Analysis of Influencing Factors on Cognition and Behavioral Responses Regarding Green Development of Farming Households in Tibetan Areas—Taking Hezuo City as an Example

Abstract

As an ecologically fragile and agriculture-dominated region in China, the Tibetan area is in urgent need of green transformation. Based on the survey data of 59 farmers in 16 villages in Hezuo City, this paper empirically examines the influencing factors and decision-making logic of the green production behaviors of farmers in Hezuo City using the Double Hurdle model and the moderated model, and the results show the following: (1) Cognitive norms and environmental regulations are the key elements determining the green production intentions and behavioral responses of farmers, and the driving effect of cognitive norms on behavioral response shows a declining trend in comparison with behavioral intention. Compared with behavioral intention, the effect of cognitive norms on behavioral response showed a downward trend. (2) The consistency between group social cognition and green production cognition significantly influences the behavioral intentions and behavioral responses of farmers regarding green production in Hezuo City. This is conducive to promoting farmers’ intentions to engage in and their continuous response to green production. (3) The role of environmental regulation in enhancing green production intention and behavioral response is more consistent and significant. However, it cannot continuously promote a green production response by influencing green production intention again. The green development strategy can help to align farmers’ cognitive and behavioral responses to green production. It is recommended that the government use environmental regulation as the primary means of driving the current green transformation in Tibetan areas.

1. Introduction

Since the end of the twentieth century, China has made great strides in green agriculture, driven by the construction of an ecological civilization and the rural revitalization strategy [1,2,3,4]. The green agricultural transition requires farmers to pay more attention to green practices in their agricultural activities and to obtain economic gains through agricultural production and ecological compensation mechanisms while promoting ecological restoration initiatives such as forest protection and land reclamation [5,6,7]. Therefore, the green agriculture paradigm shift is both an effective means to achieve sustainable agricultural development and an important way to improve ecological efficiency. The Qinghai–Tibetan Plateau is an important frontier barrier, ecological and environmental protection zone, and natural resource-rich area in China. Grassland agriculture and animal husbandry are important parts of the agricultural and rural economy on the Qinghai–Tibet Plateau, where the level of agricultural and animal husbandry production is basic and lacks innovation due to the long-standing rough and fragmented mode of operation and inefficient and backward production methods [8]. In terms of grassland ecological protection, over-emphasis has long been placed on the grazing and breeding functions of grasslands, neglecting the natural ecological protection functions of grasslands; overloading and overgrazing, indiscriminate mining and reclamation, and other phenomena damaging the grasslands have seriously affected the development of pastoral areas. In recent years, with the introduction of a series of agricultural support policies by the government, agricultural production conditions for farmers in Tibetan areas have improved. In addition, these measures have made it possible to restore land resources and the ecological environment. However, in practice, limited perception of green production has hindered significant progress in the development of green agriculture in Tibetan areas. Farmers are increasingly pursuing high-nutrient fertilizers and pesticides for economic gain, which negatively affects soil quality and farmland environmental pollution [9]. This not only hinders the development of green agriculture, but also highlights the need for sustainable agricultural practices that seek a balance between productivity and ecology.

Based on this, this paper takes the green production cognition of Tibetan farmers and pastoral communities as its starting point. It integrates existing research findings both domestically and internationally to analyze questionnaire data collected from these groups within Hezuo City in Tibet. Through empirical analysis, this study identifies key factors influencing farmers’ green production cognition and behavioral responses. Based on these findings, targeted countermeasures are proposed with the aim of encouraging farmers to adopt more extensive and sustainable green agricultural practices. This research seeks to provide practical insights for promoting eco-friendly agricultural development at both the local and broader regional levels in Tibet.

2. Current Status of Research

From the construction of ecological civilization to the current rural revitalization strategy, the issue of “green agricultural production behavior” has been attracting much attention in theory and practice. Green agriculture aims to achieve a win–win situation between increasing farmers’ production and promoting the green transformation of agriculture, and to ensure the long-term sustainable development of the agricultural environment. Laurence elaborated on the concept of the green development of agriculture, and carried out in-depth research and an analysis on the public policy challenges faced by the green development of agriculture [10]. Scholars emphasize that the establishment of a sound and effective green agriculture mechanism is a necessary foundation for green agricultural production. It is important to build a green agriculture framework and incentives [11], establish a mechanism to coordinate the role of agricultural development and the ecological environment, and design a policy mechanism that adheres to the rights and responsibilities of farmers and can vigorously promote the innovation of the mechanism for achieving the green development of agriculture [12]. In order to achieve the green development of agriculture, it is possible to establish a participatory mechanism for diversifying the green development of agriculture by improving the policy design; improving the governance system; and encouraging the participation of local stakeholders, guiding the participation of a variety of market players, and improving the effectiveness of decision-making and governance [13,14,15]. Making full use of the scale advantage and management of agricultural cooperatives can reduce the production risk of smallholder farmers and achieve a new pattern of multi-win–win situations in agricultural green development [16]. And expanding financial support can promote the development of sustainable agricultural systems [17,18].

Green production is the starting point for achieving the ideal of the rational economic person, but the reality of farmers’ conditions means that in the specific implementation of this process, their behavior regarding green production generally manifests as the “will and behavioral deviation” phenomenon. In recent years, many scholars have shifted their research perspective to individual farmers, studying the willingness of individual farmers to engage in green production and the factors affecting their willingness. When farmers face a green transition, they are mainly influenced by individual characteristics, green production knowledge, policy incentives, and other factors [19].

