The Impact of Environmental Regulation and Cognition of Manure Treatment on the Resource Utilization Behaviors of Swine Farmers

Abstract

The resource utilization of swine manure represents a critical pathway for advancing sustainable agricultural development. This study, based on survey data from 509 swine farmers in Sichuan Province, employs the Ordered Probit (Oprobit) model and the Conditional Mixed Process (CMP) model to analyze the mechanisms and pathways through which cognition about manure treatment, environmental regulation, and their interaction influence farmers’ behaviors towards manure resource utilization. It further delves into the heterogeneous characteristics of influencing factors. The findings reveal the following: (1) Farmers possess a high level of cognition regarding manure treatment, while environmental regulation is moderately implemented. The principal methods of manure resource utilization focus on recycling to fields and organic fertilizer production, with over 95% of farmers adopting at least one method of resource utilization. (2) Both cognition of manure treatment and environmental regulation significantly promote the behavior of manure resource utilization. There are substitutive or complementary effects between moral cognition and constraint regulation, as well as capability cognition and guidance regulation. (3) Among the farming community, the behavior of large-scale farmers is mainly influenced by moral cognition, whereas non-large-scale farmers are more affected by capability cognition and guidance regulation; middle-aged and young farmers are predominantly influenced by capability cognition, incentives, and guidance regulation, whereas the older generation of farmers is driven more by moral cognition and guidance regulation. Based on these insights, this study proposes targeted strategies for enhancing cognition and regulatory alignment across different groups, aiming to elevate the level of manure resource utilization and promote the green transformation of livestock farming.

1. Introduction

China stands as the global leader in both the production and consumption of swine, harboring the world’s largest inventory of swine which comprises approximately 37.95% of the global total [1]. In recent years, the Chinese swine livestock farming sector has been undergoing a profound transformation from traditional, scattered husbandry to more intensive and large-scale operations [2]. This transformation has significantly enhanced industrial efficiency; as per data from the National Bureau of Statistics, by 2022, swine farms with an annual output of more than 500 heads accounted for about 70% of the national total output. However, as the scale of swine farming in China continues to expand, the volume of manure produced has increased proportionally with the swine inventory [3]. According to public data released by the Ministry of Agriculture and Rural Affairs, such as the “Opinions on Accelerating the Resource Utilization of Livestock and Poultry Breeding Waste,” the annual production of swine manure has exceeded 600 million tons, with approximately 40% still not effectively treated or utilized as resources. The indiscriminate discharge of untreated manure not only contributes to the pollution of soil, water bodies, and the atmosphere but also poses a severe threat to regional ecological security. It potentially jeopardizes the health and quality of life of nearby residents [4]. Thus, promoting the clean treatment of swine manure has become a crucial component of China’s “pollution reduction and green growth” strategy. This effort also serves as a critical foundation for the sustainable development of the global swine industry. Currently, swine farmers face a dual dilemma in the field of manure treatment: on one hand, constrained by traditional farming concepts and technical levels, some farmers do not sufficiently prioritize manure treatment, and their methods remain extensive [5]; on the other hand, the high costs and technical barriers associated with manure treatment facilities render the farmers deficient in both subjective will and objective capability, ultimately leading to a disconnect between their cognition and practice [6]. In this context, advancing the resource utilization of swine manure becomes an important strategy to address pollution problems associated with livestock farming.

Environmental regulation serves as a pivotal policy instrument for addressing the externalities of environmental pollution. It has increasingly attracted the attention of environmental protection agencies [7]. However, flaws in policy design, deviations in implementation, and limitations in functional coverage often result in a discrepancy between the actual efficacy of environmental governance and its anticipated objectives [8]. Agricultural behavioral economics suggests that the decision-making logic of farmers regarding the adoption of new technologies is influenced by external factors related to policy regulation. It is also affected by cognitive-derived reputation risks and social pressures [9]. According to studies by Lu et al. and Sun et al. [10,11], farmers first assess the value and social impact of technologies before deciding whether to adopt them. For example, to maintain their reputation and neighborly relations, farmers are willing to invest more resources in pro-environmental behaviors [12]. Although existing research has thoroughly investigated incentive strategies for the resource utilization of livestock manure, it has rarely focused on the key role of manure treatment cognition. It also overlooks reputation transmission mechanisms within the social context of rural China, which are crucial to the decision-making processes of livestock owners [13]. In fact, cognitive factors play a decisive role in the formulation and implementation of environmental policies, directly shaping the resource utilization behaviors of farmers regarding livestock manure. However, the literature still falls short in exploring the transmission mechanisms among environmental regulation, farmers’ cognition of manure treatment behaviors, and their resource utilization actions. Analyzing the intrinsic logical relationships among these elements is vitally important. It is essential both theoretically and practically for refining the system of livestock manure resource utilization and promoting the green transformation of the livestock industry.

Based on the analysis presented above, this study explores the synergistic mechanism between environmental regulation and cognition of manure treatment. The study employs the cognition of manure treatment by farmers as an endogenous driver and environmental regulation as an exogenous constraint, to develop a decision analysis model for the utilization of manure as a resource by swine farmers. Utilizing data from 509 swine farmers, this study applies Oprobit, IV-Probit, and CMP models to further reveal the mechanisms and pathways through which cognition and environmental regulation influence resource utilization behaviors. It also conducts a detailed analysis of the heterogeneous effects of these influences. This study holds significant theoretical and practical implications for enhancing the mechanisms of resource utilization of manure and advancing the green transformation of livestock farming in China and other developing countries.

