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Article

Policy-Driven Changes in Rural Waste Separation: Evidence from a Quasi-Experimental Study

by
Zhaoyun Yin
1,2 and
Jing Ma
3,*
1
School of Humanities, Chang’an University, Xi’an 710064, China
2
Key Laboratory of Behavioral Science and Public Policy of Shaanxi Higher Education Institutions, Chang’an University, Xi’an 710064, China
3
School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(11), 4747; https://doi.org/10.3390/su17114747
Submission received: 17 April 2025 / Revised: 20 May 2025 / Accepted: 20 May 2025 / Published: 22 May 2025
(This article belongs to the Section Sustainable Urban and Rural Development)
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Abstract

Waste management plays a crucial role in sustainable development efforts in a national context. However, waste separation in rural areas has received limited research attention, restricting the generalizability of the urban-focused findings to these regions. Furthermore, the influence of policy interventions on waste separation behavior among rural residents remains poorly understood. Therefore, sustainability in rural areas is challenging. Here, we used a quasi-experimental design to examine the causal relationship between policy and rural residents’ waste separation behavior. We identify policy information perception as the key explanatory factor mediating this relationship and validate information dissemination as a robust instrumental variable for policy information perception. Furthermore, we explore the urban–rural disparities in policy information perception and provide recommendations to enhance policy effectiveness. These findings enhance the understanding of the behavioral mechanisms driving rural waste separation, explain how policy information influences ecological behavior from a perceptual perspective, and provide practical guidance for optimizing environmental policy implementation.

1. Introduction

Global economic development and improved living standards have driven overall consumption growth, resulting in a rapid increase in waste generation. In rural areas, household-sourced waste, such as food scraps, low-value plastics, packaging materials, and other daily consumables, has become a critical challenge because of limited collection and treatment infrastructure [1]. Unlike urban settings, rural communities often lack centralized waste management systems, and their proximity to natural ecosystems exacerbates the environmental risks of improper disposal (e.g., open burning or dumping) [2]. These practices directly threaten local biodiversity, soil quality, and water resources, while endangering human health [3]. Given these vulnerabilities, rural regions urgently require context-specific waste management strategies that address the composition and dispersal of household-generated waste.
Waste separation by rural residents is the most crucial step in waste management in rural areas. It determines the quantity and quality of waste that flows in subsequent processing procedures and significantly affects the comprehensive utilization of rural waste management systems [4,5,6]. However, since most individuals do not choose to engage in waste separation voluntarily, governments need to implement specific policies to intervene and promote such behavior [7,8,9].
Except for a few industrialized countries (e.g., Germany and Japan), the deployment of waste separation policies (WSPs) is still limited in many nations [10]. Therefore, the effectiveness of WSP has garnered the attention of researchers. Some studies have suggested that these policies have a positive impact. For instance, Li et al. [11], Liao et al. [12], and Zhang et al. [13] found that WSP can influence residents’ intentions to separate waste. Conversely, other studies indicate that the impact of these policies may be limited. For example, Pedersen and Manhice [14] observed that the WSPs in the European Union did not achieve the expected success. Issock et al. [15] found that WSP does not affect social and moral norms regarding intention to separate. These studies have contributed to the understanding of the effectiveness of WSPs. However, they have notable deficiencies. First, they did not employ randomized controlled trials, which may have obscured the influence of other endogenous variables on the results. For instance, along with the implementation of the WSP, the education level of residents has gradually improved, and education is also considered an important factor influencing waste separation behavior [16,17]. Therefore, without using scientific methods (such as randomized controlled trials), it is challenging to determine whether the policy itself has a real impact on waste separation behavior. Second, while some scholars have acknowledged the existence of a black box between policy and residents’ waste separation behavior [18], there is a lack of research that directly treats WSPs or their outcomes as core explanatory variables. The mechanisms through which policy influences waste separation behavior may not have been thoroughly explored, and further investigation is needed to determine why WSPs may be effective or ineffective. Third, research on the effectiveness of WSP has predominantly focused on urban areas, with minimal attention given to rural regions. Consequently, there is a significant knowledge gap regarding the effectiveness of WSPs in rural areas. The integrated development of urban and rural waste management continues to face substantial challenges, particularly in terms of effective policy implementation. As highlighted by Pressman and Wildavsky’s foundational work on implementation, and further elaborated by Graham [19] and Wegrich [20], the success of public policy depends not only on its design but also on the complex, multi-actor processes through which it is translated into practice. Understanding and addressing the “implementation gap” is therefore essential for ensuring that waste separation policies achieve their intended outcomes in diverse local contexts.
To address these gaps, this study makes several contributions. It advances theoretical understanding by introducing policy information perception as a central explanatory variable, building upon Lewin’s psychological field theory to demonstrate how external policy signals must be cognitively processed before influencing behavior [21]. This framework provides a more nuanced alternative to the existing models. While the Theory of Planned Behavior (TPB) explains general pro-environmental actions [22], our approach specifically accounts for policy-driven actions, whereas the Institutional Analysis and Development Framework [23] emphasizes policy design. Our model bridges the critical gap between policy formulation and behavioral responses through a perceptual mechanism. Furthermore, the study generates robust empirical evidence through a quasi-experimental design in rural settings, offering causal insights into the policy perception–behavior relationship that previous qualitative approaches [24] could not establish. Additionally, by focusing on rural China, a context representing similar challenges faced by many developing nations, the findings provide practical insights into the broader sustainable development discourse, particularly regarding how policy interventions can be effectively tailored to rural communities with unique socioeconomic and infrastructure constraints. Together, these contributions provide both theoretical and empirical clarity on the previously understudied aspects of waste management policy effectiveness [25].
The remainder of this paper is organized as follows. Section 2 outlines the study’s methodology. The analysis results are presented in Section 3. Finally, conclusions and recommendations are presented in Section 4.