In terms of individual characteristic factors, Shiferaw believes that farmers’ behavior can influence agroecology [19]. Farmers’ enthusiasm for agricultural production, their own quality of life, and their family environment have an important impact on their cognition of their agricultural production behavior [20,21]; a farmer’s age has a negative impact on their engagement in green production, and younger farmers are more willing to adopt green production [22]. Smallholder part-time farming can significantly increase the level of socialized adoption of agricultural machinery, and these farmers choose to join new management subjects and are more willing to accept green production technology [23,24]. In terms of green production cognition, farmers’ trust in green agriculture has a significant impact on their production behavior, and farmers do not understand the economic benefits and advantages brought by green technology and only consider the cost, which reduces their willingness to adopt it [25,26]. Green production cognition has a positive effect on farmers’ green production willingness and behavior, and can prompt farmers to engage in green production behavior; studies have concluded that the higher the farmer’s cognition of green production, the stronger their willingness to adopt it, which, in turn, promotes the development of green production [27,28]. In terms of policy incentives, government departments must vigorously implement agricultural subsidies and publicity and promotion policies, improve environmental regulations according to local conditions, and give full play to the role of the system in promoting the adoption of green production by farmers. It is believed that government support is a strong determinant of smallholder technology adoption [29], and it is believed that different types of policy implementation affect the green production promotion effect of agricultural subsidies [30]. Agricultural subsidies have positive incentive effects on farmers and can significantly increase agricultural productivity. Reducing the risk of planting and directly increasing the economic income of farmers leads farmers to increase their inputs and promotes the transition of smallholder farmers to green production methods [31,32,33,34,35]. The form and standard of agricultural subsidies can improve crop yield and quality. For example, the use of drip irrigation subsidies instead of direct agricultural subsidies can directly increase farmers’ economic income as well as improve crop yields due to technological upgrading [34,36,37,38]. Agricultural subsidies must take into account the value recognition, experience and economic returns of farmers in order to work well. Agricultural subsidy policies can only work well if smallholders recognize the environmental crisis and have a certain value identity [39]. In the implementation of agricultural subsidy policy, it is necessary to pay attention to the experience of and benefits to smallholder farmers, to help smallholder farmers recognize the potential benefits, and to stimulate the awareness of green production of smallholder farmers, and thus promote green production [40]. Agricultural subsidies can enable farmers to take the initiative to use green technologies and promote green production. The implementation of a subsidy policy helps to increase the input of agricultural machinery and promote the efficiency of fertilizer application [41,42,43,44]. The ease of borrowing financial capital significantly and positively affects the willingness to adopt green technology [45]. However, some scholars have put forward a different opinion, stating that the role of agricultural subsidies is not significant in the early stage of the development of the green agricultural transition [46].

In summary, many scholars have carried out extensive investigations around the driving factors of green production behavior and the reality of obstacles, and have studied the factors influencing the green production willingness and behavior of smallholder farmers from different perspectives, which provides a favorable reference base for this study. The green production willingness and behavior of farmers are driven by a variety of factors, and the degree of influence of each factor varies. However, there is still room for expansion of the above studies in terms of analyzing perspectives and research objects. On the one hand, most of the existing literature focuses on how to make smallholder farmers reduce their use of chemical fertilizers and pesticides, and less on the starting point and actual needs of smallholder farmers to adopt green production. On the other hand, the traditional living habits and religious beliefs of Tibetan farmers are consistent with many of the requirements of green development, and there is a significant difference in balancing the ecological environment and economic benefits, but the important value of this research object has not been fully reflected in the existing literature. Therefore, this paper will collate and summarize the above research results and try to make a marginal contribution to the academic research on green production in agriculture. Starting from both theoretical research and a current situation analysis, from the perspective of farmers, taking Tibetan farmers in Hezuo City as an example, based on the results of a questionnaire, this paper explores farmers’ willingness to carry out green production and the factors influencing their green production behaviors, constructs a model and conducts an empirical analysis in order to improve the participation of farmers in green production in Hezuo City, and puts forward policy suggestions for the development of green agriculture in Hezuo City.

3. Theoretical Analysis Framework for Cognitive and Behavioral Responses of Farmers in Tibetan Areas

3.1. Cognitive Norms and Environmental Regulation

Research on group decision-making has shown that individuals construct a “model” of the external world within their minds. This mental model significantly influences their production and consumption behavior. Farmer behavior refers to the decisions made by farmers during the agricultural production process, based on comprehensive analyses of available information. It encompasses a wide range of areas, including production behavior, business behavior, investment behavior, and other aspects. These behaviors are shaped by both subjective and objective factors. Subjective factors include personal characteristics such as age, gender, education level, etc., while objective factors include social environment, government policies, market demand, and so on. Additionally, the awareness of homestead property rights, land transfer, agricultural insurance, and environmental protection among farmers directly affects their willingness to participate and their economic decisions. Even when focusing specifically on “green production cognition”, the aforementioned influence logic remains largely applicable. In specific production practices, social norms such as customs, habits, moral constraints, and other social regulations can effectively regulate farmer behavior, indirectly reflecting the impact of external environmental factors on agricultural production decisions [47]. Farmers’ green production willingness is influenced by a variety of factors, including their attitudes toward green production, their subjective norms, and their understanding of green production [48]. To achieve sustainable agricultural development, it is necessary to reasonably regulate farmers’ agricultural production costs while also improving the economic benefits they receive. Therefore, the theory of farmer behavior plays a crucial role in determining whether farmers can participate in green production.

The transition of farmers to green production methods in Hezuo City is a systematic decision-making process. Farmers’ behavioral decisions are primarily influenced by internal factors, such as cognitive norms, which represent an individual’s values, as well as their inherent moral and societally dictated tendencies. These cognitive norms can be regarded as norms to be observed during cognitive activities, serving both cognitive and normative functions. They play a crucial role in regulating farmers’ behavior and awakening their sense of responsibility. Thus, green production behavior among farmers can be seen as a conscious decision made on the basis of effective cognition. External factors also significantly impact farmers’ green production decisions. The “Hawthorne effect” demonstrates that individuals are not purely rational but are influenced by social factors as well. In the case of Tibetan farmers, their production decisions are likely to be affected by external environmental factors such as government policies and market demands. Green production behavior reflects a concentrated manifestation of government regulations aimed at promoting the economic productivity of farmers. This includes three main types of regulations: guidance, incentives, and constraints. As an external institutional factor, environmental regulations have been widely recognized for their important influence on farmers’ production decisions.

