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Article

The Role of Environmental Knowledge and Perceived Ecological Benefits in Shaping Farmers’ Pro-Environmental Behaviour Towards Rural Solid Waste

by
Menglei Jiang
1,
Yong Liu
1,
Bo Xia
2 and
Shiwang Yu
1,*
1
School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, 928, No. 2 Street, Xiasha, Qiantang District, Hangzhou 310018, China
2
School of Architecture and Built Environment, Queensland University of Technology (QUT), 2 George Street, Brisbane 4001, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(12), 5258; https://doi.org/10.3390/su17125258
Submission received: 3 March 2025 / Revised: 7 May 2025 / Accepted: 3 June 2025 / Published: 6 June 2025
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Abstract

Encouraging farmers to adopt pro-environmental practices for the management of rural solid waste is a sustainable solution that can prevent significant harm to rural residents. However, there is limited research focused on the pro-environmental behaviour of rural residents in relation to rural solid waste, and the determinants influencing it. To address this gap, a questionnaire survey was conducted in Guizhou Province, China, and 240 valid responses were collected. A regression equation for the determinants influencing pro-environmental behaviour was developed using IBM SPSS Statistics 26 software, and the model was cross-validated using partial least squares structural equation modelling analysis to ensure the reliability of the results. The data analysis shows that environmental commitment, subjective norms, and attitude are fundamental predictors of pro-environmental behaviour. Additionally, environmental knowledge and perceived ecological benefits are significant determinants of farmers’ environmental commitment, subjective norms, and attitude. This study presents practical and theoretical implications for farmers and local authorities, along with recommendations for improving the current situation of rural solid waste management and enhancing the pro-environmental behaviour of farming households.

1. Introduction

China’s rapid economic development has been accompanied by many environmental problems, such as air pollution, water pollution, soil pollution, domestic waste, and haze, which have gradually come to the fore, causing serious impacts on people’s production and life [1,2]. Rural areas are also prominent in this regard. Under the influence of urbanisation and other factors, the rural population has been significantly reduced, but the total amount of rural solid waste production is increasing, and the production of waste per capita in rural areas shows a large growth trend. It was estimated in 2021 that China produces approximately 3.8 billion tonnes of livestock manure and 1 billion tonnes of straw annually [3]. With the development of the rural economy, rural solid waste emissions are increasing, and littering is commonplace due to the poor environmental awareness of rural residents and inadequate rural management. In rural areas, the most important rural solid waste includes crop straw, livestock and poultry excrement, rural domestic waste, pesticide waste, and flower, fruit, and vegetable waste. Among them, crop straw, which is widely distributed and produced, is a renewable energy source and is potentially harmful to the rural ecological environment if it is not recycled; almost every household has chickens, ducks, geese and other animals most commonly found in rural areas, producing large amounts of faecal excrement outdoors, including on the roads; rural domestic waste is mainly made up of food waste, ash and plastic, and with the increased use of industrial and plastic products in the countryside, the composition of rural domestic waste is becoming more complex and tends to be urbanised; China produces a large amount of waste from agriculture every year, yet China has a low utilisation rate of rural solid waste [4]. The proliferation of rural solid waste hinders the development of the rural economy, affects the construction of beautiful villages, and threatens the health of rural residents.
China is traditionally a largely agricultural country, and rural residents are the foundation of the country. The country has always attached more importance to rural issues, whether they are rural agricultural development issues or environmental issues. The Chinese government has provided significant support for rural solid waste management, issuing several policies to promote the resourceful use of livestock and poultry manure, strengthen the control of agricultural film pollution, improve the comprehensive use of straw and encourage source separation and harmless treatment of rural domestic waste [5]. However, these measures will be reduced to empty talk if they are carried out without the cooperation of rural residents. The pro-environmental behaviour (PEB) of rural residents helps to reduce resource and harmless rural solid waste and improve the agricultural and rural ecological environment [6]. Rural residents’ knowledge of environmental protection, their perception of its benefits, and the environmental climate around them, directly or indirectly influence their attitudes towards the waste they produce and the way they dispose of it in agricultural production [7].
Although considerable progress has been made in understanding PEB in rural contexts, existing studies have predominantly focused on the management of household waste and residents’ environmentally friendly practices related to domestic refuse, such as kitchen waste and everyday household garbage [8]. Such research tends to narrowly define rural environmental issues within the confines of domestic waste, thereby overlooking the diversity and complexity inherent in rural solid waste. Meanwhile, some studies have focused on the psychological and cognitive determinants of PEB, such as environmental awareness, value orientation, and behavioural intention [9,10,11], while others have emphasised the role of external contextual factors, including social norms, institutional support, and the availability of environmental infrastructure [12,13]. However, the majority of these studies have been conducted in urban contexts or have addressed general environmental behaviours, resulting in insufficient attention to PEB in rural settings—particularly within the specific context of rural solid waste management. To fill this gap, this study integrates both internal and external factors to examine the formation mechanisms of PEB among rural residents and provides corresponding policy recommendations for reducing rural solid waste generation and promoting the resourcefulness of rural solid waste.
The main innovations and contributions of this study include: (i) investigating farmers’ pro-environmental behaviour (PEB) in rural solid waste management and its influencing factors; (ii) validating the structural model using regression and structural equation modelling (SEM); and (iii) identifying the significant role of environmental knowledge and perceived ecological benefits in enhancing PEB. The findings provide actionable insights for policymakers aiming to enhance rural environmental governance and advance sustainable waste management practices in rural China.
This paper is structured as follows: Section 2 introduces the concept and practice of rural solid waste management, the PEB of rural residents, and the factors influencing it; Section 3 explains the research methodology and data analysis methods; Section 4 presents a discussion of the data analysis results; Section 5 deals with the theoretical and practical implications of the study; and Section 6 summarises the study’s findings and provides suggestions for improving the management of rural solid waste.

2. Literature Review and Research Hypotheses

In recent years, as the phenomenon of “rubbish siege” has become a concern, many scholars have been paying attention to rural solid waste treatment research. The main research hotspots for scholars are related to the treatment of rural household waste, the resource utilisation of rural solid waste, and the revitalisation of the countryside.

2.1. Rural Solid Waste and Its Impact

Rural solid waste (RSW) refers to organic materials discarded during agricultural production, including plant, animal and process residues, and household waste [14]. In China, rural solid waste includes crop straw, livestock and poultry manure, animal remains, and rural domestic waste [15]. Among them, crop straw includes maise straw, wheat straw, and rice straw, while livestock and poultry manure include pig manure, cow manure, sheep manure, and poultry manure, and rural domestic waste mainly consists of food waste, ash, rubber, and plastic [16]. Additionally, it has been noted that rural solid waste includes farm and horticultural waste, wastewater, livestock and poultry manure, household waste, and agricultural residues [17]. In recent years, the growing emissions of agricultural waste, livestock manure, and household garbage in rural areas have led to worsening environmental pollution due to improper disposal.
Improper management of rural solid waste can lead to a range of problems, including contamination of soil, surface, and groundwater; inadequate disposal of drugs in toilets or household waste, leading to the emergence of antibiotic-resistant superbugs; the emission of persistent organic pollutants such as flame retardants from some electronic equipment near where animals breed, generating large amounts of manure residues that lead to high levels of odour and bacterial contamination, high greenhouse gas emissions, and high organic matter and nutrient loads; and the rate of sound disposal of livestock manure, crop residues, rural household waste, and sewage can also contribute to significant water pollution [18].
In China, the volume of rural solid waste is substantial, and inadequate management remains a pervasive issue. Studies indicate that the disorderly accumulation and indiscriminate disposal of rural solid waste not only directly impact the rural ecological environment but also pose potential health risks to rural residents [19,20]. Consequently, in recent years, there has been an increasing focus on rural solid waste management measures and policies within China. Numerous studies have proposed various technical methods and management strategies to enable the effective recycling and resource utilisation of rural waste [21]. As agricultural waste and livestock manure continue to increase, the government and relevant authorities have begun implementing measures, such as promoting crop straw return-to-field technologies and advancing the harmless treatment and resource utilisation of livestock and poultry manure. Effectively managing and treating rural solid waste is not only critical for improving rural environmental quality but is also an essential task in promoting sustainable rural development.

