Normative Influences on Carbon Offset Behavior: Insights from Organic Farming Practices

Abstract

The production of green agricultural products and carbon sink compensation play a crucial role in mitigating climate change. Farmers’ behaviors are influenced by both social norms and personal norms. This study aims to explore how these norms shape farmers’ carbon sink compensation behaviors and to provide a theoretical basis for formulating effective policies and incentive mechanisms. A mixed-methods approach was adopted in this study, involving in-depth interviews with 13 agricultural workers and a survey of 409 individuals from China, Japan, and South Korea who are or were engaged in agriculture-related work. The results indicate that the activation of personal norms is primarily driven by economic costs rather than mere moral responsibility. Subjective norms serve as a significant mediator between personal norms and behavior. Social norms indirectly influence behavior through policy guidance and community support. Based on these findings, specific strategies to strengthen personal norms, optimize social norms, and improve policy incentives were proposed to enhance farmers’ willingness to participate in carbon sink compensation and promote sustainable low-carbon agriculture. To effectively promote farmers’ participation in carbon sink compensation, it is necessary to foster a positive social atmosphere at the community level while addressing farmers’ personal needs by enhancing environmental awareness and engagement through policy guidance and incentives. This study employs grounded theory, combining open, axial, and selective coding to thoroughly analyze the interaction between social and personal norms and their positive impact on farmers’ behavior, specifically regarding green agricultural product carbon sink compensation. Concrete policy and community-level pathways are proposed, providing clear guidance for both theory and practice.

1. Introduction

Global climate change has become one of the most significant global issues of the 21st century, profoundly affecting ecosystems, food security, and economic development. Against this backdrop, the carbon sink mechanism, as an important strategy to mitigate climate change, has increasingly attracted the attention of scholars and policymakers [1]. The carbon sink mechanism provides a vital pathway for achieving global climate goals by increasing soil organic carbon storage and reducing greenhouse gas emissions.

Straw, as an agricultural by-product, holds multiple utilization values. On one hand, its average chemical composition includes 4–5% P₂O₅, 25–30% K₂O, and 15–20% C, demonstrating considerable energy utilization potential. Its calorific value ranges from 16.61 to 18.98 MJ/kg, with combustion efficiency varying under different humidity conditions [2]. Additionally, returning straw to the field enhances soil fertility, reduces fertilizer use, and increases soil organic carbon storage, providing a feasible pathway for achieving carbon neutrality in agricultural systems. In the early stages of agriculture, straw was typically used as firewood or directly returned to the field, posing no environmental burden. With the development of mechanized agriculture, harvesting efficiency improved, but it generated large amounts of waste straw. To facilitate farming, farmers began burning straw, leading to severe air pollution. Straw burning has become an environmental issue in China’s agricultural production, with its resulting air pollution and long-term threats to soil health requiring urgent resolution. Research indicates that straw burning is one of the main sources of PM2.5 emissions, significantly exacerbating air pollution levels [3]. Through proper management, these straws can significantly enhance the carbon sink capacity of agricultural systems while reducing fertilizer use and greenhouse gas emissions. Studies show that every ton of straw returned to the field can provide the soil with 30 kg of potassium and 5 kg of phosphorus. Additionally, the ash produced after its burning contains up to 60% potassium [4]. These characteristics endow straw with significant application potential in agricultural carbon cycle management.

Agriculture plays a dual role in the carbon cycle. On the one hand, it is a major source of global greenhouse gas emissions, accounting for 24% of total global emissions, with livestock farming and fertilizer use being the primary contributors. On the other hand, through optimized management practices, agriculture also has significant carbon sequestration potential. For example, techniques such as cover crops and agroforestry systems can effectively enhance soil organic carbon storage [5]. The study by Antle and Sturmvogel indicates that the carbon sink compensation mechanism can not only significantly increase rural incomes but also stabilize soil carbon storage and promote the sustainability of agricultural systems [4]. This further underscores the importance of agricultural carbon sink compensation in achieving carbon neutrality goals. In recent years, the promotion of green agriculture and organic agricultural products has further demonstrated the immense potential of the agricultural sector in achieving carbon sequestration and environmental sustainability. Meanwhile, as an innovative approach, the carbon sink compensation mechanism integrates carbon sequestration with economic benefits through carbon credit markets and policy incentives. This provides a critical opportunity for advancing sustainable agricultural development [6]. Although the carbon sink compensation mechanism has made some progress at the technical and policy levels, its promotion faces numerous challenges. The primary issue is the insufficient willingness of farmers to participate. Complex policy rules, a lack of targeted economic incentives, and distrust of policies significantly hinder the promotion of carbon sink projects in agriculture [7]. Furthermore, the mechanisms by which social norms and personal norms influence farmers’ behaviors remain unclear. For example, how do community support and group pressure shape farmers’ environmental behaviors? How do individual values and trust in policies drive behavioral changes? The answers to these questions are crucial for optimizing policy design and enhancing the effectiveness of carbon sink compensation mechanisms.