3.2. “Cognitive–Intentional–Behavioral” Decision-Making Frameworks

The Theory of Planned Behavior (TPB) is an integrative theoretical framework designed to study social behavior and predict individual behavioral intentions or behaviors. It provides a comprehensive analysis and explanatory model of individual behavioral decision-making processes, grounded in information processing theory and attitudinal expectancy-value theory. The TPB includes five key components: attitudes, subjective norms, behavioral intentions, perceived behavioral control (PBC), and behavioral norms. In the context of the Tibet region’s adoption of green production practices, these five elements are closely interrelated. Specifically, farmers’ positive attitudes toward green production are positively related to their behavioral willingness—i.e., the stronger their attitude, the greater their willingness to adopt green production. When farmers subjectively desire to carry out green production, they are likely to develop a positive behavioral attitude. Regarding subjective normative elements, the stronger the agricultural subsidy policy, the more willing smallholder farmers are to adopt green production practices. In terms of perceived behavioral control, when smallholder farmers are highly confident in carrying out green production, they are more inclined to commit themselves to such practices. The willingness of smallholder farmers to adopt green production has a positive impact on their actual behavior toward green production. The purpose of this study is to apply the Theory of Planned Behavior (TPB) to thoroughly analyze the relationship between subjective norms, behavioral attitudes, perceived behavioral control, and farmers’ willingness to carry out green production, as well as their actual green production behaviors. By understanding these interconnections, this study aims to provide a scientific foundation for formulating relevant policies.

Therefore, on this basis, we establish the following hypotheses:

Hypothesis 1:

Tibetan farmers perceived norms and governmental environmental regulations play a significant role in influencing Tibetan farmers’ green production intentions and behavioral responses.

Hypothesis 2:

The congruence of group cognitive norms such as religious beliefs and neighborhood-driven roles with perceptions of green production is conducive to driving farmers’ intentions and sustained responses regarding green production.

Ajzen’s Theory of Planned Behavior constructs a complete “cognitive–intentional– behavioral” individual behavioral response process. Farmers’ cognition serves as the precursor to production decision-making and lays the foundation for the occurrence of intention. According to theories of rational behavior, willingness is a sufficient condition for individual behavior; its formation depends on the combined effects of personal circumstances and individual cognition, and it can be regarded as a mediator connecting cognition and behavior. In summary, based on field surveys and theoretical thinking in this study area, drawing from the analytical framework developed by Cheng et al. [42], this paper selects “cognitive norms” and “environmental regulation” as latent variables and integrates them with the frameworks of “rational smallholder farming” and the intention–behavior transformation process within planned behavior to construct a theoretical analysis framework (cognitive norms→behavioral intention→behavioral response), incorporating contextual factors (Figure 1).

In this study, we will first establish an indicator system based on “cognitive norms” and “environmental regulations”, verifying the key elements influencing farmers’ green production behavioral intentions and behavioral responses in Tibet using this model. Subsequently, drawing from this framework, the paper will employ “environmental regulation” as a moderating variable to examine its impact at two distinct stages of the individual behavioral response process.

On this basis, we establish:

Hypothesis 3:

Environmental regulation as a moderating variable can effectively influence the whole process from cognitive norms to behavioral intention to behavioral response to promote the practice of green production among farmers in Tibetan areas.

4. Materials and Methods

4.1. Regional Overview

Hezuo City is located in the northeastern edge of the Tibetan Plateau and has an extremely important ecological, cultural, and economic status, with China’s Yellow River and the upper reaches of the Yangtze River forming the important green ecological barrier area; but also, in the region where there is a confluence of Tibetan Buddhist culture, Islamic culture, and Chinese culture, historically known as the ‘Sino-Tibetan Corridor’, Gan, Qinghai, and Ningxia agriculture and animal husbandry are intertwined, representing an important animal husbandry base.

As shown in Figure 2, Hezuo City now has 6 townships and 4 street offices, 8 community neighborhood committees, 39 village committees, and 258 village groups. It has a land area of 29.7 × 10⁴ hm², including a grassland area of 17.9 × 10⁴ hm², an arable land area of 1 × 10⁴ hm², a forest area of 4.3 × 10⁴ hm², an urban area of 0.1 × 10⁴ hm², and an area of other land types (mountainous areas, rivers, etc.) of 3.7 × 10⁴ hm².

Specifically, the harsh climatic conditions and geographical features of the city have created an extremely vulnerable ecological environment. Over time, the city’s natural grassland has been severely degraded, with 72.4% of the grassland area now falling into different stages of degradation. Among these, the moderately degraded region accounts for 5.7 × 10⁴ hm² (49.1%), and the severely degraded area constitutes 5.3 × 10⁴ hm² (30.5%) of the available grassland area. These findings are based on the reference values of fresh grass production at 3120 kg/hm² and a grass coverage rate of 97%. Compared to the 1950s, there has been a 95% decline in natural grassland grass production; compared to the 1970s, the vegetation cover on the natural grassland has decreased by 19%, while good pasture grass cover has decreased by 20%. Simultaneously, weeds (both poisonous and general) have increased by 25%. The severe degradation of the city’s grasslands has led to a dramatic decline in wetland areas, biodiversity loss, intensified soil erosion, and an exacerbated water resource issue. The area experiencing soil erosion is 910.9 km² (23% of the total land area), with an annual soil erosion modulus of 2 × 10⁴ t/km² and total annual sand loss of 97.9 × 10⁴ t. These ecological deficits significantly weaken the river’s ability to replenish water resources, posing a direct threat to the economic and social development of the entire Yellow River Basin.

4.2. Research Design

In this study, based on a thorough review of relevant policy documents and the literature, a questionnaire was designed that is closely aligned with the research topic and the individual characteristics of the respondents. Given that the target population is primarily Tibetan farmers, the questionnaire content was designed to ensure simplicity and ease of understanding. The questionnaire is divided into six sections: the personal information of smallholder farmers, the behavioral process of green production in agriculture, the behavioral process of green production in pastoralism, the behavioral process of green production in non-agricultural industries, the process of sustainable urbanization, and the perception of the value of a green transition. The questionnaire was designed in a logical and clear manner, thus ensuring the reliability and credibility of the data collected in this paper.