2.2. International Situation of Rural Solid Waste Management

In various regions around the world, the management and disposal of rural solid waste is an ongoing issue: in Ethiopia, burning large amounts of crop biomass, open burning of crop residues and straw, direct burning of crop biomass, and open burning of biomass after harvest and in household cooking are common practices [22]; in Brazil, due to economic reasons limiting the transport of recyclable materials and influenced by environmental education and political variables, inorganic domestic solid waste is either burnt, recycled, or improperly disposed of in open areas [23]; in India, the rural energy demand problem can be largely solved by adopting various rural solid waste energy conversion technologies [24], such as using rural solid waste as raw material for biogas production [25], which makes rational use of rural solid waste while alleviating energy demand at the same time; and the Al-Ghat region of Saudi Arabia uses crop residues as organic fertiliser wherever possible, and recycles resources according to the organic nature of the waste [26]. In general, composting is the most viable technology for solid waste disposal in rural areas [27] followed by the conversion of the solid waste into charcoal briquettes to provide a source of cheap clean-burning fuel [28,29]. Either way, it is necessary to have a good economic base, a uniform collection of waste, and well-developed waste collection equipment and waste disposal sites [30]. Sesame hulls are a readily available rural solid waste and may be a new alternative for removing pollutants from wastewater; in 2013, the world and Iranian production of sesame seeds was 156,752 tonnes and 28,000 tonnes, respectively [31]. Globally, agriculture produces 140 billion tonnes of biomass per year and is estimated to produce approximately 500 million tonnes of crop residues each year [32]. Nova Scotia farms produce up to 900 tonnes of plastic waste per year, most of which is not recycled [33].
In developed regions, waste separation and recycling systems have become more mature. In Canada, a few jurisdictions have developed successful recycling projects for agricultural plastics waste, either through legislative means or voluntary initiatives [33]. The European Plastics Strategy for the Circular Economy, released in 2018, identifies a series of measures aimed at reducing plastic waste in the EU [34], and the four parts per thousand initiative aims to offset current CO2 emissions whilst contributing to food security and a healthier environment [35]. These developments demonstrate that policies and regulations play a critical role in the management of environmental pollution.

2.3. Research Hypotheses

PEB is a set of behaviours implemented to reduce negative impacts on the environment and contribute to environmental sustainability, including reduction and resourceisation behaviours [11]. Farmers’ perceptions of the knowledge risk and time risk aspects of the psychological risk of rural solid waste resourceisation are high, with the policy context and group pressure aspects being the next highest and the behavioural consequence aspects being the lowest, thus reflecting a lack of environmental awareness among rural residents. Scholars have proposed that PEB should include a compliance-based aspect, consumption-based aspect, conservation-based aspect, and proactive aspect [36,37]. Studies of PEB suggest that it should at least include resourcefulness and minimisation as part of a solid waste management strategy.
Drawing on social learning theory [38], pro-environmental behaviours in rural communities are significantly shaped by observational learning and peer influence, particularly in collectivist cultural contexts [39]. A supportive community climate, coupled with personal normative beliefs, creates a dual mechanism that fosters environmentally responsible actions [40]. Group-level social pressure and mutual supervision further reinforce behavioural compliance, highlighting the role of community dynamics in shaping pro-environmental behaviour.
Institutional support also plays a pivotal role by enhancing infrastructure and introducing incentive–constraint mechanisms (e.g., training subsidies, penalties), thereby encouraging normative behaviour [41,42]. However, structural constraints such as limited economic development, inadequate facilities, and weak policy communication, along with low environmental awareness and entrenched habits, continue to impede behavioural transformation in rural areas [43]. Notably, when policies emphasise the tangible benefits of resource recovery, farmers’ recognition and willingness to act significantly improve, revealing a strong link between policy perception and behavioural response—an insight critical for optimising rural environmental governance [44,45].
H1a. 
Environmental atmosphere has a direct positive influence on environmental commitment.
H1b. 
Environmental atmosphere has a direct positive influence on subjective norms.
H2. 
Government support has a direct positive influence on subjective norms.
H3. 
Policy perception has a direct positive influence on subjective norms.
Cognitive factors play a critical role in shaping rural residents’ pro-environmental behaviour (PEB) decision-making. Drawing on social cognitive theory, existing studies suggest that the accumulation of environmental knowledge directly influences farmers’ environmental commitment by highlighting the specific harms associated with improper waste disposal—such as soil contamination from agricultural plastics and health risks from open burning [11,46]. At the same time, enhanced environmental knowledge promotes the internalisation of subjective norms by improving policy comprehension and perceived efficacy [47]. Prior research also indicates that improving the efficiency of governmental environmental governance contributes positively to individuals’ PEB, underscoring the importance of strengthening environmental management and individual environmental education in fostering environmentally responsible behaviours [42].
Furthermore, among the perceived ecological benefits of rural solid waste resource utilisation, farmers report the highest awareness of environmental benefits [45], indicating that perceived ecological efficacy is a key determinant of PEB. Such perceptions can strengthen collective behavioural norms through measurable outcomes—for example, the improvement of soil organic matter via straw return to fields—and enhance individual attitudes based on cost–benefit analyses, such as household biogas systems generating annual savings of approximately CNY 1520 per household [42,45].
H4a. 
Environmental knowledge has a direct positive influence on environmental commitment.
H4b. 
Environmental knowledge has a direct positive influence on subjective norms.
H5a. 
Perceived ecological benefits have a direct positive influence on subjective norms.
H5b. 
Perceived ecological benefits have a direct positive influence on attitude.
The psychological mechanisms underlying behavioural decision-making in rural environmental contexts exhibit a multi-level motivational structure. Environmental commitment functions as an intrinsic driver, which is reflected in farmers’ internalised sense of responsibility toward waste resource utilisation [11,48] and the moral anxiety experienced when deviating from such pro-environmental obligations [49]. Subjective norms act as external constraints, wherein social pressures—such as expectations from village committees and family members—jointly influence pro-environmental behaviour (PEB) [50]. Among rural residents, behavioural intention is primarily shaped by their attitudes, followed by perceived behavioural control, while subjective norms serve as significant predictors of recycling intention [50,51]. According to the Theory of Planned Behaviour (TPB), key latent psychological determinants of behavioural intention include attitude toward the behaviour, subjective norms, and perceived behavioural control [52].
H6. 
Environmental commitment has a direct positive influence on pro-environmental behaviour.
H7. 
Subjective norms have a direct positive influence on pro-environmental behaviour.
H8. 
Attitude has a direct positive influence on pro-environmental behaviour.
In summary, there are many factors that influence PEB, but there is a lack of research on the PEB of rural solid waste and the factors that influence it, especially those that include subjective norms, environmental commitment, environmental knowledge, environmental climate, government support, and perceived ecological benefits. Therefore, this study aims to fill this gap. This study will examine the PEB of farmers and their personal and external influences on rural solid waste management.

3. Methodology

3.1. Overview of the Study Area

The research study area for this study is Guizhou Province, China, where 92.5% of the landform is characterised by mountains and hills. The terrain of the province slopes from west to east, with an average elevation of approximately 1100 m [53]. Due to its unique geographical location and complex topography, Guizhou exhibits diverse climatic and ecological conditions, abundant vegetation, and distinct features of three-dimensional agriculture. The annual grain sowing area in Guizhou Province is approximately 2.789 million hectares, the vegetable planting area is about 1.233 million hectares, and the orchard area is around 0.657 million hectares. In the absence of environmentally friendly countermeasures, Guizhou generates a significant amount of rural solid waste annually. For instance, from 2020 to 2022, the crop straw resource in Guizhou increased year by year, exceeding 25.95 million tons, with a collectable amount of approximately 23.05 million tons and a comprehensive utilisation rate of 88.82% [54]. Nevertheless, agricultural activities still produce a substantial volume of crop straw and plastic film residues, which may contribute to environmental pollution [55]. As shown in Figure 1, as a landlocked province in Southwest China, Guizhou, with its complex terrain and frequent agricultural activities, faces more severe challenges in agricultural waste management.