2. Literature Review and Theoretical Analysis

2.1. The Impact of Farmers’ Cognition of Manure Treatment on the Resource Utilization of Swine Manure Behavior

Research in the field of manure treatment cognition shows that farmers’ value cognition significantly increases their likelihood of adopting behaviors that utilize livestock manure resources effectively. Ecological value cognition is particularly influential [14]. Factors such as concern for the rural environment, emphasis on environmental conservation, and behaviors related to waste utilization are positively correlated with farmers’ willingness to utilize livestock manure resources. However, the effectiveness of manure resource utilization in practice is often hindered by both objective external factors, such as economic development levels, and subjective internal factors, like the farmers’ cognitive levels [15]. In China, common issues include low educational levels among farmers and aging rural populations [16]. Many farmers show a willingness to treat livestock manure resources, but the transition from willingness to actual behavior has not been empirically verified [17]. This study introduces a three-dimensional analytical framework of cognition—capability, moral, and relational—to explore how cognition influences decisions to adopt technology. On one hand, farmers view technology adoption as a way to demonstrate forward-thinking environmental principles. Early adopters gain recognition and respect from their community, which enhances their social reputation and motivates them to adopt resource utilization technologies. On the other hand, cognition can lead to a conservative approach to adopting new technologies. If new technologies carry high risks or might lead to peer skepticism, farmers may prefer traditional methods to mitigate risks. This behavior partly explains why it is challenging to promote environmental technologies in rural areas. As environmental regulations tighten and public opinion shifts towards sustainable development, farmers who do not adopt new resource utilization technologies face not only competitive disadvantages in the market but also potential reputational damage within their communities. As the concept of green development gains widespread acceptance, behaviors that reject environmental technologies are increasingly seen as “backward” and indicative of a “lack of social responsibility,” directly harming the social reputation of farmers and their families [18]. Consequently, under the dual constraints of environmental regulation and public opinion, the cognition of manure treatment becomes a significant internal driver for farmers to engage in the resource utilization of swine manure. Accordingly, this study proposes the following hypothesis:

H1. 

The cognition of manure treatment by swine farmers has a significant positive impact on their behavior concerning the resource utilization of swine manure.

2.2. The Impact of Environmental Regulation on the Behavior of Farmers Toward Resource Utilization of Swine Manure

Environmental regulation involves the government’s adjustment of farmers’ economic production behaviors. This adjustment balances environmental protection with economic development through relevant regulatory measures. These measures include incentives, constraints, and guidance [19]. Extensive research indicates that government-implemented environmental regulations promote the adoption of environmentally friendly production methods among farmers [20]. These regulations include incentive regulations, which involve government subsidies and material rewards. Such incentives reduce production costs and enhance the economic expectations of farmers, motivating them to utilize swine manure resources [21]. Constraint regulations involve the government enforcing monitoring, explicit regulations, and notifications of censure. These measures effectively encourage environmentally friendly behaviors among farmers. If farmers contravene these policies, they face severe accountability and penalties, which support their participation in the resource utilization of swine manure [22]. Guidance regulations are also essential. They include public communication and educational training. Surveys indicate that over 90% of farmers are unaware of the government’s quality inspections of agricultural products. This lack of awareness reduces their willingness and enthusiasm for safe production [23]. Current studies show that government-led informational campaigns significantly improve farmers’ understanding and practical engagement in manure resource utilization. These findings highlight the critical role of governmental communication and guidance in ensuring safe agricultural production. Overall, environmental regulation significantly influences farmers’ decision-making regarding their participation in the resource utilization of swine manure. This influence occurs through mechanisms of incentives, constraints, and guidance. Therefore, this study proposes the following hypothesis:

H2. 

Environmental regulation has a significant positive impact on the behavior of farmers toward the resource utilization of swine manure.

2.3. The Interactive Effects of Cognition of Manure Treatment and Environmental Regulation on Farmers’ Behavior Regarding the Resource Utilization of Swine Manure

The theory of externalities suggests that the eco-friendly production behaviors of agricultural producers are influenced by the interplay between technological cognition and environmental regulation, both of which demonstrate a significant interactive mechanism [24]. Consequently, the influence of manure treatment cognition and environmental regulation on farmers’ behaviors concerning the resource utilization of swine manure is likely not characterized by a singular effect; instead, it involves an interaction between various influencing mechanisms. In the absence of manure treatment cognition among farmers, environmental regulations impose constraints and offer guidance, promoting the initiation of resource utilization of manure. Conversely, when farmers possess manure treatment cognition, a synergistic effect with environmental regulation is observed. The higher the level of farmers’ cognition in manure treatment, the stronger the influence of environmental regulation on the behavior of manure resource utilization. Similarly, an increase in the strength of environmental regulation also enhances the promotive effect of cognition on farmers’ resource utilization behavior [25]. For instance, under strict environmental regulations, measures such as penalties and public criticism can significantly increase the participation of farmers, who are concerned with the reputation of their village collectives, in pro-environmental behaviors, thereby underscoring the practical significance of their interaction. This leads to the following hypothesis:

H3. 

The interactive effects of farmers’ cognition of swine manure treatment and environmental regulation significantly and positively influence the behavior of resource utilization.

The theoretical framework of this study is shown in Figure 1.

3. Data, Variables, and Models

3.1. Data Source

The data for this study were collected by the research team during field surveys conducted in July and August 2023 in Sichuan Province. Six counties within the municipalities of Chengdu, Meishan, Leshan, Luzhou, Mianyang, and Yibin were selected as the survey areas. The sample selection combined typical and random sampling methods. A total of 550 questionnaires were distributed during the survey. After data cleansing, which involved removing questionnaires with missing crucial information or logical inconsistencies, 509 valid questionnaires were obtained, resulting in a validity rate of 92.55%. The questionnaire was comprehensive, covering various dimensions including individual and family characteristics of the respondents, governmental actions, and cognition regarding livestock manure treatment, thus providing robust data support for the study.

3.2. Basic Characteristics of Sampled Farmers

Based on the statistical analysis of data from

Table 1. Characteristics of respondents and their households.