The research team employed a combination of random sampling and stratified sampling methods inspired by Fengni Li et al. [49]. As shown in Figure 3, this study was conducted in Hezuo City by first randomly selecting 8 townships (Le Xiu Township, Zuo Gai Manma Township, Na Wu Township, Kajiaman Township, Yihegang Street, Dang Zhou Street, Jianmukeer Street, Tongqin Street); then, 2–3 sample villages were randomly selected from each sample township; and finally, 2–3 farmers were randomly selected from each village to conduct the study. To ensure the credibility of the data, relevant officials from the Department of Agriculture and Rural Affairs and township governments in Hezuo City were visited during the field research to gather primary information, through interviews, about the current adoption of green production by smallholder farmers, their awareness of publicity and promotion efforts, environmental protection regulations, and policy preferences. The sample point selection covered key construction areas within the rural area of Hezuo City, ensuring a representative distribution across the region. The dataset documented in the study includes basic information about the individual and family backgrounds of the smallholder farmers participating in the study; details on their agricultural practices, willingness to adopt green production, cognition related to green production, and behavior patterns associated with green production; and the external environmental factors influencing their decisions.

Given the limited population of Hezuo City (90,000 residents) and its concentration in urban areas, the rural population comprises mostly elderly people and children. Based on the challenges posed by the small population base and urbanization, the first questionnaire survey was carried out from June to July 2023, with a total of 51 questionnaires collected through field research visits. These efforts yielded 46 valid responses after excluding invalid questionnaires and abnormal values, resulting in a questionnaire recovery effectiveness rate of 90.19%. In addition to the initial survey, we conducted a supplementary questionnaire survey between November and December 2024, which yielded 13 valid responses. The specific sample sites is shown in

Table 1. Sample distribution and regional statistics.

Shore	Townships and Towns (Streets)	Sample Sites (Villages)	Sample Size
Center of urban area	Dangzhou Street	Zhihema Village, Nanmulou Village	6
	Jianmukeer street	Jialagama village, Jialagongma village	10
	Tongqin Street	Wumai village	4
	Yiheang Street	Danzinang, Amuquhunang	5
	Nawu town	Dasa village, Magang village, Yangde village, Huangkeyihe	15
Northern	Kajiaman town	Xinji and Bora villages	9
Southern	Lexiu town	Deng Ying Gao and Luowa villages	7
Eastern	Zuogaimanma town	Ehela village	3
Total	8	16	59

Note: The data used in this paper come from a micro-study of Tibetan farming households conducted in Hezuo City in June–July 2023 and November–December 2024, with team members conducting exhaustive household interviews in Hezuo City in stages.

.

4.3. Variable Selection and Descriptive Statistics

Frequency statistics were obtained on the personal characteristics of the samples, such as gender, age, number of laborers, whether there are party members in the family, and the occupation they are engaged in, and the results are shown in

Table 2. Descriptive statistics of personal characteristics.

Dimension	Observed Variable	Number	Percentage
Sex	Male	27	45.76%
	Female	32	54.24%
Age	Under 16	13	22.03%
	16–60 years	27	45.76%
	60 years and over	19	32.20%
Number of laborers	1	14	23.73%
	2	35	59.32%
	≥3	10	16.95%
Family party members	Yes	12	20.34%
	No	47	79.66%
Occupation	Farmer	39	66.10%
	Part-time farming	17	28.81%
	Non-agricultural	3	5.08%
Intention	Yes	54	91.53%
	No	5	8.47%
Educational level	Primary and below	30	50.85%
	Junior high school	22	37.29%
	High school and above	7	11.86%
Faith	Buddhist	58	98.31%
	None	1	1.69%

below. Specific descriptive statistics results are shown in Table 2.

Following the cognition–intention–behavior theoretical framework and related research on the impact of green cognition on smallholder green production, the cognitive norms variable in this study consists of two dimensions: cognition and norms. Cognition reflects the underlying personal beliefs and thought processes behind the change in farmers’ green production behavior, while norms reflect farmers’ education, group environmental norms, and religious beliefs. Environmental regulation is mainly composed of guidance regulation, incentive regulation, and constraint regulation. At the level of farmers’ cognition, farmers in Tibetan areas usually aim to maximize their expected benefits and make production decisions after considering information on costs and benefits. At the same time, the potential ecological benefits and the transformation of ecological value should also be included in the cost–benefit analysis. In this case, different green production behavior perceptions of individual Tibetan farmers will lead to fluctuations in the marginal cost curve or marginal benefit curve.

In addition, according to the research group’s micro-survey, farmers in Hezuo City preferred James Scott’s concept of a ’moral economy’ when making green production decisions. That is, farmers provide informal social security through reciprocity and patronage relationships and redistribute resources to achieve the goal of group survival. It is worth noting that farmers’ perceptions not only influence household behavioral intentions and decisions, but are also closely related to ecological efficiency and group interests. At the level of farm household norms, social norms are the behavioral norms and guidelines that groups and their members understand about certain events, which have a certain guiding effect on individual behavior. In addition, for agricultural production, individual cognitive norms, such as the technical training that farmers participate in and their own business capacity, also influence business decisions. It is worth noting that Tibetan Buddhism has a profound impact on Tibetan farmers, and the concept of harmony between human beings and nature in its teachings will have a profound impact on their green production perceptions and practices of. For measuring the religious belief variable, firstly, whether the farmer had faith or not was defined based on the questionnaire; secondly, Tibetan farmers with religious beliefs were interviewed, and based on the questionnaire, questions were asked about the degree of similarity between green production and traditional teaching and about how green agriculture is expressed in Tibetan Buddhism. The degree of religious belief was measured from the farmers’ subjective answers to these two questions.

In summary, farmers’ cognition plays a key role in household behavioral intention and decision-making, which is not only related to production and operation benefits, but also closely related to ecological benefits, group interests, and other elements. In addition, social norms and individual cognitive norms also have an important influence on the decision-making of farm households. The influence of Tibetan Buddhism further enriches the cognitive dimension of green production.