Existing literature primarily focuses on the impact of technological pathways and economic factors on carbon sink behavior, such as the economic benefits of carbon credit markets and the carbon sequestration potential of biochar technology [8]. However, while research on the role of norms has received some attention, it primarily focuses on the direct effects of social norms, neglecting the potential complex interactions between social norms and personal norms [9]. Huber et al. [10] mentioned that social norms significantly enhance individuals’ willingness to engage in pro-environmental behaviors through group feedback, but they did not explore its interactions with intrinsic norms such as personal responsibility. Meanwhile, the study by Steentjes et al. [11] analyzed the role of norms in climate behaviors from a psychological perspective, emphasizing the importance of personal values and social expectations, but it also did not further reveal how the two jointly shape specific behaviors.

This deficiency limits a comprehensive understanding of the decision-making pathways in carbon sink compensation behaviors, particularly in complex social contexts where balancing the external influence of community norms and the internal drive of personal norms is crucial. Carbon sink compensation behaviors involve complex motivations and norm interactions, encompassing both the intrinsic drive of personal responsibility and the external constraints of community support and group norms. Existing literature often focuses on a single aspect, neglecting the potential synergistic or conflicting effects between the two types of norms. This theoretical gap hinders the optimization of policies for carbon sink compensation mechanisms and also limits the targeted implementation of practical promotion efforts.

Existing research indicates that straw burning not only depletes the organic matter content in soil but also affects the sustainability of agricultural ecosystems in the long term. The study by Guo and Zhao [12] highlights that straw burning significantly reduces soil organic carbon storage and microbial activity while negatively impacting the composition of humus. Additionally, the high temperatures during the burning process cause significant nutrient volatilization in the straw, thereby diminishing its potential resource value. The study by Raheem et al. [13] further reveals that straw burning has a significant impact on soil microbial communities, particularly reducing the population of beneficial bacteria critical for nutrient cycling and plant growth, thereby weakening soil health and productivity.

Current research mainly focuses on technological pathways for straw resource utilization, including field return, energy use, and fertilizer production. However, relatively little research has been conducted on behavior-driven approaches to reduce straw burning. Therefore, this study aims to investigate from a normative perspective, systematically exploring the combined influence pathways of social norms and personal norms on farmers’ carbon sink compensation behaviors. By integrating grounded theory, this study constructs a behavioral decision-making model for farmers and conducts an in-depth analysis of the role of norms in promoting carbon sink compensation mechanisms. Specifically, the innovations of this study include:

Innovative Research Perspective: The Interaction of Dual Norms. Addressing the lack of research on the interaction mechanisms between social norms and personal norms in the existing literature, this study adopts an “interaction perspective” to examine the issue, systematically analyzing the synergistic effects and potential conflicts between social norms and personal norms in farmers’ carbon sink compensation behaviors. The study by Batzke and Ernst [14] indicates that the dynamic impacts of social norms and personal norms on behavioral decisions differ significantly. Social norms change rapidly and are more influenced by external environmental changes, while personal norms are more enduring, reflecting individual behavioral adaptability across different contexts. Exploring this dual norm interaction pathway helps to comprehensively reveal the complexity of farmers’ behavioral decisions, providing a new theoretical foundation for the optimization of carbon sink compensation policies.

Methodological Innovation: The Integration of Grounded Theory and Structural Equation Modeling (SEM) and Its Unique Advantages. This study employs a combination of grounded theory and structural equation modeling (SEM). Grounded theory, through open coding, axial coding, and selective coding, enables the in-depth exploration of the potential influence pathways of norms on behavioral decisions from qualitative interview data. This bottom-up theory-building approach is particularly effective in exploring complex social phenomena. Coulibaly et al. [15] emphasize that through the activation of normative theoretical frameworks, qualitative research can deeply reveal how community norms motivate individual behaviors, providing a methodological basis for the application of grounded theory in this study.

