Impact of Capital Endowment and Environmental Literacy on Farmers’ Willingness to Pay and Level of Payment for Domestic Waste Management

Abstract

China’s rural rejuvenation rationale reveals that domestic waste management (DWM), as a gateway to habitat enhancement, is a critical policy pathway for achieving sustainable rural development. This paper analyzes the influence of capital endowment (CE) and environmental literacy (EL) on farmers’ willingness to pay (WTP) for DWM through a binary logistic model, focusing on the Yangtze River Delta region, with 571 farmers contributing validated responses. It also conducts a more in-depth exploration of the regulatory role of EL and the influence of CE on WTP for DWM. The findings are as follows: (1) CE and sub-dimensions of economic capital and psychological capital yield a substantial positive effect on WTP for DWM. (2) CE and sub-dimensions of economic capital, human capital, and psychological capital yield a substantial positive effect on LOP for DWM. (3) EL and sub-dimensions of ER, EP, and EKS exert a notably positive influence on WTP and LOP for DWM. (4) EL functions as a moderator in the effect of CE on WTP for DWM. Based on this, this paper puts forward some policy suggestions to improve farmers’ WTP for DWM from two aspects: optimize the structure of farmers’ capital endowment and foster a positive climate for the entire society to safeguard the environment and strengthen the environmental literacy education system.

1. Introduction

Building eco-friendly and habitable rural living settings has emerged as a critical core goal in China’s overall rural regeneration strategy. This is not only a matter of personal interest and quality of life for the great majority of farmers, but it is also a critical measure for achieving long-term rural development and encouraging coordinated urban and rural areas. The rural habitat environment encompasses many components of the rural natural environment, including living conditions, infrastructure, and public services, and the degree of improvement has a direct impact on the overall image and development potential of the countryside.

The economy, society, and culture of China’s rural areas are currently undergoing enormous and profound transformations at all levels, which have gradually affected farmers’ attitudes and living habits, as well as their pursuit of a greater quality of life. However, despite these beneficial developments, environmental issues have become more prevalent. Rural domestic waste is the leading cause of rural pollution [1]. The types and quantities of rural domestic waste have increased dramatically as the rural economy has developed and farmers’ living standards have improved, and the composition of waste and the difficulty of treatment have become increasingly complex, putting significant strain on the rural ecological environment. Domestic waste management (DWM) is critical to the improvement of rural habitats. With the rapid development of the rural economy and the astonishing improvement in farmers’ living standards, the volume of rural domestic waste has increased dramatically. According to statistics, the total amount of rural garbage in China was close to 300 million tons in 2022, and this figure is continually increasing. Such a large volume of waste puts a strain on the rural environment, and if not managed properly, it would pollute the land, water, and air in the countryside, affecting the health of farmers and the ecological balance of the country.

However, the current situation of rural DWM is not promising. Although substantial progress has been achieved in garbage disposal, less than half of domestic waste can be processed safely, implying that a large volume of waste is still being improperly disposed of and may cause environmental harm. What is more serious, is that one-quarter of the waste is not effectively collected and treated [2], and is casually discarded or piled up in the open air, destroying the countryside landscape while also easily breeding bacteria and mosquitoes and spreading diseases.

Rural DWM has typical public goods characteristics, such as high management costs and benefits that are difficult to properly quantify; therefore, government departments have primary responsibility for waste management. The government must commit significant human, material, and financial resources to the construction of garbage treatment facilities, as well as waste collection and transportation. The majority of farmers, as producers of rural household waste, generally benefit from improvements in the human environment, such as clean air and a pleasant living environment, but due to a lack of effective incentives, they frequently lack the initiative to participate in household waste management. This has put huge financial pressure on governments of all levels to support rural household waste management [3]. Not only must the government cover the construction and operation costs of waste treatment facilities, but it also must invest heavily in publicity, education, oversight, and management, which, to some extent, limits the in-depth growth of rural waste management.

To effectively address this issue and promote the long-term development of rural waste management, the Five-Year Action Program for the Improvement and Upgrading of the Rural Habitat Environment (2021–2025) explicitly proposes investigating the establishment of a farmers’ payment system for rural domestic waste treatment, as well as the establishment and improvement of a long-term mechanism for the improvement of rural habitat. Allowing farmers to suffer some of the costs of waste disposal can raise their understanding of environmental preservation and responsibility, as well as their enthusiasm for participating in DWM. At the same time, it can relieve the government’s financial burden, allowing more funds to be directed toward improving the technical level and facility development of rural DWM.

Based on this, this study focuses on the critical question of how to successfully increase rural families’ willingness to pay (WTP) and level of payment (LOP) for DWM. Through an in-depth analysis of farmers’ WTP and its affecting elements, tailored policies and initiatives are developed to improve farmers’ understanding and recognition of DWM, as well as to encourage active engagement in payment. This has significant practical implications for relieving the government’s demand on environmental governance and improving the quality of the rural human environment, as well as providing strong theoretical support and practical direction for the long-term promotion of rural household waste management.

When actors make various judgments and choices, their willingness to select is not limitless, free, and arbitrary; rather, they frequently confront capital endowment (CE) limits. The concept of capital endowment encompasses a wide range of factors, including not only the amount of monetary funds owned by the actor, but also the cultural level and other intangible assets that represent the actor’s ability and advantages in a specific field and can bring potential benefits to the actor. Due to capital endowment limits, actors must decide whether to give up when faced with multiple behavioral options with potential value. This abandonment conduct closely correlates with a lesser willingness to engage in the relevant behaviors. Actors are not unwilling to pursue better growth possibilities or achieve greater goals; rather, they are constrained by their own poor capital endowment, unable to translate their ambition into tangible actions. At the structural level, the lack of economic capital tends to create rigid constraints, while the ecological fragility of natural capital exacerbates the dilemma; at the cognitive level, the inadequacy of human capital creates implicit barriers to knowledge, the path dependence of cultural capital manifests itself in the suppression of innovative behaviors by traditions, and psychological capital emphasizes the divergence of preferences. This effect of capital endowment constrains actors’ willingness to choose and is especially evident in a social environment of uneven economic development and unequal distribution of resources, where there is a significant gap in behavioral choices and expressions of willingness between different actors due to differences in capital endowment [4]. Many scholars have investigated the impact of CE on rural households’ adoption of environmentally friendly technologies [5,6,7] and green production behavior [8,9,10]. Previous studies have proved that CE can effectively encourage farmers to participate in village environmental governance [11,12,13,14]. It was discovered that CE has a considerable favorable influence on rural households’ WTP for rural human settlement improvement [15]. However, CE is not a monolithic concept; rather, it interacts and functions in concert with a wide range of other elements. Micro-level analysis reveals a strong correlation between an individual’s environmental literacy (EL) and CE. Farmers who are more environmentally literate are more aware of the value of enhancing rural habitat and are more prepared to pay a higher price for better environmental conditions. In addition to being a body of information, EL is a set of values and a code of conduct that affects farmers’ daily decisions and actions. It can only play a better role by combining the talents and abilities that people have learned in practice. However, few studies have examined WTP by combining personal EL with farmers’ CE. An essential antecedent variable for forecasting environmental behavior is EL. It was discovered that farmers’ behavior, willingness, or decision-making is significantly influenced by EL, as well as the environmental awareness and environmental responsibility (ER) dimensions [16,17,18]. Farmers with a higher sense of responsibility were thought to be more willing and invested in DWM in terms of WTP [19]. Xie et al. confirmed that rural residents’ WTP for centralized DWM grows more robust the greater their environmental emotion and the higher their level of environmental cognition [20].