In this study, our dependent variables are twofold: one measures the “behavioral intention” towards green production, indicated by binary choices (1 for no intention and 0 for having an intention). The other variable assesses the “behavioral response”, reflecting the increasing degree of farmers’ participation in green practices. We selected 15 observational variables based on our theoretical framework and the existing research literature to investigate the behavioral intentions and responses toward green production among Tibetan farmers. To ensure accurate measurement, we acknowledged that some variables are best captured using structured tools like the Likert scale. The five-level Likert scale ranges from “Completely Disagree (Very Poor)” to “Strongly Agree (Well)”, offering a nuanced understanding of farmers’ perceptions and dispositions. Four control variables were selected at both the individual and household levels to account for potential confounding factors that might influence farming behavior indirectly. Variables such as the number of family laborers and age, while not directly tied to green production, may impact farming practices and thus need to be controlled for to isolate the specific effects of green initiatives. Finally, we provide a detailed indicator system and descriptive statistics in

Table 3. Variable definitions and descriptive statistics.

Dimension	Observed Variable (Percentage)	Mean	Standard Deviation
Behavioral intention	Willingness of farmers in Tibetan areas to adopt green production practices (0,1)	0.807	0.447
Behavioral responses	Level of response from Tibetan farmers (1–5)	2.525	0.489
Perceived economic benefits	Increased crop yields (1–5)	3.667	1.037
	Increase in household income (1–5)	3.121	1.026
Perceived ecological effectiveness	Upgrading land quality (1–5)	2.569	0.713
	Conservation of biodiversity (1–5)	2.734	0.852
Perception of social well-being	Promoting environmental awareness (1–5)	3.325	0.698
	Improving living environment (1–5)	3.329	0.993
	Promotion of non-farm employment (1–5)	1.908	1.321
Personal norm	Own production capacity (1–5)	2.924	0.879
	Regular training services (1–5)	1.773	0.967
Social norm	neighborhood-driven roles (1–5)	2.429	0.884
	Pollution status of villages (1–5)	3.659	1.647
	Religious belief (1–5)	4.383	0.354
Regulatory guidance	No fertilizer guidance (1–5)	3.778	1.245
Incentive regulation	Ecological compensation incentives (1–5)	2.761	1.433
Restrictive regime	Grazing ban constraint (1–5)	4.286	0.052
Control variable	Age
	Educational level
	Number of laborers	1.932	0.639
	Family party members (0,1)	0.203	0.405

. In conclusion, this section lays a robust foundation for our research by selecting relevant variables at both dependent and control levels. Through structured measurement tools like Likert scales and careful control for confounding factors, we ensure that our findings are both reliable and credible.

Cronbach’s coefficient was used to test the reliability of the variables in this study. The Cronbach’s alpha value indicates the internal consistency of the data as well as the extent to which the variables are interrelated. The value of this coefficient lies between 0 and 1. The higher the alpha coefficient, the higher the reliability of the scale. The judgment criteria are as follows: when the Cronbach’s α value is lower than 0.5, there is no need for research; between 0.5 and 0.7, the reliability is acceptable; between 0.7 and 0.9, the reliability is good; and greater than 0.9, the reliability is very good. As shown in

Table 4. The results of the reliability test of the total scale.

Dimension	α Coefficient	Terms
Total	0.796	16
Perceived economic benefits	0.903	2
Perceived ecological effectiveness	0.876	2
Perception of social well-being	0.851	3
Personal norm	0.771	2
Social norm	0.887	3
Regulatory guidance	0.941	1
Incentive regulation	0.785	1
Restrictive regime	0.764	1

, the alpha coefficient of the total scale is 0.796, which is between 0.7 and 0.9, indicating that the reliability of the total scale is good.

The KMO test was used to analyze the validity of the questionnaire, and the criteria for judging the validity of the questionnaire through the KMO value were as follows: when the KMO value is less than 0.5, it indicates that the validity of the questionnaire is relatively poor; when the KMO value ranges from 0.5 to 0.7, it indicates that the questionnaire is of average validity; when the KMO value ranges from 0.7 to 0.9, it indicates that the questionnaire is of good validity; and when the KMO value is greater than 0.9, it indicates that the questionnaire is of very good validity. From the results, it can be seen that the KMO value is 0.815, which is between 0.7 and 0.9, indicating that the reliability of the questionnaire is relatively good.

4.4. Empirical Modeling

The team used a combination of random and stratified sampling in the Hezuo City regions of Nawu Township, Lexiu Township, Zuogaimanma Township, Kajiaman Township, Yiheang street, Dangzhou Street, Jianmukeer street, and Tongqin Street. In each of the 8 townships and streets, we took 1–2 administrative villages and randomly selected 2–3 Tibetan farming households in each village to start the questionnaire survey. In order to verify the credibility of the data, interviews with government staff in each township and street were used as an indirect basis. The sample points were selected to approximately cover the key construction areas in the villages of Hezuo City.

$$\begin{gathered} {\mathrm{d}}_{\mathrm{p}} = {\mathrm{Z}}_{\mathrm{p}}\theta + {\epsilon }_{\mathrm{p}} \\ {\mathrm{y}}_{\mathrm{p}} = {\mathrm{X}}_{\mathrm{p}}\beta + {\mu }_{\mathrm{p}} \\ {\mathrm{y}}_{\mathrm{p}\ast } = {\mathrm{y}}_{\mathrm{p}},\mathrm{i}\mathrm{f} {\mathrm{d}}_{\mathrm{p}} > 0 \\ {\mathrm{y}}_{\mathrm{p}\ast } = 0, \mathrm{o}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{w}\mathrm{i}\mathrm{s}\mathrm{e} \end{gathered}$$

(1)

Based on this, the research team constructed the Double Hurdle model. In Equation (1), d_p is the behavioral intention of farmer p to operate a Tibetan farm, which takes a value of 0 or 1; y_p reflects the degree of the farmer’s response to green production; Z_p and X_p are the same set of economic and social variables jointly affecting BR and BI; the parameters θ and β are used to reveal whether there are any unintentional and actual degrees of response; and ε_p and μ_p are the random perturbation terms obeying an independent homogeneous distribution. In particular, y_p∗ is the conditioning variable generated. When a farmer’s d_p > 0, it indicates the existence of behavioral intention, in which case, d_p = 1 and y_p∗ = y_p; and when d_p = 0, there is no behavioral intention, in which case, it is treated as if y_p = 0 regardless of the degree of behavioral response of the farmer. This setup is able to overcome the effect of outliers on the estimation results of the BR and does not generate sample selection bias. Cragg suggests that the maximum likelihood principle should be used for the estimation of the Double Hurdle model. The likelihood function is of the form

$$\ln L={\displaystyle \sum _{\mathrm{yi}=0}\ln \varphi }\left[1-\left(Z\mathrm{p}\theta \right)\right]+{\displaystyle \sum _{\mathrm{yi}>0}\left[\ln \varphi \left(Z\mathrm{p}\theta \right)+\ln \frac{{\left({\mathrm{y}}_{\mathrm{p}}-{\mathrm{X}}_{\mathrm{p}}\right)}^2}{{{\displaystyle \sigma }}^2}-\ln \varphi \frac{{\mathrm{X}}_{\mathrm{p}}\beta }{\sigma }\right]}$$