Structural equation modeling (SEM) further quantifies the conceptual framework generated by grounded theory, validating causal relationships and their significance among variables. This mixed research method, combining qualitative and quantitative approaches, provides a robust tool for the empirical examination of the interaction between social and personal norms. Moreover, given the complexity of relationships among variables in social science research, SEM can simultaneously analyze direct and indirect effects across multiple pathways, thereby more accurately uncovering the multidimensional driving mechanisms of farmers’ behaviors. Zhang et al. [16], by comparing TPB and VBN theories, demonstrate the advantages of SEM in explaining complex behavioral decision-making pathways, providing a foundational validation tool for the model in this study.

2. Literature Review

In recent years, straw burning has become a significant environmental issue in rural China. Statistics show that approximately 30–50% of agricultural waste straw is burned annually. This practice not only directly results in substantial greenhouse gas emissions but also significantly increases atmospheric PM2.5 concentrations, becoming one of the primary sources of winter smog. The study by Huang et al. [17] evaluated the effectiveness of China’s straw burning ban, pointing out that open-field straw burning is one of the main contributors to PM2.5 emissions, especially during peak burning periods when PM2.5 concentrations can significantly increase, further deteriorating air quality and adversely impacting public health. The comprehensive utilization of straw resources has received widespread attention. For example, Migo-Sumagang et al. [18] proposed that the calorific value of straw can be utilized for rice drying and energy production, a process that not only reduces greenhouse gas emissions but also offers new directions for sustainable development in agricultural regions. Additionally, the ash from burned straw, used as a long-lasting potassium fertilizer, can significantly improve crop yields, further confirming the critical role of straw resource utilization in agricultural systems [7].

2.1. Carbon Sequestration Compensation Behavior

Carbon sequestration compensation behavior reflects farmers’ participation in achieving economic and environmental benefits through low-carbon agricultural practices and carbon credit markets. Agricultural management practices significantly enhance soil organic carbon storage, becoming key pathways for promoting carbon sequestration compensation [19].

The rise of green agriculture has further reinforced the implementation of carbon sequestration compensation behaviors, especially under policy support and technological incentives [20]. However, complex market rules may limit farmers’ willingness to participate, necessitating policy design optimization to enhance fairness and attractiveness [21].

The implementation effects of carbon sequestration compensation behaviors globally exhibit significant regional characteristics due to differences in natural conditions, agricultural practices, and policy support. For example, in China, straw return as a sustainable agricultural practice not only significantly increases soil organic carbon storage and crop yields but also enhances farmers’ economic returns due to its applicability in humid climates and high-fertility soil areas [22]. Meanwhile, in Vermont, USA, regenerative agricultural practices have significantly increased soil carbon storage within a decade, and policy recommendations related to ecosystem service payments have been proposed [23].

These studies suggest that by integrating pathways of social norms, subjective norms, and personal norms, the sustainability of carbon sequestration compensation behaviors can be significantly enhanced [24]. Regional differences in studies further underscore the necessity of cross-cultural and cross-regional policy design and technology promotion, providing a reference for the global implementation of carbon sequestration compensation mechanisms.

2.2. Subjective Norms

Subjective norms refer to an individual’s perception of the expectations of significant others and the pressure to fulfill those expectations, which act as mediating variables in behavioral pathways, significantly influencing carbon sequestration compensation behavior. Liang et al. [25] noted that feedback mechanisms and social expectations can significantly strengthen the role of subjective norms in promoting low-carbon behaviors. Kaushal and Rosendahl [7] found that community cooperation strengthens farmers’ acceptance of environmental policies by increasing social pressure. Francaviglia et al. [1] emphasized that subjective norms, through interaction with personal norms, drive participation in carbon credit markets and the adoption of low-carbon agricultural technologies.

Furthermore, policy interventions influence behavioral decisions by enhancing subjective norms. Chen [26] demonstrated that subsidies and carbon credit incentives in policies can indirectly strengthen farmers’ subjective norms, increasing their willingness to engage in carbon sequestration compensation behaviors. Hermann, Sauthoff, and Musshoff [27] further revealed that policy support mechanisms, such as fixed subsidies and market certificates, can effectively enhance farmers’ subjective norms through incentive pathways, boosting their enthusiasm for soil carbon sequestration.

Personal norms are defined as a higher-order latent variable, comprising two sub-dimensions: sense of responsibility and moral obligation. Based on the Norm Activation Model (NAM) and Value-Belief-Norm (VBN) theory, the sense of responsibility emphasizes an individual’s perception of the consequences of environmental behavior and the intrinsic responsibility arising from it, while moral obligation reflects an individual’s self-imposed requirement for environmental behavior based on internalized moral values [28]. This structure illustrates the intrinsic motivation of individuals when confronting environmental issues, providing crucial theoretical support for understanding the formation of carbon compensation behavior [29].

