The Impact of Tea Farmers’ Cognition on Green Production Behavior in Jingmai Mountain: Chain Mediation by Social and Personal Norms and the Moderating Role of Government Regulation

Abstract

China’s agricultural sector faces significant challenges, including fragmented farming practices, limited farmer knowledge of sustainable production, and outdated pest control technologies. These issues result in improper fertilization, pesticide application, and disposal of agricultural inputs, contributing to agricultural non-point source pollution and hindering the transition to a green economy. Thus, promoting green production behavior among farmers is critical for achieving carbon peaking, carbon neutrality, and harmonious coexistence between humans and nature. However, the existing literature on this topic is still relatively scarce. This study aims to investigate the impact of farmers’ cognition on their green production behavior (GPB). Considering the role of policy, this study also examines the moderating effect of government regulation in this relationship. An analysis of 306 survey responses from tea farmers in Jingmai Mountain, Pu’er City, Yunnan Province, reveals that farmers’ cognition exerts a significant and positive impact on GPB. Social norms and personal norms serve as chain mediators in the relationship between farmers’ cognition and GPB. Moreover, government regulation moderates the influence of farmers’ cognition on social norms, further amplifying its impact on them. This study advances the theoretical understanding of farmers’ behavior and offers practical insights for fostering the sustainable development of the tea industry.

1. Introduction

Promoting green development is a crucial pathway for achieving modernization with Chinese characteristics [1,2]. This strategy aims to achieve both carbon peaking and carbon neutrality [3], while fostering harmonious coexistence between humanity and nature [4]. Agricultural green production is a critical component of the green development strategy [5]. According to the China Agricultural Green Development Report released in 2023, China’s usage of chemical fertilizers remains high at 51.91 million tons, while pesticide usage stands at 248,300 tons, with the recycling rate of pesticide packaging waste at just 58.6%. These figures suggest significant potential for reducing agricultural inputs and improving the recycling rate of agricultural waste. Hence, enhancing farmers’ awareness and fostering green production practices are crucial for advancing the transformation towards green agriculture.

Farmers hold a pivotal position within the agricultural production chain, and their sustained engagement in green production practices is essential to advancing agricultural green development. Nonetheless, farmers currently encounter a range of challenges in implementing green production, such as limited awareness [6], over-reliance on agricultural inputs [7], and inefficient agricultural waste disposal [8]. These challenges impede the broader adoption of green production practices and undermine the long-term sustainability of agricultural green development.

Tea is one of the oldest and most widely consumed beverages worldwide [9] and is also a key economic crop in China [10]. The development of the tea industry and the promotion of tea culture have strengthened China’s position during the wave of economic globalization, carrying both significant economic and social implications. Ensuring the green and sustainable development of the tea industry necessitates continuous efforts to promote green tea production. Enhancing tea farmers’ awareness and ensuring their sustained implementation of green production practices are critical to achieving a green transformation in tea production. However, in practice, tea plantations and management are relatively fragmented [11], and tea farmers often lack adequate knowledge of green production. This results in outdated pest control techniques, inefficient use of fertilizers and pesticides, and improper disposal of agricultural inputs [12], which hinder the green development of the tea industry and the comprehensive management of agricultural environmental issues. These problems contribute to non-point source pollution and ecological risks, thereby impeding the advancement of sustainable green development in the tea industry.

A review of existing research reveals numerous studies examining the impact of farmers’ cognition on green production behaviors. Vignola et al. [13] argue that farmers’ ecological cognition significantly influences their behavior; higher levels of environmental knowledge increase farmers’ inclination to adopt green agricultural technologies and practices. Adnan et al. [14] suggest that farmers with heightened environmental awareness and concern are more likely to engage in green production practices in response to perceived ecological degradation, aiming for improved environmental outcomes. Ren et al. [15] assert that social and personal norms, integral to farmers’ cognition, positively influence their decisions regarding green production. Luo et al. [16] propose that heightened awareness of the COVID-19 pandemic significantly increased farmers’ willingness to adopt green production practices. While scholars have extensively explored the theoretical and practical aspects of the relationship between farmers’ cognition and green production behaviors, gaps remain in the existing research.

Building on this foundation, the paper constructs an analytical framework, Farmers’ Cognition—Social Norms and Personal Norms—Green Production Behavior, which is grounded in normative activation theory, farmer behavior theory, and government regulation theory. Using field research data, the study explores the pathways and magnitudes of influence among farmers’ cognition, social norms, personal norms, and green production behavior within the tea farming community in Lancang County. It further evaluates the moderating effect of government regulation on tea farmers’ green production practices. The primary goal of this paper is to broaden perspectives and advance theoretical research on green production behavior, focusing on the following key questions: (1) Does farmers’ cognition affect tea farmers’ green production behavior? (2) What factors mediate and moderate this influence? (3) How can we effectively enhance farmers’ cognition and promote green production behaviors?

The rest of the paper is organized as follows. Section 2 contains an extensive review of the literature and a theoretical framework for the research hypothesis. Section 3 presents sample selection, variable measurement, and the research model. Section 4 shows the results. Section 5 discusses these results. Section 6 concludes and suggests future research.

2. Theoretical Background and Hypotheses Development

To examine the role of farmers’ cognition in promoting green production behavior, this study integrates cognitive behavioral theory, the theory of planned behavior, normative activation theory, and government regulation theory. According to Jaccard et al. [17], behavior encompasses any identifiable action undertaken by an individual, group, or living system. Therefore, farmers’ activities on their farms can be regarded as practical applications of behavioral theory. By analyzing the factors influencing these behaviors, we can more effectively encourage pro-environmental practices among farmers.