Variable	Category	Sample Size	Proportion (%)	Variable	Category	Sample Size	Percentage (%)
Gender	Male	424	83.3	Party Membership	Yes	115	22.59
	Female	85	16.7		No	394	77.41
Age/Years	≤45	111	21.81	Green Revenue Cognition	Disagree	19	3.37
	45~60	360	70.73		Neutral	53	10.41
	>60	38	7.74		Agree	437	85.85
Education/Years	≤9	361	70.92	Risk Preference	High Risk	103	20.24
	9~13	96	18.86		Moderate Risk	109	21.41
	≥13	52	10.22		Low Risk	297	58.35

, the majority of the respondents in the sample are middle-aged males with an educational attainment of junior high school or lower. This demographic characteristic is closely aligned with the physical and health demands of the swine breeding industry. Middle-aged men possess distinct advantages in terms of physical labor intensity and the sustained capacity for breeding operations. In comparison, the elderly and women face limitations in absorbing breeding techniques and participating in high-intensity physical labor. Additionally, the lower educational level observed aligns with the prevalent characteristics of workers in the swine breeding sector. From a socio-political perspective, non-party members constitute 77.41% of the sample. In terms of cognition of green revenue, after data consolidation, 85.85% of respondents recognize the income-enhancing effects of manure resource utilization, while only 3.73% hold a contrary position. A survey on risk preference reveals that 58.35% of respondents exhibit a low risk preference, with high-risk takers accounting for 20.24%, which corresponds with the general risk-averse inclination of the farm household demographic. Collectively, these individual characteristics of sampled farm households are representative, effectively reflecting the current state of those engaged in swine breeding within rural China.

3.3. Variable Definitions

3.3.1. Dependent Variable

In accordance with the “Regulations on Pollution Prevention of Livestock and Poultry from Large-scale Breeding” in China, and integrating field research findings, this study categorizes the resource utilization of swine manure into three primary methods: manure return to farmland, organic fertilizer production, and biogas fermentation. To quantitatively assess the extent of resource utilization by farmers, this study adopts the method from existing research [26] to quantify the number of methods employed by the farmers. Specifically, the resource utilization behaviors of the sample farmers are classified into four categories: ‘not adopted,’ ‘adopted one method,’ ‘adopted two methods,’ and ‘adopted three methods,’ which are sequentially assigned values from 1 to 4. Higher values indicate a higher level of resource utilization of swine manure by the farmers.

3.3.2. Independent Variables

The fundamental definition of cognition concerning manure treatment is defined as “the systematic understanding, attitude, and willingness to adopt practices that consider the environmental risks, resource value, treatment technologies, policies, and regulations, as well as the responsibilities and cost–benefit analysis associated with one’s actions by the farming subject and relevant stakeholders.” Based on a review of previous literature and methodologies from existing studies [27], cognition related to manure treatment is categorized into three dimensions: relational cognition, capability cognition, and moral cognition. This study employs these dimensions to measure farmers’ cognition regarding manure treatment: relational cognition is assessed by “the impact of direct discharge of swine manure on one’s reputation among neighbors,” moral cognition by “feelings of guilt and remorse associated with the direct discharge of swine manure,” and capability cognition by “the demonstration of one’s environmental responsibility and ability through the resource utilization of swine manure.” All indicators are measured using a Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). In terms of measuring environmental regulation, the study selects three dimensions to construct the index system: incentives, constraints, and guidance. Incentive regulation includes economic rewards and material subsidies implemented by the government to promote the resource utilization of manure; constraint regulation includes explicit regulations and supervision penalties aimed at restricting non-compliant discharge behavior; guidance regulation focuses on environmental training and other policy-driven guidance measures. Detailed variable definitions and values are provided in

Table 2. Measurement of cognition and environmental regulation related to manure treatment.

Dimension	Variable Definition and Values	Mean	Std. Dev.
Capability Cognition	Resource utilization of swine manure reflects my environmental responsibility and ability: 1 = strongly disagree; 2 = disagree somewhat; 3 = neutral; 4 = agree somewhat; 5 = strongly agree	4.202	1.117
Moral Cognition	Direct discharge of swine manure causes feelings of guilt and remorse: 1 = strongly disagree; 2 = disagree somewhat; 3 = neutral; 4 = agree somewhat; 5 = strongly agree	4.393	0.961
Relational Cognition	Direct discharge of swine manure impacts my reputation among neighbors: 1 = strongly disagree; 2 = disagree somewhat; 3 = neutral; 4 = agree somewhat; 5 = strongly agree	4.462	0.794
Constraint Regulation	Government supervision and punishment for swine manure management: 1 = very lenient; 2 = somewhat lenient; 3 = neutral; 4 = fairly strict; 5 = very strict	3.754	1.118
Guidance Regulation	Government training intensity on resource utilization of swine manure: 1 = none; 2 = low; 3 = moderate; 4 = high; 5 = very high	3.190	1.263
Incentive Regulation	Government rewards and subsidies for on-site manure resource utilization: 1 = very low; 2 = low; 3 = moderate; 4 = high; 5 = very high	3.248	1.451

.

3.3.3. Control Variables and Instrumental Variables

In this study, drawing upon paradigms established in the existing literature [28,29], we systematically examine the factors influencing the behavior of swine farmers regarding manure resource utilization from four dimensions: individual characteristics, family operation, production conditions, and cognitive perceptions. We have constructed a system of control variables as follows: Individual characteristics include variables such as age, years of education, risk preference, and political status. Family operation characteristics encompass total family members, number of laborers involved in farming, and whether the household is that of a cadre. Production characteristics include years involved in swine farming and the proportion of income derived from farming. Considering the pivotal role of economic factors in production decisions, the research posits that “manure resource utilization increases income” as a cognitive characteristic variable. The specific settings and values for these variables are detailed in

Table 3. Descriptive statistics of variables.

