	−151.3 ** (0.0235)	-(-)	-(-)	-(-)
OcupationType_corporate sector	-(-)	-(-)	-(-)	-(-)	−220.259 ** (0.0206)
OcupationType_student	-(-)	76.657 ** (0.0372)	-(-)	6.6096 * (0.0517)	-(-)
OcupationType_govenment, military	-(-)	-(-)	-(-)	-(-)	-(-)
OcupationType_free lancer and other	-(-)	86.928 ** (0.0372)	-(-)	7.8935 * (0.0828)	-(-)
LowCarbonAwareness	8.1523 * (0.0896)	-(-)	-(-)	-(-)	24.6027 (0.1443)
Neighborhood level variables
FloorAreaRatio	-(-)	-(-)	-(-)	8.3602 * (0.0801)	234.1052 (0.1025)
BuiltYear	21.5089 ** (0.0308)	83.379 * (0.0907)	163.0800 *** (0.0023)	8.4460 *** (0.0020)	255.1668 ** (0.0362)
GreeningRate	-(-)	-(-)	-(-)	-(-)	-(-)
RatioGreenLandinBuff	-(-)	-(-)	-(-)	23.676 ** (0.0279)	−283.676 ** (0.0279)
Model Goodness of Fitness
AIC	1897.717	1208.947	1672.354	1398.919	1517.806
BIC	1930.801	1256.700	1691.005	1448.769	1586.525
R-square (lower bound)	0.0533	0.0999	0.0759	0.1425	0.1845
R-square (upper bound)	0.0510	0.0995	0.0754	0.1379	0.1805

Note: * p < 0.1; ** p < 0.05; *** p < 0.01.

.

To further explore the factors influencing respondents’ payment amounts for the measures they were willing to pay for, we applied interval regression models for each of the five waste management measures. This method is appropriate for payment amounts that fall within set ranges on the questionnaire and provides a more accurate measure of the factors that lead residents to offer higher or lower amounts. The results, shown in Table 5 (example), revealed the following:

Significant Positive Effect of Income Level

Higher-income respondents were not only more likely to pay but also tended to offer higher amounts, particularly for “community-level” public measures (such as the community self-management fund and food waste treatment facilities).

New Residents vs. Long-Term Residents

Residents who had moved to Hangzhou in the past five years showed significantly higher payment amounts for “differentiated waste charging”, “community-based food waste treatment”, and “community self-management fund”, possibly due to a greater willingness to improve the urban environment or a more open attitude toward new measures.

Education Level and Public Good Payment

Higher-education respondents were more likely to pay for measures with direct benefits or within a controllable scope, such as “differentiated waste charging” or “food waste resource treatment”. However, for more public-oriented measures, such as “public waste recycling facilities” or “community funds”, the increase in payment amounts was less significant. Some respondents believed that public facilities should be funded by the government or specialized agencies, rather than residents.

Occupation Type and Demographic Characteristics

Students and freelancers were generally more willing to invest in measures like “community volunteer supervision systems” or “intelligent recycling platforms”, possibly due to community identification or interest in innovative initiatives. In contrast, respondents working in public institutions were more cautious about paying for measures like “differentiated waste charging” and “public funds”, likely due to their strong identification with government fiscal responsibilities.

Differences in Residential Environment

Residents in older communities tended to offer higher payment amounts for all five measures, likely due to the lack of facilities and strong demand for environmental improvements. Conversely, residents in communities with higher green coverage or more advanced waste sorting infrastructure tended to be more conservative in their willingness to pay.

4.2.2. Impact of Additional Information on Residents’ WTP

To test the impact of “providing or not providing environmental benefits information” for waste management projects on WTP, we set up two scenarios in the survey. Without environmental benefits information: only the basic operation model and potential benefits of the measure were introduced. With environmental benefits information: additional details were provided on how the measure could reduce waste, increase resource recycling, decrease carbon emissions, and improve the urban environment.

The results (compared with the data in the

Table 6. Two-sample paired t-test of WTP ratio and payment amount between scenarios with and without known environmental benefits.