Research on climate change adaptability has traditionally been grounded in the cognitive and behavioral sciences [18]. In this context, farmers’ cognition is viewed as a key factor in understanding their green production behavior. The theory of planned behavior, first proposed by Ajzen and Madden [19], suggests that behavior is influenced by attitudes, intentions, and perceived behavioral control. Psychological research further confirms that cognition plays a crucial role in shaping behavior [20]. Subjective norms refer to an individual’s perception of social pressures concerning whether or not to engage in a specific behavior [21].

Furthermore, government environmental regulations are vital tools for promoting sustainable development and exert a considerable influence on farmers’ green behavior. By employing economic, administrative, and legal mechanisms, government regulation serves a crucial institutional function in agricultural production, guiding and regulating farmers’ green practices to achieve macro-level governance goals. These theoretical frameworks are not only highly relevant to sustainable development in agriculture and rural areas but also offer valuable insights for the formulation of related policies.

2.1. The Impact of Farmers’ Cognition on Green Production Behavior(GPB)

Cognition is a fundamental concept in psychology. As Double et al. [22] explain, cognition includes the processes of perception and comprehension, arising from internal psychological mechanisms. The core of cognition involves an individual’s distinctive way of perceiving and interpreting the external world, which allows the acquiring of knowledge and the addressing of problems. Averbuch et al. [23] describe farmer cognition as a collection of knowledge, skills, and ideas that can mitigate or reduce negative external effects, equipping farmers to proactively respond to environmental shifts and make informed decisions regarding their crops.

The theory of farmer behavior posits that cognition plays a crucial role in shaping farmers’ actions. Behavior emerges from the dynamic interaction of various cognitive processes. Unlike traditional agricultural practices, green production behavior (GPB) necessitates a certain cognitive threshold. Through cognitive processes, farmers assimilate knowledge pertaining to economic, social, and ecological dimensions, which ultimately influences their preferences, adoption, and execution of GPB. Cognitive–behavioral theory, which merges cognitive theory with behaviorism, underscores the interrelationship and consistency between cognition and behavior. Social behaviorists assert that cognition underpins behavior, with farmers’ cognitive capacities determining their preferences and, subsequently, their actual actions [24].

Somerville et al. [25] suggest that the dynamic interplay between cognition, affective traits, social context, and behavioral patterns provides an effective framework for explaining why individuals adopt certain behaviors while avoiding others. In the context of farmers’ decision-making, cognition assumes a pivotal role. This study conceptualizes farmer cognition as their understanding of the value and impact of green production across economic, social, and ecological domains, encompassing economic, ecological, and social cognitions.

Economic cognition refers to farmers’ understanding and perception of the economic indicators associated with green production [26]. Ecological cognition relates to farmers’ awareness of the rural ecological environment, reflecting an individual psychological trait. This encompasses their understanding of production methods and technologies that affect the rural ecological environment, indicating whether farmers are informed and responsible for environmental protection [15]. Social cognition, in turn, highlights the social impacts generated by green production [27,28].

Overall, the higher the level of cognition, the better farmers understand the relationship between the environment and themselves, and the more likely they are to engage in green production practices. Individuals with a strong cognitive foundation are more likely to lead by example, facilitating the coordination of economic and ecological benefits, thereby achieving personal fulfillment, gaining social recognition, and assuming responsibility for environmental protection. For example, farmers’ cognition of green agriculture can motivate them to reduce their use of chemical fertilizers [29]. Foguesatto et al. [30] found that green cognition significantly fosters sustainable agricultural behaviors, suggesting that enhancing farmers’ green cognition can promote corresponding green production practices. Li et al. [31] observed that farmers’ cognition of pesticides significantly influences their pesticide use behavior in green production, playing a crucial role in pesticide selection and waste management. Fan et al. [32] showed that farmers’ cognition can encourage green production behaviors, with higher-income households being more inclined to adopt green production technologies. Zhang et al. [33] found that ecological cognition significantly boosts the adoption and depth of green production technologies, indicating that higher levels of ecological cognition increase both the likelihood and extent of adopting such technologies.

In summary, the higher the farmers’ cognition of ecological, social, and economic benefits, the more likely they are to adopt green production behaviors. Therefore, we propose the following hypothesis:

H1. 

Farmers’ cognition has a positive impact on GPB.

2.2. The Mediating Effect of Social Norms and Personal Norms between Farmers’ Awareness and GPB

The Theory of Normative Social Behavior (TNSB) [34] posits that social norms provide individuals with a reference point in ambiguous situations, thereby shaping their behavior [35]. In social psychology, descriptive and injunctive norms are two central concepts. Descriptive norms refer to behaviors that most people typically engage in, whereas injunctive norms pertain to the expectations or perceived approval of certain behaviors by others. These norms are activated and reinforced through different mechanisms. Descriptive norms reflect an individual’s perception of how common a behavior is within a particular group, while injunctive norms represent societal approval or disapproval of the behavior, particularly the attitudes of significant others or reference groups [36]. According to TNSB, social norms can influence behavior directly or indirectly by interacting with other factors [37].

Social norms play a crucial role in shaping human behavior across private, professional, and public contexts [38]. As a set of widely accepted behavioral guidelines within groups, these norms dictate appropriate actions in specific situations [36]. They provide individuals with meaningful guidance, encouraging them to make choices that align with group expectations. In essence, social norms can be understood as a set of shared behaviors, attitudes, beliefs, and standards within a particular group [39].