These studies collectively indicate that subjective norms serve as an essential bridge between personal and social norms, significantly driving carbon sequestration compensation behaviors under the combined influence of policy support and social expectations.

2.3. Social Norms

Social norms influence individual behavior through community rules and group behavior constraints, serving as a crucial social force in promoting low-carbon behaviors. Zheng et al. [30] noted that community support and group feedback significantly enhance the driving force of social norms on behaviors, particularly among farmers. Thorman et al. [31] emphasized that social norms strengthen the role of subjective norms through group interactions while increasing farmers’ acceptance of policies. The study by Saracevic and Schlegelmilch [32] showed that cultural context significantly moderates the effects of social norms, with the influence of social norms being particularly pronounced in collectivist cultures. Additionally, social norms promote regional environmental behaviors through localized normative information [33]. Community organizations, such as agricultural cooperatives, also increase farmers’ willingness to adopt low-carbon agricultural technologies by enhancing their sense of social belonging [34].

Furthermore, the implementation effects of social norms vary significantly across different regional contexts. Gang Cui and Zhicheng Liu [35] found that social norms significantly influence farmers’ behavior in reducing fertilizer use by strengthening community support and social network interactions, especially in regions with stricter environmental regulations, where the mediating role of social norms is more pronounced. This further underscores the regional adaptability and cultural moderation of social norms.

Subjective norms, as a higher-order latent variable, consist of two sub-dimensions: social pressure and expectations from significant others. Based on the Theory of Planned Behavior (TPB), social pressure reflects an individual’s perception of societal expectations regarding their behavior, while expectations from significant others are further refined to encompass concerns for specific close groups, such as family or community [36]. This dual-dimensional structure indicates that subjective norms can enhance an individual’s intention to engage in low-carbon behaviors through external social influences, providing a crucial theoretical foundation for decision making in carbon compensation behavior [37].

2.4. Personal Norms

Personal norms are behavioral drivers based on individuals’ intrinsic sense of responsibility and values, playing a crucial role in implementing low-carbon behaviors. Zeng et al. [38] mentioned that individual environmental awareness directly increases farmers’ willingness to adopt low-carbon agricultural technologies, particularly under policy incentives. Moreover, Mouro and Duarte [39] showed that the internalization of personal responsibility significantly enhances the longevity and stability of environmental behaviors. The balance between economic benefits and environmental values is particularly prominent in personal norms.

In recent years, the Chinese government has attempted to reduce straw burning by issuing policies banning burning and providing subsidies. However, farmers’ straw management decisions are influenced not only by economic incentives but also by social norms. For example, under community cooperation and social supervision, farmers are more likely to choose straw return to the field or centralized processing. Existing research lacks sufficient analysis of the association between social norms and personal behaviors, requiring further exploration.

Economic analysis shows that straw burning has significant impacts on air pollution and health hazards, while effective policies and market tools can internalize these social costs to improve straw management practices, thereby better guiding farmers toward sustainable management practices [40]. Wei et al. [41] noted that combining economic incentives with personal environmental values amplifies farmers’ willingness to engage in carbon sequestration compensation behaviors. Further studies show that personal norms, under the combined influence of economic incentives and social support, significantly strengthen farmers’ low-carbon behaviors. Bopp et al. [42] emphasized that farmers’ intrinsic motivation plays a key role in the effectiveness of policy incentives. For farmers with low intrinsic motivation, policy incentives significantly increase their likelihood of participating in sustainable agricultural practices, whereas for farmers with high intrinsic motivation, the impact of policies is relatively weaker.

Zeweld et al. [43] based their study on the theory of planned behavior, highlighting the significant role of social capital and training in shaping norms and enhancing smallholder farmers’ intentions for sustainable agricultural practices. Agricultural management practices driven by personal norms also become key pathways for achieving carbon sequestration goals [44]. This suggests that strengthening individual environmental awareness and intrinsic responsibility, combined with economic incentives and social support, can effectively promote farmers’ long-term investment in low-carbon agricultural technologies and carbon sequestration compensation behaviors.

