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.
4. Results
4.1. Reliability and Validity Test
As shown in
Table 5, 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 and
Table 7 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 and
Table 9 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 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.