Theories of cognitive behavior and planned behavior provide crucial frameworks for understanding how individuals make decisions through cognitive processes. These theories propose that farmers’ economic, social, and environmental cognition significantly shapes their behavior, particularly in economic practices, social interactions, and environmental conservation efforts. Norms, especially social norms, may stem from behavioral expectations within a group (descriptive norms) or from widely accepted societal standards (injunctive norms) [40]. Even without legal enforcement, social norms can effectively guide individual actions.

In recent years, the impact of social norms on the adoption of sustainable agricultural practices has garnered increasing scholarly attention. Empirical research suggests that social norms can effectively encourage farmers to adopt environmentally friendly techniques, reduce their reliance on chemical inputs, and transition towards more sustainable agricultural practices.

First, social norms influence farmers’ decision-making through social pressure, where behaviors adopted by neighbors, relatives, and peers serve as crucial reference points. For instance, Villamayor-Tomas et al. [41] found that the “farmer recommendation effect” significantly boosts participation in agro-environmental schemes. Farmers who observe others successfully adopting green agricultural practices are more likely to follow suit, suggesting that social norms play a pivotal role in spreading sustainable behaviors.

Second, social norms influence behavior through internalization, where they are incorporated into personal moral standards. According to Norm Activation theory, once internalized, social norms act as personal moral obligations that guide behavior. Li et al. [42] found that higher education levels among farmers increased their susceptibility to social norms, which, in turn, heightened environmental awareness and the adoption of green practices, such as organic fertilizer use. This suggests that the internalization of social norms—shaped by cognitive processes—promotes sustainable agricultural behavior.

Social norms also function through informal peer-monitoring mechanisms within farming communities, adding an additional layer of accountability. In the context of pesticide use, for instance, farmers who understand that improper application can harm the environment and negatively affect their community are more likely to adhere to green practices to avoid social disapproval.

In specific ecological contexts, social norms can exert significant influence on the adoption of sustainable practices. For example, Liu et al. [29] found that, in ecologically sensitive areas, cooperatives providing technical training and information services enhanced farmers’ awareness and shaped their behavior through community norms, leading to reduced fertilizer use. Similarly, Vignola et al. [13] emphasized that social norms strongly shaped soil conservation efforts in Costa Rica, where peer influence encouraged the adoption of sustainable land management practices, such as minimizing excessive tilling and fertilizer application.

Thus, social norms emerge as pivotal drivers of the green transition in agriculture. By reinforcing positive group behaviors and showcasing successful examples of sustainable practices, social norms can enhance voluntary adherence to environmentally friendly techniques. Norm Activation theory suggests that, when internalized, social norms foster self-regulation through personal feelings of pride or guilt [43]. In agricultural communities, these personal norms—understood as guiding principles in the absence of legal obligations [44], can be particularly influential.

Furthermore, farmers’ cognitive abilities, shaped by environmental factors and information-processing capacities, significantly impact the internalization of social norms. Li et al. [42] found that higher cognitive abilities among rice farmers intensified the influence of social norms, particularly regarding the use of organic fertilizers. Therefore, cognition enhances the soft regulatory power of social norms by increasing individuals’ sensitivity to social pressure. As Li et al. [45] demonstrated, improving farmers’ cognitive abilities not only reinforced social norms but also facilitated land transfers, diversified income streams, and promoted more sustainable agricultural practices.

Personal norms pertain to the moral obligation to undertake or avoid certain actions [43]. Recognition and positive reinforcement from relatives and friends can encourage farmers to adhere to these norms, thereby enhancing their willingness to adopt environmentally sustainable practices [46]. Personal norms play a pivotal role in influencing farmers’ decisions to embrace environmentally friendly agricultural techniques. As farmers’ cognitive capacities advance, their engagement in sustainable practices is expected to rise. This is because enhanced cognition strengthens the activation of key elements of personal norms, particularly awareness of consequences and the assignment of responsibility.

Wang et al. [47] identified that both awareness of consequences and the assignment of responsibility indirectly influence farmers’ willingness to engage in ecological farming through the mediation of personal norms. Similarly, Guo et al. [48] demonstrated that individuals tend to internalize personal norms when they recognize the social and environmental harms of food waste. In the context of farming, when farmers recognize that their actions carry social and environmental consequences or are subject to evaluation by their social groups, they are more likely to adopt personal norms that align with societal expectations. For instance, farmers who fully understand the environmental damage caused by excessive pesticide use may deliberately reduce their pesticide application [49]. Based on this, we propose the following hypothesis:

H2. 

Farmers’ cognition has a positive impact on social norms.

H3. 

Farmers’ cognition has a positive impact on personal norms.

Norm Activation Theory examines the relationship between social norms and personal norms, asserting that external social norms shape internal personal norms. Subjective norms precede personal norms because the accepted standards and importance of specific behaviors in society are dictated by subjective norms. Subsequently, these subjective norms may be internalized as personal norms under perceived pressure from significant others [50]. Due to individual cognitive differences, widely recognized social norms impact individuals during social interactions, thereby influencing the formation of personal norms. Le et al. [51] combined the Theory of Planned Behavior and the Norm Activation Model to explore organic food purchase intention, confirming that social norms significantly and positively influence personal norms. Zhang et al. [52] noted that personal norms represent an individual’s self-expectation to perform specific behaviors in certain situations, which are internalized social norms. Violating personal norms can result in internal moral condemnation and self-reproach. Therefore, we propose the following hypothesis:

H4. 

Social norms have a positive impact on personal norms.