At present, the research on farmers’ WTP and LOP for domestic waste has made some progress, but there is still potential for improvement in the following areas: (1) Studies focused on the influence of CE on farmers’ WTP for DWM are primarily investigated from an overall perspective, with few studies examining its impact in depth from the standpoint of farmers’ CE. (2) The impact of the environment on farmers’ WTP for DWM is mostly focused on external elements such as sanitation facilities, publicity and training, and institutional restrictions. There is also research from individual viewpoints, such as farmers’ environmental cognition and ecological cognition, although few studies include farmers’ EL as a whole and its sub-dimensions as influencing elements for discussion. (3) The existing literature seldom addresses the potential regulating role of EL in the relationship between CE and farmers’ WTP for DWM. According to farmer behavior theory, the study of farmers’ payment behavior for DWM, in general, may be separated into two decision-making stages: “if they are willing to pay for domestic waste management” (willingness to pay) and “how much to pay” (level of payment). Farmers’ readiness to pay impacts their desire to participate in home waste management, whereas payment level indicates their response to various payment criteria. Based on the above, this study, based on the survey data of farm households in the Yangtze River Delta region, uses the binary logistic model and ordered logistic model to analyze the impacts of CE and EL on the WTP and LOP for DWM, and further explores the differences in the WTP for DWM under different EL levels. The purpose of this research is to increase the universality of WTP studies and provide an opportunity to investigate a feasible payment system for rural household waste management, and provide theoretical support and a reference base for ensuring the long-term mechanism of rural human environment improvement.

2. Theoretical Analysis and Research Hypothesis

2.1. Influence of CE on WTP and LOP for DWM

According to the French sociologist Pierre Bourdieu, economic behavior is influenced by both field and habitus. CE is the stock of physical and non-physical capital accumulated by an actor at a specific point in time, which has multiple dimensions. Farmers’ behavior is partially influenced by their own resources. CE has a significant favorable effect on farmers’ willingness to participate in ecological compensation for straw collection, as well as a significant negative effect on the compensation level [21]. It was discovered that farmers’ CE has a direct impact on their willingness and actions to pay for the improvement of human settlements [22]. Referring to previous studies [13,23,24], this paper discusses how CE affects the WTP for DWM from five aspects: farmers’ economic capital, human capital, cultural capital, natural capital, and psychological capital.

H1. 

Farmers’ WTP and LOP for DWM are positively influenced by CE.

As one of the most fundamental kinds of CE, the impact of economic capital on farmers’ WTP for DWM manifests itself primarily in two ways: on the one hand, DWM has the potential to improve the environment. In general, the higher the household income level, the higher the demand for quality of life, and correspondingly, the demand for a good ecological environment is also increased [25], so that farmers with a high level of economic level may have a more positive attitude toward paying for the management of household wastes; on the other hand, farmers with a strong economic capacity will be able to resist the risks, and thus, are more likely to take the initiative. Farmers with better human capital may be more concerned with the influence of environmental sanitation on quality of life, and studies have indicated that health status has a positive and significant impact on rural residents’ WTP for DWM [26,27]. Cultural capital is directly linked to the development of subjective consciousness. Jia and Zhao, for example, discovered that rural households’ desire to participate in the management of domestic trash is significantly positively correlated with their degree of education [28]. In general, farmers with high cultural capital are more likely to grasp the dangers of waste pollution and the long-term advantages of environmental management, to be more environmentally conscious, and to favor paying for management. Natural capital refers to natural resources or services that can be used to sustain a living, such as land and water. Farmers who rely on natural resources (for example, agricultural revenue) stay in the village for longer periods of time, have more opportunities to participate in the public realm, and are more likely to invest in environmental governance to ensure their livelihood. Psychological capital is an intangible, unconventional psychological resource. The greater farm households’ psychological capital, the more positive their outlook on life, the stronger their resistance to setbacks, the more inclined they are to exercise their own initiative, the more likely they are to believe that the government can effectively use governance funds, and the higher their WTP. The following hypotheses are proposed based on the analytical findings:

H1a. 

Economic capital has a positive influence on WTP and LOP for DWM.

H1b. 

Human capital has a positive influence on WTP and LOP for DWM.

H1c. 

Cultural capital has a positive influence on WTP and LOP for DWM.

H1d. 

Natural capital has a positive influence on WTP and LOP for DWM.

H1e. 

Psychological capital has a positive influence on WTP and LOP for DWM.

2.2. Influence of EL on WTP and LOP for DWM

Roth established the theoretical foundations of EL, originally conceptualizing it as “a comprehensive competency system encompassing environmental knowledge, affect, values, skills, and behaviors” [29]. EL refers to an individual’s cognitive ability, attitude, and behavioral predisposition toward environmental challenges, which is inextricably linked to individual environmental decision-making and behavior. Scholars generally agree that improving EL contributes to better environmental conditions. Using data from field surveys, it was discovered that improving EL can motivate farmers to become more actively involved in human settlement improvement [30]. Based on the empirical findings, the study hypothesis is formulated as follows:

H2. 

Farmers’ WTP and LOP for DWM are positively influenced by EL.