	WTP Ratio	WTP Amount
Differential Waste Charging	t(520) = −4.80, p < 0.001 ***	t(330) = −6.76, p < 0.001 ***
Implementation of Smart Recycling Points Platform and Community Engagement	t(520) = −5.44, p < 0.001 ***	t(310) = −5.77, p < 0.001 ***
On-Site Organic Waste Resource Recovery in Communities	t(520) = −5.54, p < 0.001 ***	t(282) = −6.51, p < 0.001 ***
Establishment of Waste Sorting Volunteer and Public Supervision System	t(520) = −5.23, p < 0.001 ***	t(273) = −6.12, p < 0.001 ***
Establishment of Community Self-Management Fund	t(520) = −5.53, p < 0.001 ***	t(301) = −6.37, p < 0.001 ***

Note: *** p < 0.01.

) showed that, in the “with information” scenario, the WTP probability and payment amounts for measures like “differentiated waste charging”, “intelligent recycling platforms”, “community-based food waste treatment”, and “volunteer and public supervision systems” all significantly increased. Paired t-tests showed that these differences were statistically significant (p < 0.001).

Who Is More Affected by Information?

Further analysis revealed that “new residents” who had moved to Hangzhou in the past five years were most sensitive to the additional information provided (p = 0.090, significant at the 0.1 level). This could be because they had less knowledge of the local waste management situation, and once informed about the potential benefits of the projects in terms of emission reductions, cost savings, and community health, they were more willing to change their previous attitudes.

4.3. Preliminary Connection with Discussion

This section presents the systematic results of the study, highlighting the variations in respondents’ willingness to pay (WTP) for the five waste management measures, the distribution of payment amounts, and the key influencing factors. These findings lay the foundation for further discussion. The following key insights can be drawn:

4.3.1. Differences in WTP for Different Waste Management Measures

Measures like differentiated waste charging and community-based food waste treatment, which offer direct benefits to individuals or the community, tend to receive higher levels of acceptance and higher WTP amounts. In contrast, for more public-oriented initiatives, such as community self-management funds and volunteer systems, many residents believe these responsibilities should be borne by the government or developers.

4.3.2. The Crucial Role of Public Awareness and Information Transparency

Residents with a deeper understanding of waste sorting and its benefits are generally more willing to pay for waste management measures. Additionally, in scenarios where extra information was provided, the WTP rates and amounts for most measures significantly increased. This underscores the importance of enhancing public education and clearly communicating the environmental and economic benefits of these initiatives to garner broader public support.

4.3.3. Differences Between New Residents and Long-Term Residents

The study found that new residents, who had moved to Hangzhou more recently, are more sensitive to information and are more likely to show a positive attitude toward waste management measures. They also tend to offer higher financial support if their economic situation permits. This group may have higher expectations for urban environmental improvements.

4.3.4. Comparison with Other Domestic and International Studies

The findings align with common trends, such as the higher WTP associated with higher income levels, while also reflecting local characteristics (e.g., the more positive attitudes of new residents) and policy context (e.g., a stronger emphasis on government responsibility for public goods). Future research could involve broader horizontal comparisons with other cities to further explore these dynamics.

5. Discussion

5.1. Summary of Hypothesis Testing

By deploying multiple quantitative analyses, this study investigated Hangzhou residents’ willingness to pay (WTP) for the following five municipal solid waste (MSW) management measures: differentiated waste charging, smart recycling and community co-construction, on-site kitchen waste resource recovery, volunteer-based supervision of waste sorting, and a community self-governance fund. We then tested five hypotheses derived from prior research. This section briefly summarizes the extent to which our data support each hypothesis. Before turning to these hypotheses, we introduced Hangzhou as our case study, emphasizing its rapid urbanization, newly merged districts, and its predominantly government-led approach to MSW management—a structure that has so far provided stability and uniform standards amid ongoing urban expansion.

In order to provide a concise overview,

Table 7. Summary of hypotheses, key variables, and findings.