Variable	Variable Definition and Coding	Mean	Std. Dev.
Resource Utilization Behavior	Number of resource utilization behaviors by farmers: 1 = none; 2 = one type; 3 = two types; 4 = three types	2.275	0.473
Manure Treatment Cognition	Calculated based on factor scores	0.000	1.000
Environmental Regulation	Calculated based on factor scores	0.000	1.000
Age	Actual age in years	50.650	8.399
Years of Education	Number of years of education	9.024	3.237
Risk Preference	Preferred investment type: 1 = high risk; 2 = moderate risk; 3 = low risk	2.010	0.645
Communist Party Membership	Whether a member of the Communist Party: Yes = 1; No = 0	0.226	0.419
Number of Family Members	Number of people in the household	5.002	1.643
Cadre Household Status	Whether the household includes a cadre: Yes = 1; No = 0	0.194	0.578
Number of People Engaged in Swine Farming	Number of people in the household engaged in swine farming	1.833	0.940
Years Engaged in Swine Farming	Number of years engaged in swine farming	13.569	9.592
Proportion of Farming Income	Proportion of farming income to total family income (%)	65.614	27.848
Cognition of Green Revenue	Perception of increased income through manure resource utilization: 1 = strongly disagree; 2 = disagree; 3 = neutral; 4 = agree; 5 = strongly agree	4.234	0.834

.

3.4. Model Specification

3.4.1. Factor Analysis Method

To mitigate the issue of multicollinearity among variables, this study employs SPSS 26.0 software to conduct exploratory factor analysis on the cognition and environmental regulation of manure treatment among swine farmers. Initially, the reliability and validity of the manure treatment cognition variables were assessed. The Cronbach’s Alpha reliability coefficient was found to be 0.696, the Kaiser-Meyer-Olkin (KMO) value was 0.640, and Bartlett’s Test of Sphericity yielded an approximate chi-square value of 322.465 (sig = 0.000). These results indicate that the evaluative indicators of manure treatment cognition are suitable for factor analysis. Subsequently, factor rotation was performed using the varimax method, and principal component analysis was utilized to extract a single common factor of manure treatment cognition, which accounted for 64.043% of the cumulative variance. All factor loadings exceeded 0.5, indicating good construct validity. The specific calculation formula is presented below:

$${\mathrm{F}}_{\mathrm{j}}={\beta }_{\mathrm{j}1}{\mathrm{X}}_1+{\beta }_{\mathrm{j}2}{\mathrm{X}}_2+\dots +{\beta }_{\mathrm{jp}}{\mathrm{X}}_{\mathrm{p}}$$

(1)

In Equation (1), ${\mathrm{F}}_{\mathrm{j}}$ represents the score of the j-th factor for the sample farmers. ${\mathrm{X}}_1-{\mathrm{X}}_{\mathrm{P}}$ represent the variables encompassing the cognition of manure treatment, and ${\beta }_{\mathrm{j}1}-{\beta }_{\mathrm{jp}}$ are the respective weights of these variables. Given that only a single common factor was extracted from the cognition dimension, this factor score serves as a comprehensive index for manure treatment cognition.

Similarly, the Cronbach’s Alpha coefficient for environmental regulation was 0.721, the KMO value was 0.662, and Bartlett’s Test of Sphericity resulted in an approximate chi-square value of 322.289 (p < 0.001), indicating suitability for factor analysis. Using the varimax maximization method for factor rotation and principal component analysis, one common factor representing environmental regulation was extracted, which accounted for a cumulative variance contribution of 64.814%. As with manure treatment cognition, the score of this common factor is used as a comprehensive index for environmental regulation.

3.4.2. Ordered Probit Model

In order to examine whether the cognition of manure treatment and environmental regulations can positively influence the behavior of resource utilization of swine manure, and considering that the dependent variable represents the extent of resource utilization behavior, which is categorized into ordinal values of 1, 2, 3, and 4, indicating a clear progressive relationship, the Oprobit model is deemed appropriate. The specific model expression is as follows:

$${\mathrm{Adoption}}^{*}=\beta {\mathrm{X}’}_{\mathrm{k}}+{\mathsf{\epsilon }}_{\mathrm{k}}$$

(2)

where ${\mathrm{Adoption}}^{*}$ represents an unobservable latent variable, ${\mathrm{X}’}_{\mathrm{k}}$ denotes variables that may influence the farmers’ behavior towards the resource utilization of manure, including cognition about manure disposal and environmental regulations, along with a set of control variables. $\beta$ is the coefficient to be estimated, and ${\mathsf{\epsilon }}_{\mathrm{k}}$ represents an error term that follows a standard normal distribution. Based on the relationship between the observable sample of farmers’ resource utilization behavior, $\mathrm{Adoption}$, and the unobservable latent variable, ${\mathrm{Adoption}}^{*}$, the model expression is derived as follows:

$$\mathrm{Adoption}=\left\{\begin{array}{c}1\left(0 \mathrm{behavior}\right),\mathrm{i}\mathrm{f} {\mathrm{y}}^{*}\le {\mathrm{r}}_0\\ 2\left(1 \mathrm{behavior}\right),\mathrm{i}\mathrm{f} {\mathrm{r}}_0\le {\mathrm{y}}^{*}\le {\mathrm{r}}_1\\ 3\left(2 \mathrm{behavior}\right),\mathrm{i}\mathrm{f} {\mathrm{r}}_1\le {\mathrm{y}}^{*}\le {\mathrm{r}}_2\\ 4\left(3 \mathrm{behaviors}\right),\mathrm{i}\mathrm{f} {\mathrm{y}}^{*}>{\mathrm{r}}_2\end{array}\right.$$

(3)