Referring to previous studies [16,31,32,33,34], this paper divides EL into three dimensions: ER, environmental perception (EP), and environmental knowledge and skills (EKS). The exact mechanism on farmers’ WTP for DWM is as follows: (1) ER investigates farmers’ sense of responsibility for actively implementing environmental practices to reduce environmental concerns. ER has a beneficial impact on rural households’ desire to invest in rural waste treatment, as well as DWM investment levels [19]. Individuals have good emotional resonance with the environment and a strong desire to protect it, which increases the likelihood of producing WTP for DWM. (2) EP refers to farmers’ intuitive feelings about the living and ecological environments in their permanent communities. Farmers are more sensitive to changes in their surroundings and have a higher degree of EP, which makes it easier to improve their WTP for DWM. (3) EKS assesses individuals’ understanding and mastery of environmental behaviors, as well as farmers’ abilities to identify, analyze, and deal with environmental problems in their surroundings. DWM consists mostly of waste generation, classification, collection, transportation, transit, and treatment. Information intervention can considerably increase the classification effect of rural residents’ domestic waste, demonstrating that farmers’ mastery of waste classification knowledge is a necessary condition for improving the classification effect [35]. It can be seen that improving farmers’ EKS is beneficial to establishing a green concept; therefore, they are prepared to pay for DWM. Based on previous debate, this study presents the following hypotheses:

H2a. 

ER has a positive influence on WTP and LOP for DWM.

H2b. 

EP has a positive influence on WTP and LOP for DWM.

H2c. 

EKS has a positive influence on WTP and LOP for DWM.

2.3. The Regulating Role of EL

CE, as a reflection of farm households’ accumulation of economic resources, is an important supporting factor in the field of rural household DWM. Specifically, it allows farmers to pay for waste management. However, simply having CE does not guarantee that farmers actively pay for waste management. Farmers with higher EL are more likely to enable the conversion of capital endowment into actual WTP for household DWM. EL is a collection of farmers’ beliefs, attitudes, and values regarding environmental issues, which influence their behavioral choices. Farm households with higher environmental literacy are well aware of the dangers that domestic waste poses to the rural environment, ecology, and health, as well as the necessity of garbage management in improving the rural living environment and quality of life. As a result, people are more prepared to pay for DWM with their own CE and to engage in rural DWM by taking tangible steps to promote its continued development. Guo’s work provides compelling evidence for the function of EL in rural environmental governance. She discovered that EL plays an important regulatory role in the relationship between “guiding environmental regulation-farmers’ environmental behavior” and “encouraging environmental regulation-farmers’ environmental behavior” [18]. Environmental regulation raises farmers’ awareness and attention to environmental issues through publicity, teaching, and demonstration, and farmers with higher EL are better able to understand and accept these guiding messages, resulting in more active adoption of pro-environmental behaviors. Incentive-type environmental regulation encourages farmers to participate in rural environmental management through economic incentives, policy concessions, and so on. Farmers with a high EL can quickly detect these incentive signals and use their CE to respond favorably to the incentive program, converting their capital into genuine WTP. Based on the foregoing results, this study draws the following testable propositions:

H3. 

EL acts as a beneficial moderator, enhancing the influence of CE on farmers’ WTP to pay for DWM.

The theoretical model framework is shown in Figure 1.

3. Materials and Methods

3.1. Data Sources

The data were sourced from a questionnaire survey completed by farmers in the Yangtze River Delta (including Shanghai, Zhejiang, Jiangsu, and Anhui) from March to April 2024 (Supplementary Materials). In this study, the Questionnaire Star platform was chosen to conduct online research and targeted distribution through the agricultural and rural departments in the Yangtze River Delta region to WeChat groups of administrative villages under their authority. The survey distributed 584 questionnaires, of which 571 were confirmed as valid responses. The results of the sample are exhibited in

Table 1. Sample statistical characteristics.

Characteristics	Description	Mean	Standard Deviation
Gender	Male = 1, Female = 2	1.53	0.50
Age	<18 = 1; 18–24 = 2; 25–34 = 3; 35–44 = 4; 45–54 = 5; 55–64 = 6; ≥65 = 7	3.24	0.90
Education level	Educational attainment in years	5.38	0.87
Occupation	Full-time farming = 1; Part-time farming with migrant work = 2; Full-time migrant work = 3; Government official/village cadre = 4; Student = 5; Retired = 6; Other occupation = 7; Unemployed = 8	3.16	1.39
Household size	Number of permanent rural residents in household: 1 = 1; 2 = 2; 3 = 3; 4 = 4; ≥5 = 5	3.77	1.02
Region	Jiangsu = 1; Zhejiang = 2; Shanghai = 3; Anhui = 4	2.50	1.12

. The questionnaire involves farmers’ CE, farmers’ EL, and WTP for DWM.

3.2. Variable Measurement

The explained variable is farmers’ WTP for DWM, which is based on “I am willing to pay a certain fee for the treatment of domestic waste.” To characterize, select “yes” to assign a value of one and “no” to assign a value of zero.

The explanatory variables are CE and EL.

Based on previous research, this paper selects five indicators—income level, health status [8], education level, status of cultivated land [36], and happiness [23,37]—to characterize farmers’ economic capital, human capital, cultural capital, natural capital, and psychological capital, respectively, and computes the overall CE by weighting each indicator via the entropy method, ensuring an objective assessment of system efficiency. See

Table 2. Explanation and statistical characteristics of CE.

Capital Endowment Dimension	Indicator	Definition	Average	Standard Deviation
Economic capital	Income level	<CNY 10,000 = 1; CNY 10,000–30,000 = 2; CNY 30,001–60,000 = 3; CNY 60,001–100,000 = 4; >CNY 100,000 = 5	1.69	0.77
Human capital	Health status	Chronic illness = 1; Not very healthy = 2; Fair = 3; Good = 4; Very healthy = 5	4.08	0.73
Cultural capital	Education level	Educational attainment in years	5.38	0.87
Natural capital	Status of cultivated land	Cultivated land is the most important source of my household income. No = 0; Yes = 1	0.27	0.44
Psychological capital	Happiness	Overall life satisfaction in the permanent residence village: Very unhappy = 5; Somewhat happy = 4; Neutral = 3; Somewhat unhappy = 2; Very unhappy = 1	3.86	0.75

for a more complete discussion of the indicators and their statistical properties.