2. Methods

2.1. Research Design

A counterfactual causal analysis framework is generally used [26]. However, for policy information perception research, conducting strict randomized controlled experiments is not feasible. Instead, causal inference must be based on the concept of a quasi-experimental design. A quasi-experiment (also known as a natural or field experiment) is a research method that falls between observational studies and laboratory experiments. It utilizes observational data and exogenous shocks to create an environment that resembles that of randomized controlled trials. In this study, the implementation of the waste separation policy (WSP) serves as an exogenous shock, which is an already occurring fact. Rural residents do not have the option to choose their policy information perception (they may or may not perceive it), and researchers do not have the power to manipulate the policy information perception of rural residents. Consequently, this study met the basic requirements of a quasi-experiment [27].
Following the methods established in previous studies [18,28], rural residents who were aware of the local government’s WSP formed the experimental group, while those who were unaware constituted the control group. Specifically, residents in the experimental group were aware of the implementation time and main content of the WSP and had a clear perception of the effect of policy implementation, whereas the residents in the control group did not have such perceptions. We assessed the residents’ perceptions by conducting oral interviews regarding their understanding of WSP. In this study, residents’ knowledge of WSP was used solely as an indicator of their perception of policy-related information. This variable distinguishes the experimental group (residents with policy information perception) from the control group. It is important to clarify that this variable is not intended to directly represent behavioral changes. Rather, it reflects a prerequisite condition for exploring whether policy information perception can influence residents’ waste separation behavior. Therefore, knowledge about WSP is not treated as a direct causal factor in behavior change but as a foundational element in policy communication and behavioral response pathways.
This approach ensured that policy information perception was the only exogenous intervention variable in the experiment. However, different groups may have varying capacities to receive information, which could lead to variations in the perception outcomes. To ensure a random selection of experimental subjects, this study employed a sample-trimming method to randomly adjust some subjects [29]. Specifically, random numbers were generated using Excel, and redundant subjects in the control group were randomly excluded according to the population structure ratio of the experimental group. By comparing the experimental and control groups, we can assess whether the policy influenced rural residents’ waste separation behavior. Subsequently, we focus on the effects of policy information perception to reveal the underlying mechanisms mentioned in the introduction.
A quasi-experiment was conducted using a field survey. The questionnaires were designated as Questionnaire A (to be completed by the experimental group) and Questionnaire B (to be completed by the control group). The difference between the two questionnaires is that Questionnaire B did not include questions measuring policy information perception. In addition to measuring waste separation behavior, policy information perception, and demographic characteristics, other potential determinants of waste separation behavior, such as attitude, subjective norms, perceived behavioral control, waste separation intention, and information intervention, were also included in the questionnaire design.
A five-point Likert scale (strongly disagree, disagree, neither, agree, and strongly agree) was used to measure the question items (variables). Although Likert scales inherently possess ordinal characteristics, they are often treated as interval scales in statistical analyses [30]. When Likert scales have sufficient response categories (≥5) and approximately normal distributions, treating responses as interval-level data is empirically supported [31], though their ordinal nature should be noted. Additionally, numerous case studies have successfully applied the Likert scale to path analysis of environmental behavior [22]. Given the ordinal nature of Likert scales, these results should be interpreted as exploratory, though prior research supports the robustness of such analyses with well-designed scales.