In Equation (3), ${\mathrm{r}}_0$, ${\mathrm{r}}_1$, and ${\mathrm{r}}_2$ are unknown thresholds for the adoption of manure resource utilization technology, with ${\mathrm{r}}_0<{\mathrm{r}}_1{<\mathrm{r}}_2$. Consequently, the probabilities of a sample farmer adopting zero, one, two, or three resource utilization practices are given by:

$${\displaystyle \genfrac{}{}{0pt}{}{\mathrm{P}\left(\mathrm{Adoption}=1\mid \mathrm{X}\right)=\mathsf{\varphi }\left({\mathrm{r}}_1-{{\mathrm{X}}^{’}}_{\mathrm{k}}\beta \right)}{\begin{array}{c}\mathrm{P}\left(\mathrm{Adoption}=2\mid \mathrm{X}\right)=\mathsf{\varphi }\left({\mathrm{r}}_2-{{\mathrm{X}}^{’}}_{\mathrm{k}}\beta \right)-\mathsf{\varphi }\left({\mathrm{r}}_1-{{\mathrm{X}}^{’}}_{\mathrm{k}}\beta \right)\\ \mathrm{P}\left(\mathrm{Adoption}=3\mid \mathrm{X}\right)=\mathsf{\varphi }\left({\mathrm{r}}_3-{{\mathrm{X}}^{’}}_{\mathrm{k}}\beta \right)-\mathsf{\varphi }\left({\mathrm{r}}_2-{{\mathrm{X}}^{’}}_{\mathrm{k}}\beta \right)\\ \mathrm{P}\left(\mathrm{Adoption}=4\mid \mathrm{X}\right)=1-\mathsf{\varphi }\left({\mathrm{r}}_3-{{\mathrm{X}}^{’}}_{\mathrm{k}}\beta \right)\end{array}}}$$

(4)

In Equation (4), $\mathsf{\Phi }$ represents the cumulative density function of the standard normal distribution. The parameters of the Oprobit model are estimated using the method of Maximum Likelihood Estimation (MLE).

4. Results

4.1. Benchmark Regression Analysis

Considering the potential multicollinearity among the selected variables influencing manure resource utilization, a multicollinearity test was conducted using Stata 16 software before the main effect analysis. The results showed that the variance inflation factors (VIF) ranged from 1.05 to 1.71, all of which are below 5, indicating no multicollinearity among the chosen variables and confirming their appropriateness for the analysis.

To systematically dissect the impact mechanisms of manure treatment cognition and environmental regulation on the resource utilization behavior of swine manure by farmers, this study employs factor analysis to construct a composite index system. Regression estimations and marginal effects analyses are then carried out using Model 2, with detailed results presented in

Table 4. Main effect test.

Variable	Oprobit Model		Oprobit Model (Marginal Effect/%)
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Manure Treatment Cognition		0.148 ** (0.064)	−0.237 (0.002)	−4.389 ** (0.019)	4.366 ** (0.019)	0.259 (0.002)
Environmental Regulation		0.239 *** (0.062)	−0.381 * (0.002)	−7.069 *** (0.018)	7.032 *** (0.017)	0.418 * (0.002)
Capability Cognition	0.137 ** (0.067)
Moral Cognition	0.141 * (0.082)
Relational Cognition	−0.096 (0.098)
Constraint Regulation	−0.036 (0.067)
Guidance Regulation	0.136 ** (0.063)
Incentive Regulation	0.106 ** (0.050)
Age	0.001 (0.008)	0.002 (0.008)	−0.002 (0.000)	−0.045 (0.002)	0.045 (0.002)	0.003 (0.000)
Education Years	0.052 ** (0.021)	0.056 *** (0.021)	−0.089 (0.001)	−1.648 *** (0.006)	1.639 *** (0.006)	0.097 (0.001)
Risk Preference	0.107 (0.093)	0.102 (0.093)	−0.163 (0.002)	−3.028 (0.027)	3.012 (0.027)	0.179 (0.002)
Party Membership	−0.162 (0.153)	−0.177 (0.152)	0.282 (0.003)	5.235 (0.045)	−5.208 (0.045)	−0.309 (0.003)
Household Members	−0.071 * (0.037)	−0.068 * (0.037)	0.108 (0.001)	2.003 * (0.011)	−1.992 * (0.011)	−0.118 (0.001)
Cadre Household	0.113 (0.099)	0.117 (0.099)	−0.187 (0.002)	−3.461 (0.029)	3.443 (0.029)	0.205 (0.002)
Engagement in Swine Farming	−0.008 (0.067)	−0.007 (0.067)	0.011 (0.001)	0.208 (0.020)	−0.207 (0.020)	−0.012 (0.001)
Farming Years	0.009 (0.007)	0.009 (0.007)	−0.014 (0.000)	−0.260 (0.002)	0.259 (0.002)	0.015 (0.000)
Farming Income Proportion	0.002 (0.002)	0.002 (0.002)	−0.004 (0.000)	−0.068 (0.001)	0.068 (0.001)	0.004 (0.000)
Green Revenue Cognition	−0.017 (0.073)	0.016 (0.001)	0.290 (0.021)	−0.289 (0.021)	−0.017 (0.001)	0.016 (0.001)
LR chi2	48.31 **	40.21 ***
Pseudo R2	0.074 **	0.062 ***

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively; parentheses contain standard errors.

. Empirical results demonstrate that both manure treatment cognition and environmental regulation exert a statistically significant positive impact on the resource utilization behavior of farmers at significance levels of 5% and 1%, respectively, thereby successfully validating the research hypotheses H1 and H2.

Results from Model 1 indicate that both capability cognition and moral cognition exhibit positive effects at the significance levels of 5% and 10%, respectively. At the level of moral cognition, the environmental pollution caused by the disorderly discharge of swine manure can induce a sense of moral guilt among farmers, who, motivated by the desire to preserve their social reputation, tend to proactively reduce polluting behaviors [30]. From the perspective of social cognition, environmental practices have become a crucial measure of an individual’s social responsibility and competence. Farmers, aiming to demonstrate their environmental commitment within their community, are more motivated to engage in the resource utilization of manure. This conclusion is theoretically congruent with existing research on the synergistic effects of behavioral cognition and environmental regulation [31].