Social norms, as a higher-order latent variable, consist of two sub-dimensions: descriptive norms and injunctive norms. According to the Descriptive and Injunctive Norms Theory, descriptive norms reflect an individual’s observation and imitation of commonly exhibited group behaviors, while injunctive norms represent the explicit expectations and constraints imposed by society on individual behavior [25]. By integrating these two dimensions, social norms can not only guide individual choices through exemplary behaviors but also enhance the social acceptance of low-carbon behaviors through societal expectations [45].

2.5. Summary

A review of the literature on carbon sink compensation behavior, personal norms, subjective norms, and social norms reveals a gap in existing research regarding interaction mechanisms, particularly the lack of systematic empirical analysis on multi-level normative interaction pathways. Therefore, this study proposes a theoretical model of “social norms-subjective norms-personal norms-carbon sink compensation behavior” based on the Norm Activation Model (NAM), the Theory of Planned Behavior (TPB), and the Value-Belief-Norm (VBN) theory, aiming to explore the impact of multi-level normative interaction mechanisms on farmers’ carbon sink compensation behavior.

This theoretical model not only extends the application of normative theory in the field of environmental behavior but also provides a new perspective for policy formulation and practical implementation.

3. Theoretical Support and Hypotheses

3.1. Theoretical Foundation

This study integrates the Norm Activation Model (NAM) [46], the Theory of Planned Behavior (TPB) [47], the Value-Belief-Norm (VBN) theory [48], and the Social Impact Theory (SIT) [49] to construct a carbon sink compensation behavior model centered on social norms, personal norms, and subjective norms. According to the Norm Activation Model (NAM), personal norms are activated when individuals become aware of the consequences of their behavior and perceive personal responsibility, thereby driving moral actions [46].

However, the activation of personal norms alone is insufficient to fully explain complex social behaviors, necessitating the introduction of subjective and social norm mediation mechanisms. Ajzen [37], in the Theory of Planned Behavior (TPB), stated that subjective norms, i.e., individuals’ perceptions of important others’ expectations, significantly influence behavioral intentions. Additionally, Latane [49] proposed in the Social Impact Theory (SIT) that social norms influence subjective norms through group pressure and social expectations, thereby indirectly affecting individual behavior.

To further refine the model, this study incorporates the Value-Belief-Norm (VBN) theory, which emphasizes that social norms enhance environmental beliefs and internalize responsibility into personal norms, thus, promoting pro-environmental behaviors [49]. Based on this, the study proposes a theoretical model of “social norms-subjective norms-personal norms-carbon sink compensation behavior”, aiming to reveal the behavioral decision-making pathway under multi-level normative interactions.

3.2. Relationship Between Personal Norms and Subjective Norms

According to the Norm Activation Model (NAM) theory, when an individual becomes aware of the potential consequences of their actions and perceives personal responsibility, their personal norms are activated, thereby influencing their response to external social expectations. Ajzen’s [37] Theory of Planned Behavior (TPB) further states that subjective norms reflect an individual’s perception of others’ expectations, and this perception typically stems from the internalization of a sense of responsibility.

The literature indicates that personal norms not only directly influence behavior through a sense of responsibility but also indirectly amplify behavioral intentions through subjective norms. For example, Thøgersen [49] found that, in environmental behavior, personal norms can significantly activate subjective norms, thereby strengthening intrinsic motivation for behavior. Bamberg, Hunecke, and Blöbaum [50] further confirmed the close relationship between the two, demonstrating that subjective norms can activate and amplify the influence of personal norms on behavior.

Based on this, the following hypothesis is proposed:

H1. 

Personal norms positively influence subjective norms.

3.3. Relationship Between Personal Norms and Social Norms

According to Social Impact Theory (SIT), individual behavioral norms gradually expand through group interactions, thereby shaping social norms [51]. In collectivist cultures, this expansion effect is particularly pronounced, as individual behaviors are often regarded as exemplars of group behavior.

Studies indicate that personal norms can drive the evolution of social norms through demonstration effects and dissemination. For example, Huber et al. [52] pointed out that, when an individual’s pro-environmental behavior is accepted and recognized by the community, this behavioral norm gradually evolves into a common standard within the community.

Accordingly, it is inferred that, when more consumers choose green products based on personal norms, their behavior gradually spreads within the group, enhancing social acceptance of green consumption. Based on this, the following hypothesis is proposed:

H2. 

Personal norms positively influence social norms.

3.4. Relationship Between Personal Norms and Carbon Sink Compensation Behavior

The Value-Belief-Norm (VBN) theory suggests that an individual’s environmental values and sense of responsibility can directly stimulate their willingness to engage in pro-environmental behavior [53]. This effect is particularly evident in carbon compensation behavior, especially when individuals have a clear awareness of the consequences of their carbon footprint.