Subjective norms (SNs) refer to the perceived social pressure individuals experience concerning certain behaviors, encompassing the approval or disapproval of these behaviors by others or society [53]. In decision-making, individuals are frequently constrained by social norms and influenced by those with whom they maintain close relationships. For farmers, green production practices may be shaped by influences from the government, neighbors, relatives, and friends. Social norms function as fundamental indicators for assessing and evaluating individual behavior under the influence of external factors. In agricultural production, the behavior of most individuals is likely to be shaped by those with whom they maintain close relationships [54]. To gain social approval, farmers may consistently imitate the successful practices of those around them, internalizing these practices as behavioral standards and acting under the potential pressure of social expectations [55]. As Villamayor-Tomas et al. [41] observed, the “farmer recommendation” effect can positively influence farmers’ participation in agri-environmental schemes. Therefore, this study explores the impact of social norms on tea farmers’ green production behavior and proposes the following hypothesis:

H5. 

Social norms have a positive impact on GPB.

Norm Activation Theory elucidates how personal norms, functioning as internalized guidelines and standards, directly influence behavioral implementation. Personal norms, representing an individual’s internalized sense of responsibility for specific actions, exert significant influence on pro-environmental behaviors. In the environmental domain, individuals with strong personal norms for engaging in pro-environmental actions experience a moral obligation to act accordingly [56]. In the context of agricultural practices, tea farmers’ adherence to green production behaviors can be viewed as an extension of their personal norms.

Stern et al. [57] posit that the stronger the moral obligation to undertake environmental actions, the greater the likelihood of individuals participating in green production behaviors (GPBs). Personal norms are activated when individuals become aware of the consequences of their actions and recognize their responsibility to mitigate negative impacts. For tea farmers, the adoption of green production practices—such as reducing chemical inputs, applying organic fertilizers, and conserving water—is primarily driven by their internalized environmental moral beliefs. Based on the literature discussed, the following hypothesis is presented:

H6. 

Personal norms have a positive impact on GPB.

Individual behavior is shaped by both the perception of and adherence to social norms [42]. In the context of agricultural production, social norms not only influence farmers’ awareness of green production but also function as a crucial mediator between their cognition and behavior. Farmers’ cognition of green production, defined as their understanding and recognition of environmentally friendly agricultural practices, is a key factor in promoting their engagement in green production behaviors [58,59]. However, whether farmers translate this cognition into actual behavior largely depends on their social environment and their perception of social norms [60,61]. When farmers perceive strong societal expectations for green production within their community or society, such social norms can motivate them to engage more actively in green production behaviors.

Individual behavior is shaped not only by environmental awareness but also by an internal sense of moral responsibility, often reflected in personal norms [62,63]. For farmers, green production behavior depends not only on their cognition of environmental sustainability and ecological farming [64,65] but also on the influence of their internal personal norms [59]. Farmers’ cognition of green production, which encompasses their understanding of environmental protection and sustainable agriculture, can shape moral expectations for their behavior, commonly referred to as personal norms. When farmers recognize that their actions significantly impact the environment, this awareness may trigger their internal sense of responsibility, prompting them to adopt more environmentally friendly production practices. Thus, personal norms may play a critical mediating role between farmers’ cognition and their green production behaviors.

Individual behavior is shaped not only by the cognition of specific issues but also by the combined influence of social norms and personal norms [66,67]. In the development of green production behaviors in agriculture, social norms and personal norms may jointly form a chain-like mediating effect, influencing the process by which farmers translate their cognition into actual behavior. Specifically, farmers’ cognition of green production first affects their perception of social norms, i.e., their understanding of community, peer, or societal expectations regarding environmentally friendly agricultural practices. Strong social norms further reinforce farmers’ personal norms [39], stimulating their internal moral responsibility and motivating them to engage more actively in green production behaviors. Thus, social norms and personal norms may together form a chain-like mediating effect that influences farmers’ green production behaviors [68]. Based on this, we propose the following hypotheses:

H7. 

Social norms mediate the relationship between farmers’ cognition and green production behavior.

H8. 

Personal norms mediate the relationship between farmers’ cognition and green production behavior.

H9. 

Social norms and personal norms form a chain mediation effect between farmers’ cognition and green production behavior.

2.3. The Moderating Role of Government Regulation

Government regulations are actions implemented by relevant governmental bodies to protect and achieve specific public interests and social objectives [69]. As an incentive mechanism, government regulations can influence farmers’ behavior through subsidies, tax incentives, and access to favorable loans [70]. Additionally, the government can raise farmers’ environmental awareness through educational training, policy dissemination, and technical support. The formulation of government regulations typically involves establishing rules (such as laws, policies, and administrative measures) intended to guide and constrain individuals or groups to meet societal expectations.

Punitive regulations typically employ mandatory or coercive rules to direct and regulate individual behavioral intentions, while incentive-based regulations aim to enhance farm income through subsidies and rewards, thereby reducing the marginal cost of safe production. Such normative regulations may induce a herd mentality among farmers, leading them to base their production decisions on the anticipated goals and prevailing values of ecological protection. As external drivers, the stronger the policy incentives and administrative constraints, the greater their impact on farmers’ cognitive levels [71].

Dong et al. [72] observed that higher levels of government regulation can reshape farmers’ perceptions of development opportunities and external conditions, thereby enhancing their technical knowledge and ability to participate. When farmers become aware of societal expectations related to environmental protection—such as reducing pesticide use or adopting eco-friendly farming techniques—they may become more inclined to adhere to these norms to gain social approval. Sang et al. [73] also highlighted that government regulations can influence farmers’ behavior by shaping social norms. Such economic incentives or restrictions can prompt farmers to adopt behaviors that align with government regulations, thereby shaping or transforming social norms on a broader scale.