Hypothesis	Key Variables	Expected Outcome	Findings	Significance	Interpretation
H1: Socioeconomic and Demographic Factors (age, education, gender, income)	Age, education, gender, income	Younger, wealthier, more educated populations exhibit higher WTP.	Partially Supported: Age is not consistently significant. Education and income are important predictors; more educated/higher-income respondents are likely to pay more. Male respondents tend to invest larger sums in technologically intensive measures.	p < 0.05 for education and income; mixed for age and gender	Sample bias may exaggerate WTP among young/educated. Some well-educated respondents prefer direct household benefits over public-goods measures.
H2: Social and Local Attachment (marital status, children, home ownership)	Marital status, children, home ownership, household size	Strong local affiliation correlates with higher WTP.	Not Supported Overall: Few of these variables were significant. “New Hangzhou residents” (<5 years) displayed a higher WTP, contradicting typical assumptions about long-term affiliation.	Mostly non-significant	Economic and educational factors may overshadow local attachment. New residents’ stronger motivation may relate to higher expectations for local environment or a desire to integrate socially.
H3: Awareness of Waste Management Initiatives	Awareness Index (correct ID of existing policies and measures)	Greater knowledge of MSW measures leads to higher WTP.	Supported: Correct identification of multiple policies correlated strongly with higher WTP for all five measures.	p < 0.01	Demonstrates the importance of clear, accessible policy information in boosting support for waste management programs.
H4: Providing Environmental Benefit Information	Detailed info on landfill reduction, resource utilization, etc.	Stated WTP increases when respondents learn potential environmental gains.	Supported: Paired t-tests indicated significant increases in both WTP probability and amount after additional information. Newcomers were particularly influenced.	p < 0.01	Tangible benefits can sway hesitant participants, though real-world results may differ if respondents are not fully accountable for stated preferences (hypothetical bias).
H5: Built Environment Conditions (building age, green coverage, layout)	Building age, green coverage, floor–area ratio, spatial layout	Different community characteristics influence preferences for certain MSW measures.	Partially Supported: High-density areas favored differentiated charging and tech solutions. Older complexes more inclined toward volunteer oversight or local composting.	Some variables p < 0.05	Reflects neighborhood-specific demands: older areas may require infrastructural revitalization, while dense urban zones face greater waste pressure, thus preferring faster, more systematic interventions.

summarizes each hypothesis, the key variables, expected outcomes, empirical findings, and a brief interpretation of the results.

5.1.1. Socioeconomic and Demographic Factors (H1)

Based on the existing literature, the study hypothesized that variables such as age, education, gender, and income significantly shape residents’ intentions and the amounts they are willing to pay for waste management interventions (H1). However, our findings present mixed support for this assumption. Although the overall sample size (n = 521) provides a basis for statistical analysis, we acknowledge that younger and more educated respondents are slightly over-represented compared to the broader Hangzhou population. This demographic bias may, in part, explain some of the observed trends—for example, higher WTP levels among respondents with greater financial or educational resources. As suggested in Marques et al. [56], income and educational attainment are particularly pivotal in influencing environmental behaviors, warranting future use of sample weighting or stratified sampling to ensure stronger representativeness.

Age: Overall, age did not exhibit a strong effect on the probability or the amount of payment. Nevertheless, subtle differences emerged for specific measures: younger participants tended to allocate higher contributions to community-oriented innovations (e.g., smart recycling platforms, volunteer-based oversight), potentially reflecting their greater receptivity to new technologies and communal engagement.

Education: More educated respondents displayed a stronger inclination to fund measures closely related to immediate household benefits, such as on-site kitchen waste composting or differentiated waste charging, presumably due to clear economic and environmental gains. Conversely, they were less willing to spend on measures with more pronounced public goods attributes—like community self-governance funds or volunteer supervision—believing the local government or property management companies should bear these costs. Some references note that lower-status groups often demonstrate higher prosocial tendencies [56], which aligns with our finding that less-educated participants were more supportive of community-wide waste initiatives, perceiving broader collective benefits.

Gender and income: Male respondents were more likely to pay larger sums for technologically demanding or capital-intensive measures (e.g., advanced recycling systems, specialized equipment for MSW management). Income level was less decisive for whether a respondent paid at all, but once individuals chose to pay, those in higher income brackets contributed more substantial amounts. This pattern resonates with experiences in certain affluent neighborhoods of Hangzhou, where economic flexibility facilitates greater investment in public services.

5.1.2. Social and Local Attachment (H2)

We posited that stronger community or urban attachment—reflected in marital status, children, home ownership, and other indicators—would drive higher WTP and spending levels for MSW management (H2). Contrary to expectations, the results did not generally validate this hypothesis. Beyond households already equipped with advanced classification tools or kitchen waste processing facilities (who showed higher support for on-site resource recovery), variables such as home ownership, marital status, or household size did not exhibit significant overall correlations with WTP.