(2)

In Equation (2), σ is the density function, and the natural logarithmic sum of the first term on the right side of the equation corresponds to the result of the probit model; if the farmer’s intention to participate in green production is not zero, then Z_p > 0. The natural logarithmic sum of the second term on the right side of the equation corresponds to the result of the broken-tailed regression, reflecting the degree of behavioral response of the farmers in the Tibetan area.

5. Results

5.1. Model Fit Tests

In this paper, the econometric model is tested and estimated with the help of Stata19 software, and

Table 5. Results of model fit test.

Project	Tobit	Probit	Truncreg
Waldχ2 (LRχ2)	205.1	397.2	107.9
Prob > χ2	0.000 ***	0.000 ***	0.000 ***
Log likehood	−472.501	−403.777	−494.734
Pseudo R2	0.179	0.292	-

Note: *** indicates that the test passes at the 1 per cent levels of significance.

shows the results of the validity test of the model. It can be concluded from the statistical quantities, such as the pseudo-judgment coefficient (Pseudo R²) and the value of the log likelihood function (Log likehood), that the fitting effect of farmers’ behavioral intention with the help of the Probit model is better, and the truncated Gaussian regression model is also suitable for the study of the green production behavior of farmers in the Tibetan area. Therefore, combining the above judgements, it is more reasonable for this paper to apply Double Hurdle model to empirically test BI and BR in two stages.

5.2. Empirical Results and Analyses

The maximum value of the correlation coefficient between variables is 0.47, and the maximum value of the variance inflation factor (VIF) is 3.97, which meets the criterion of a correlation coefficient of less than 0.6 and a VIF of less than 10, and parameter estimation can be carried out. The chi-square values of the Probit model and Truncreg model are significant at the 1% level, which indicates that the models have good explanatory power, and the estimation results are shown in

Table 6. Results of two-column model estimation (N = 59).

Latent Variable	Observed Variable	Probit (BI)		Truncreg (BR)
		Coef	Std.Err	Coef	Std.Err
Farmers’ perceptions	Increase crop yield	0.302 ***	0.110	0.147 *	0.059
	Increasing household income	0.371	0.297	0.205	0.391
	Upgrading land quality	0.177 ***	0.059	0.211 ***	0.057
	Conservation of biodiversity	0.374	0.207	0.305	0.051
	Promoting Environmental Awareness	0.147 **	0.091	0.071 *	0.059
	Promotion of non-farm employment	0.119 ***	0.070	0.190	0.101
	Improving the living environment	0.155 ***	0.036	0.168 ***	0.086
Farmers’ norms	Own production capacity	0.009	0.050	−0.077	0.043
	Regular training services	0.072	0.297	0.209 ***	0.257
	Neighborhood-driven role	0.901 ***	0.199	0.991 ***	0.062
	Pollution status of villages	−0.037	0.077	−0.041	0.093
	Religious belief	0.257 ***	0.178	0.193 ***	0.024
Environmental regulation	No fertilizer guidance	0.142 *	0.059	0.079 *	0.053
	Eco-compensation incentives	0.201 ***	0.071	0.299 ***	0.105
	Grazing ban constraint	−0.120 ***	0.095	−0.113 *	0.043
Control variable	Age	−0.309 ***	0.051	−0.159 ***	0.204
	Educational level of head of household	−0.301	0.299	−0.213	0.047
	Number of household laborers	0.377 *	0.204	0.350 ***	0.151
	Any party members or cadres in the household	0.197 **	0.091	0.90 ***	0.039
BI→BR	Behavioral intention			0.286 *	0.179
cons		9.747 ***	0.577	2.519 ***	0.439

Note: ***, **, and * indicate that the test passes at the 1 per cent, 5 per cent, and 10 per cent levels of significance, respectively.

.

In terms of cognitive norms (cognition), the variable “increase crop yield”, which measures farmers’ perceptions of economic efficiency, has a more significant effect on behavioral intentions. However, when considering specific behavioral responses, the impact of this variable diminishes. Furthermore, “increasing household income” is not significant in both stages of the model, indicating that business income is not the most important driving force for Tibetan farmers to engage in green farming practices. This finding aligns with the behavioral logic observed among farmers in Hezuo City within the theoretical framework, suggesting that ecological and environmental considerations take precedence over purely economic motivations.

The variable “upgrading land quality”, which observes the perception of ecological efficacy, shows a highly significant relationship in both models, indicating that there is a strong willingness to improve the ecological benefits of green production, especially the quality of arable land and grassland. This not only helps to increase the intention of farmers to engage in green production, but also stimulates actual behavioral responses from the farmers. On the other hand, the conservation of biodiversity was not a major concern for farmers in either stage. Thus, the above analyses also reflect that farmers in Hezuo City prioritize “ecological rationality” over “economic rationality” in their decision-making process regarding Tibetan farming practices.

Additionally, variables characterizing social well-being perceptions—promoting environmental awareness and promoting non-farm employment—are both more significant in the first stage than in the second stage of the model. However, the variable “improving the living environment” is more significant in both stages compared to the second stage. This suggests that considerations about public welfare can positively influence farmers’ behavioral intentions. Direct social welfare perceptions, such as improving the living environment, strongly drive green practices, while indirect social welfare perceptions may hinder the effect of driving green practices. This also reflects that farmers in Hezuo City prioritize “living rationality” over “ecological rationality” in their decision-making process regarding Tibetan farming practices.