Enhanced government regulation not only raises farmers’ awareness of the importance of environmental protection but also intensifies social pressure, prompting them to adhere to relevant norms. Consequently, government regulation can enhance the influence of farmers’ cognition on social norms, leading those with higher cognitive levels to be more likely to comply with environmental regulations. Based on this, we propose the following hypothesis:

H10. 

Government regulation positively moderates the direct effect of farmers’ cognition on social norms.

Combining the above research hypotheses, we arrive at the theoretical model of this study, as shown in Figure 1.

3. Methodology

3.1. Selection of the Case

Lancang County, situated in the southwest of Yunnan Province, derives its name from its proximity to the Lancang River. It is the second largest county in Yunnan Province. As of 2023, Lancang County contains a total of 436,000 mu of tea gardens, involving 63,000 households and encompassing 294,000 people. From 10 to 25 September 2023, at the 45th World Heritage Committee meeting in Riyadh, Saudi Arabia, the Cultural Landscape of Old Tea Forests of Jingmai Mountain in Pu’er was inscribed on the UNESCO World Heritage List. This marked the first tea-themed World Heritage site designated by UNESCO [74]. The ancient tea forest cultural landscape of Jingmai Mountain in Pu’er is primarily situated in Jingmai and Mangjing villages, Huimin Town, Lancang County (Figure 2). The total area of these ancient tea gardens is approximately 28,000 mu, with over 10,000 mu of mature, contiguous tea-picking areas. These tea gardens were cultivated by the local Blang and Dai ethnic groups and have a history spanning over 1800 years. The tea industry and its historical and cultural context in Lancang County provide a rich empirical foundation for this study, offering significant research value.

3.2. Variables and Measurement

This study investigates five key research variables: Green Production Behavior (GPB) among tea farmers, farmers’ cognition, social norms, personal norms, and government regulation. GPB is measured using five items, primarily adapted from He et al. [75] and Long et al. [76]. Farmers’ cognition is assessed using scales derived from Wang et al. [77], Zhang et al. [78], and supplemented by insights from Finhler et al. [79]. This construct is conceptualized as a second-order factor, encompassing economic, ecological, and social cognition, and is measured using eight items. Social norms and personal norms act as chain mediating variables; social norms are informed by Guo et al. [80] and Kim et al. [81], while personal norms are drawn from Doran et al. [22] and Li et al. [82]. Government regulation, serving as a moderating variable, is assessed through policy, funding, and support perspectives, as suggested by Rakhmawati et al. [83] and Zhang et al. [84]. The final measurement scales are presented in

Table 1. Questionnaire Items.

Construct	Code	Item
Economic Cognition	EC1	Can green production increase the price of tea?
	EC2	Can green production improve the quality of tea and gain consumer recognition?
	EC3	Can green production reduce agricultural production costs?
Ecological Cognition	ECo1	Can green production help protect the local environment?
	ECo2	Can green production promote biodiversity in tea gardens?
	ECo3	Can green production reduce the use of chemical fertilizers and pesticides?
Social Cognition	SC1	Can green production improve the health and safety of tea farmers?
	SC2	Can green production enhance the reputation of the local tea industry?
Green Production Behavior	GPB1	In recent years, have you reduced the amount of chemical fertilizers used per mu in tea cultivation and production?
	GPB2	Do you use organic fertilizers (such as farmyard manure) instead of chemical fertilizers in tea cultivation and production?
	GPB3	In recent years, have you reduced the amount of pesticides used per mu in tea cultivation and production?
	GPB4	Do you use biological control techniques in tea cultivation and production?
	GPB5	Do you recycle agricultural products (such as pesticide and fertilizer bags, plastic mulch, etc.) in the tea cultivation and production area?
Social Norms	SN1	Have other villagers already adopted or are they currently adopting green production practices?
	SN2	Have relatives or friends already adopted or are they currently adopting green production practices?
	SN3	Do other villagers believe that you should adopt green production practices?
	SN4	Do relatives or friends believe that you should adopt green production practices?
Personal Norms	PN1	I should adopt green production practices.
	PN2	I believe that adopting green production practices is important.
	PN3	I feel responsible for adopting green production practices.
Government Regulation	GR1	The government and relevant departments have introduced various measures to encourage green production.
	GR2	Various levels of government provide certain preferential policies for green production.
	GR3	Policies from various levels of government prioritize support for farming households.
	GR4	Government policies are beneficial for implementing green production practices.
	GR5	Government funding can assist farmers in adopting green production practices.
	GR6	If farmers encounter difficulties in implementing green production, the government will provide certain support and assistance.

Notes: EC = Economic Cognition, ECo = Ecological Cognition, SC = Social Cognition, GPB = Green Production Behavior, SN = Social Norms, PN = Personal Norms, GR = Government Regulation.

. All variables are rated on a 5-point Likert scale, where 1 indicates strong disagreement and 5 indicates strong agreement.

3.3. Data Collection

Data for this study were collected through a questionnaire comprising six sections: the Farmers’ Cognition Scale, the Green Production Behavior Scale, the Subjective Norms Scale, the Personal Norms Scale, the Government Regulation Scale, and Socio-Demographic Variables. The first section gathered demographic information, including gender, age, years of cultivation, and type of tea garden. The second section, the core of the questionnaire, includes five latent variables and 26 measurement items.