Combined with the previous analysis, this paper divides EL into three dimensions—ER, EP, and EKS—which are derived using factor analysis and dimensionality reduction. Responses are evaluated on a 5-point Likert scale: 1 = Strongly disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly agree. Among them, ER assesses farmers’ environmental identification awareness and ER for the prompt classification and management of waste in rural everyday life, and investigates four items: “I am willing to voluntarily engage in environmental protection activities,” “I am willing to encourage my family and friends to adopt environmental protection practices,” “I strongly believe that domestic waste classification is essential,” and “The village is our shared home, and environmental protection requires collective efforts from everyone.” EP refers to farmers’ intuitive thoughts about the interaction between rural domestic waste and the surrounding environment, which primarily comprises knowing one’s immediate living surroundings, perceptions of nearby DWM systems, and perceptions of DWM’s environmental benefits. EKS refers to farmers’ mastery of domestic waste classification skills, which is separated into two components: content comprehension and policy understanding.

Table 3. Explanation and statistical characteristics of EL.

Environmental Literacy Dimension	Items	Definition	Mean	Standard Deviation
Environmental responsibility (ER)	ER1	I am willing to voluntarily engage in environmental protection activities.	3.68	0.81
	ER2	I am willing to encourage my family and friends to adopt environmental protection practices.	3.58	0.91
	ER3	I strongly believe that domestic waste classification is essential.	3.63	0.84
	ER4	The village is our shared home, and environmental protection requires collective efforts from everyone.	3.43	0.96
Environment perception (EP)	EP1	I am satisfied with the general situation of the current environment in my permanent village.	4.14	0.84
	EP2	I am satisfied with the general situation of domestic waste management in my permanent village.	4.07	0.83
	EP3	The implementation of domestic waste management has improved the village environment in my permanent village.	4.27	0.81
Environmental knowledge and skills (EKS)	EKS1	I can accurately classify domestic waste and understand the specific categorization criteria.	4.39	0.7
	EKS2	I am aware of the waste classification policies implemented in my permanent residence village.	3.87	0.71

provides a thorough description and statistics for EL.

SPSS 27.0 was used to conduct the KMO and Bartlett’s sphericity tests on EL measurement items. A KMO value of 0.830 and a p-value of less than 0.001 in Bartlett’s sphericity test suggested factor analysis appropriateness. Exploring the EL factor analysis using principal component analysis and the maximum variance rotation approach resulted in the extraction of three common factors, with an overall factor variance explanation of 67.107%. This demonstrates that it is reasonable to divide EL into three dimensions: ER, EP, and EKS, which correspond to the changing settings.

In this paper, the gender, age, size of household, and treatment methods for domestic waste serve as control variables to eliminate the effect of missing variables.

Table 4. Variable meaning and description statistics.

Variables		Definition	Mean	Standard Deviation
Explained variable	Willingness to pay (WTP) for domestic waste management (DWM)	Considering the actual situation of my family, I am willing to pay a certain fee for the treatment of domestic waste. No = 0; Yes = 1	0.69	0.46
	Level of payment (LOP) for domestic waste management (DWM)	Considering the actual situation of my family, how much would I be willing to pay annually for rural household waste management? CYN 0/household year = 0; CYN 1–24/household year = 1; CYN 25–60/household year = 2; CYN 61–120/household year = 3; CYN 121–240/household year = 4; more than CYN 240/household year = 5	1.70	1.52
Explanatory variable	Capital endowment (CE)	The stock of physical and non-physical capital: calculated by the entropy method	0.34	0.27
	Environmental literacy (EL)	Integration of perceptions, attitudes, and behavioral tendencies towards environmental issues: calculated by factor analysis	0.00	1.73
Control variable	Gender	Male = 1, Female = 2	1.53	0.50
	Age	<18 = 1; 18–24 = 2; 25–34 = 3; 35–44 = 4; 45–54 = 5; 55–64 = 6; ≥65 = 7	3.24	0.90
	Size of household	Number of permanent rural residents in household: 1 = 1; 2 = 2; 3 = 3; 4 = 4; ≥5 = 5	3.77	1.02
	Treatment methods for domestic waste	My family classifies domestic waste. No = 0; Yes = 1	0.84	0.37

provides definitions and descriptive summaries for the important variables.

3.3. Model Selection

The main focus of this research is on the effects of CE and EL on WTP and LOP for DWM. Farmers’ WTP for DWM is a 0–1 explanatory variable, and the binary logistic model is configured as follows:

$$Y_i={\alpha }_0+{\alpha }_1·E_i+{\alpha }_2·X_i+{\mu }_i$$

(1)

Among them, Y_i represents the WTP for DWM, whereas E_i stands for CE (economic, human, cultural, natural, and psychological capital) and EL (ER, EP, and EKS). The random disturbance term is μ_i, while the control variable is X_i.

Additionally, the level of remuneration farmers receive for managing domestic garbage is an ordinal categorical variable, and the ordinal logistic model is configured as follows:

$$\log ({\displaystyle \frac{P\left(Y_i=j\right)}{P(Y_i=5)}})={\beta }_{j0}+{\beta }_{j1}·E_i+{\beta }_{j2}·X_i+{\mu }_i$$

(2)

Among them, Y_i represents the LOP for DWM, whereas E_i stands for CE (economic capital, human capital, cultural capital, natural capital, and psychological capital) and EL (ER, EP, EKS). The random disturbance term is μ_i, while the control variable is X_i, and j = 0, 1, 2, 3, 4, 5.

4. Results

4.1. Reliability and Validity Test

As shown in

Table 5. Results of the reliability and validity test (n = 571).

Latent Variables	Items	Loading	Cronbach’s α	p-Value	CR	AVE
ER	ER1	0.74	0.79	0.00	0.84	0.56
	ER2	0.78
	ER3	0.79
	ER4	0.68
EP	EP1	0.78	0.75	0.00	0.83	0.61
	EP2	0.81
	EP3	0.76
EKS	EKS1	0.85	0.67	0.00	0.77	0.63
	EKS2	0.73

, the factor analysis results demonstrate that almost all standardized factor loadings for the measures surpass 0.7, which is greater than the recommended value of 0.5, indicating that the measures work well together. Additionally, the reliability test indicators for ER, EP, and EKS have Cronbach’s α coefficients of 0.79, 0.75, and 0.67, respectively; all exceed 0.6, indicating high reliability. Meanwhile, the standardized loading coefficients of each ER, EP, and EKS measurement question item are all greater than 0.6, the combined reliability (CR) for ER, EP, and EKS items is 0.84, 0.83, and 0.77, respectively, all of which exceed 0.7, and the average variance extracted (AVE) is greater than the standard value of 0.5, indicating strong convergent validity.