Both guidance and incentive regulations show positive effects at the 5% significance level. Based on the “rational economic agent” assumption, governmental incentives such as cash rewards and material subsidies effectively stimulate farmers’ participation by reducing the cost of technology adoption and enhancing expected returns, whereas guidance regulations, such as training and education, promote behavioral change by enhancing cognition at the conceptual level [32]. Compared to incentive regulations, guidance regulations are favored by local governments due to their cost-effective implementation. On the other hand, constraint regulations exhibit certain limitations in practice. Since farmers’ decisions on resource utilization are heavily dependent on actual production conditions, reliance solely on supervisory and punitive measures can negatively impact farming efficiency and provoke resistance among farmers. Research indicates that moderate supervision aids in behavior standardization, but excessive regulation can lead to policy backfires [33]. Marginal effects analysis further reveals that cognition related to manure treatment and environmental regulation have the most significant impacts on farmers’ adoption of two methods of manure resource utilization, with marginal effects of 4.366% and 7.032%, respectively. This means that for each unit increase in these variables, the probability of farmers adopting two resource utilization methods increases by 4.366% and 7.032%, respectively. It is noteworthy that the “Relational Cognition” variable did not demonstrate statistical significance, and this finding itself carries important theoretical implications. It reveals the core mechanism underlying Chinese swine farmers’ environmental decision-making: when policy pressure and techno-economic feasibility become sufficiently defined, the decision-making framework shifts decisively from social relationship orientation to a cost–benefit and compliance-oriented approach, marking a transition in environmental governance from “relationship-dominated” to “rule-dominated” paradigms.

4.2. Endogeneity Correction

Given that farmers’ cognition of manure treatment falls under the category of subjective consciousness, it is imperative to consider endogeneity issues when analyzing its impact on the behavior of resource utilization of swine manure. On one hand, there may exist a bidirectional causality between manure treatment cognition and resource utilization behavior: enhanced cognition can promote the adoption of resource utilization behaviors by farmers, while effective implementation of such behaviors may deepen their cognition of ecological benefits, thereby strengthening their environmental ethics and sense of responsibility, which in turn affects their cognition of manure treatment [34]. On the other hand, omitted variables and unobservable factors leading to measurement errors could result in biased estimation results. To address these endogeneity issues, this study adopts the conditional mixed process (CMP) method combined with the instrumental variable approach (IV-Oprobit), drawing on existing research methodologies [35]. Based on the principle that an instrumental variable must be highly correlated with the endogenous variable and uncorrelated with the error term, the number of visits to relatives and friends by the farmers has been chosen as an instrumental variable. Within the cultural context of rural China, “visiting relatives and friends” effectively reflects an individual’s social status, interpersonal network, and social influence, and is significantly related to manure treatment cognition [28]; meanwhile, it has no direct association with the behavior of resource utilization of swine manure, meeting the criteria for the selection of an instrumental variable.

In this study, the number of visits to relatives and friends is measured by the “number of relatives and friends you frequently interact with,” and categorized into five levels based on the distribution characteristics of the data (<5 people = 1; 5–20 people = 2; 20–40 people = 3; 40–50 people = 4; ≥50 people = 5). Upon testing for exogeneity and validity, the results indicate that the instrumental variable does not significantly impact the farmers’ resource utilization behavior but has a significant impact on manure treatment cognition, suggesting a reasonable setting of the instrumental variable. The endogeneity test results (

Table 5. Endogeneity test of the impact of manure treatment cognition on resource utilization behavior.

Variable	IV-Oprobit
	First Stage	Second Stage
Number of Visits	0.165 *** (0.055)
Manure Treatment Cognition		0.817 *** (0.192)
Controlled Variables	Controlled
atanhrho_12	−0.863 **

Note: The dependent variable in the first stage is manure treatment cognition; the dependent variable in the second stage is the level of resource utilization behavior of swine manure by farmers. *** and ** indicate significance at the 1% and 5% levels, respectively; parentheses contain standard errors.

) demonstrate that the IV-Oprobit model, through the atanhrho_12 test, validates the superiority of the CMP method compared to the standard Oprobit model. The two-stage estimation results are significantly positive, and the direction and significance of the impact of manure treatment cognition are consistent with the main effect. After controlling for endogeneity, the coefficient increases, confirming that endogeneity indeed caused the original model to underestimate the impact of manure treatment cognition on the behavior of manure resource utilization by farmers, thereby further supporting Research Hypothesis 1.

4.3. Analysis of Interaction Effects

The behavior of farmers in the resource utilization of swine manure is not solely influenced by single factors such as cognitive awareness or environmental regulation. There may exist significant interaction effects between cognition and environmental regulations. Following the interaction effect modeling method established by prior research [36], an interaction term is generated by multiplying the two variables, which is then incorporated into the model for examination. The estimation results are presented in

Table 6. Analysis of interaction effects between manure treatment cognition and environmental regulation.

Variable	Oprobit
	Coefficient	Standard Error
Moral Cognition × Constraint Regulation	−0.246 ***	0.106
Capability Cognition × Guidance Regulation	0.189 *	0.065
Control Variables	Controlled
Pseudo R2	0.089
LR chi2	58.27 ***

Note: *** and * indicate significance at the 1% and 10% levels, respectively; parentheses contain standard errors.

.

From Table 6, it is evident that the interaction terms formed by relational cognition and environmental regulation did not pass the significance test. However, the interaction term composed of moral cognition and regulatory constraints significantly negatively influenced farmers’ behavior towards the resource utilization of swine manure at a 1% significance level. Previous research suggests a potential substitution relationship between these factors, indicating that when one’s influence on the behavior of resource utilization of swine manure is weak or absent, the other can serve as a substitute mechanism [23]. In scenarios where governmental constraint mechanisms are insufficient to affect the behavior of farmers regarding the resource utilization of swine manure, farmers’ moral cognition acts as a substitute mechanism. The higher the moral cognition of the farmers, the stronger their feelings of guilt and remorse for environmentally harmful behaviors, leading to a self-driven correction of their environmental practices. Consequently, an enhancement in moral cognition can crowd out and weaken the effects of regulatory constraints [30]. Additionally, the coefficient of the interaction term between capability cognition and guidance mechanisms is significantly positive at the 10% statistical level, indicating that enhancement in one factor strengthens the impact of the other on the farmers’ behavior towards resource utilization of swine manure, thereby successfully validating research hypothesis H3. Specifically, for farmers possessing capability cognition, viewing the effective treatment of swine manure as a necessary action for environmental protection signifies a motivation or readiness to adopt resource-efficient practices. In this context, when the government employs guidance mechanisms such as training and education for these farmers, it significantly enhances the level of capability cognition among them regarding the adoption of resource utilization behaviors. From another perspective, guidance mechanisms such as training and promotional activities also promote the adoption of resource utilization behaviors in swine manure by farmers. When farmers possess a strong sense of environmental responsibility and capability, this reduces the resistance to the implementation of governmental guidance measures, thereby enhancing the effectiveness of guidance mechanisms in influencing farmers’ behavior towards the resource utilization of swine manure.