The survey was conducted in Huimin Town, Jiujing Township, Menglang Town, and Fubang Township, located in Lancang Lahu Autonomous County, Pu’er City, Yunnan Province, where tea is the dominant agricultural industry. From October to December 2023, farmers from these four tea-producing areas were surveyed using convenience sampling and in-person interviews. The questionnaire covered personal and family information, farmers’ cognition, green production behavior, social norms, personal norms, and government regulation. A total of 327 questionnaires were distributed, with 306 valid responses, resulting in a validity rate of 93%. The data and distribution of the survey areas are shown in

Table 2. Distribution of Survey Data by Region.

Region	Number of Questionnaires Distributed	Percentage (%)
Huimin Town	76	23.24
Jiujing Township	84	25.67
Menglang Town	85	26.01
Fubang Township	82	25.08
Total	327	100%

.

To better understand the fundamental conditions of the surveyed tea farmers, this study examined their personal, family, and production conditions. As presented in

Table 3. Socio-Demographic Characteristics (N = 306).

Indicator	Characteristic	Frequency (N)	Percentage (%)
Gender	Male	167	54.48
	Female	139	45.42
Age	30 yrs or younger	24	7.84
	31–40 yrs	95	31.05
	41–50yrs	114	37.25
	51–60 yrs	33	10.78
	61 yrs or older	40	13.08
Years Engaged in Tea Cultivation	11–20 yrs	132	43.14
	21–30 yrs	59	19.28
	31 yrs or more	18	5.88
Proportion of Tea Income	20% or less	29	9.48
	21–40%	93	30.39
	41–60%	99	32.35
	61–80%	62	20.26
	More than 80%	23	7.52
Tea Garden Type	Terrace Tea Garden	115	37.58
	Ancient Tree Tea Garden	130	42.48
	Mixed-Type Tea Garden	61	19.94

, 54.58% of the respondents are male, and 45.42% are female. In terms of age, 7.84% are 30 years old or younger, 31.05% are between 31 and 40 years old, 37.25% are between 41 and 50 years old, 10.78% are between 51 and 60 years old, and 13.08% are 61 years old or older. The highest proportion is in the 41–50 age group, while the lowest is in the 30 years and younger group. This reflects the reality in rural China, where younger individuals often migrate for work, leaving farming to older generations.

In terms of years engaged in tea production, 31.7% have been involved for 10 years or less, 43.14% for 11–20 years, 19.28% for 21–30 years, and 5.88% for 31 years or more. Overall, the largest proportion of respondents has been involved in the tea industry for 11–20 years, aligning with the local history of tea industry development.

In terms of tea income, 9.48% of respondents earn 20% or less of their income from tea, 30.39% earn 21–40%, 32.35% earn 41–60%, 20.26% earn 61–80%, and 7.52% earn more than 80%. This suggests that tea production plays a significant role in the local income structure.

Regarding tea garden types, 37.58% have terrace tea gardens, while 42.48% have ancient tree tea gardens, and 19.94% have mixed-type tea gardens. This suggests that local tea cultivation optimally utilizes ancient tea tree resources, with ancient tea trees representing one of the primary varieties cultivated and maintained in the region.

3.4. Statistical Analysis

To further process the collected data, this study employed SPSS 26.0 and Amos 26.0 for statistical analysis. First, the reliability and validity of the scales were tested. Subsequently, Structural Equation Modeling (SEM) was applied for path analysis and the construction of the conceptual model. The Bootstrap method was adopted to assess mediation effects, while the Process tool in SPSS 26.0 was ultimately employed to examine these mediation effects.

4. Data Analysis and Results

4.1. Common Method Bias (CMB)

To control for the impact of such bias on the research process and conclusions, this study utilized SPSS 26.0 in conducting a Harman’s single-factor test on the collected scale data. Following the inclusion of all items in the exploratory factor analysis, the results indicated that the total variance explained was 25.823%, which is below 40%. These results suggest that there is no substantial issue of common method bias in the model [85].

4.2. Reliability Analysis

In Structural Equation Modeling (SEM) studies, reliability is typically assessed using Cronbach’s alpha, Average Variance Extracted (AVE), and standardized loadings. All item factor loading >0.70, and Cronbach’s alpha >0.70, indicating sufficient reliability. The intercorrelations among the constructs are much lower than the square root of the AVE. This is illustrated in

Table 4. Reliability and Validity Analysis Results.

Variable	Item	Factor Loading	Cronbach’s Alpha	AVE	CR
Economic Cognition	EC1	0.766	0.802	0.578	0.804
	EC2	0.738
	EC3	0.777
Ecological Cognition	ECo1	0.759	0.821	0.607	0.822
	ECo2	0.796
	ECo3	0.782
Social Cognition	SC1	0.838	0.800	0.667	0.800
	SC2	0.795
Social Norms	SN2	0.777	0.841	0.570	0.841
	SN3	0.751
	SN4	0.729
Personal Norms	PN1	0.763	0.804	0.579	0.805
	PN2	0.794
	PN3	0.758
GPB	GR1	0.731	0.882	0.599	0.882
	GR2	0.806
	GR3	0.783
	GR4	0.747
	GR5	0.75
	GR6	0.784

CR  =  Composite reliability, AVE  =  average variance extracted (N  =  306).

and

Table 5. Inter-Item Correlation Matrix.

	Economic Cognition	Ecological Cognition	Social Cognition	Social Norms	Personal Norms	GPB
Economic Cognition	0.761
Ecological Cognition	0.471	0.779
Social Cognition	0.470	0.422	0.817
Social Norms	0.231	0.164	0.140	0.755
Personal Norms	0.299	0.208	0.206	0.285	0.761
GPB	0.291	0.211	0.258	0.317	0.349	0.774

Notes: Diagonal values represent the square root of the AVE (N  =  306).