“Own production capacity” does not significantly influence farmers’ intentions towards green farming or their behavioral responses. The negative regression coefficient for this variable in the behavioral response stage suggests a lack of impact. “Regular training service” reaches significance at the 1% level within the behavioral response model, indicating that while technical training may not strongly influence farmers’ green farming preferences overall, farmers who respond positively to such training are more likely to engage in behavior-oriented practices. This implies that a deficiency in production skills could hinder green production decision-making. However, technical barriers do not serve as a decisive factor in preventing farmers from engaging in behavior-oriented practices. In contrast, factors such as “neighborhood-driven role” and “religious belief” have more significant effects on farmers’ green production behavioral intentions and responses compared to the negative influence of “village pollution.” These findings underscore the crucial roles of herd mentality, cohort effects, and religious influences on farmers’ values and moral tendencies. Additionally, they highlight the indispensable nature of social norms and social attributes in understanding smallholder decision-making.

Among the moderating variables, “no fertilizer guidance” has a positive effect on both behavioral intention and behavioral response. This suggests that fertilizer subsidies remain an important guarantee and incentive for farmers to engage in green business behavior in Tibetan areas. However, their statistical significance is inconsistent, which may be linked to relatively low farmer production levels in Hezuo City and regional disparities in supporting policies and implementation efforts. In contrast, “eco-compensation incentives” have a significant positive impact on farmers’ behavior, aligning with the decision-making processes and incentive responses of small-scale farmers. However, research indicates that some farmers express concerns about the stability of ecological subsidies and income growth and optimism regarding long-term income from traditional agricultural and livestock practices.

These findings suggest that addressing concerns about ecological subsidies and ensuring steady increases in farming income alongside ecological compensation are critical for enhancing green production intentions and sustaining green practices. Finally, “grazing ban constraints” exhibit significant negative correlations across both models, highlighting the multifaceted challenges faced by farmers in Hezuo City’s production decision-making processes, such as production vacuums caused by grazing indicator restrictions.

Among the control variables, “age” exhibited a significantly negative impact on farmers’ green production willingness and behavior, aligning with findings from studies examining the adoption of green technologies, where older farmers are found to be more comfortable adopting traditional production methods. Furthermore, the variables “number of household laborers” and “presence of a party member in the household” demonstrated significant effects in both models, emphasizing that labor resource endowments and the party’s ideological and production guidance to farmers are essential prerequisites for their decision-making regarding green production.

Through comparison, it was found that the influence directions and degrees of many dependent variables in this study closely aligned across the two models. The overall fit significance in the first stage of the model was higher than in the second stage, reflecting stronger explanatory power for predicting behavioral intentions than predicting behavioral responses. According to the Double Hurdle model test results, the estimated coefficient of Tibetan farmers’ green production behavioral intentions on their behavioral response was 0.286 and passed the 10% significance level, indicating that green production behavioral intention has certain explanatory strength and promoting effects on behavioral responses. However, along the transformation path from BI (behavioral intention) to BR (behavioral response), farmers’ behavior is often influenced by a combination of internal and external factors, leading to differentiation in their actions. To achieve a wide range of green production behavioral responses, beyond enhancing group norms and improving life rationality among farmers in Tibet, it is essential to provide sufficient external stimuli along the practical path of green transformation. Additionally, farmers should be supported in developing their technical production skills under individualized norms, enabling them to learn about and respond to green production initiatives autonomously.

5.3. Robustness Tests

In order to further validate the estimation results of the Double Hurdle model, this paper intends to conduct a robustness test on the results of the empirical analyses to verify the reliability of the estimation results. In econometric analyses, “replacing the dependent variable” and “selecting subsamples” are common ways to measure the robustness of a model. Given that the two dependent variables in this paper can achieve the effect of mutual corroboration under the premise of the same indicators, this paper intends to take the “exclusion of the elderly households sample” approach to model re-estimation and robustness test regression estimation using heteroskedasticity robust standard errors to achieve more accurate parameter valuation. The specific estimation results are shown in

Table 7. Robustness test estimation results.

X	Probit (BI)		Truncreg (BR)		Probit (BI)		Truncreg (BR)
	Coef	Std.Err	Coef	Std.Err	Coef	Std.Err	Coef	Std.Err
Increase crop yield	0.275 ***	0.143	0.113 *	0.055	0.326 ***	0.477	0.131 *	0.066
Increasing household income	0.280	0.386	0.158	0.362	0.332	0.433	0.183	0.438
Upgrading land quality	0.161 ***	0.077	0.162 ***	0.053	0.191 ***	0.065	0.188 ***	0.064
Conservation of biodiversity	0.282	0.269	0.235	0.047	0.334	0.227	0.272	0.057
Promoting Environmental Awareness	0.134 **	0.118	0.055 *	0.055	0.159 **	0.449	0.063 *	0.066
Promotion of non-farm employment	0.108 ***	0.091	0.146	0.094	0.129 ***	0.542	0.170	0.113
Improving the living environment	0.141 ***	0.047	0.129 ***	0.080	0.167 ***	0.087	0.150 ***	0.096
Own production capacity	0.007	0.065	−0.059	0.040	0.008	0.377	−0.069	0.048
Regular training services	0.054	0.386	0.161 ***	0.238	0.064	0.433	0.187 ***	0.288
Neighborhood-driven role	0.820 ***	0.258	0.763 ***	0.057	0.973 ***	0.219	0.885 ***	0.069
Pollution status of villages	−0.028	0.100	−0.032	0.086	−0.033	0.491	−0.037	0.104
Religious belief	0.234 ***	0.231	0.149 ***	0.022	0.278 ***	0.196	0.172 ***	0.027
No fertilizer guidance	0.129 *	0.077	0.061 *	0.049	0.153 *	0.476	0.071 *	0.059
Eco-compensation incentives	0.183 ***	0.092	0.230 ***	0.097	0.217 ***	0.476	0.267 ***	0.118
Grazing ban constraint	−0.109 ***	0.123	−0.087 *	0.040	−0.130 ***	0.543	−0.101 *	0.048
Age of head of household					−0.334 ***	−0.056	−0.142 ***	0.228
Educational level of head of household					−0.325	−0.329	−0.190	0.053
Number of household laborers					0.407 *	−0.224	0.313 ***	0.169
Any party members or cadres in the household					0.213 **	−0.100	0.804 **	0.044
cons	0.359 *	0.350	2.593 ***	0.739	3.497 **	0.439	2.335 **	2.430
N	59	59	59	59	59	59	59	59

Note: ***, **, and * indicate that the test passes at the 1 per cent, 5 per cent, and 10 per cent levels of significance, respectively.