Of particular note, “new Hangzhou residents” (those who arrived within the last five years) showed a greater willingness to pay and were likely to contribute higher amounts for most MSW measures. However, we cannot definitively conclude that recent relocation is the sole driver; factors such as educational background or higher socioeconomic status might also characterize this group. Departing from the conventional view that long-term residents display stronger local affiliation, our data suggest that newcomers may be more sensitive to local environmental conditions and, thus, more motivated to invest in improving community waste management.

5.1.3. Awareness of Waste Management Initiatives (H3)

We further hypothesized that individuals more knowledgeable about existing waste management practices and policies would exhibit greater willingness to pay (H3). The findings revealed that, although Hangzhou residents displayed reasonable familiarity with “basic sorting and recycling systems”, they had a limited understanding of newer or more detailed policies (e.g., specifics of community self-governance funds or differential charging structures). However, respondents who could correctly identify multiple initiatives already in place consistently exhibited higher WTP for all five measures. Hence, hypothesis H3 is strongly supported; enhancing residents’ awareness of MSW programs appears to be a key lever for elevating their financial commitment.

5.1.4. Providing Information on Environmental Benefits (H4)

The analysis underscored the importance of communicating these measures’ environmental impacts—such as reduced landfill loads, improved resource utilization, and lowered ecological harm (H4). Paired t-tests showed that, once participants were informed of each measure’s potential environmental and economic payoffs (e.g., higher compost utilization rates, increased recycling via point-based platforms), their likelihood of paying and the amounts pledged both rose significantly. Recent arrivals (new Hangzhou residents) were especially sensitive to additional information, relative to long-term dwellers who perhaps already held more stable views.

Although providing supplementary details improved WTP in our survey context, real-world outcomes may differ. Respondents might have offered optimistic “best-case” answers that overestimate actual financial commitments. Further discrete choice experiments or field trials could validate whether households would still pay once multiple measures are simultaneously implemented.

5.1.5. Built Environment Conditions (H5)

We hypothesized that neighborhood characteristics—such as building age, green coverage, and spatial layout—could influence individuals’ investment in waste management initiatives (H5).

The results suggest the following: Floor–area ratio and green coverage: Residents in high-density communities with a high floor–area ratio tended to support smart recycling and differentiated waste charging more, likely because heightened waste pressures call for more robust sorting or incentive mechanisms. Meanwhile, those in greener areas showed stronger preferences for on-site composting or “differentiated charging + composting” to maintain or enhance their environmental quality.

Demands from older neighborhoods: Respondents in older complexes indicated a higher willingness to pay for volunteer oversight programs or local composting, possibly due to weaker existing infrastructure and a more urgent need for improvement. This finding aligns with ongoing policies to revitalize older communities in Hangzhou, suggesting that a strategic focus on modernizing waste infrastructure in these areas could yield tangible benefits.

5.2. Comparisons with Existing Literature and Practical Implications

Although our findings provide valuable insights into residents’ WTP for MSW measures, potential biases inherent in self-reported contingent valuation methods warrant caution. Specifically, hypothetical bias may arise, where respondents overstate their actual WTP due to the hypothetical nature of survey scenarios. Furthermore, social desirability bias—where participants respond in ways perceived as socially acceptable—may inflate reported WTP values. Although we have implemented measures to mitigate these biases (e.g., ensuring respondent anonymity, neutral framing of questions, and clear instructions emphasizing no correct or incorrect answers), residual biases could still affect the robustness of our findings. Future studies may enhance validity by incorporating complementary approaches, such as discrete choice experiments (DCE) or revealed preference methods to cross-validate findings and reduce potential estimation biases.

The WTP patterns for MSW measures in Hangzhou share notable parallels with studies from other regions. Both in developed cities (e.g., Copenhagen, Singapore) and emerging urban areas (e.g., parts of South America and Southeast Asia), there is a common expectation for government to fund “public good” components of waste management, whereas individuals are often more ready to pay for interventions yielding clear private or local benefits [57]. This underscores the critical roles of economic rationale and policy transparency in shaping public attitudes and actions.