2.2. Site Selection

As shown in Figure 1, Guilin, located northeast of the Guangxi Zhuang Autonomous Region of China, was selected as the research area for this study. Guilin has been one of the eight pilot cities to promote waste separation in China. Unlike other pilot cities (i.e., Beijing, Shanghai, Guangzhou, Shenzhen, Hangzhou, Nanjing, and Xiamen), Guilin is more representative of Chinese cities because it is located inland and has a GDP close to the national average [5]. Since being designated as a pilot city, Guilin has implemented the WSP in its rural areas. Notably, in October 2016, the Guilin Municipal Government formulated the “Two-Year Action Plan for Special Treatment of Rural Waste in Guangxi”, which explicitly mandates Guilin to “actively promote the separation of rural waste to achieve source reduction” [32].

2.3. Measures

2.3.1. Policy Information Perception

According to previous studies [18,33], the measurement of policy information perception examined individuals’ favorable or unfavorable views regarding waste separation policy, including: “Waste separation policy has improved the level of environmental management”, “Waste separation policy has improved residents’ quality of life”, “The content of waste separation policy is scientific and reasonable”, “The implementation effect of waste separation policy is good”, and “Waste separation policy is effective in reducing waste”. The survey questions are presented in Appendix A (Table A1).

2.3.2. Other Measures

To measure the other variables involved in this study, the items were adapted from previous similar studies [10,34,35]: (i) attitude, evaluated using three items (e.g., “Waste separation is good”); (ii) subjective norm, captured through three items (e.g., “My family expects me to separate waste”); (iii) perceived behavioral control, assessed with three items (e.g., “Whether or not I separate waste is entirely up to me”); and (iv) information intervention, measured using five items (e.g., “Information intervention can make residents aware of waste separation benefits”).

2.4. Participants

Research data were collected from a field survey in the rural areas of Guilin from August to December 2022. Guilin’s rural areas are vast, with scattered settlements and generally low economic levels. Therefore, this study did not use economic level as a sampling criterion, but instead divided rural communities or villages based on their spatial distance from the urban center into three categories: suburban (urban–rural fringe), outer suburban, and mountainous hinterland. Due to the sparse population of most villages, this study randomly selected survey points within the spatial range and then used convenience sampling to survey the residents of nearby villages. Overall, sampling in rural areas adhered to the principles of probability sampling, allowing for the inference of the general characteristics of rural residents. Data entry was performed using the EpiData 3.1 application. Following initial data entry, we conducted two rounds of data verification to ensure accuracy. Following the principles of quasi-experiment, rural residents who perceived the policy were designated as the experimental group, while those who did not were designated as the control group. A comparison of the groups indicated that there was a higher proportion of residents aged >65 years in the control group. To satisfy the requirements of random control, we used a sample-trimming method to adjust the control group data. The chi-squared test results for the adjusted data for both groups are shown in Table 1. The chi-square test results indicated no significant differences in the data structure between the experimental group in the rural areas and the adjusted control group (p > 0.1). Therefore, a randomized controlled analysis of the quasi-experiment was conducted.
Based on professional statistical calculations (e.g., using a sample size calculator) and assuming a 95% confidence level with a 50% response variability (i.e., the most conservative estimate where responses are evenly split), the sample size in this study results in a sampling error margin of only 5%.