Empirical studies have further validated this theory. For example, Chen found in a study on airline passengers’ participation in carbon offset programs that personal norms are the key determinant of participation willingness. Even in the absence of external incentives, a sense of personal responsibility can still drive carbon compensation behavior [54]. Blasch and Ohndorf further confirmed this through carbon sink compensation experiments conducted in Switzerland and the United States, demonstrating that moral norms (personal norms) play a significant role in carbon sink compensation behavior and may be reinforced by social recognition and altruism. When an individual’s internal norms are activated, they are likely to voluntarily engage in compensation behavior even in the absence of external requirements [55].

Based on this, it is inferred that in the context of green consumption, personal norms will significantly promote carbon sink compensation behavior by enhancing environmental responsibility awareness and moral obligation. This, in turn, drives carbon sink compensation behavior. Based on this, the following hypothesis is proposed:

H3. 

Personal norms positively influence carbon sink compensation behavior.

3.5. Relationship Between Subjective Norms and Carbon Sink Compensation Behavior

According to the Theory of Planned Behavior (TPB), subjective norms reflect an individual’s perception of the expectations of significant others, and this perception can significantly influence behavioral intentions and choices [37]. In the context of carbon compensation behavior, subjective norms amplify individuals’ willingness to act through group pressure and policy environments.

The literature indicates that under policy support, subjective norms have a particularly strong impact on low-carbon behaviors. For example, Ritchie et al. (2019) found in a study conducted in Australia that subjective norms can significantly enhance consumers’ recognition of carbon compensation behavior, particularly in contexts where social awareness is high [56]. Tao, Duan, and Deng (2021) found that subjective norms have a significant impact on consumers’ willingness to voluntarily participate in carbon sink compensation, surpassing the influence of attitudes and personal moral norms [57].

Accordingly, it is inferred that subjective norms significantly promote carbon sink compensation behavior through the perception of others’ expectations. Based on this, the following hypothesis is proposed:

H4. 

Subjective norms positively influence carbon sink compensation behavior.

3.6. Relationship Between Social Norms and Subjective Norms

The Descriptive and Injunctive Norms Theory states that social norms, as behavioral expectations at the group level, are gradually internalized into individuals’ subjective norms through cultural dynamics, thereby influencing individual behavioral intentions [58]. This process is particularly dependent on cultural context; for instance, in collectivist cultures, the influence of social norms is significantly stronger than in individualist cultures [32].

Empirical studies further indicate that social norms can shape subjective norms by strengthening individuals’ awareness of group behaviors. For example, Sieverding et al. found that descriptive social norms can significantly enhance individuals’ perception of subjective norms regarding low-carbon behaviors [59]. Based on this, the following hypothesis is proposed:

H5. 

Social norms positively influence subjective norms.

3.7. Relationship Between Social Norms and Carbon Sink Compensation Behavior

Social Impact Theory emphasizes that social norms can significantly promote individuals’ pro-environmental behavior through group feedback and policy signals [48]. In the context of carbon compensation behavior, social norms enhance individuals’ willingness to participate by fostering a supportive environment.

The literature indicates that when social norms exhibit high levels of group acceptance, individuals’ willingness to engage in carbon compensation behavior increases significantly. For example, Araghi et al. demonstrated through a choice experiment that a higher group participation rate can significantly enhance individuals’ recognition of and willingness to engage in carbon compensation behavior [60].

In the context of carbon sink compensation, an experiment conducted by Huber, Anderson, and Bernauer [52] confirmed that if group information (e.g., others’ attitudes and behaviors) and institutional signals (e.g., the social desirability of government policies) are introduced, the proportion of individuals voluntarily paying for carbon sink compensation increases significantly.

Based on this, it is inferred that when carbon sink compensation behavior becomes a prevalent norm in the social environment, individuals will be more inclined to conform to this trend. Therefore, the following hypothesis is proposed:

H6. 

Social norms positively influence carbon sink compensation behavior.

3.8. Model Construction

This study’s model is based on the integration of multiple theories, incorporating the NAM’s explanation of the activation pathway of personal norms, the TPB’s emphasis on the mediating role of subjective norms, and the VBN theory’s elucidation of the internalization mechanisms of social norms and values. By integrating these theories, this study systematically analyzes, for the first time, the impact of multi-level norm interactions on carbon compensation behavior, providing an innovative perspective for theoretical research.