3.2. Questionnaire Survey

Questionnaires are a widely used survey method. Using rural residents in Guizhou as the research target, a questionnaire was developed on the environmental protection behaviour of rural residents and the factors influencing it, to help understand rural people’s views on factors such as rural solid waste generation and disposal, and environmental protection behaviour. The questionnaire was presented in the form of questions, and a five-point Likert scale was used to measure respondents’ responses to the questions as follows: strongly disagree (1), disagree (2), moderate (3), agree (4) and strongly agree (5). The questionnaire consisted of 2 main sections: the personal information section and the PEB and its influencing factors, with 33 sub-questions, of which the basic information section consisted of 7 questions and the PEB and its influencing factors consisted of 26 sub-questions. The questions were developed based on previous literature and adapted to fit the current state of rural solid waste management and PEB, as shown in Table 1.
A stratified random sampling method was employed to ensure representativeness. Two counties or districts were randomly selected from each of Guizhou Province’s 9 prefecture-level regions (6 cities and 3 autonomous prefectures), yielding 18 county-level units. Within each, 3–4 townships were selected based on the proportion of the agricultural population, followed by the random selection of 3–4 administrative villages per township. Farmers were then identified using a random number table. This sampling strategy captures the province’s geographic diversity—including mountainous, hilly, and basin areas—and ensures coverage of both poverty-alleviated and more developed regions.
Data were collected from farmers in various regions of Guizhou Province from 1 March to 30 October 2024. During the questionnaire delivery and collection process, a variety of measures to improve the efficiency and quality of the questionnaire collection process were adopted. Before sending the questionnaire, prospective respondents were contacted to explain the purpose and procedure of the questionnaire; after sending the questionnaire, the meaning of the measurement questions in the questionnaire was explained to ensure that the interviewees’ understanding of the questionnaire items was consistent and correct. A total of 250 questionnaires were distributed in this study. During the data cleaning process, 10 invalid responses were excluded based on predefined criteria to ensure data quality. Specifically, invalid questionnaires were identified according to the following conditions: (1) 6 questionnaires exhibited logical inconsistencies due to apparent misinterpretation of item descriptions; (2) 3 questionnaires demonstrated an excessive repetition of responses (e.g., selecting the same option across 20 consecutive items); and (3) 1 questionnaire lacked essential information. Following this rigorous screening process, 240 valid responses were retained for subsequent analysis, yielding an effective response rate of 96%. Among them, 52.92% of the respondents were male, and the highest percentage of respondents had junior and senior secondary education, at 35.8% and 30.4%, respectively. The age distribution of the respondents was relatively even, with the largest number of respondents being in the 21–30 age group, followed by the 41–50 age group. Detailed demographic information on the respondents is shown in Figure 2.
The collected data were collated and then statistically analysed. Sub-descriptive statistical analysis, confidence analysis, factor analysis, correlation analysis, and regression analysis were first conducted using IBM SPSS Statistics 26 software to obtain regression equations for influencing PEB and to develop a structural model. Subsequently, partial least squares structural equation modelling analysis was conducted using SmartPLS 4.0 to ensure that the structural model of the relationship between PEB and its influencing factors is reliable.
In this study, SPSS regression analysis was first employed as a preliminary step to identify key predictors of PEB and to examine potential multicollinearity among variables, thereby establishing a solid empirical basis for subsequent model development. Following this, structural equation modelling (SEM) using SmartPLS 4.0 was conducted to investigate the complex causal relationships among latent constructs.

4. Results

4.1. Descriptive Statistical Analysis

Four questions were set to investigate the status of rural solid waste disposal in rural China, as shown in Table 2. Maise straw was most used for firewood (66.3%), followed by local burning (55.0%), and fertilisation (52.1%). The most common disposal methods for livestock manure were composting (60.4%) and fermentation for biogas (44.6%). It is important to note that there are still many people who choose to discharge directly into drains (31.3%) or leave their waste untreated (33.3%). Respondents reported that the most common types of rural solid waste generated in agricultural production were mulch (63.8%) and fertiliser bags (60.8%), while more than half also chose pesticide bottles (57.9%) and maise straw (55.0%). In terms of environmentally friendly practices for rural solid waste, more than half of respondents chose to recycle fertiliser bags, followed by reducing mulch, and maise straw waste.

4.2. Factor Analysis

Factor analysis is a reduced dimensional correlation analysis method used to examine the structure of covariance or correlation coefficients between a set of variables and to explain the association between these variables and a smaller number of factors. The most used methods are principal component analysis and common factor analysis. In this study, SPSS 26.0 was used to carry out factor analysis on the initially selected indicators, and the specific analysis steps were as follows. Firstly, the variables needed for the analysis were selected, and the factors extracted based on the calculation of the correlation coefficient matrix between the variables; the cumulative contribution rate was greater than 80%. The key factor indicators of this study were extracted based on a loading of 0.7 or above. In this study, nine factors were identified in the factor analysis, and the factor loadings and other indicators met the criteria previously set. The results of the reliability analysis showed that Cronbach’s alpha of each factor was greater than 0.7, as shown in Table 3.

4.3. Correlation Analysis

Correlation analysis is the study of whether there is some kind of dependence between phenomena and explores the direction and degree of correlation for specific phenomena that have a dependence. A correlation is a non-deterministic relationship, and the correlation coefficient is a measure of the degree of linear correlation between the variables studied. Table 4 presents the correlation coefficients between PEB and its influencing factors. Specifically, environmental commitment (0.355, p < 0.05), subjective norms (0.478, p < 0.05), and attitude (0.400, p < 0.05) show significant positive correlations with PEB. Additionally, environmental atmosphere (0.315, p < 0.05), government support (0.483, p < 0.05), policy perception (0.408, p < 0.05), environmental knowledge (0.325, p < 0.05), and perceived ecological benefits (0.337, p < 0.05) also demonstrate significant positive correlations. These results indicate that the selected influencing factors are strongly associated with PEB, thereby providing preliminary empirical support for the theoretical framework of this study.
All p-values were less than 0.05, indicating a high level of statistical significance. According to the classification criteria proposed by Cohen (1992) [72] and Field (2018) [73], correlation coefficients in the range of 0.10–0.29 are considered weak, 0.30–0.49 moderate, and ≥0.50 strong (as shown in Table 5). Based on these standards, the results suggest that the observed correlations are generally moderate to strong, demonstrating substantial associations among the variables.

4.4. Regression Analysis

Due to the intricacy of objective linkages, changes in a dependent variable are often influenced by two or more independent variables. To fully reveal such complex dependencies, accurately measure their quantitative changes, and improve the accuracy of prediction and control, more independent variables are needed, and multiple regression models are built to find models that fit better. Based on the results of the above analysis, a linear regression model was developed using PEB, environmental commitment, subjective norms, and attitude as the dependent variables and the remaining influencing factors as the independent variables. To evaluate the goodness-of-fit of each regression model, both the R and R2 values were taken into consideration. As shown in Table 6, Model 1 reports an R value of 0.536, indicating a moderate strength of association between the predictors and the dependent variable. The R2 value of 0.287 suggests that the model explains 28.7% of the variance in pro-environmental behaviour (PEB). In the context of social science research, this level of explanatory power is considered acceptable, particularly given the multifaceted nature of rural environmental behaviour. Similarly, the R values for Models 2 to 4 range from 0.492 to 0.611, indicating a moderate explanatory capacity in predicting attitude, subjective norms, and environmental commitment, respectively.
The Variance Inflation Factor (VIF) is used to assess multicollinearity among independent variables, with lower values indicating more reliable coefficient estimates. Model 1, as depicted in Table 6, shows that the VIF values for environmental commitment, subjective norms, and attitude were all <2, meaning that there was no co-linearity; the t-values of the three variables were 5.060, 3.56, and 2.09, respectively. The t-values for the three variables are 5.060, 3.56, and 2.091, respectively, and the significance is as required (p < 0.05). Thus, environmental commitment, subjective norms, and attitude were the key predictors of PEB. Similarly, Model 2 reveals that the predictors of attitude include perceived ecological benefits, environmental knowledge, and government support; Model 3 reveals that the key predictors of subjective norms include government support, environmental knowledge, policy perception, perceived ecological benefits, and environmental atmosphere; and Model 4 shows the predictors of environmental commitment. Figure 3 illustrates the close relationship between the above variables.

4.5. Structural Equation Modelling

To further validate the causal relationship between PEB and its influencing factors, SmartPLS was used to perform partial least squares structural equation modelling. The results of the data analysis show t-values > 2 for all causal relationships between the two variables, and all meet the significance requirements. The soundness and reliability of the structural model were further validated; see Table 7.
Environmental commitment, subjective norms, and attitudes significantly and positively influence PEB. Environmental commitment is shaped by environmental atmosphere and environmental knowledge, while subjective norms are influenced by environmental atmosphere, government support, policy perception, environmental knowledge, and perceived ecological benefits. Attitudes are positively affected by perceived ecological benefits.