4.2. Binary Logistic Regression Analysis

The effect of CE and EL on farmers’ WTP for DWM was investigated using stepwise regression in STATA 17.

Table 6. Influence of CE on farmers’ WTP for DWM.

	Model 1	Model 2
CE	1.90 * (0.74)
Economic capital		1.43 *** (0.11)
Human capital		1.14 (0.16)
Cultural capital		1.02 (0.12)
Natural capital		1.24 (0.37)
Psychological capital		1.17 * (0.10)
Gender	0.70 ** (0.14)	0.75 (0.15)
Age	1.56 *** (0.18)	1.36 ** (0.17)
Size of household	1.04 (0.10)	1.07 (0.10)
Treatment methods for domestic waste	1.83 *** (0.24)	1.64 *** (0.22)
Constant	0.07 *** (0.05)	0.01 *** (0.01)

Note: Standard errors (in parentheses) are reported alongside *, **, and ***, which are significant at the 10%, 5%, and 1% levels, respectively.

and

Table 7. Influence of EL on farmers’ WTP for DWM.

	Model 3	Model 4
EL	1.67 *** (0.12)
ER		1.62 *** (0.16)
EP		1.65 *** (0.19)
EKS		1.73 *** (0.18)
Gender	0.71 * (0.14)	0.71 (0.15)
Age	1.35 ** (0.16)	1.35 ** (0.16)
Size of household	0.98 (0.10)	0.97 (0.10)
Treatment methods for domestic waste	1.12 (0.17)	1.13 (0.18)
Constant	1.15 (1.01)	1.11 (0.99)

Note: Standard errors (in parentheses) are reported alongside *, **, and ***, which are significant at the 10%, 5%, and 1% levels, respectively.

present the results.

Model 1 depicts the effect of the total dimension of CE on farmers’ WTP for DWM. Statistical research demonstrates that CE has a favorable influence on WTP for DWM, with significance at the 10% level, implying that farmers with higher CE will greatly improve the chance of their WTP for DWM, which aligns with expectations. H1 has been verified. This could be because farmers with higher CE are more likely to have free time and extra income, as well as pay more attention to environmental contamination, resulting in a higher WTP. In terms of the control variables, gender, age, and household waste treatment technologies all had a statistically significant beneficial effect (5%, 1%, and 1%, respectively) on farmers’ WTP for DWM.

Model 2 illustrates the impact of CE on farmers’ WTP for DWM. Empirical evidence suggests that economic capital and psychological capital contribute positively to WTP for DWM, and at the 1% and 10% significance levels, respectively, both factors exhibit a strong statistical association, implying that farmers with higher income levels and happiness will significantly improve their WTP for DWM, which is consistent with expectations. H1a and H1e have been verified. Farmers with stronger economic capital have a higher ability to pay, providing a tangible assurance for farmers’ WTP for DWM. Farmers in better economic circumstances typically have higher expectations for quality of life and are more likely to pay for environmental governance to improve their living conditions. Farmers with more psychological capital are more optimistic about the future and are more certain that paying for waste disposal will result in long-term environmental improvements; thus, they are more prepared to pay. However, human capital, cultural capital, and natural capital have no substantial impact on WTP for DWM, which could be attributed to differing views of environmental concerns and insufficient policy cognition. In terms of control factors, the age and treatment techniques of domestic waste have a statistically significant beneficial effect on WTP for DWM at 5% and 1%, respectively.

Model 3 depicts the impact of the entire dimension of EL on farmers’ WTP for DWM. Empirical evidence shows that EL has a positive effect on the WTP for DWM, with statistical significance at the 1% level, implying that farmers with higher EL will greatly improve the probability of their WTP for DWM, which is consistent with expectations. H2 has been verified. Control variables such as gender (p < 0.1) and age (p < 0.05) positively impact the WTP for DWM.

Model 4 illustrates the effect of EL on farmers’ WTP for DWM. The findings indicate that ER, EP, and EKS all had a 1% positive effect on the WTP for DWM, and H2a, H2b, and H2c have been confirmed. This suggests that farmers’ increased feeling of environmental responsibility is associated with a deeper concern and perception of environmental concerns, and the better their understanding of environmental-related knowledge, the greater their WTP for DWM. Age (p < 0.05) has a significant impact on WTP for DWM among control variables.

4.3. Ordered Logistic Regression Analysis

The effect of CE and EL on farmers’ LOP for DWM was investigated using stepwise regression in STATA 17.

Table 8. Influence of CE on farmers’ LOP for DWM.

	Model 5	Model 6
CE	0.46 * (0.28)
Economic capital		0.30 *** (0.06)
Human capital		0.34 *** (0.12)
Cultural capital		0.01 (0.09)
Natural capital		0.13 (0.22)
Psychological capital		0.16 ** (0.07)
Gender	−0.45 *** (0.15)	−0.35 ** (0.16)
Age	0.31 *** (0.09)	0.25 ** (0.10)
Size of household	0.20 ** (0.07)	0.21 ** (0.08)
Treatment methods for domestic waste	0.70 *** (0.12)	0.55 *** (0.12)

Note: Standard errors (in parentheses) are reported alongside *, **, and ***, which are significant at the 10%, 5%, and 1% levels, respectively.

and

Table 9. Influence of EL on farmers’ LOP for DWM.

	Model 7	Model 8
EL	0.46 *** (0.06)
ER		0.44 *** (0.09)
EP		0.46 *** (0.10)
EKS		0.48 *** (0.08)
Gender	−0.40 ** (0.15)	−0.40 ** (0.16)
Age	0.20 ** (0.09)	0.20 ** (0.09)
Size of household	0.14 * (0.08)	0.14 * (0.08)
Treatment methods for domestic waste	0.27 ** (0.12)	0.28 ** (0.13)

Note: Standard errors (in parentheses) are reported alongside *, **, and ***, which are significant at the 10%, 5%, and 1% levels, respectively.

present the results.

Model 5 depicts how the overall dimension of CE affects farmers’ LOP for DWM. Statistical research demonstrates that CE has a favorable influence on WTP for DWM, with significance at the 10% level, implying that farmers with higher CE will greatly improve the probability of their LOP for DWM, as expected. In terms of the control variables, gender, age, household size, and domestic waste treatment technologies all had a statistically significant beneficial effect on farmers’ LOP for DWM (1%, 1%, 5%, and 1%, respectively).