3. Results and Discussion

3.1. Results of the Quasi-Experiment

Psychological research suggests that behavior generally includes behavioral intention and actual behavior [36]. Behavioral intention is a conceptual variable that requires indirect estimation using appropriate observational indicators. Following the approach of Ma et al. [5], we measure behavioral intention using indicators such as “I am willing to spend time on waste separation”, “I plan to separate waste”, and “I will make an effort to separate waste”. Additionally, we measured actual waste separation behavior using the indicator “I never/often separate my household waste”. Waste separation intention and behavior were used as the outcome variables in the quasi-experiment.
After implementing the quasi-experiment, this study compared the raw data on waste separation intention and behavior between the experimental and control groups of rural residents. The results are shown in Figure 2.
As shown in Figure 2, the experimental group demonstrated a stronger behavioral intention toward waste separation, with a median score of 5.0 compared to the control group’s median of 4.67. The interquartile range (IQR) was narrower for the experimental group (IQR = 0.67) than for the control group (IQR = 1.33), indicating more consistent responses among the participants who received the intervention. Similarly, for actual waste separation behavior, the experimental group showed superior performance (median = 4.0) compared to the control group (median = 3.0). The tighter data distribution in the experimental group (IQR = 1.0 versus 2.0 for the control group) further confirms that the treatment produced more uniform behavioral outcomes. However, whether this difference is statistically significant requires further investigation.
The Mann–Whitney U test was employed as the analytical method for testing the difference between the experimental and control groups because it is suitable for ordinal Likert-scale data, requiring no assumptions about data distribution or interval-level measurement properties [37]. The results showed that the level of waste separation intention in the experimental group in rural areas was significantly higher than that in the control group (p < 0.001, z = −3.930), and the level of waste separation behavior in the experimental group was also significantly higher than that in the control group (p < 0.001, z = −6.281). This indicates that policy information perception has a causal effect on waste separation behavior, enhancing the waste separation behavior of rural residents. Additionally, the survey data revealed that the mean levels of waste separation behavior in both the experimental and control groups were relatively low, indicating that rural residents rarely engage in waste separation. This also reflects the poor effectiveness of the waste separation policy (WSP) in rural areas.
In addition, we were interested in the correlation between the differences observed in the experimental and control groups (i.e., whether residents have policy information perception) and their age and education level. In this study, whether residents had policy information perception was treated as the dependent variable, while age and education level were used as independent variables. A regression analysis revealed that age was significantly and positively associated with the dependent variable (β = 0.020, p < 0.1), whereas educational level was significantly and negatively associated with it (β = −0.068, p < 0.01). These findings suggest that younger individuals and those with higher education levels are less likely to perceive policy information. A possible explanation is that younger residents are primarily students who may have limited access to policy-related information because of the relatively closed nature of school environments. Meanwhile, individuals with higher educational levels are often preoccupied with academic or professional commitments, leaving them with limited attention to the implementation of the WSP.