Similar cities with comparable demographics or economic standing could benefit from these findings: tailoring communication and incentive strategies for distinct groups—such as newcomers vs. long-term locals or high-income vs. lower-income households—can reduce inequities and foster more sustainable, inclusive waste systems.

5.2.1. Public Awareness and Educational Interventions

Our data reveal that people lack a thorough understanding of certain MSW measures (e.g., smart recycling programs, differentiated charging), but once informed of specific environmental and economic gains, their WTP increases significantly. This suggests that local authorities should publicize concrete numerical or illustrative evidence of waste reduction, circular economy benefits, and community advantages. In international examples, sustained public outreach—often through visual aids like infographics on total waste outputs—significantly raised the acceptance of sorting and waste reduction [58]. Hangzhou, likewise, could leverage bulletin boards, social media (e.g., WeChat), and metro advertisements to highlight the merits of various initiatives.

5.2.2. Tailoring Strategies for Different Resident Groups

The study shows that “new Hangzhou residents” are particularly open to funding waste governance and respond strongly to supplemental information. Municipal agencies and neighborhood committees could, therefore, organize “orientation packages” or “community welcome sessions” aimed at newly relocated residents, providing guidelines on waste classification, point-based recycling platforms, and composting programs. For longer-term residents, events promoting local interactions, like “waste sorting competitions”, may bolster belonging and heighten collective buy-in. Experiences in Melbourne suggest that segmenting educational programs for different demographic clusters can significantly boost the efficacy of community-led waste initiatives [59].

5.2.3. Minimizing Implementation Disruption

Numerous respondents expressed concerns about potential noise, trash overflow, and service interruptions during upgrades or construction related to waste sorting infrastructure. Enhancing acceptance for differential charging or local composting facilities, thus, requires clear advance communication on timelines, mitigation measures, and anticipated benefits. European “green renovation” initiatives show that early disclosure of noise and dust control strategies—and scheduling key activities outside peak resident hours—can markedly raise satisfaction [60]. Hangzhou’s ongoing renovation projects in older neighborhoods may adopt these best practices (e.g., real-time monitoring of noise and air quality, public updates on work progress) to convey transparency and reduce disruptions.

5.2.4. Addressing Misconceptions About Novel Measures

A portion of participants voiced doubts about maintaining and safely operating new technologies (e.g., smart recycling devices, composting equipment) or concerns about the fairness of volunteer supervision. Yet, successful precedents elsewhere demonstrate that many perceived drawbacks reflect outdated information or overstated risks. By combining official communications, media outreach, and pilot demonstrations that invite the public to observe or test the technology, misunderstandings may be countered and trust gradually developed. Experiences in Oslo, where decentralized kitchen waste composting was rolled out, and in San Francisco, where household-by-household sorting oversight was employed, indicate that transparency and modest subsidies can strongly motivate resident adoption and overcome stereotypes regarding “unreliable” facilities or “intrusive” interventions [61,62].

6. Conclusions

Drawing on a survey of Hangzhou residents and subsequent statistical modeling, this study examined public attitudes and willingness to pay (WTP) for the following five key waste management measures: differentiated waste charging, smart recycling points, on-site kitchen waste resource recovery, volunteer-based waste sorting supervision, and a community self-governance fund. The results identify important factors shaping whether and how much residents are willing to invest in these interventions. Nevertheless, certain caveats must be emphasized. First, our sample—though sufficient for initial statistical inferences—slightly over-represents younger and more educated populations, which may inflate overall WTP figures. Second, the payment scale (PS) method can sometimes lead to optimistic or socially desirable answers, particularly if participants are not forced to make trade-offs across multiple policy options. Finally, correlations in our dataset should not be interpreted as definitive causation; additional research with more diverse samples and longitudinal designs could shed further light on causal links.

6.1. Contribution to Urban Governance

This study enriches the literature on sustainable urban management by illustrating how socioeconomic status, demographic features, and environmental awareness jointly influence public support for waste reduction and treatment. In Hangzhou’s transition toward becoming a “zero waste” city, coordinated efforts among stakeholders—along with targeted policies and outreach—can heighten citizens’ active engagement in waste reduction, resource recovery, and safe disposal [63].