5. Discussion and Implications

5.1. Discussion

The regression results (Table 5) demonstrate that environmental commitment, subjective norms, and attitudes significantly predict pro-environmental behaviour (PEB), aligning with the Theory of Planned Behaviour [74]. This study further identifies perceived ecological benefits, environmental knowledge, and government support as key antecedents of environmental attitudes, consistent with existing findings that highlight the role of cognition and policy in shaping pro-environmental tendencies [75]. Specifically, perceived ecological benefits serve as a foundation for positive attitudes by reflecting expected outcomes—an effect emphasised in both TPB and Value-Belief-Norm (VBN) theory [76]. Environmental knowledge enhances individuals’ competence and confidence, forming a necessary basis for favourable attitudes [77]. Government support strengthens attitudes by promoting trust in environmental policies and facilitating access to relevant information [78].
Subjective norms are shaped by a range of external variables, including government support, environmental knowledge, policy perception, perceived ecological benefits, and the broader environmental atmosphere [79]. These variables reflect both institutional and sociocultural dimensions, collectively influencing individuals’ perceptions of normative behavioural expectations. In rural contexts, environmental atmosphere—manifested through local traditions, shared values, and collective identity—assumes heightened significance, often serving as a key determinant of community-driven pro-environmental behaviours [80]. Furthermore, perceived ecological benefits have been shown to influence not only subjective norms but also individual attitudes toward behaviour, thereby reinforcing their dual role in shaping behavioural intention [81].
As shown in Table 7, SEM provides further insight into the mechanisms driving rural residents’ PEB. Higher education levels and greater environmental knowledge are associated with stronger behavioural intentions, supporting the “knowledge–behaviour” pathway [82,83]. Moreover, environmental knowledge and perceived ecological benefits positively shape attitudes toward solid waste reuse, echoing prior findings that cognition and perceived personal or collective benefits are core drivers of PEB [84]. Practical experiences further validate these mechanisms. In Shandong Province, China, initiatives such as “Ecological Civilization Classrooms” integrate environmental education into schools, Party training, and village assemblies, successfully transforming knowledge into behavioural norms [85]. Similarly, Kenya’s “Community-Led Total Sanitation” project mobilises community-led training and public evaluation to enhance waste management participation [86]. These cases underscore the importance of tailoring environmental education to local cultures and governance structures to enhance knowledge uptake and behavioural change.
Government support and policy awareness are also pivotal. Beyond shaping norms directly, policy interventions enhance environmental literacy and perception, thereby reinforcing the foundation for PEB [87]. For example, the “Ten Thousand Villages Project” in China’s Zhejiang Province significantly boosted rural waste classification through clear directives, subsidies, and publicity campaigns. Prior research confirms that accurate policy understanding strengthens residents’ willingness to engage in waste recycling and reuse [88].

5.2. Implications

This study investigates the PEB of farmers towards rural solid waste and the factors that influence it, bringing many practical implications for farmers and local authorities, as well as helping to enrich the content and scope of research on rural solid waste management.

5.2.1. Practical Implications

Enhancing rural residents’ awareness of solid waste issues is essential, and the implementation of effective economic incentives represents a key strategy in this regard. In provinces such as Zhejiang and Jiangsu, certain localities have introduced a “green account” points system, whereby residents accumulate points for correctly sorting household waste, which can then be redeemed for daily necessities. Additionally, some counties and districts have offered financial subsidies for the purchase of environmentally friendly facilities, such as household composting bins for food waste, and have institutionalised fines for littering through village regulations. These economic incentive mechanisms have contributed positively to increasing public engagement and environmental consciousness among rural populations. Nevertheless, such approaches also face challenges, particularly regarding their long-term sustainability and the adequacy of financial support. The findings of this study offer valuable insights for enhancing environmental awareness, fostering active participation among farmers, and developing more robust systems of incentives and penalties in rural solid waste management.
Improving the living environment of rural residents and creating an environmentally friendly atmosphere is also essential. In China, the “Beautiful Countryside” initiative has achieved notable success in many regions. Various villages have established local environmental supervision mechanisms, conducted public awareness campaigns, formed volunteer groups, and set up community bulletin boards to enhance residents’ environmental awareness and participation. These measures have gradually fostered green and low-carbon lifestyles among rural populations. This study contributes to identifying key barriers in rural solid waste management and proposes practical policy recommendations that can be implemented by governments at all levels, particularly agricultural authorities. These efforts are expected to promote continuous improvements in rural environmental conditions and ultimately enhance the quality of life and well-being of rural residents.
It is also important to reduce the amount of waste generated, organically resource the waste and promote a balanced development of the rural economy and waste management. Hence, reduce the amount of waste generated at the source and in the process, and then recycle organic and recyclable waste. The economic development of rural areas cannot be achieved without the cultivation of crops, and organic cultivation cannot be achieved without fertilisers. Crops play an important role in the quality of life in rural areas; they provide conditions for rural residents to produce waste, as well as better conditions and technology for waste disposal.

5.2.2. Theoretical Implications

In recent years, with the continuous advancement of ecological civilisation construction, rural environmental governance has gradually become an important topic in environmental sociology and environmental behaviour research. A significant body of research highlights the unique challenges faced by rural areas, such as resource constraints, an incomplete governance system, and weaker environmental awareness among residents, factors that are distinct from the institutional and technological conditions present in urban areas. In this context, understanding the motivations behind rural residents’ pro-environmental behaviours (PEB) holds significant theoretical value for the development of governance mechanisms and behavioural intervention strategies tailored to local conditions. This is particularly crucial in a stage where waste management technologies are not yet widespread, and institutional enforcement is weak. At such a stage, the “operability” of behaviour becomes a key link connecting intention with practical behaviour, yet academic research in this area is still in its early stages.
This study focuses on rural areas in Guizhou Province, China, to explore the issue of rural solid waste management. As a typical representative of relatively underdeveloped regions in western China, the environmental governance of rural areas in Guizhou holds unique research value. Through systematic analysis, we identify the key factors influencing farmers’ pro-environmental behaviour (PEB), providing a new research perspective on rural solid waste management. These findings not only carry significant theoretical importance but also provide scientific evidence to inform improvements in rural environmental governance practices.

6. Conclusions

This study focuses on designing the PEB of rural residents and the factors influencing it. This study confirms the importance of environmental commitment, subjective norms, and attitude toward PEB. The study also reveals the following: factors influencing environmental commitment include environmental climate, environmental knowledge, and perceived ecological benefits; factors influencing subjective norms include environmental climate, government support, policy perceptions, environmental knowledge, and perceived ecological benefits; and factors influencing attitude include environmental knowledge and perceived ecological benefits. In view of the above findings, the following recommendations are provided to improve the current situation of rural solid waste management and enhance the PEB of farm households.
(1)
Vigorously promote the resourcefulness of rural solid waste. On the one hand, relevant no-burning policies have been formulated to strengthen the supervision of straw burning, while on the other hand, the handling of perishable items in rural domestic garbage has been strengthened through on-site treatment and resource utilisation. Furthermore, the resourcefulness of straw has been vigorously promoted to improve the use of livestock manure for biogas and composting, while achieving the reduction and resourcefulness of rural solid waste.
(2)
Strengthen supervision and publicise environmental protection knowledge to create a good environmental protection atmosphere. Education and publicity on environmental protection knowledge were conducted in villages, and rural residents were organised to shoot environmental protection videos for uploading to short video platforms such as ShakeYin, including videos on the proper handling of rural solid waste, the hazards of rural solid waste, and environmental pollution. Make people aware of the seriousness of the problem and create a good environmental protection atmosphere.
(3)
Strengthen government management and urge dedicated personnel to implement initiatives. It is important to strengthen the support of government personnel for rural solid waste management and to arrange for special people to go to the countryside to effectively disseminate information to build awareness of rural solid waste reduction and resourcefulness among rural residents. The government should introduce incentive policies to encourage villagers to become advocates and promoters of rural waste reduction and resourcefulness and the implementation of environmentally friendly knowledge promotion behaviours.
(4)
Improve the laws and regulations on rural solid waste disposal and strengthen law enforcement. The government should formulate and improve relevant laws and regulations on rural solid waste, disallow rural residents from discarding waste on hillsides and farmland, and strengthen law enforcement against illegal acts by rural residents.
Although this study benefits from stratified random sampling, which improves the representativeness of the sample, the relatively small sample size of 240 limits the ability to conduct detailed regional comparisons. Future research should expand the sample size to enhance statistical power and the robustness of comparative analyses. Additionally, while this study emphasises the roles of environmental commitment, subjective norms, and individual attitudes in shaping pro-environmental behaviour (PEB), it does not consider other potential factors such as economic incentives, infrastructural conditions, cultural norms, trust in local authorities, or perceived health risks. Future studies could address these limitations by incorporating these additional variables and adopting mixed-methods approaches for a more comprehensive understanding. The focus on a single county in Guizhou also limits the generalizability of the findings to other regions with different socio-economic and cultural contexts. Therefore, further research is needed to validate the model in other provinces.