The use of higher-order variables follows the research recommendations of Becker et al. [61] emphasizing that in complex social behavior modeling, higher-order structures can effectively integrate factors from different dimensions, enhancing the explanatory power and adaptability of the model. Based on the results of in-depth interviews guided by grounded theory and existing literature, this study constructs a behavior decision-making model with social norms, personal norms, and subjective norms as core variables.

The model is as follows (see Figure 1):

4. Research Methods

This study adopts a mixed-methods approach combining qualitative and quantitative methods, using both in-depth interviews and surveys to comprehensively analyze the influence pathways of social norms and personal norms on farmers’ carbon sink compensation behavior. The mixed-methods approach effectively integrates qualitative and quantitative data, enhancing the explanatory power and generalizability of the research [62].

4.1. In-Depth Interviews

The design of this study’s interview outline is based on the preliminary coding process of grounded theory. During the design phase, the research topic was clarified through a literature review, and the question framework was refined based on the open coding results of a small sample of pilot interviews.

For example, in the dimension of personal norms, the outline focuses on exploring respondents’ sense of responsibility and moral obligation. In the dimension of social norms, the questions address the influence of descriptive and injunctive norms.

The design of open-ended questions aims to capture respondents’ diverse understandings of low-carbon behavior and its driving factors, while also providing preliminary themes and data support for quantitative research. To explore the influence pathways of social norms and personal norms in carbon sink compensation behavior, this study employed in-depth interviews, collecting and analyzing qualitative data from 13 agricultural workers. Following the case study and qualitative research design principles proposed by Yin [63], a detailed interview outline was prepared prior to the interviews, and the sequence and content of the questions were dynamically adjusted during the interviews to enhance data authenticity and achieve theoretical saturation. The interviews focused on the following themes:

Farmers’ perceptions of the carbon sequestration compensation mechanism;

The impact of social norms on their behaviors;

The role of personal values in decision making.

The interview content was recorded, transcribed, and coded to ensure data integrity and reliability. Semi-open-ended questions were used to guide respondents to freely express their views, thus, uncovering potential influencing factors and providing theoretical support for questionnaire design and subsequent analysis.

This in-depth interview method is highly valuable for exploring complex social behaviors. Mills et al. [64] revealed through 60 qualitative interviews how social and psychological drivers influence British farmers’ environmental management behaviors, demonstrating the effectiveness of in-depth interviews in uncovering behavioral drivers and barriers. Additionally, Farani et al. [65], based on the theory of planned behavior, analyzed the impact of environmental attitudes and responsibility perceptions on behaviors through a survey of 400 Iranian farmers, emphasizing the critical role of mixed methods combining interviews and questionnaires in uncovering behavioral pathways.

This study draws on the aforementioned methods, conducting in-depth interviews to comprehensively analyze the influence of social norms and personal values on farmers’ carbon sequestration compensation behaviors, providing a scientific basis for subsequent questionnaire design.

4.2. Indicator Sources

The questionnaire design is based on the interview results and measurement frameworks from existing literature and is divided into the following three parts:

Screening Questions: To confirm whether the respondent is currently or has previously been engaged in agricultural production activities;

Basic Information: Including gender, age, education level, income level, and country of residence;

Variable Measurement: According to the research hypotheses, the scales for measuring the four core variables are as follows:

Personal Norms: Using the personal responsibility and consequence awareness scale by Setiawan et al. [66], combined with items on environmental values and economic motivations (e.g., “I believe straw return to the field should be implemented.”) to validate the activation role of personal norms on behavior;

Subjective Norms: Subjective norms refer to individuals’ perceptions of the expectations of significant others and the pressure to fulfill those expectations. The questionnaire design references Kim et al. [67], emphasizing the core roles of normative beliefs and compliance motivations in subjective norms, and suggests evaluating specific referents and willingness to comply. This study uses measurement items based on these frameworks, such as “I feel responsible to resist straw burning to reduce negative impacts.”;

Social Norms: Based on the dynamic social norms model by Gelfand et al. [68] and the measurement framework by Cui and Liu [35] on the impact of social norms and environmental regulations on farmers’ behaviors, items such as “Everyone has the obligation to implement straw return to the field to address climate change” were designed to reflect the formation and change mechanisms of social norms;

Carbon Sequestration Compensation Behavior: A combined measurement method of behavioral intention and actual behavior is used [69], with items such as “I am willing to implement straw return to the field.” To ensure scientific measurement, this study references Hermann et al. [27], which experimentally analyzed the positive effects of policy incentives on farmers’ efforts to increase soil carbon sequestration, validating the policy feasibility of measuring carbon sequestration compensation behaviors. Additionally, combining the research of Gruijter et al. [70], specific items for quantifying carbon sequestration compensation behaviors were designed in this questionnaire (See Appendix A for more details), to reflect the importance of farm-level soil carbon storage audits based on random sampling and predictive tools. This auditing method provides precision support for behavioral measurement and verifies the feasibility of farmers’ behaviors through model optimization.