3.2. The Mechanism of Policy Information Perception in Rural Areas

As shown in Figure 3, a model was proposed to reveal the mechanism of policy information perception regarding rural residents’ waste separation behavior. According to this definition, policy information perception may directly affect waste separation behavior [38]. On the other hand, psychologists believe that there are a series of mediating factors between stimuli and responses that cannot be directly observed but can be inferred based on the antecedents that cause a behavior and the final behavioral outcome itself [39,40]. As an outcome of policy stimulus, policy information perception may influence the final behavioral response through a mediating process. Furthermore, residents’ practice of waste separation behavior tends to be a conscious pursuit of goals [41], while the Theory of Planned Behavior (TPB) emphasizes the role of conscious choice in the process of goal pursuit [42]. Therefore, we consider that policy information perception may indirectly affect waste separation behavior through the TPB model.
Based on the aforementioned quasi-experiment, this study used data from an experimental group to analyze the mechanism of policy information perception. This approach avoids erroneous statistical results caused by including subjects who are not influenced by policy information perception. Before conducting a path analysis of the theoretical model, it was necessary to test the measurement model. Given the relatively small sample size of the experimental group obtained from the survey in rural areas and the skewed nature of the data, this study employed the partial least squares structural equation modeling (PLS-SEM) approach, which is suitable for small samples and does not require strict data distribution assumptions. Statistical computations were performed using SmartPLS 4.0, with the bootstrap sampling set to 5000 iterations.
Through reliability and validity testing of the model, it was confirmed that all the indicators of the measurement model met the required standards. Specifically, as shown in Table A2, the Cronbach’s alpha (α) of the proposed model structure is between 0.817 and 0.913 (suggested threshold is 0.5), the average variance extracted (AVE) is between 0.606 and 0.853 (suggested threshold is 0.36), and the composite reliability (CR) is between 0.892 and 0.945 (suggested threshold is 0.6). All indices were within the recommended threshold values, indicating that the proposed model is reliable. Meanwhile, as shown in Table A3, the component scores of each construct were higher than those of the other constructs, indicating that the model had good discriminant validity [43]. The standardized root-mean-square residual (SRMR) of the model was 0.060, which is below the threshold of 0.1, indicating a good model fit [44]. The path analysis results revealed that policy information perception did not have a significant effect on waste separation intention (β = 0.096, p > 0.1), whereas all other paths were significant. This suggests that, in rural areas, policy information perception can directly influence residents’ actual waste separation behavior and indirectly influence their waste separation behavior through attitude, subjective norms, and perceived behavioral control (PBC).
The path analysis results indicate that policy information perception has varying degrees of direct and indirect effects on rural residents’ waste separation behavior. Specifically, compared to the indirect effects, the direct impact of policy information perception on rural residents’ waste separation behavior is relatively weak. This is because policy information perception does not influence the intention to separate waste, but it can affect the actual separate behavior. Although behavioral intention is an important explanatory variable for actual behavior, it is not the only one. For example, perceived ease of use is closely related to actual behavioral control. Perceived ease of use essentially reflects an individual’s practical perception of the convenience provided by WSP implementation. Actual behavioral control indicates the extent to which an individual possesses the necessary skills, resources, and other prerequisites for performing a certain behavior [45]. Therefore, perceived ease of use and actual behavioral control are highly interrelated. From the content of the policy information perception measurement, it is evident that policy information perception includes perceived ease of use. When individuals perceive that there are sufficient waste separation containers around them, they have better resource control conditions to adopt waste separation behavior. Ajzen, the founder of the TPB, pointed out that the successful performance of a behavior depends not only on behavioral intention but also on a sufficient level of behavioral control [46]. Actual behavioral control can serve as a proxy for perceived control and can be used for behavior prediction. Therefore, policy information perception has a direct impact on the actual waste separation behavior of rural residents.
From an indirect impact perspective, policy information perception can influence rural residents’ waste separation behavior through attitudes, subjective norms, and PBC. (i) For the mediating role of attitude, scholars generally believe that, in collectivist countries, the main purpose of public policy is to create a collectivist culture [47]. This collectivist culture ensures that the majority of society’s members share good and relatively unified values, such as a positive attitude toward environmental protection. The implementation of the WSP also has a similar cultural function. Policy information perception formed by the implementation of the WSP can easily transform into a positive attitude towards waste separation behavior in a collectivist cultural atmosphere. Additionally, numerous studies continue to support the reliability of TPB [48,49,50]. Therefore, attitudes can mediate the relationship between policy information perception and rural residents’ waste separation behavior. (ii) For the mediating role of subjective norms, public policies are often viewed as substitutes or supplements to laws and regulations. The content of public policies represents the specific content of social norms, which individuals internalize to form subjective norms [51]. Therefore, policy information perception affects subjective norms, which, in turn, can mediate the relationship between policy information perception and rural residents’ waste separation behavior. (iii) For the mediating role of PBC, PBC refers to an individual’s perceived difficulty or control over performing a certain behavior [46]. Positive policy information perception can lead individuals to believe that they have good conditions for implementing waste separation, reduce the perceived difficulty, and enhance the controllability of the behavior. Therefore, there is a significant correlation between policy information perception and PBC, which mediates the relationship between policy information perception and rural residents’ waste separation behavior.

3.3. Robustness Check Based on Instrumental Variable Method

Empirical research often encounters endogeneity issues, which must be properly addressed to ensure the reliability of empirical conclusions. To minimize endogeneity, this study conducted a quasi-experiment to infer the causal effect of policy information perception before engaging in a theoretical discussion. However, the risk of endogeneity still exists objectively. For instance, this study primarily uses survey data, and some research suggests that survey data may not meet strict homogeneity assumptions [52], thus failing to ensure the robustness of causal relationship tests. To address this, this study employs the instrumental variable method and conducts a two-stage least squares regression analysis to reexamine the endogeneity issue, aiming to obtain more robust research conclusions.
Previous research has confirmed that information dissemination is closely related to policy information perception but does not directly influence waste separation behavior [10]. Therefore, this study hypothesizes that information dissemination serves as an instrumental variable in policy information perception. In the first stage of the regression, the model uses information dissemination as the explanatory variable and policy information perception as the dependent variable, with demographic characteristics such as gender, age, education level, and monthly income as the control variables. As shown in Table 2, Column (1) reports the regression results without control variables, indicating that the effect of information dissemination on policy information perception is significant (p < 0.001). In Column (2), after adding the control variables, the effect of information dissemination on policy information perception remained significant (p < 0.001), and the F-statistic exceeded the recommended threshold value of 10. This indicates that information dissemination is not a weak instrumental variable for policy information perception [53].
Table 3 presents the results of the second-stage regression analysis. Columns (3) and (5) report the regression results without the control variables, demonstrating that policy information perception significantly influences waste separation behavior (p < 0.001). In columns (4) and (6), after adding the control variables, policy information perception still showed a significant effect (p < 0.001). This suggests that the endogeneity of the model is somewhat mitigated, and policy information perception continues to have a positive impact on waste-sorting behavior in rural areas. This indicates that the main conclusions regarding the role of policy information perception are robust and that the study is reliable. Additionally, this demonstrates the existence of the “information dissemination → policy information perception → waste separation behavior” pathway, which can supplement the understanding of the mechanism of policy information perception and clarify the role of information dissemination in the academic community [10].