Model 6 illustrates the impact of CE on farmers’ LOP for DWM. Empirical research reveals that economic capital, human capital, and psychological capital all contribute favorably to the LOP for DWM, at the 1%, 1%, and 5% significance levels, respectively. However, cultural and natural capital had no substantial effect on LOP for DWM, which could be attributed to differing perceptions of environmental concerns and insufficient policy cognition. In terms of the control variables, gender, age, household size, and domestic waste treatment technologies all had a statistically significant positive effect on LOP for DWM at the 5%, 5%, and 1% levels, respectively.

Model 7 illustrates how the overall dimension of EL affects farmers’ LOP for DWM. Empirical research suggests that EL has a favorable effect on the LOP for DWM, with statistical significance at the 1% level, implying that farmers with higher EL will greatly enhance their chance of LOP for DWM, which is consistent with expectations. Control variables such as gender (p < 0.05), age (p < 0.05), household size (p < 0.1), and domestic waste treatment procedures (p < 0.01) positively impact the LOP for DWM.

Model 8 illustrates the effect of EL on farmers’ LOP for DWM. The results demonstrate that ER, EP, and EKS all had a 1% positive influence on DWM’s LOP. Control variables such as gender (p < 0.05), age (p < 0.05), household size (p < 0.1), and domestic waste treatment procedures (p < 0.01) have a substantial impact on DWM’s LOP.

4.4. Robustness Test

To assess robustness, the OLS model and ordered probit model were used instead of the binary logistic model and ordered logistic model. There was no substantial difference between the benchmark regression findings and the models, indicating that the regression results are robust.

4.5. Regulatory Effect

The grouping regression approach was used to investigate the moderating influence of EL. The moderator variable, environmental literacy (EL), was determined by component analysis and had a mean value of 0. As a result, in this study, values greater than or equal to 0 were classified as high environmental literacy groups, and values less than 0 were classified as low environmental literacy groups. Differences in the effects between the groups were presented directly using group regression, a method that intuitively quantifies the heterogeneous effects of environmental literacy. To check the validity of the conclusions, we employed Fisher’s combined test to confirm the significance of the coefficient difference between the high- and low-regulation groups. According to prior research [38], there may be differences when comparing the significance level of sub-sample coefficients alone; thus, it is required to analyze the statistical significance of coefficient discrepancies between groups. Based on preceding studies, this paper employs the bdiff command to perform a Fisher combination test on 1000 bootstrap samples to determine the coefficient difference between groups and whether the adjustment impact of income level is significant. See

Table 10. Regulatory role of EL.

Variable	Model 9
	Low EL Group	High EL Group	Test of Coefficient Difference Between Groups
CE	0.32 * (1.71)	0.01 (0.05)	Prob > chi2 = 0.02
Gender	−0.06 (−0.98)	−0.05 (−1.09)	Prob > chi2 = 0.37
Age	0.09 ** (2.51)	0.03 (1.28)	Prob > chi2 = 0.16
Size of household	−0.06 (−1.48)	0.06 (1.40)	Prob > chi2 = 0.04
Treatment methods for domestic waste	0.16 ** (2.35)	0.05 (1.18)	Prob > chi2 = 0.07
Constant	0.20 (1.06)	0.65 *** (5.20)	Prob > chi2 = 0.00

Note: Standard errors (in parentheses) are reported alongside *, **, and ***, which are significant at the 10%, 5%, and 1% levels, respectively.

for specific details.

Model 9 is a regression analysis that looks at the impact of CE on farmers’ WTP for DWM in various EL groups. Among them, CE is found to enhance the WTP for DWM in the low EL group but has no significant impact in the high EL group, and the coefficient difference between groups (Prob > chi2 = 0.02) is statistically significant at the 5% level, implying that EL moderates the relationship between CE and farmers’ WTP for DWM, and H3 has been verified. Furthermore, treatment procedures for domestic waste were shown to improve WTP for DWM in the low EL group but had no meaningful impact in the high EL group, with a coefficient difference between groups that was statistically significant at the 10% level.

5. Discussion

The purpose of this study is to thoroughly explore the influence of CE on farmers’ WTP for DWM, to provide a scientific foundation for developing more effective policy measures. To accomplish this research goal, this study includes EL as a crucial explanatory variable. EL considers a variety of factors, including farmers’ knowledge of the environment, care for environmental issues, intuition of the surrounding environment, and environmental awareness and values. EL is regarded as a key mediator in the connection between CE and WTP for DWM. It may moderate the relationship between CE and WTP for DWM by influencing farmers’ perceptions of the relevance of household DWM, acceptance of payment expenses, and motivation and initiative to participate in management. The empirical investigation, which included a survey and data analysis of a sample of farmers, revealed that both CE and EL have a direct favorable effect on WTP for DWM. CE provides a material and psychological foundation for farmers’ behavioral engagement. At the material level, farmers with high CE have more economic resources and a stable income, making it cheaper for them to shoulder payment responsibilities when faced with the expense of home waste management. At the psychological level, the accumulation of CE provides farmers with a better sense of security and self-confidence, making them more eager to participate in public concerns such as household waste management, believing that their efforts would result in tangible benefits and returns. EL, on the other hand, supports the transformation of resources into actionable commitments by improving farmers’ comprehensive cognitive ability. Farm households with greater EL have a more in-depth knowledge and comprehension of environmental issues, which pushes them to pay closer attention to DWM and be willing to pay the associated costs.

Furthermore, this study reveals the multilevel driving mechanism of farmers’ WTP and LOP for DWM by comparing the binary logit model to the ordered logit model. The model results reveal that CE improves both WTP and LOP, although the particular path of action varies significantly across models. According to the binary logit model results, economic capital and psychological capital are the primary factors determining whether farmers participate in paying for government services, with significance levels (1% and 10%) indicating that the material basis of the ability to pay and the subjective psychological identity serve as the dual thresholds of the payment decision. This finding is consistent with the characteristics of “rational choice” in rural environmental governance: farmers with higher economic capital have not only the ability to pay, but also an endogenous demand for environmental improvement due to their quality of life aspirations; whereas those with higher psychological capital tend to view payment as an investment in long-term environmental benefits based on their trust in the policy’s effectiveness. It is worth noting that human capital failed the significance test in the WTP model but had a large positive effect at the 1% level in the payment model. This distinction may be due to the staged nature of human capital’s role: while improvements in educational attainment or health status do not generally increase farmers’ payment participation, they do significantly increase the refined demand for governance services among the already-paying group. The comparative examination of control variables emphasizes the contextual complexity of behavioral decisions. The age variable had strong significance at the 1% level in the binary model, but it dropped to 5% in the ordered model, implying that older age groups are more conservative in their decision-making about whether or not to pay, but that the actual amount to be paid may be moderated by economic capital.