. Through a comparative analysis, we find that the direction and significance level of the main independent variables and control variables selected in the model presented in this paper show high consistency with the estimation results in Table 6. This indicates that the above model has strong applicability for the estimation of variables, and therefore, the parameter valuation of the model is stable and reliable.

5.4. Tests of the Moderating Effect of Environmental Regulation

North’s theory of institutional change provides important insights into the validation of the relationship between individual cognition and behavioral decision-making in this study, while Hoffman found that environmental regulation, as an important situational factor, may have a role to play in the relationship between cognitive norms and behaviors through the study of external environmental factors. Thus, based on the test results of the Double Hurdle model, this paper sets environmental regulation as the target variable and, with the help of a moderating model, examines the relationship between the other latent variables and the dependent variables, and the fitting results of SPSS21 are shown in

Table 8. Moderated effects test (N = 59).

Variable Name	Behavioral Intention (BI)	Behavioral Response (BR)
Environmental regulation × Cognitive norm	0.019 **	0.035 *
	(0.016)	(0.019)
Environmental regulation × Farmers’ norms	0.166 ***	0.070 ***
	(0.0191)	(0.013)
Environmental regulation × Perceived economic benefits	0.037 **	0.099
	(0.017)	(0.131)
Environmental regulation × Perceived ecological effectiveness	0.072 ***	0.062 ***
	(0.018)	(0.013)
Environmental regulation × Perception of social well-being	0.019	0.013
	(0.018)	(0.017)
Environmental regulation × Behavioral intention		0.017
	-	(0.016)
Prob > chi2	0.000 ***	0.000 ***

Note: ***, **, and * indicate that the test passes at the 1 per cent, 5 per cent, and 10 per cent levels of significance, respectively. The cognitive norms and behavioral attitudes variables are weighted averages of their specific indicators, which are cross-multiplied with the environmental regulation variables separately to produce interaction terms.

. Overall, the moderating role and direction of environmental regulation between the two dependent variables and the other latent variables are relatively consistent. Environmental regulation, as an important contextual variable, can effectively compensate for the lack of cognitive norms of farmers in Tibetan areas and exert influence, and then effectively induce farmers’ green production behavioral responses. In particular, although the variable characterizing farmers’ norms is not highly significant for the two dependent variables in the two-threshold model, farmers’ norms are highly significant for behavioral intention and behavioral response after interacting with environmental regulation. After subdividing the latent variable cognitive norms, we found that the significant coefficients and levels of the interaction terms of ecological efficiency and environmental regulation were higher than those of the interaction terms of economic efficiency and environmental regulation, which once again verified the “eco-economist” nature of the business decision-making of the farmers in the ecologically fragile Tibetan area. However, environmental regulations do not contribute to the limited cognition of smallholders, so the actual behavioral response often fails to take into account social well-being. Finally, in the pathway of cognitive norms→behavioral intentions→behavioral responses, environmental regulations cannot induce sustained green production responses by reinfluencing farmers’ green production intentions, but only moderate their responses in the first half of the pathway.

6. Discussions and Conclusions

Due to the communication difficulties encountered in this research and the complexity of smallholder behavior itself, this study still has some shortcomings. Firstly, this study only focuses on the left-behind farmers in the study area, so the overall sample size is insufficient, and it can only roughly reflect the basic situation of the green production behavior of farmers in Hezuo City. Secondly, people’s green production behavior is multi-dimensional, and this study mainly focuses on their daily agricultural production. It is difficult to fully reflect the implementation of green production in Hezuo City.

Based on the field survey data of 59 farmers, this study innovatively introduced religious factors into cognitive norms, empirically tested the influencing factors and decision-making logic of the green production behavior of farmers in Hezuo City under the green development strategy by using the Double Hurdle model and the moderated model, and drew three conclusions:

(1) The current green production practices of Hezuo City farmers show a non-ideal path of ‘low cognition, high intention and low behavioral response’. Farmers’ cognitive norms and government environmental regulations are the key factors determining their green production intention and behavioral response, and the driving effect of cognitive norms on behavioral response shows a downward trend compared with behavioral intention.

(2) Group cognitive norms, such as religious beliefs and neighborhood-driven roles, significantly influenced the two stages of farmers’ green production intentions and behavioral responses in Hezuo City. The consistency between group social cognition and green production cognition is conducive to promoting farmers’ green production intentions and sustained responses.

(3) Environmental regulations can significantly influence the green production behavior of smallholder farmers in Tibetan areas. Specifically, the role of environmental regulations in enhancing green production intention and behavioral response is consistent and significant. However, environmental regulations cannot influence green production intention again to promote sustainable green production responses, and their role is limited to half of the path regulation.

Farmers in Tibetan areas with strong religious foundations are influenced by Tibetan Buddhist ideas of harmony between humans and nature, being born in nature, respecting the elements of nature in religious rituals, and so on, and their knowledge of, willingness to, and response to green production are of a special nature. Believers focus more on ecological and social well-being benefits rather than economic benefits because they are more consistent with religious tenets and better able to bring spiritual peace and abundance to believers. This study suggests that in order to promote widespread green production behavioral responses, sufficient external incentives must be provided, and at the same time, positive factors such as current ecological awareness and neighborhood herd effects among Tibetan farmers must be captured so as to induce a sustained response by strengthening the perception of social efficacy, effective publicity and guidance, and coordinating policies among industries and sectors. In the short term, environmental regulatory instruments are used as the main tool for encouraging green production behavioral responses. In the future, we can conduct in-depth research on the perceptions and behavioral responses to green production in the whole industry and the process of the Tibetan people, and dig deep into the perceptions and practices of the Tibetan people regarding green transformation under the influence of religion, so as to enrich the content of related research.