3.4. Further Discussion: Urban–Rural Differences in Policy Information Perception

China’s long-standing urban–rural dual structure has led to various adverse effects, one of which is the increasing irrationality of resource allocation [54]. The implementation of WSP inevitably requires a corresponding resource investment. Otherwise, it will be difficult to ensure the expected effectiveness of policy implementation. Rural areas differ significantly from urban areas in terms of household waste composition, energy structure, and residents’ habits [55,56,57,58]. Therefore, the implementation status of WSP in rural areas may differ greatly from that in urban areas. An important research topic in this study is whether there are differences in the characteristics and patterns of policy information perceptions regarding waste separation among residents in these two areas. To address this, we conducted a survey of policy information perception using stratified random sampling in the urban areas of Guilin.

3.4.1. The Urban–Rural Differences in Policy Information Perception Scope

In the grouping of the survey data from rural areas (divided into experimental and control groups), the policy information perception rate was 68.49%. In contrast, among the 862 valid questionnaires collected from urban areas in Guilin, 653 respondents were aware of the WSP (experimental group) and 209 were not (control group), with a policy information perception rate of 75.75%, nearly 7 percentage points higher than that of the rural areas. To explore whether there is a difference in policy information perception rates between urban and rural areas, this study divided the total sample of social surveys in Guilin into four sub-samples based on distance from the city center: urban area, suburban area, outer suburban area, and mountainous hinterland. The policy information perception rates for residents in each of these subsamples were then calculated. The results are shown in Figure 4.
Figure 4 shows that policy information perception rates in urban and suburban areas are essentially the same. However, the policy information perception rates in the outer suburban and mountainous hinterland areas are relatively low, falling below 70%. Overall, as the distance from the city center increased, the policy information perception rate gradually decreased. Whether this difference was statistically significant requires further investigation. Since the four groups of data mainly come from two different populations (urban and rural residents) and do not follow a normal distribution, this study employs the Kruskal–Wallis non-parametric test. The test results indicate a significant difference in policy information perception rates between urban areas and mountainous hinterlands (p < 0.05). This suggests a declining trend in policy information perception rates from the urban center to the mountainous hinterland, with rural residents having lower policy information perception rates than urban residents.
The reasons for low policy information perception among rural residents are multifaceted, with one primary reason being the relative scarcity of information dissemination channels in rural areas. This study confirms that information dissemination is an effective instrumental variable for policy information perception. Information dissemination indirectly influences residents’ waste separation behavior through the perception of policy information. Conversely, residents gain a certain degree of perception of policy information through information dissemination. However, information dissemination relies on information dissemination channels. Urban areas are rich in public resources and have well-developed channels for information dissemination. By contrast, rural areas have relatively insufficient information dissemination channels. For example, we found that 32.60% of residents in the urban areas of Guilin obtained information related to waste separation through print media (e.g., newspapers, manuals, and books), while only 26.04% of rural residents obtained such information through this channel. We also found that the proportion of people obtaining waste separation information through waste separation manuals was roughly the same in both urban (29.12%) and rural areas (29.69%). This indicates that the lower proportion of rural residents obtaining waste separation information through print media is not due to a preference for reading (urban residents have a higher interest in reading compared to rural residents) but rather due to the relative lack of print media resources in rural areas.

3.4.2. The Urban–Rural Differences in the Mechanism of Policy Information Perception

In the analysis of the mechanism of policy information perception, it was found that policy information perception in urban areas does not directly influence residents’ waste separation behavior, whereas in rural areas, policy information perception can directly influence waste separation behavior. This was the only difference in the mechanism of policy information perception between the two regions. A reasonable explanation is that, compared to urban areas, rural residents have relatively lower expectations for the effectiveness of WSP. Therefore, rural residents are more likely to experience the efficacy of policy implementation (reaching the critical value of efficacy-driven behavioral change, i.e., the “absolute threshold of perception”), thereby facilitating behavioral change [59]. Intuitive evidence for this is that, when rural residents were asked, ‘If a specific waste separation policy was introduced in our city in the second half of the next year, what would be your expected evaluation of its implementation effectiveness?’, they generally gave positive responses, with an average score of 4.018 (S.D.) = 0.850). By contrast, the average score for urban residents for this question was 3.839 (S.D. = 0.800). Both sets of data followed a normal distribution (p > 0.1), and an independent-sample t-test revealed that the difference in the mean scores was statistically significant (p < 0.001).