.

4.3. Hypothesis Testing

This study employs the maximum likelihood estimation method in the AMOS 26.0 software and utilizes structural equation modeling to test the proposed model. The structural model demonstrates a good fit with the data (χ²/df = 1.126, RMR = 0.045, RMSEA = 0.02, TLI = 0.989, CFI = 0.988).

Hypothesis testing is conducted using the STD. Estimate, C.R., and p values are obtained from the path analysis, as shown in

Table 6. Path coefficient test results (N = 306).

Path	STD.Estimate	C.R.	p	Hypothesis
H1: Farmers’ Cognition → GPB	0.123	3.267	**	Supported
H2: Farmers’ Cognition → Social Norms	0.139	3.911	***	Supported
H3: Farmers’ Cognition → personal norms	0.156	4.379	***	Supported
H4: Social Norms → personal norms	0.228	3.206	**	Supported
H5: Social Norms → GPB	0.253	3.447	***	Supported
H6: personal norms → GPB	0.248	3.090	**	Supported

Note: ** p < 0.01, *** p < 0.001.

. The results indicate that all six hypothesized paths are supported, confirming the validity of the proposed paths.

4.4. Mediation Effect Test

The Bootstrap technique in AMOS26.0 is utilized to iterate 5000 times and examine if the bias-corrected 95% confidence interval encompasses 0 to validate the significance of the indirect impact. The findings are displayed in

Table 7. Results of mediating effect test (N = 306).

Indirect Effect	Estimate	95% CI
		Lower	Upper
H7: Farmers’ Cognition → Social Norms → GPB	0.009	0.012	0.074
H8: Farmers’ Cognition → Personal Norms → GPB	0.008	0.018	0.084
H9: Farmers’ Cognition → Social Norms → Personal Norms → GPB	0.001	0.002	0.020

.

The analysis reveals that, for H7, the path Farmer Cognition → Social Norms → Green Production Behavior (GPB) has a Bootstrap 95% confidence interval of [0.012, 0.074]. Since the interval does not include 0 and is significant at the 1% level, H7 is supported, indicating a significant mediation effect for this path. For H8, the Bootstrap 95% confidence interval is [0.018, 0.084], which also does not include 0 and is significant at the 1% level, confirming a significant mediation effect for H8. Similarly, for H9, the Bootstrap 95% confidence interval is [0.002, 0.020], excluding 0 and significant at the 1% level, demonstrating a significant chain mediation effect for H9.

4.5. The Moderating Effect Test

For testing, use SPSS 26’s PROCESS feature. The following are the outcomes.

The model (N = 306), with Farmer Cognition as the independent variable, Green Production Behavior as the dependent variable, and Social Norms and personal norms as mediating variables, was tested. Model 83 in Process was used to validate the moderation effect, with the Bootstrap sample size set to 5000. The results, as shown in

Table 8. Results for the Moderated Mediating Effect of Government Regulation (N = 306).

Level	Effect Size	Boot SE	Boot LLCI	Boot ULCI
Low Government Regulation, (M − 1SD)	−0.039	0.044	−0.125	0.047
Mean	0.132	0.033	0.068	0.197
High Government Regulation, (M + 1SD)	0.304	0.048	0.210	0.398

, indicate that the interaction term between Social Norms and Government Regulation has a significant regression coefficient (β = 0.033, p < 0.001), with a 95% confidence interval of [0.021, 0.045], which does not include 0. This supports the preliminary validation of Hypothesis H10.

To further explore the moderating effect of government regulation, we conducted a simple slope analysis at one standard deviation above and below the mean. The results indicated that, under high government regulation, the 95% confidence interval for its effect did not include zero, while at low regulation, the interval included zero. This suggests a significant difference in the effect of farmer cognition under high and low regulation, confirming the presence of a moderating effect. The simple slope plot (Figure 3) further demonstrates the moderating effect of government regulation in the relationship between farmer cognition and social norms. Specifically, the interaction term (farmer cognition * government regulation) has a positive and statistically significant impact on social norms, suggesting that the strength of this moderating effect is contingent on the level of government regulation.

In the context of tea farmers’ green production practices, this interaction is especially pertinent. Tea farmers who are more cognizant of sustainable practices are more capable of aligning their behavior with established social norms when government regulations are stringent. The enhanced regulatory environment offers a robust framework and enforcement mechanisms to promote adherence to green production standards. Conversely, in areas with weaker government regulation, tea farmers may lack sufficient external incentives or pressures to fully embrace these norms, thereby weakening the linkage between cognition and green production behaviors.

To illustrate this interaction, we plotted the effects of farmer cognition and government regulation on social norms at both high and low levels, based on the mean score of institutional development. The plot shows that, when government regulation is stronger, the relationship between farmer cognition and social norms becomes stronger, consistent with hypothesis H10. However, in regions with weaker government regulation, this relationship tends to weaken. The corresponding results are presented in Table 8.

5. Discussion and Conclusions

This study, based on cognitive behavioral theory, planned behavior theory, and Norm Activation theory, empirically examines the mechanisms through which farmers’ cognition influences their green production behavior. The findings confirm a positive relationship between farmers’ cognitive awareness and their engagement in sustainable practices, consistent with previous studies emphasizing the importance of cognitive awareness in promoting green production [86,87].