The household size variable was only significant (5%) in the level of payment model, indicating that household size influences the choice of payment bracket via the mediating effect of waste creation, but has a lower effect on the underlying decision to pay. This distinction highlights the distinction between “participation decision” and “intensity decision” in farmers’ environmental behavior: the former is dominated by individual characteristics and policy perceptions, whereas the latter is closely related to households’ material conditions and the intensity of their environmental needs. Furthermore, the relevance of the waste disposal method (1%) is consistent across the two models, implying that it has a broad impact on farmers’ payment behavior. The complementarity of the two models suggests that rural environmental governance policies should be designed in a “ladder” fashion: first, economic incentives to expand coverage of the paying group, then educational inputs to optimize the payment structure, and finally, synergies between governance cost-sharing and service quality improvement.

The data further highlight the significant moderating influence of EL in the relationship between CE and farmers’ WTP for DWM. Farmers with a high EL are better able to comprehend and appreciate the significance and value of CE in family DWM, allowing them to use their CE more effectively to support management efforts. For example, farmers with a high EL will recognize that paying for DWM is not just a way to better their own living conditions, but is also a statement of responsibility to future generations, and they will respond more actively to the government’s appeal to take the initiative and accept the responsibility to pay. Simultaneously, they will increase governance efficiency and accomplish optimal resource allocation through rational planning and capital utilization. This demonstrates that EL is an effective factor in promoting the development of long-term habitat remediation mechanisms, which strengthens the relationship between CE and farmers’ WTP for DWM, allowing CE to be more effectively translated into practical actions for household DWM and promoting better habitat remediation results.

Furthermore, this study demonstrates that the two sub-dimensions of CE, economic capital and psychological capital, as well as the three sub-dimensions of EL, ER, EP, and EKS, have a significant favorable impact on farmers’ WTP for DWM. According to the sub-dimension of CE, economic capital, as the foundation of farmers’ material life, directly impacts farmers’ affordability when it comes to household waste management costs. Farm households with greater economic capital can not only afford the treatment charges, but may also have the extra funds to invest in more advanced waste treatment facilities or services. This economic advantage encourages more initiative and drive in domestic waste treatment, resulting in a large increase in WTP. On the other hand, psychological capital reflects farmers’ psychological condition and self-perception. Farmers with high psychological capital have a more favorable attitude toward household waste management, believing that they can participate in and promote the management activity and are confident in its success. This optimistic attitude makes people more inclined to donate to DWM and pay the necessary fees. The three sub-dimensions of EL also have a substantial impact on farmers’ willingness to pay for home waste treatment. ER has a deep-seated sense of mission and responsibility for environmental protection among farmers. Farmers with a strong ER intentionally perceive DWM as an obligation and see paying for management as a sign of environmental stewardship. They will not only take the initiative to follow rubbish classification standards, but they will also aggressively promote the notion of environmental protection to others, encouraging more people to join in management activities. EP refers to farmers’ perceptions and emotions about the surrounding environmental conditions. When farmers understand the serious pollution caused by domestic waste in the rural environment, such as soil pollution, water pollution, air pollution, and so on, as well as the negative impact of such pollution on their own health and quality of life, they will place a higher value on DWM and be willing to pay for it. EKS, on the other hand, assesses farmers’ environmental knowledge and ability to safeguard the environment. Farmers with high EKS are better able to appreciate the necessity and procedures of household waste management, including how to correctly separate waste and limit waste creation. This collection of knowledge makes individuals more comfortable with the treatment procedure and more able to perceive the treatment’s actual results, raising their WTP.

Furthermore, this study reveals the multilevel driving mechanism of farmers’ WTP and LOP for DWM by comparing the binary logit model to the ordered logit model. The model results reveal that CE improves both WTP and LOP, although the particular path of action varies significantly across models. According to the binary logit model’s results, economic capital and psychological capital are the primary factors determining whether farmers participate in paying for governance, with significance levels (1% and 10%) indicating that the material basis of the ability to pay and the subjective psychological identity serve as the dual thresholds for payment decisions. This finding is consistent with the characteristics of “rational choice” in rural environmental governance: farmers with better economic capital have not only the means to pay, but also an endogenous demand for environmental improvement due to their quality of life ambitions. Those with stronger psychological capital, on the other hand, see payment as an investment in long-term environmental benefits because they believe the policy is effective. It is worth noting that human capital fails the significance test in the willingness to pay model but has a large positive effect at the 1% level in the payment model. This distinction may be due to the staged nature of human capital’s role: while improvements in educational attainment or health status do not generally increase farmers’ payment participation, they do significantly increase the refined demand for governance services among the already-paying group. The comparative examination of control variables emphasizes the contextual complexity of behavioral decisions. The age variable has strong significance at the 1% level in the binary model, but drops to 5% in the ordered model, implying that older age groups are more conservative in their decision-making about whether or not to pay, but that the actual amount to be paid may be moderated by economic capital. The household size variable was only significant (5%) in the level of payment model, indicating that household size influences the choice of payment bracket via the mediating effect of waste creation, but has a lower effect on the underlying decision to pay. This distinction highlights the distinction between “participation decision” and “intensity decision” in farmers’ environmental behavior: the former is dominated by individual characteristics and policy perceptions, whereas the latter is closely related to households’ material conditions and the intensity of their environmental needs. Furthermore, the relevance of the waste disposal method (1%) is consistent across the two models, implying that it has a broad impact on farmers’ payment behavior. The complementary character of the two models implies that rural environmental governance policies must be designed in a “stepwise” manner: first, economic incentives should be employed to increase the paying group’s coverage, followed by payment structure optimization based on educational inputs, and last, the synergy between governance cost-sharing and service quality improvement should be realized.