Author Contributions

Conceptualisation, S.Y.; methodology, S.Y.; software, B.X.; formal analysis, M.J.; investigation, M.J.; resources, Y.L.; data curation, B.X.; writing—original draft preparation, M.J. and Y.L.; visualisation, B.X.; project administration, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Philosophy and Social Science Foundation of Zhejiang Province, China (Grant No. 25NDJC057YBMS); the Research Foundation of the Education Bureau of Zhejiang Province, China (Grant No. Y202045460); the Internal Startup Fund of Zhejiang Sci-Tech University (Grant No. 24052159-Y); and the Construction Research Project of Zhejiang Provincial Department of Housing and Urban Rural Development (Grant No. 2024K316).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Acknowledgments

The authors thank all the reviewers who helped to improve this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Wu, H.; Yuan, H.; Wang, J.; Ouyang, L.; Li, Z. An Investigation of Demolition Waste Management: Case of Shenzhen in China. In Proceedings of the 20th International Symposium on Advancement of Construction Management and Real Estate; Springer: Berlin/Heidelberg, Germany, 2017; pp. 1157–1167. [Google Scholar]
  2. Liu, J.; Ma, C. Optimization of the Construction and Demolition Waste Recycling Facilities Location Based on GIS-AHP: A Case Study of Panyu District, Guangzhou City. J. Eng. Manag. 2020, 34, 74–79. [Google Scholar]
  3. Yan, B.; Yan, J.; Li, Y.; Qin, Y.; Yang, L. Spatial Distribution of Biogas Potential, Utilization Ratio and Development Potential of Biogas from Agricultural Waste in China. J. Clean. Prod. 2021, 292, 126077. [Google Scholar] [CrossRef]
  4. Zhang, M.; Shi, A.; Ajmal, M.; Ye, L.; Awais, M. Comprehensive Review on Agricultural Waste Utilization and High-Temperature Fermentation and Composting. Biomass Convers. Biorefinery 2023, 13, 5445–5468. [Google Scholar]
  5. Wang, B.; Li, M.; Wen, X.; Yang, Y.; Zhu, J.; Belzile, N.; Chen, Y.-W.; Liu, M.; Chen, S. Distribution Characteristics, Potential Contribution, and Management Strategy of Crop Straw and Livestock-Poultry Manure in Multi-Ethnic Regions of China: A Critical Evaluation. J. Clean. Prod. 2020, 274, 123174. [Google Scholar] [CrossRef]
  6. Koul, B.; Yakoob, M.; Shah, M.P. Agricultural Waste Management Strategies for Environmental Sustainability. Environ. Res. 2022, 206, 112285. [Google Scholar] [CrossRef]
  7. Xu, X.; Zhang, Z.; Kuang, Y.; Li, C.; Sun, M.; Zhang, L.; Chang, D. Waste Pesticide Bottles Disposal in Rural China: Policy Constraints and Smallholder Farmers’ Behavior. J. Clean. Prod. 2021, 316, 128385. [Google Scholar] [CrossRef]
  8. Zhang, R.; Zheng, H.; Zhang, H.; Hu, F. Study on the Influence of Social Capital on Farmers’ Participation in Rural Domestic Sewage Treatment in Nanjing, China. Int. J. Environ. Res. Public Health 2020, 17, 2479. [Google Scholar] [CrossRef]
  9. Tam, K.-P. Mind Attribution to Nature and Proenvironmental Behavior. Ecopsychology 2015, 7, 87–95. [Google Scholar] [CrossRef]
  10. De Groot, J.I.; Steg, L. Relationships between Value Orientations, Self-Determined Motivational Types and pro-Environmental Behavioural Intentions. J. Environ. Psychol. 2010, 30, 368–378. [Google Scholar] [CrossRef]
  11. Guo, N.; Hao, J.L.; Zheng, C.; Yu, S.; Wu, W. Applying Social Cognitive Theory to the Determinants of Employees’ pro-Environmental Behaviour towards Renovation Waste Minimization: In Pursuit of a Circular Economy. Waste Biomass Valoriz. 2022, 13, 3739–3752. [Google Scholar] [CrossRef]
  12. Rajapaksa, D.; Islam, M.; Managi, S. Pro-Environmental Behavior: The Role of Public Perception in Infrastructure and the Social Factors for Sustainable Development. Sustainability 2018, 10, 937. [Google Scholar] [CrossRef]
  13. Farrow, K.; Grolleau, G.; Ibanez, L. Social Norms and Pro-Environmental Behavior: A Review of the Evidence. Ecol. Econ. 2017, 140, 1–13. [Google Scholar] [CrossRef]
  14. Maji, S.; Dwivedi, D.H.; Singh, N.; Kishor, S.; Gond, M. Agricultural Waste: Its Impact on Environment and Management Approaches. Emerg. Eco-Friendly Green Technol. Wastewater Treat. 2020, 18, 329–351. [Google Scholar]
  15. Yang, G.; Li, J.; Liu, Z.; Zhang, Y.; Xu, X.; Zhang, H.; Xu, Y. Research Trends in Crop–Livestock Systems: A Bibliometric Review. Int. J. Environ. Res. Public Health 2022, 19, 8563. [Google Scholar] [CrossRef]
  16. Zhou, J.; Zhang, Y.; Khoshnevisan, B.; Duan, N. Meta-Analysis of Anaerobic Co-Digestion of Livestock Manure in Last Decade: Identification of Synergistic Effect and Optimization Synergy Range. Appl. Energy 2021, 282, 116128. [Google Scholar] [CrossRef]
  17. Esmaeili, H.; Farhadi, M. Prognosis of Access to Biomass Residue Resources in Rural Areas to Provide Energy for Villagers. SN Appl. Sci. 2020, 2, 430. [Google Scholar] [CrossRef]
  18. Khan, A.H.; López-Maldonado, E.A.; Khan, N.A.; Villarreal-Gómez, L.J.; Munshi, F.M.; Alsabhan, A.H.; Perveen, K. Current Solid Waste Management Strategies and Energy Recovery in Developing Countries-State of Art Review. Chemosphere 2022, 291, 133088. [Google Scholar] [CrossRef]
  19. Kaur, M.; Singh, A.; Kaur, A. Challenges and Consequences of Improper Waste Disposal in Rural Tourism. In Solid Waste Management and Disposal Practices in Rural Tourism; IGI Global: Hershey, PA, USA, 2025; pp. 317–352. [Google Scholar]
  20. Ziraba, A.K.; Haregu, T.N.; Mberu, B. A Review and Framework for Understanding the Potential Impact of Poor Solid Waste Management on Health in Developing Countries. Arch. Public Health 2016, 74, 55. [Google Scholar] [CrossRef]
  21. Wan, C.; Shen, G.Q.; Choi, S. Waste Management Strategies for Sustainable Development. In Encyclopedia of Sustainability in Higher Education; Springer: Berlin/Heidelberg, Germany, 2019; pp. 2020–2028. [Google Scholar]
  22. Atinkut, H.B.; Yan, T.; Arega, Y.; Raza, M.H. Farmers’ Willingness-to-Pay for Eco-Friendly Agricultural Waste Management in Ethiopia: A Contingent Valuation. J. Clean. Prod. 2020, 261, 121211. [Google Scholar] [CrossRef]
  23. Bernardes, C.; Günther, W.M.R. Generation of Domestic Solid Waste in Rural Areas: Case Study of Remote Communities in the Brazilian Amazon. Human Ecol. 2014, 42, 617–623. [Google Scholar] [CrossRef]
  24. Chauhan, A.; Saini, R.P. Renewable Energy Based Off-Grid Rural Electrification in Uttarakhand State of India: Technology Options, Modelling Method, Barriers and Recommendations. Renew. Sustain. Energy Rev. 2015, 51, 662–681. [Google Scholar] [CrossRef]
  25. Kapoor, R.; Ghosh, P.; Kumar, M.; Sengupta, S.; Gupta, A.; Kumar, S.S.; Vijay, V.; Kumar, V.; Vijay, V.K.; Pant, D. Valorization of Agricultural Waste for Biogas Based Circular Economy in India: A Research Outlook. Bioresour. Technol. 2020, 304, 123036. [Google Scholar] [CrossRef]
  26. Al-Shayaa, M.S.; Al-Wabel, M.; Herab, A.H.; Sallam, A.; Baig, M.B.; Usman, A.R. Environmental Issues in Relation to Agricultural Practices and Attitudes of Farmers: A Case Study from Saudi Arabia. Saudi J. Biol. Sci. 2021, 28, 1080–1087. [Google Scholar] [CrossRef] [PubMed]
  27. Patwa, A.; Parde, D.; Dohare, D.; Vijay, R.; Kumar, R. Solid Waste Characterization and Treatment Technologies in Rural Areas: An Indian and International Review. Environ. Technol. Innov. 2020, 20, 101066. [Google Scholar] [CrossRef]
  28. Jana, K.; De, S. Polygeneration Using Agricultural Waste: Thermodynamic and Economic Feasibility Study. Renew. Energy 2015, 74, 648–660. [Google Scholar] [CrossRef]
  29. Yu, S.; Lew, V.; Ma, W.; Bao, Z.; Hao, J.L. Unlocking Key Factors Affecting Utilization of Biomass Briquettes in Africa through SWOT and Analytic Hierarchy Process: A Case of Madagascar. Fuel 2022, 323, 124298. [Google Scholar] [CrossRef]