Drawing on these foundational theories, our study provides deeper insights into the mediating roles of social and personal norms in the relationship between cognition and green production behavior. Previous research (e.g., Stern et al. [56]; Gifford & Nilsson, [62]) has highlighted the importance of personal responsibility and societal expectations in promoting pro-environmental behavior. Our study expands on this body of work by revealing a chain mediation effect: farmers’ cognition first shapes social norms, which in turn strengthen personal norms, which ultimately guides their behavior. This extends Norm Activation theory by illustrating how social pressures can reinforce internal moral obligations, an important insight that builds on Rezaei et al.’s [48] work.

Moreover, our analysis demonstrates that government regulation intensifies the impact of social norms on green production behavior, emphasizing the intricate interaction between external regulations and internalized norms. This implies that, although cognition serves as the foundation for green behaviors, external institutional support, such as government policies, substantially strengthens the effectiveness of normative mechanisms. This finding aligns with previous research emphasizing the government’s role in promoting sustainable agriculture (Sang et al. [73]).

In contrast to much of the existing literature, which typically emphasizes the direct impact of cognition on behavior, our study highlights the significance of exploring the interaction among social norms, personal norms, and external regulatory factors. This approach provides a more nuanced insight into how farmers’ cognition translates into action within a structured regulatory environment. By doing so, our study contributes to the literature by expanding cognitive–behavioral models to integrate both social and regulatory dimensions, offering a more comprehensive framework for understanding green production behavior in the agricultural sector.

6. Policy Implications and Future Outlook

This section presents the key findings of our research, outlines policy implications for different stakeholders, emphasizes the potential for increased stakeholder engagement, and addresses the limitations of the study, offering suggestions for future research.

6.1. Policy Implications

Our research provides valuable insights with direct implications for government agencies, tea companies, and civil society organizations (CSOs). The findings offer actionable strategies to promote green production practices in the tea industry. Below, we outline specific recommendations for each group of stakeholders.

The study highlights the critical importance of robust government regulations and policies in promoting green production. It is recommended that authorities not only strengthen existing regulations but also improve communication with tea farmers through comprehensive educational campaigns and technical support programs, clearly conveying the importance of sustainable practices. Additionally, to encourage the adoption of green production methods, governments should implement stricter penalties for non-compliance and offer financial incentives, such as subsidies, low-interest loans, or grants, aimed at promoting eco-friendly agricultural practices. Ongoing training programs are also recommended to improve farmers’ understanding of the long-term economic and environmental benefits of green production.

Tea-producing companies play a pivotal role in promoting green production by integrating corporate social responsibility (CSR) into their business strategies and investing in sustainable farming techniques. These companies can support farmers by providing resources, such as organic fertilizers, bio-pesticides, and other environmentally friendly products, potentially at subsidized rates. Collaborating with local cooperatives to promote fair trade and sustainable practices can further encourage the adoption of green production methods. Furthermore, it is essential for companies to ensure that the premiums gained from eco-friendly products are passed on to farmers, thereby enhancing their financial motivation to engage in sustainable practices.

Non-governmental organizations (NGOs) and community-based organizations play a crucial role in raising awareness among both farmers and consumers about the benefits of green production. CSOs can serve as intermediaries between farmers and policymakers, advocating for and delivering training programs focused on sustainable agricultural practices. Additionally, CSOs can promote peer-learning initiatives, enabling farmers to share their experiences in successfully adopting green technologies. CSOs also play a critical role in monitoring and evaluating green production practices at the grassroots level, ensuring accountability and transparency across various initiatives.

6.2. Enhancing Stakeholder Engagement

Our research underscores the critical role that stakeholders currently play in promoting sustainable agricultural practices. However, their involvement can be further strengthened through collaboration. Specifically:

(1)

Governments should collaborate closely with local businesses and civil society organizations (CSOs) to ensure that all efforts to promote sustainable agricultural practices are well-aligned and cohesive. Such partnerships can enhance the efficiency and broaden the scope of sustainability initiatives.

(2)

Tea companies can form public–private partnerships with governmental agencies to co-develop educational campaigns. These campaigns can strengthen companies’ influence on farmers’ decision-making processes and cultivate a deeper understanding of the benefits of sustainable production methods.

(3)

CSOs should advocate for greater government involvement in regions where sustainable production practices are less common, thereby encouraging broader and more consistent adoption of sustainable methods.

In conclusion, the successful promotion of sustainable production in the tea industry depends on the coordinated and complementary efforts of government bodies, tea companies, and CSOs. Strengthening stakeholder engagement is essential to ensure farmers receive the necessary support to fully embrace and benefit from sustainable agricultural practices.

6.3. Limitations and Future Research Directions

While this study provides valuable insights, several limitations must be acknowledged. First, the sample is limited to tea farmers in Lancang County, which may impact the external validity and generalizability of the findings. Future research could expand the geographic scope to include a broader range of regions, thereby enhancing the applicability of the results across diverse agricultural contexts.

Secondly, the use of survey questionnaires limited respondents to predefined answers, potentially restricting the exploration of the full complexity of their real-world experiences. Future studies could benefit from employing mixed methods of data collection, such as in-depth interviews or field observations, to capture a more nuanced understanding of farmers’ behavioral patterns and decision-making processes related to sustainable production.

Moreover, the model used in this study may have omitted important variables that influence green production behavior. Future research could consider incorporating additional factors, such as social norms, organizational support, and market demand, to strengthen the model’s explanatory power and provide a more comprehensive understanding of the factors driving sustainable agricultural practices.

Further investigation into these areas could offer policymakers, businesses, and civil society organizations more in-depth insights, ultimately aiding the promotion of comprehensive green production behaviors and contributing to the sustainable transformation of the agricultural sector.