Practically, our findings offer valuable insights for policymakers and urban managers in cities facing similar socio-economic challenges as Hangzhou. By understanding the diverse factors driving residents’ willingness to pay, municipalities can better design targeted waste management strategies that enhance community participation and improve policy acceptance. Specifically, our results suggest that cities should emphasize tailored communication and education efforts aimed at different demographic groups, particularly newcomers and higher-income residents, who display higher WTP when informed of clear environmental benefits. Additionally, promoting transparent and measurable outcomes of waste management measures (e.g., waste diversion rates or recycling achievements) through regular feedback channels can effectively sustain resident engagement. These evidence-based strategies can help urban governments in comparable settings achieve greater efficiency, fairness, and long-term sustainability in their waste management systems.

6.2. Limitations and Future Directions

Due to questionnaire length constraints, this research does not cover all potential waste management strategies (e.g., large-scale waste-to-energy plants or hazardous waste solutions). Additionally, the over-representation of certain demographic groups may limit the generalizability of our findings to the entire Hangzhou population. Future studies could adopt longitudinal or mixed methods to track changes in public attitudes over time and incorporate psychological perspectives (e.g., social norms, environmental values) to better understand the multiple factors driving individuals’ willingness to invest. More robust justification of the chosen econometric models, particularly regarding region-specific factors, could also strengthen the methodology. Employing alternative valuation methods, such as discrete choice experiments (DCE), could also help minimize possible overestimation of stated WTP.

6.3. International Applicability and Replicability

Key drivers of public WTP for waste management initiatives—such as household economic capacity, policy knowledge and trust, and community involvement—are relevant worldwide. Hence, this study’s approach has potential for application in other cities. Nevertheless, caution is warranted when extending these findings beyond Hangzhou, as differences in local governance structures, economic conditions, and cultural norms may significantly alter public willingness to pay. We, therefore, recommend additional cross-regional research or pilot studies in diverse settings to verify whether these insights hold true and to adapt strategies to the unique context of each city or nation.

6.4. Policy Perspectives

The findings underscore the importance of education programs, transparency, and continuous monitoring in encouraging effective waste management. Local authorities could employ points-based incentives, public awareness campaigns, and technology support to promote proactive participation among households and neighborhoods. For example, establishing a digital platform where residents earn credits for correct sorting or for bringing recyclables to community drop-off points can both gamify the process and provide valuable data for city planners. Additionally, subsidies for differential waste charging systems or the installation and upkeep of community composting facilities can help reduce end-disposal pressure. To further boost participation, policymakers might consider the following strategies: Cross-district collaboration: encourage neighboring districts to share best practices and coordinate on consistent waste sorting rules, thus reducing confusion for residents who move or commute frequently. Community-led oversight: partner with local volunteers or NGOs to conduct regular waste audits, ensuring that residents’ sorting behaviors align with established guidelines. Infrastructure upgrades: invest in reliable collection infrastructure (e.g., well-maintained bins, prompt pickups, odor management) to reassure the public about the tangible benefits of correct waste disposal. Targeted outreach for newer residents: since newcomers often respond favorably to additional information, “welcome packages” explaining local sorting requirements and available recycling points can bolster early and sustained compliance. Regular feedback mechanisms: share community-specific metrics—such as landfill diversion rates, reduced waste volume, or cost savings—through social media or neighborhood bulletins to maintain engagement and celebrate achievements. Tailored outreach strategies—especially for newer residents—can expand voluntary engagement across diverse income and age groups, while ongoing feedback loops help maintain momentum. By aligning financial incentives with clearly communicated environmental and social benefits, municipalities can more effectively harness public support for sustainable waste management initiatives.

6.5. Recommendations for Broader Adoption

We recommend that other cities adopt a similar integrated framework combining questionnaire design, WTP analysis, and hypothesis testing to assess public views on waste management policies. This approach is especially relevant for cities with socioeconomic and structural conditions comparable to Hangzhou. Nonetheless, local adaptation is crucial; each city should adjust specific measures and incentives to reflect its own waste management systems, cultural preferences, and mobilization practices. By addressing issues of sample representativeness, valuation method choice, and causal interpretation, future research can further refine our understanding of public willingness to invest in sustainable waste governance.