4. Conclusions and Recommendations

This study advances our understanding of policy-driven behavioral change through a quasi-experimental investigation of rural waste separation policies, revealing several key insights. The findings demonstrate that policy information perception serves as a critical mediator between policy implementation and behavioral outcomes, with rural residents exhibiting stronger waste separation intentions and behaviors when they possess a clearer understanding of policy content. Importantly, this mediation operates primarily through indirect pathways, where attitude formation, social norms, and perceived behavioral control collectively translate policy awareness into action. We establish information dissemination as a valid instrumental variable that activates this perception–behavior pathway, although its effectiveness diminishes with the distances from urban centers, reflecting the urban–rural divide in policy communication efficiency. Notably, the mechanism exhibits fundamental contextual differences. While urban settings show decoupled perception–behavior relationships due to established environmental governance systems, rural areas demonstrate direct perception–action linkages, likely because residents’ lower expectations of policy efficacy make personal understanding more behaviorally determinant. These results collectively suggest that policy effectiveness in rural contexts depends heavily on closing the “last mile” of information delivery and addressing the unique cognitive filters through which rural populations interpret policy signals.
Based on the above conclusions, this study proposes the following recommendations. First, it emphasizes the importance of policy information perception among rural residents and formulates a waste separation policy according to their policy information perception characteristics to enhance the effectiveness of policy implementation in rural areas. Second, target key populations and areas in rural regions to eliminate “blind spots” in policy information perception scope and expand the “radiation area” of policy information perception. Third, we focused on cultivating positive psychological mediating variables for waste separation behavior in rural areas to better leverage the indirect effects of policy information perception. Fourth, it effectively enhances the sense of policy efficacy among rural residents and focuses on the convenience of the construction of rural communities. Finally, we emphasize the functional roles of information dissemination channels that significantly impact information dissemination and improve the effectiveness of information dissemination in rural areas.
Limitations and future research: One limitation of this study was that we did not directly measure individual perceived ease of use regarding waste separation practices. The ease of use may be associated with age, potentially influencing residents’ willingness or ability to engage in waste separation. Future research should incorporate specific measures of perceived ease of use and examine their interaction with demographic factors such as age to better understand the mechanisms underlying behavioral adoption.

Author Contributions

Conceptualization, J.M. and Z.Y.; methodology, J.M. and Z.Y.; validation, Z.Y. and J.M.; formal analysis, J.M. and Z.Y.; data curation, J.M.; writing—original draft preparation, Z.Y.; writing—review and editing, J.M. and Z.Y.; visualization, Z.Y.; supervision, J.M.; funding acquisition, Z.Y. and J.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 72150410448, the Innovation Capability Support Program of Shaanxi, grant number 2025KG-YBXM-145, the Basic Scientific Research Operating Expenses for Central Universities, grant number SK2024116, and the Fundamental Research Funds for the Central Universities, grant number 300102114607.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are not publicly available in accordance with the consent provided by participants on the use of confidential data.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Questionnaire of the survey.
Table A1. Questionnaire of the survey.
ConstructsItemsQuestions
Policy information perception (PIP)PIP1Waste separation policy has improved the level of environmental management.
PIP2Waste separation policy has improved residents’ quality of life.
PIP3The content of waste separation policy is scientific and reasonable.
PIP4The implementation effect of waste separation policy is good.
PIP5Waste separation policy is effective in reducing waste.
PIP6I am satisfied with waste separation policy.
Attitude (ATT)ATT1Waste separation is good.
ATT2Waste separation is rewarding.
ATT3Waste separation is responsible.
Subjective norm (SN)SN1My families expect me to separate waste.
SN2My friends expect me to separate waste.
SN3My neighbors expect me to separate waste.
Perceived behavioral control (PBC)PBC1Waste separation is easy.
PBC2I have enough time to separate waste.
PBC3Whether or not I separate waste is entirely up to me.
Waste separation intention (WSI)SI1I am willing to spend time on waste separation.
SI2I plan to separate waste.
SI3I will make an effort to separate waste.
Waste separation behavior (WSB)WSBI never/often separate my household waste.
Information intervention (II)II1Information intervention can make residents aware of waste separation benefits.
II2Information intervention can make residents understand waste separation importance.
II3Information intervention can make residents feel responsible for waste separation.
II4Information intervention can make residents learn proper waste disposal methods.
II5Information intervention can make residents access practical waste separation guidance.
Table A2. Reliability measurement of each item.
Table A2. Reliability measurement of each item.
ConstructsItemsCronbach’s AlphaCRFactor LoadingsAVE
Policy information perception (PIP)PIP10.8670.9020.7320.606
PIP2 0.815
PIP3 0.790
PIP4 0.764
PIP5 0.778
PIP6 0.789
Attitude (ATT)ATT10.8500.9120.8970.775
ATT2 0.884
ATT3 0.860
Subjective norm (SN)SN10.8680.9200.8710.793
SN2 0.906
SN3 0.894
Perceived behavioral control (PBC)PBC10.8170.8920.7890.733
PBC2 0.899
PBC3 0.877
Waste separation intention (WSI)WSI10.9130.9450.9300.853
WSI2 0.918
WSI3 0.922
Table A3. Test results of discriminant validity.
Table A3. Test results of discriminant validity.
12345
1Subjective norm0.890
2Waste separation intention0.4820.923
3Attitude0.4050.3480.880
4Perceived behavioral control0.2890.6610.1030.856
5Policy information perception0.3600.3820.5950.2550.778