  30. Huang, R.; Sun, X.; Tan, X.; Han, B.; Zhao, S.; Ma, D.; Sun, T.; Wei, Q.; Yang, Y.; Zhang, L. Decision-Making Analysis on Collection and Transportation Distance of Rural Domestic Waste: Case Study of Guangxi Province, China. J. Mater. Cycles Waste Manag. 2020, 22, 2081–2091. [Google Scholar] [CrossRef]
  31. Ghasemi, S.M.; Ghaderpoori, M.; Moradi, B.; Taghavi, M.; Karimyan, K.; Mehdipour, F. Optimization of Cr (VI) Adsorption by Modified Sesame Hull from Aqueous Solutions Using Response Surface Methodology. Int. J. Environ. Anal. Chem. 2022, 102, 3094–3108. [Google Scholar] [CrossRef]
  32. Paul, A.S.; Panwar, N.L.; Salvi, B.L.; Jain, S.; Sharma, D. Experimental Investigation on the Production of Bio-Oil from Wheat Straw. Energy Sources Part A Recovery Util. Environ. Eff. 2024, 46, 9777–9792. [Google Scholar] [CrossRef]
  33. Muise, I.; Adams, M.; Côté, R.; Price, G.W. Attitudes to the Recovery and Recycling of Agricultural Plastics Waste: A Case Study of Nova Scotia, Canada. Resour. Conserv. Recycl. 2016, 109, 137–145. [Google Scholar] [CrossRef]
  34. Pandey, P.; Dhiman, M.; Kansal, A.; Subudhi, S.P. Plastic Waste Management for Sustainable Environment: Techniques and Approaches. Waste Dispos. Sustain. Energy 2023, 5, 205–222. [Google Scholar] [CrossRef] [PubMed]
  35. Chabbi, A.; Lehmann, J.; Ciais, P.; Loescher, H.W.; Cotrufo, M.F.; Don, A.; SanClements, M.; Schipper, L.; Six, J.; Smith, P. Aligning Agriculture and Climate Policy. Nat. Clim. Change 2017, 7, 307–309. [Google Scholar] [CrossRef]
  36. Hao, J.L.; Yu, S.; Tang, X.; Wu, W. Determinants of Workers’ pro-Environmental Behaviour towards Enhancing Construction Waste Management: Contributing to China’s Circular Economy. J. Clean. Prod. 2022, 369, 133265. [Google Scholar] [CrossRef]
  37. Zareie, B.; Navimipour, N.J. The Impact of Electronic Environmental Knowledge on the Environmental Behaviors of People. Comput. Human Behav. 2016, 59, 1–8. [Google Scholar] [CrossRef]
  38. Bandura, A. Social Foundations of Thought and Action; Prentice-Hall, Inc.: Englewood Cliffs, NJ, USA, 1986; Volume 1986, p. 2. [Google Scholar]
  39. Yang, Y.; Yuan, Y.; Liu, P.; Wu, W.; Huo, C. Crucial to Me and My Society: How Collectivist Culture Influences Individual pro-Environmental Behavior through Environmental Values. J. Clean. Prod. 2024, 454, 142211. [Google Scholar] [CrossRef]
  40. Zientara, P.; Zamojska, A. Green Organizational Climates and Employee Pro-Environmental Behaviour in the Hotel Industry. J. Sustain. Tour. 2018, 26, 1142–1159. [Google Scholar] [CrossRef]
  41. Savari, M.; Zhoolideh, M.; Khosravipour, B. Explaining Pro-Environmental Behavior of Farmers: A Case of Rural Iran. Curr. Psychol. 2021, 42, 7752–7770. [Google Scholar] [CrossRef]
  42. Sereenonchai, S.; Arunrat, N. Farmers’ Perceptions, Insight Behavior and Communication Strategies for Rice Straw and Stubble Management in Thailand. Agronomy 2022, 12, 200. [Google Scholar] [CrossRef]
  43. Smith, L.E.; Siciliano, G. A Comprehensive Review of Constraints to Improved Management of Fertilizers in China and Mitigation of Diffuse Water Pollution from Agriculture. Agric. Ecosyst. Environ. 2015, 209, 15–25. [Google Scholar] [CrossRef]
  44. Yang, L.; Jiang, Y.; Zhang, W.; Zhang, Q.; Gong, H. An Empirical Examination of Individual Green Policy Perception and Green Behaviors. Int. J. Manpow. 2020, 41, 1021–1040. [Google Scholar] [CrossRef]
  45. Wang, B.; Dong, F.; Chen, M.; Zhu, J.; Tan, J.; Fu, X.; Wang, Y.; Chen, S. Advances in Recycling and Utilization of Agricultural Wastes in China: Based on Environmental Risk, Crucial Pathways, Influencing Factors, Policy Mechanism. Procedia Environ. Sci. 2016, 31, 12–17. [Google Scholar] [CrossRef]
  46. Drangert, J.-O.; Kiełbasa, B.; Ulen, B.; Tonderski, K.S.; Tonderski, A. Generating Applicable Environmental Knowledge among Farmers: Experiences from Two Regions in Poland. Agroecol. Sustain. Food Syst. 2017, 41, 671–690. [Google Scholar] [CrossRef]
  47. Wang, P.; Liu, Q.; Qi, Y. Factors Influencing Sustainable Consumption Behaviors: A Survey of the Rural Residents in China. J. Clean. Prod. 2014, 63, 152–165. [Google Scholar] [CrossRef]
  48. Xue, M.; Zhao, Y.; Wang, Z.; Zhang, B. Behavioural Determinants of an Individual’s Intention to Adapt to Climate Change: Both Internal and External Perspectives. Environ. Impact Assess. Rev. 2021, 91, 106672. [Google Scholar] [CrossRef]
  49. Wang, W.; Wang, L.; Gu, L.; Zhou, G. Understanding Farmers’ Commitments to Carbon Projects. Sci. Total Environ. 2021, 784, 147112. [Google Scholar] [CrossRef]
  50. Jiang, L.; Zhang, J.; Wang, H.H.; Zhang, L.; He, K. The Impact of Psychological Factors on Farmers’ Intentions to Reuse Agricultural Biomass Waste for Carbon Emission Abatement. J. Clean. Prod. 2018, 189, 797–804. [Google Scholar] [CrossRef]
  51. Shi, X.; Song, Z. The Silent Majority: Local Residents’ Environmental Behavior and Its Influencing Factors in Coal Mine Area. J. Clean. Prod. 2019, 240, 118275. [Google Scholar] [CrossRef]
  52. Bagheri, A.; Emami, N.; Damalas, C.A. Farmers’ Behavior in Reading and Using Risk Information Displayed on Pesticide Labels: A Test with the Theory of Planned Behavior. Pest Manag. Sci. 2021, 77, 2903–2913. [Google Scholar] [CrossRef]
  53. Christopher Mittelstaedt, J. Culture for the Masses: Building Grassroots Cultural Infrastructure in China. Mod. China 2024, 50, 607–640. [Google Scholar] [CrossRef]
  54. Jiang, T.; Wang, M.; Zhang, W.; Zhu, C.; Wang, F. A Comprehensive Analysis of Agricultural Non-Point Source Pollution in China: Current Status, Risk Assessment and Management Strategies. Sustainability 2024, 16, 2515. [Google Scholar] [CrossRef]
  55. Yang, Y.; Zhu, Y.; Wang, X.; Li, Y. The Perception of Environmental Information Disclosure on Rural Residents’ pro-Environmental Behavior. Int. J. Environ. Res. Public Health 2022, 19, 7851. [Google Scholar] [CrossRef] [PubMed]
  56. Haddoud, M.Y.; Onjewu, A.-K.E.; Nowiński, W. Environmental Commitment and Innovation as Catalysts for Export Performance in Family Firms. Technol. Forecast. Soc. Change 2021, 173, 121085. [Google Scholar] [CrossRef]
  57. Bagheri, A.; Bondori, A.; Allahyari, M.S.; Damalas, C.A. Modeling Farmers’ Intention to Use Pesticides: An Expanded Version of the Theory of Planned Behavior. J. Environ. Manag. 2019, 248, 109291. [Google Scholar] [CrossRef] [PubMed]
  58. Mohammadrezaei, M.; Meredith, D.; McNamara, J. Subjective Norms Influence Advisors’ Reluctance to Discuss Farm Health and Safety. J. Agric. Educ. Ext. 2023, 29, 627–651. [Google Scholar] [CrossRef]
  59. Hu, G.; Wang, J.; Fahad, S.; Li, J. Influencing Factors of Farmers’ Land Transfer, Subjective Well-Being, and Participation in Agri-Environment Schemes in Environmentally Fragile Areas of China. Environ. Sci. Pollut. Res. 2023, 30, 4448–4461. [Google Scholar] [CrossRef]
  60. Shen, C.-C.; Chang, Y.-R.; Liu, D.-J. Sustainable Development of an Organic Agriculture Village to Explore the Influential Effect of Brand Equity from the Perspective of Landscape Resources. Sustainability 2020, 12, 7416. [Google Scholar] [CrossRef]
  61. Li, J.; Xu, X.; Liu, L. Attribution and Causal Mechanism of Farmers’ Willingness to Prevent Pollution from Livestock and Poultry Breeding in Coastal Areas. Environ. Dev. Sustain. 2021, 23, 7193–7211. [Google Scholar] [CrossRef]
  62. Prokopy, L.S.; Arbuckle, J.G.; Barnes, A.P.; Haden, V.R.; Hogan, A.; Niles, M.T.; Tyndall, J. Farmers and Climate Change: A Cross-National Comparison of Beliefs and Risk Perceptions in High-Income Countries. Environ. Manag. 2015, 56, 492–504. [Google Scholar] [CrossRef]
  63. Jiang, W.; Yan, T.; Chen, B. Impact of Media Channels and Social Interactions on the Adoption of Straw Return by Chinese Farmers. Sci. Total Environ. 2021, 756, 144078. [Google Scholar] [CrossRef]