4.4. Heterogeneity Analysis

(1)

Analysis of Resource Utilization Behavior by Farmers with Different Farming Scales

The behavior of manure resource utilization among farmers with different scales of operation may exhibit variation. This study adopts a threshold of 500 swine to differentiate between small-scale and large-scale swine farmers for the purpose of conducting segmented regression analysis [37].

Table 7. Factors influencing resource utilization of swine manure by farmers of different farming scales.

Variable	Large-Scale Farmers		Small-Scale Farmers
	Coefficient	Standard Error	Coefficient	Standard Error
Capability-oriented Cognition	0.197	0.135	0.157 ***	0.061
Moral Cognition	0.284 **	0.135	0.096	0.091
Relational Cognition	−0.150	0.278	−0.066	0.123
Constraint Regulation	0.007	0.126	−0.051	0.071
Guidance Regulation	0.024	0.114	0.181 ***	0.068
Incentive Regulation	0.065	0.112	0.108 *	0.055
Control Variables	Controlled		Controlled
Pseudo R2	0.0765		0.0955
Sample Size	104		405

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively; parentheses contain standard errors.

demonstrates significant heterogeneity in manure resource utilization behaviors among farmers of varying scales. For large-scale farmers, moral cognition positively influences manure resource utilization behaviors at a 5% significance level. This may be attributed to the higher status and prestige of large-scale farmers within the swine farming community, who are more conscious of their social reputation. Engaging in activities that compromise the communal environmental integrity could adversely impact their reputation, thereby inducing feelings of guilt and remorse. The higher the moral cognition among large-scale farmers, the greater their emphasis on manure resource utilization. Conversely, for small-scale farmers, both capability cognition and guidance regulation significantly positively influence manure resource utilization behaviors at a 1% significance level. The likely reason is that small-scale farmers primarily rely on past farming experiences and may exhibit resistance to adopting new technologies. Through robust promotion and technical training on manure resource utilization technologies, the government enhances these farmers’ cognition of such practices and stimulates their sense of social responsibility towards rural environmental protection. This, in turn, increases the adoption of manure resource utilization behaviors among small-scale farmers.

(1)

Analysis of resource utilization behaviors among different generations of farmers

The cognitive structures and behavioral patterns exhibit fundamental differences between older and newer generations of farmers. Following the study by Zhou et al. (2024) [38], farmers are categorized into two groups based on age: the older generation (older than 50 years) and the middle-aged and younger generation (50 years or younger) for segmented regression analysis.

Table 8. Factors influencing the behavior of different generations of farmers in the resource utilization of swine manure.

Variable	Young and Middle-Aged Farmers		Older Generation Farmers
	Coefficient	Standard Error	Coefficient	Standard Error
Capability Cognition	0.177 *	0.093	0.096	0.104
Moral Cognition	0.103	0.117	0.157	0.123
Relational Cognition	−0.172	0.148	−0.016	0.138
Constraint Regulation	−0.123	0.105	0.052	0.092
Guidance Regulation	0.094	0.092	−0.182 **	0.090
Incentive Regulation	0.167 **	0.072	0.069	0.074
Control Variables	Controlled		Controlled
Pseudo R2	0.068		0.203
Sample Size	248		261

Note: ** and * indicate significance at the 5%, and 10% levels, respectively; parentheses contain standard errors.

demonstrates significant heterogeneity in manure resource utilization behaviors among different generational cohorts of farmers. Within the cognitive dimensions of manure treatment, only capability-related cognition positively influences the resource utilization behavior of the middle-aged and younger farmers at a significance level of 10%. Equipped with a higher level of education, these younger farmers possess a deeper social cognition regarding environmental protection, viewing manure resource utilization as a vital means to demonstrate their environmental capabilities and societal responsibilities [14]. At the level of environmental regulation, incentive-based regulations positively impact the behavioral decisions of younger farmers at a 5% significance level. Being in their career development phase, this demographic places a greater emphasis on risk management. Economic subsidies, favorable credit conditions, and insurance protections provided by the government serve as incentives that effectively reduce the risk costs associated with technological innovation and model transformation. In contrast, only guidance-based regulations positively influence the older generation of farmers at a 10% significance level. Due to their longstanding reliance on traditional farming methods, this group faces cognitive limitations concerning new technologies and resource utilization concepts. Despite their extensive practical experience, they lack the ability to integrate new technologies. However, environmental training and other guidance measures organized by the government can assist them in updating their knowledge base, mastering modern farming technologies, and resource utilization methods. This integration of traditional experience with modern technology ultimately enhances their level of manure resource utilization.

4.5. Robustness Check

To ensure the credibility of the primary effect regression results, this study conducts robustness tests from two aspects, as detailed below. First, the regression model was substituted. Specifically, the study employed both the OLS model and the Ologit model to replace the original econometric model. The results, as shown in

Table 9. Robustness Check.