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Figure 1. Location of Guilin, China.
Figure 1. Location of Guilin, China.
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Figure 2. Violin plots of raw data for separation of the control and experimental subgroups (the dotted line indicates the median).
Figure 2. Violin plots of raw data for separation of the control and experimental subgroups (the dotted line indicates the median).
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Figure 3. Mechanism of perception of policy information on waste separation behavior. Notes: PBC = perceived behavioral control; *** p < 0.001.
Figure 3. Mechanism of perception of policy information on waste separation behavior. Notes: PBC = perceived behavioral control; *** p < 0.001.
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Figure 4. Policy information perception rates of residents in different areas.
Figure 4. Policy information perception rates of residents in different areas.
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Table 1. Chi-square test results for experimental and control groups.
Table 1. Chi-square test results for experimental and control groups.
CategoryParticipantsChi-Square
Experimental GroupControl GroupTotal
Gender p > 0.1
Male11948167
Female14463207
Age p > 0.1
Below 14612182
15–24331750
25–34271946
35–44561268
45–54351146
55–64322153
Over 65191029
Education level p > 0.1
Illiterate or elementary school613596
Junior high school9541136
Senior high school642084
Junior college or Bachelor431558
Monthly income p > 0.1
Below 2500 RMB17987266
2501–4000 RMB571471
Over 4000 RMB271037
All participants included in the survey had lived in their community for more than one year.
Table 2. First-stage results: impact of information dissemination on policy information perception.
Table 2. First-stage results: impact of information dissemination on policy information perception.
Explained VariablePolicy Information Perception
(1)(2)
Information dissemination0.547 ***
(5.88)		0.553 ***
(5.76)
Demographic variablesNoYes
F-statistics34.599 ***33.216 ***
R20.3180.331
t-statistics are shown in parentheses; *** p < 0.001.
Table 3. Second-stage results: impact of policy information perception on waste separation behavior.
Table 3. Second-stage results: impact of policy information perception on waste separation behavior.
Explained VariableWaste Separation IntentionWaste Separation Behavior
(3)(4)(5)(6)
Policy information perception0.889 ***
(6.96)		0.890 ***
(7.14)		0.919 ***
(3.75)		0.949 ***
(3.88)
Demographic variablesNoYesNoYes
R20.2560.2830.1630.176
The z-statistics are in parentheses. *** p < 0.001.
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Yin, Z.; Ma, J. Policy-Driven Changes in Rural Waste Separation: Evidence from a Quasi-Experimental Study. Sustainability 2025, 17, 4747. https://doi.org/10.3390/su17114747

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Yin Z, Ma J. Policy-Driven Changes in Rural Waste Separation: Evidence from a Quasi-Experimental Study. Sustainability. 2025; 17(11):4747. https://doi.org/10.3390/su17114747

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Yin, Zhaoyun, and Jing Ma. 2025. "Policy-Driven Changes in Rural Waste Separation: Evidence from a Quasi-Experimental Study" Sustainability 17, no. 11: 4747. https://doi.org/10.3390/su17114747

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Yin, Z., & Ma, J. (2025). Policy-Driven Changes in Rural Waste Separation: Evidence from a Quasi-Experimental Study. Sustainability, 17(11), 4747. https://doi.org/10.3390/su17114747

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