  64. Eagle, A.J.; Rude, J.; Boxall, P.C. Agricultural Support Policy in Canada: What Are the Environmental Consequences? Environ. Rev. 2015, 24, 13–24. [Google Scholar] [CrossRef]
  65. Deng, J.; Sun, P.; Zhao, F.; Han, X.; Yang, G.; Feng, Y. Analysis of the Ecological Conservation Behavior of Farmers in Payment for Ecosystem Service Programs in Eco-Environmentally Fragile Areas Using Social Psychology Models. Sci. Total Environ. 2016, 550, 382–390. [Google Scholar] [CrossRef] [PubMed]
  66. Kleijn, D.; Bommarco, R.; Fijen, T.P.; Garibaldi, L.A.; Potts, S.G.; Van Der Putten, W.H. Ecological Intensification: Bridging the Gap between Science and Practice. Trends Ecol. Evol. 2019, 34, 154–166. [Google Scholar] [CrossRef] [PubMed]
  67. Yaghoubi Farani, A.; Mohammadi, Y.; Ghahremani, F. Modeling Farmers’ Responsible Environmental Attitude and Behaviour: A Case from Iran. Environ. Sci. Pollut. Res. 2019, 26, 28146–28161. [Google Scholar] [CrossRef] [PubMed]
  68. Thompson, A.W.; Reimer, A.; Prokopy, L.S. Farmers’ Views of the Environment: The Influence of Competing Attitude Frames on Landscape Conservation Efforts. Agric. Hum. Values 2015, 32, 385–399. [Google Scholar] [CrossRef]
  69. Keshavarz, M.; Karami, E. Farmers’ pro-Environmental Behavior under Drought: Application of Protection Motivation Theory. J. Arid Environ. 2016, 127, 128–136. [Google Scholar] [CrossRef]
  70. Zhou, Z.; Liu, J.; Zeng, H.; Zhang, T.; Chen, X. How Does Soil Pollution Risk Perception Affect Farmers’ pro-Environmental Behavior? The Role of Income Level. J. Environ. Manag. 2020, 270, 110806. [Google Scholar] [CrossRef]
  71. Gholamrezai, S.; Aliabadi, V.; Ataei, P. Understanding the Pro-Environmental Behavior among Green Poultry Farmers: Application of Behavioral Theories. Environ. Dev. Sustain. 2021, 23, 16100–16118. [Google Scholar] [CrossRef]
  72. Cohen, J. Statistical Power Analysis. Curr. Dir. Psychol. Sci. 1992, 1, 98–101. [Google Scholar] [CrossRef]
  73. Field, A. Discovering Statistics Using IBM SPSS Statistics; Sage Publications Limited: London, UK, 2024. [Google Scholar]
  74. Yuriev, A.; Dahmen, M.; Paillé, P.; Boiral, O.; Guillaumie, L. Pro-Environmental Behaviors through the Lens of the Theory of Planned Behavior: A Scoping Review. Resour. Conserv. Recycl. 2020, 155, 104660. [Google Scholar] [CrossRef]
  75. Sampene, A.K.; Li, C.; Wiredu, J.; Agyeman, F.O.; Brenya, R. Examining the Nexus between Social Cognition, Biospheric Values, Moral Norms, Corporate Environmental Responsibility and pro-Environmental Behaviour. Does Environmental Knowledge Matter? Curr. Psychol. 2024, 43, 6549–6569. [Google Scholar] [CrossRef]
  76. Batool, N.; Wani, M.D.; Shah, S.A.; Dada, Z.A. Theory of Planned Behavior and Value-Belief Norm Theory as Antecedents of pro-Environmental Behaviour: Evidence from the Local Community. J. Human Behav. Social Environ. 2024, 34, 693–709. [Google Scholar] [CrossRef]
  77. Ramsey, C.E.; Rickson, R.E. Environmental Knowledge and Attitudes. J. Environ. Educ. 1976, 8, 10–18. [Google Scholar] [CrossRef]
  78. Lavergne, K.J.; Sharp, E.C.; Pelletier, L.G.; Holtby, A. The Role of Perceived Government Style in the Facilitation of Self-Determined and Non Self-Determined Motivation for pro-Environmental Behavior. J. Environ. Psychol. 2010, 30, 169–177. [Google Scholar] [CrossRef]
  79. Yin, S.; Wang, Y.; Liu, Y.; Wang, S. Exploring Drivers of Behavioral Willingness to Use Clean Energy to Reduce Environmental Emissions in Rural China: An Extension of the UTAUT2 Model. J. Renew. Sustain. Energy 2024, 16, 45903. [Google Scholar] [CrossRef]
  80. Ru, X.; Wang, S.; Yan, S. Exploring the Effects of Normative Factors and Perceived Behavioral Control on Individual’s Energy-Saving Intention: An Empirical Study in Eastern China. Resour. Conserv. Recycl. 2018, 134, 91–99. [Google Scholar] [CrossRef]
  81. Wang, Y.; Sun, J.; Liu, C.; Liu, L. Exploring the Nexus between Perceived Ecosystem Services and Well-Being of Rural Residents in a Mountainous Area, China. Appl. Geogr. 2024, 164, 103215. [Google Scholar] [CrossRef]
  82. Fang, W.-T.; Lien, C.-Y.; Huang, Y.-W.; Han, G.; Shyu, G.-S.; Chou, J.-Y.; Ng, E. Environmental Literacy on Ecotourism: A Study on Student Knowledge, Attitude, and Behavioral Intentions in China and Taiwan. Sustainability 2018, 10, 1886. [Google Scholar] [CrossRef]
  83. Safari, A.; Salehzadeh, R.; Panahi, R.; Abolghasemian, S. Multiple Pathways Linking Environmental Knowledge and Awareness to Employees’ Green Behavior. Corp. Gov. Int. J. Bus. Soc. 2018, 18, 81–103. [Google Scholar] [CrossRef]
  84. Mendes, T.; Teixeira, H.; Lopes, A.M.; Correia, A. From Environmental Knowledge to Pro-Environmental Behaviors: Paving the Way for More Sustainable Higher Education Institutions through a Mission Refocus. J. Technol. Transf. 2025, 1–34. [Google Scholar] [CrossRef]
  85. Wu, Z.; Jin, M.; Cao, H. Residents’ Environmental Behavior in Eco-community Development and Its Influencing Factors: Evidence from Dongying City, Shandong Province in China. Sustain. Dev. 2024, 32, 4338–4353. [Google Scholar] [CrossRef]
  86. Muchiri, C.N.; Opiyo, R.O. Community Adaptation Strategies in Nairobi Informal Settlements: Lessons from Korogocho, Nairobi-Kenya. Front. Sustain. Cities 2022, 4, 932046. [Google Scholar] [CrossRef]
  87. Zhang, Y.; Du, Q.; Huang, Y.; Mao, Y.; Jiao, L. Decoding Determinants of Pro-Environmental Behaviors of Higher Education Students: Insights for Sustainable Future. Int. J. Sustain. High. Educ. 2024; ahead-of-print. [Google Scholar] [CrossRef]
  88. Lu, B.; Wang, J. How Can Residents Be Motivated to Participate in Waste Recycling? An Analysis Based on Two Survey Experiments in China. Waste Manag. 2022, 143, 206–214. [Google Scholar] [CrossRef]
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Figure 1. Map of China highlighting the location of Guizhou Province.
Figure 1. Map of China highlighting the location of Guizhou Province.
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Figure 2. Specific information on respondent demographic factors.
Figure 2. Specific information on respondent demographic factors.
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Figure 3. Association of PEB and its influencing factors for farmers.
Figure 3. Association of PEB and its influencing factors for farmers.
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Table 1. Variables adopted in the questionnaire and their source.
Table 1. Variables adopted in the questionnaire and their source.
No.VariablesReferences
1Environmental commitment[49,56]
2Environmental knowledge[46,56]
3Subjective norms[57,58]
4Environmental atmosphere[59,60]
5Policy perception[61,62]
6Government support[63,64]
7Perceived ecological benefits[65,66]
8Attitude[67,68]
9Pro-environmental behaviour[69,70,71]
Table 2. Details of generation and treatment of agriculture waste.
Table 2. Details of generation and treatment of agriculture waste.
QuestionsOptionsFrequencies
(Persons)		Percentage (%)
  • The way my village disposes of its crop straw
Burning on site13255.0%
Used for firewood15966.3%
Fertilisation12552.1%
Use as feed8635.8%
Making biogas4518.8%
2.
The way livestock and poultry manure are disposed of in my village
Pesticide bottles13957.9%
Fertiliser bags14660.8%
Plastic mulch15363.8%
Maise straw13255.0%
Rice straw8836.7%
Oilseed rape straw8033.3%
3.
Rural solid wastes that I have generated during agricultural production include:
Livestock manure10041.7%
Discarded vegetables6326.3%
Pesticide bottles10443.3%
Fertiliser bags14259.2%
Plastic mulch11748.8%
Maise straw11547.9%
Rice straw6125.4%
Oilseed rape straw5522.9%
4.
Which of the following rural solid wastes have I carried out using PEB, including reduction or resource recovery?
Livestock manure4820.0%
Pesticide bottles13957.9%
Fertiliser bags14660.8%
Plastic mulch15363.8%