Variable	Model 7	Model 8	Model 9	Model 10	Model 11
Manure Treatment Cognition	0.045 **	0.264 **	0.148 **	0.150 **	0.150 **
	(0.021)	(0.123)	(0.064)	(0.064)	(0.064)
Environmental Regulation	0.082 ***	0.465 ***	0.240 ***	0.243 ***	0.245 ***
	(0.021)	(0.112)	(0.062)	(0.062)	(0.062)
Control Variables	Controlled	Controlled	Controlled	Controlled	Controlled
R2	0.0762	0.0669	0.0644	0.062	0.065

Note: *** and ** indicate significance at the 1% and 5% levels, respectively; parentheses contain standard errors.

, Models (7) and (8), indicate that farmers’ cognition of manure treatment and environmental regulation significantly and positively affect manure resource utilization. Second, missing variables were included. The variables “membership in a production organization” and “number of contacts in the mobile phone directory” were added. The regression results incorporating “membership in a production organization” are presented in Model (9), results with “number of contacts in the mobile phone directory” in Model (10), and results including both variables in Model (11). These additions consistently demonstrate that cognition of manure treatment and environmental regulation exert a significant positive influence on farmers’ manure resource utilization. In summary, the regression outcomes of this study are robust and credible.

5. Discussion

The marginal contributions of this study are reflected in the integration of research perspectives and the deepening of content: Theoretically, a “cognition–regulation” synergistic analysis framework was constructed, overcoming the limitations of previous studies that solely examined either farmers’ cognition of manure treatment or environmental regulation, and focusing on the extent of behavioral implementation. In terms of research content, the study examined the interactive effects between environmental regulation and cognition of manure treatment on resource utilization behavior, and further analyzed how different farming scales and generational differences among farmers affect resource utilization behaviors. However, this study still faces certain limitations. The sample only comes from parts of Sichuan Province and does not cover typical economic zones in ecological protection areas, limiting the generalizability of the conclusions. Additionally, the measurement of variables overlooks informal regulations and manure form. Future improvements could be made in three areas: firstly, by expanding the sample to cover areas of Sichuan not previously included and then conducting inter-provincial studies to enhance the universality of the conclusions; secondly, by optimizing variable measurement to include indicators such as village conventions and manure form, and combining field surveys to add observation dimensions that are closer to reality; and thirdly, by extending the application of the “cognition–regulation” model to other environmental behaviors of farmers, such as straw return to the field, to form a systematic analysis paradigm.

6. Conclusions and Policy Implications

This study innovatively adopts a cognitive perspective rooted in traditional rural culture and integrates governmental environmental regulations. It utilizes data from a survey of 509 swine farmers in Sichuan Province. By employing methodologies such as Oprobit, IV-Probit, and CMP, the study systematically investigates the mechanisms and pathways through which cognition of manure treatment and environmental regulations influence farmers’ behavior toward the resource utilization of swine manure. The study arrives at the following core conclusions:

Firstly, the demographic profile of the swine farmers in the sample area is predominantly middle-aged and elderly, characterized by a generally low level of education and a conservative risk preference. The cognition of manure treatment among the farming community is significant, with the intensity of environmental regulation being moderate. Methods of manure resource utilization are primarily focused on returning to the fields and organic fertilizer production, with over 95% of farmers adopting at least one method of resource utilization.

Secondly, both the cognition of manure treatment and environmental regulation significantly promote the behavior of resource utilization of manure among farmers. From the perspective of marginal effects, these factors have the most significant impact on the adoption of two types of resource utilization methods, with marginal effects of 4.366% and 7.032%, respectively. Dimensional analysis indicates that capability cognition, moral cognition, and regulatory measures such as guidance and incentives have a significant positive influence on the resource utilization behavior of farmers. Among the control variables, the duration of education correlates positively with resource utilization behavior, while the number of family members shows a negative correlation.

Thirdly, the interaction between moral cognition and constraint regulation has a significantly negative effect on the resource utilization behavior of farmers, suggesting a substitutive relationship between the two. Conversely, the interaction between capability cognition and guidance regulation exhibits a significantly positive effect, indicating a synergistic and mutually enhancing relationship.

Fourthly, the effects of manure treatment cognition and environmental regulation on the behavior of farmers exhibit significant intergenerational and scale differences. The behavior of large-scale farmers is primarily influenced by moral cognition, whereas smaller-scale farmers are more affected by capability cognition and guidance regulations. The resource utilization behavior of young and middle-aged farmers is primarily driven by capability cognition, incentive regulation, and guidance regulation, whereas the older generation of farmers is more influenced by moral cognition and guidance regulation.

Based on the findings mentioned above, this study offers the following policy recommendations:

1. Strengthen Positive Guidance in Manure Treatment Cognition: Governments should integrate economic incentives with community outreach, utilizing diverse communication channels such as short videos, village broadcasts, and visits to model households, to embed environmental knowledge into everyday management practices. Simultaneously, they should establish a credit evaluation system based on environmental behavior to promote the integration of cognitive enhancement and institutional constraints.

2. Optimize the Environmental Regulation Policy Framework: Implement compulsory pollution discharge permits and online monitoring for large-scale farms, and couple these with tax incentives or environmental subsidies to promote the upgrading of pollution control facilities. For small and medium-sized farmers, provide specialized technical support and low-cost treatment equipment, and establish regional centralized treatment centers to reduce compliance costs.

3. Construct Market-Based Collaborative Mechanisms: Promote the creation of regional manure resource trading platforms, support joint development of low-energy consumption treatment technologies by research institutions and companies, and facilitate the operation of small farms through a “green cooperative” model. Additionally, implement green certification and market promotion for resource-based products to develop sustainable operational mechanisms.

4. Implement Precision Cultivation Strategies: For middle-aged and large-scale farmers who are particularly susceptible to capability cognition and incentive regulation, it is advisable to develop online training tools and enhance market-based incentive mechanisms. For older and smaller-scale farmers who rely more on moral cognition and guidance regulations, focus should be placed on conducting onsite demonstrations, providing illustrative manuals, and equipment subsidies, along with sustained technical support to enhance policy effectiveness and systematically promote the utilization of manure resources.