This study compares the binary logit model to the ordered logit model to highlight the impact of EL on rural families’ DWM payment behavior. The model results demonstrate that EL has a large positive effect on both WTP and LOP at the 1% level, and the effects of its sub-dimensions—ER, EP, and EKS—are quite constant. It is worth noting that the synergistic effect of the three sub-dimensions of EL shows a progressive responsibility–perception–knowledge path, implying that farmers’ environmental behaviors must be emotionally driven as well as supported by a systematic cognitive framework. Comparative assessments of control variables indicated more variation in EL groups. Differences in modeling approaches offer complementary viewpoints on policy formulation. The binary logit model found that EKS had the highest independent effect (β = 0.41), indicating that fundamental environmental education is crucial for increasing coverage across paying groups. In contrast, the ordered logit model finds that ER is a more significant predictor of higher price ranges (β = 0.58). This suggests that heightened awareness of responsibility can cause farmers to pay a premium for high-quality governance services. This disparity shows that policies should be implemented in stages: first, by decreasing the participation threshold through knowledge dissemination, and then by optimizing the payout structure by instilling a sense of responsibility afterward. The substantial relevance (p < 0.01) of the waste treatment method in both models indicates the importance of technological visibility. The construction of waste treatment facilities can boost farmers’ WTP and LOP.

Finally, treatment methods of domestic waste improve WTP for DWM significantly. Adopting scientific and environmentally friendly DWM methods can help farmers see the environmental benefits of the treatment. When farmers realize that their rubbish has been efficiently processed and the village environment has grown cleaner and more beautiful, they will recognize the importance of DWM and be more prepared to pay the appropriate rates. On the other hand, if rubbish is heaped carelessly and significantly polluted, farmers will lose faith in the treatment process, and their WTP will be lowered accordingly.

The theoretical value of this study stems from the fact that it deepens the idea of EL by breaking it down into three sub-dimensions, ER, EP, and EKS, and confirms the favorable impact of these sub-dimensions on WTP and LOP for DWM. Furthermore, this study is the first to use EL as a moderating variable in the study of the relationship between CE and WTP for DWM, revealing EL’s moderating role in the relationship between CE and WTP for DWM and providing a new theoretical perspective for understanding the complex relationship between CE and WTP for DWM. In addition, this study provides a scientific foundation for developing habitat improvement policies. The study’s findings indicate that boosting rural households’ CE and EL is an important step toward increasing their WTP for DWM. To support rural development, the government should expand economic investment in rural regions, as well as construction and investment in rural household waste treatment facilities. They should also promote scientific and environmentally friendly household trash treatment technologies. Second, in terms of environmental literacy, the government should improve environmental protection publicity and education, as well as popularize EKS, so that rural households understand the necessity of domestic waste treatment and can master effective treatment methods. In addition, the government should aggressively encourage rural laborers to move to non-agricultural businesses, provide more job possibilities and training services, and assist them in improving their employability and income level.

Despite this study’s findings, significant restrictions remain. First, this study did not fully consider the long-term impact of ignoring the digital divide, which could result from ignoring groups that are unable to use any communication tools (e.g., elderly people living alone with disabilities), and future studies may combine offline questionnaires and supplement relevant data with household interviews to make the sample more comprehensive. Because China is a large country with varying socioeconomic, cultural, and environmental characteristics across areas, the WTP and LOP for DWM may be influenced by these factors. Second, while this study considered the impacts of CE and EL on farmers’ WTP and LOP, it may not have adequately considered other factors that influence WTP and LOP, such as cultural differences and the policy environment. These factors may interact with CE and EL, influencing farmers’ WTP for DWM. As a result, future research might explore how these characteristics affect farmers’ willingness to pay and how they interact with CE and EL. Third, while happiness and cultivated land status serve as pragmatic proxies under data constraints, they incompletely capture the multidimensionality of psychological and natural capital. Future studies should incorporate validated scales (e.g., the Psychological Capital Questionnaire; PCQ-24) and longitudinal data to analyze dynamic mechanisms.

6. Conclusions and Recommendations

6.1. Conclusions

Focusing on the Yangtze River Delta region, with 571 farmers contributing validated responses, this paper analyzes the influence of CE and EL on farmers’ WTP for DWM through the binary logistic model, and engages in a more in-depth exploration of the regulatory role of EL and the influence of CE on WTP for DWM. The findings are as follows: (1) CE and sub-dimensions of economic capital and psychological capital yield a substantial positive effect on WTP for DWM. (2) CE and sub-dimensions of economic capital, human capital, and psychological capital yield a substantial positive effect on LOP for DWM. (3) EL and sub-dimensions of ER, EP, and EKS exert a notably positive influence on WTP and LOP for DWM. (4) EL functions as a moderator in the effect of CE on WTP for DWM.

6.2. Recommendations

Farmers with a large capital endowment and excellent environmental literacy are more likely to actively engage with environmental concerns and recognize the impact of their actions on the ecosystem. Effective policies and well-designed measures can help farmers shift toward more sustainable practices, encouraging them to pay more for DWM. Based on the preceding study, this paper makes the following policy recommendations from a governmental perspective:

(1) Optimize the structure of farmers’ capital endowment while also thoroughly improving its level. First, promote the flow of factors and increase farmers’ economic capital. Encourage and direct the flow of high-quality resources such as skills, finance, and technology to rural areas, while also providing more chances for farmers to raise their income. Improve the rural financial service system, give consistent financial support to farmers, assist farmers in overcoming the problem of a lack of finances, and increase their economic ability to pay. Second, by improving rural infrastructure, optimizing the ecological environment, promoting industrial development, and other measures, farmers’ overall sense of acquisition, happiness, and security can be improved, as well as the psychological barrier to farmers’ willingness to pay for environmental governance be reduced.

(2) Foster a positive climate for the entire society to safeguard the environment and strengthen the environmental literacy education system. First, conduct environmental education, inform farmers about the harm and consequences of environmental pollution through case analysis and warning education, increase their sense of environmental responsibility and crisis, and then guide the formation of a positive social environment, laying the groundwork for guiding payment behavior. Second, increase the visibility of environmental governance effects by allowing farmers to experience the actual effects of environmental governance, thereby increasing their sense of identity and support for environmental governance through field visits and case exhibitions. Third, disseminate environmental protection knowledge and policies to farmers via radio, television, short videos, presentations, expert lectures, and other diverse means, increasing their awareness and understanding of environmental issues. Fourth, fully integrate policy resources, increase departmental collaboration, and prioritize encouraging households with higher environmental literacy to complete the transition from capital endowment to a willingness to pay for domestic waste management.