Maise straw13255.0%
Rice straw8836.7%
Oilseed rape straw8033.3%
Table 3. Results of factor analysis of PEB and its influencing factors.
Table 3. Results of factor analysis of PEB and its influencing factors.
FactorsItemsDetailsFactor LoadingCronbach’s Alpha
Environmental commitment1I feel obliged to support the reduction and resourcefulness of rural solid waste0.8720.839
2The interest in the reduction and resourcefulness of rural solid waste means a lot to me0.843
3I would feel guilty if I did not support the reduction and resourcefulness of rural solid waste0.838
Environmental knowledge1Plastic residues in the soil can affect soil structure and crop growth0.8330.892
2Long-term stockpiling of livestock manure can breed mosquitoes and bacteria, pollute the environment, and cause infectious diseases0.829
3Burning large quantities of straw can pollute the air with toxic gases and affect human health0.819
Subjective norms1My neighbours think I should take measures to reduce and recycle.0.8940.904
2The village council organisation thinks I should take measures to reduce and resource0.872
3Family and friends think I should take steps to reduce and resource my work.0.853
Environmental atmosphere1I am surrounded by friends and neighbours who feel that the reduction and resourcefulness of rural solid waste is a prevailing trend0.8500.756
2My friends and neighbours around me often share with each other environmentally friendly measures for rural solid waste0.784
Policy perception1I feel that the implementation of a policy of reduction and resourcefulness will help a lot in the treatment of rural solid waste0.8230.857
2I understand rural waste minimisation and resource recovery policies0.817
3I am satisfied with the policy of reducing and resourcing rural waste0.795
Government support1Local authorities have organised training on the environmental protection of rural solid waste0.8650.851
2The local government has set up a system of rewards and penalties to encourage villagers to implement rural solid waste minimisation and resource recovery0.859
3Local authorities have used online resources to push the dangers of rural solid waste0.771
Perceived ecological benefits1Crop straw return to the field and organic manure improve soil quality on farmland0.8870.900
2Reduced and resourceful use of livestock and poultry manure can reduce water pollution0.879
3A ban on the open burning of crop straw can reduce air pollution0.825
Attitude1I am in favour of the implementation of rural solid waste reduction and resource recovery0.8500.807
2The implementation of rural waste reduction and resource recovery is beneficial to environmental protection0.843
3The implementation of rural waste reduction and resource recovery can improve the living environment of villagers0.838
Pro-environmental behaviour1I often recycle rural solid waste regularly and try to avoid it0.8570.774
2I regularly collect and use livestock manure and crop straw for resource use0.779
3I have attended training on knowledge activities related to rural solid waste reduction0.798
Note: N = 240; KMO = 0.893; Extraction method: principal component analysis.
Table 4. Result of correlation analysis on PEB and its influencing factors.
Table 4. Result of correlation analysis on PEB and its influencing factors.
PEBF1F2F3F4F5F6F7F8
PEB1.0000.355 **0.478 **0.400 **0.315 **0.483 **0.408 **0.325 **0.337 **
F1 1.0000.454 **0.418 **0.281 **0.329 **0.294 **0.392 **0.410 **
F2 1.0000.438 **0.381 **0.543 **0.475 **0.411 **0.426 **
F3 1.0000.207 **0.336 **0.291 **0.281 **0.556 **
Note: N = 240; ** p < 0.01; F1-Environmental commitment, F2-Subjective norms, F3-Attitude, F4-Environmental atmosphere, F5-Government support, F6-Policy perception, F7-Environmental knowledge, F8-Perceived ecological benefits.
Table 5. Interpretation scale for correlation coefficients.
Table 5. Interpretation scale for correlation coefficients.
Correlation RangeStrength of
Association		Interpretation
0.00–0.09NegligibleExtremely weak or no meaningful association
0.10–0.29WeakMinor but potentially meaningful relationship
0.30–0.49ModerateSubstantial and practically significant relationship
≥0.50StrongHighly significant and robust association
Note. Adapted from Cohen (1992) [72] and Field (2018) [73].
Table 6. Results of regression analysis on pro-environmental behaviour and its influencing factors.
Table 6. Results of regression analysis on pro-environmental behaviour and its influencing factors.
ModelBStd. ErrortSig.VIFRR2Sig. (ANOVA)
PEB ←Attitude, subjective norms and environmental commitment
1(constant)0.8600.1774.8540.000 0.5360.2870.000
Environmental commitment0.3260.0645.0600.0001.393
Subjective norms0.2320.0653.5680.0001.257
Attitude0.1260.0602.0910.0381.222
Attitude ← F4–F8
2(constant)0.4520.1652.7390.007 0.6110.3740.000
Perceived ecological benefits0.4340.0577.6230.0001.232
Environmental knowledge0.1850.0583.2140.0011.28
Government support0.1470.0522.8280.0051.15
Subjective norms ←F4–F8
3(constant)−0.0800.188−0.4270.67 0.5360.2870.000
Government support0.2900.0585.0090.0001.480
Environmental knowledge0.2290.0574.0210.0001.300
Policy perception0.2220.0623.5830.0001.335
Perceived ecological benefits0.1700.0582.9370.0041.316
Environmental atmosphere0.1330.0552.3970.0171.300
Environmental commitment ← F4–F8
4(constant)0.5730.2102.7280.007 0.4920.2420.000
Environmental knowledge0.3080.0664.6890.0001.205
Environmental atmosphere0.2320.0593.9240.0001.041
Perceived ecological benefits0.1860.0672.7790.0061.231
Table 7. T-values and path analysis of the structural model.
Table 7. T-values and path analysis of the structural model.
No.PathsMeanSTDEVT-ValueResults
1Attitude → PEB0.2440.0733.306 **Supported
2Environmental atmosphere → Environmental commitment0.2410.0633.805 ***Supported
3Environmental atmosphere → Subjective norms0.1760.0652.678 **Supported
4Environmental commitment → PEB0.1420.0622.220 *Supported
5Environmental knowledge → Attitude0.2610.0634.139 ***Supported
6Environmental knowledge → Environmental commitment0.3120.0654.773 ***Supported
7Environmental knowledge → Subjective norms0.2230.0573.930 ***Supported
8Government support → Subjective norms0.2690.0664.092 ***Supported
9Pro-environmental behaviour → Attitude0.4560.0686.664 ***Supported
10Pro-environmental behaviour → Environmental commitment0.1660.0692.401 *Supported
11Pro-environmental behaviour →Subjective norms0.1460.0602.417 *Supported
12Policy perception → Subjective norms0.2000.0663.012 **Supported
13Subjective norms → PEB0.3280.0724.566 ***Supported
Note: * p < 0.05, ** p < 0.01, *** p < 0.001; PEB–Pro-environmental behaviour.
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Jiang, M.; Liu, Y.; Xia, B.; Yu, S. The Role of Environmental Knowledge and Perceived Ecological Benefits in Shaping Farmers’ Pro-Environmental Behaviour Towards Rural Solid Waste. Sustainability 2025, 17, 5258. https://doi.org/10.3390/su17125258

AMA Style

Jiang M, Liu Y, Xia B, Yu S. The Role of Environmental Knowledge and Perceived Ecological Benefits in Shaping Farmers’ Pro-Environmental Behaviour Towards Rural Solid Waste. Sustainability. 2025; 17(12):5258. https://doi.org/10.3390/su17125258

Chicago/Turabian Style

Jiang, Menglei, Yong Liu, Bo Xia, and Shiwang Yu. 2025. "The Role of Environmental Knowledge and Perceived Ecological Benefits in Shaping Farmers’ Pro-Environmental Behaviour Towards Rural Solid Waste" Sustainability 17, no. 12: 5258. https://doi.org/10.3390/su17125258

APA Style

Jiang, M., Liu, Y., Xia, B., & Yu, S. (2025). The Role of Environmental Knowledge and Perceived Ecological Benefits in Shaping Farmers’ Pro-Environmental Behaviour Towards Rural Solid Waste. Sustainability, 17(12), 5258. https://doi.org/10.3390/su17125258

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