All scales use a five-point Likert scale (1 = strongly disagree, 5 = strongly agree) to ensure data comparability and consistency.

4.3. Survey Process

This study adopted a combination of online and offline approaches to distribute questionnaires to ensure the representativeness of the sample and the diversity of the data.

Online questionnaires were distributed via platforms such as Enjoining, targeting individuals engaged in agriculture-related work. Online surveys offer advantages of wide coverage and strong anonymity, effectively covering a geographically dispersed sample of agricultural practitioners. Ulrich-Schad et al. [71] indicated that using online platforms for sample collection can significantly improve coverage and sampling accuracy, particularly when dealing with participants in government programs or private supplier lists.

Offline questionnaires were distributed in settings such as agricultural fairs and cooperatives, where agricultural practitioners were invited to complete the survey. The research team provided guidance during the questionnaire completion process to ensure data accuracy and reliability. Bharti et al. [72] emphasized that using the Randomized Response Technique (RRT) in questionnaires can effectively address response bias and refusal issues in sensitive topics, thereby improving data authenticity and participation rates. Through face-to-face interactions with respondents, this study minimized the cognitive burden during the questionnaire process, ensuring data authenticity and completeness.

To ensure randomness and protect privacy, the study employed electronic questionnaires to maintain anonymity and randomly arranged the variable questions to prevent subjective guessing. By combining online and offline survey methods, the representativeness and diversity of the sample were enhanced, resulting in a broad research scope with minimal limitations. This diversified survey approach effectively avoids biases that may result from a single method and improves the overall reliability and interpretability of the questionnaire results.

4.4. Description of Sample Characteristics

To ensure the scientific rigor of the survey, this study was designed to improve response rates, referring to the study of Avemegah et al. [73] and incorporating measures such as combining online and offline surveys, randomizing question order, and implementing privacy protection mechanisms, effectively enhancing data quality and survey participation.

The questionnaire in this study consists of three parts:

The first part screens respondents on whether they have engaged in agricultural product cultivation;

The second part collects basic demographic information such as gender, age, education level, and city of residence;

The third part measures four research variables, including personal norms, subjective norms, social norms, and carbon sink compensation behavior.

To ensure randomness and protect privacy, the study employed electronic questionnaires to ensure anonymity and randomized the order of variable-related questions to minimize subjective biases. The target respondents were the general public, with a broad research scope and relatively few limitations.

4.4.1. Pre-Survey

To ensure the effectiveness of the questionnaire survey, a pre-survey was conducted before the formal survey. Following statistical recommendations, each variable required 5–10 samples, and a total of 40 valid responses were collected in the pre-survey. The pre-survey was conducted from 10 June to 20 June 2024, targeting individuals from China, Japan, and South Korea who are or were engaged in agriculture, with a primary focus on individuals aged 18 and above.

The results indicate that respondents were primarily aged 26–30 (35%) and 31–40 (22.5%), reflecting strong socio-economic activity and falling predominantly within the agricultural workforce age range. In terms of city classification, third-tier cities had the highest proportion (27.5%), followed by first-tier cities (22.5%). The sample covered mid-to-high-tier cities as well as some small towns and rural areas, aligning with the characteristics of agricultural workers.

This study conducted a reliability analysis of the questionnaire using SPSS Statistics 25.0, yielding a KMO value of 0.761 (>0.7), with Bartlett’s test significance at 0.000 (<0.001), indicating that the data were suitable for factor analysis and met the basic conditions for further factor extraction and model validation. The study measured key factors using validated scales and conducted reliability analysis using Cronbach’s alpha to assess internal consistency across dimensions. The reliability coefficients ranged from 0.853 to 1, indicating excellent internal consistency, meeting the “very good” reliability standard, and ensuring high data quality. The scale design and data collection process of this study demonstrated high scientific rigor and reliability.

In the regression analysis of this pre-survey, the model’s R² was 0.697, indicating that independent variables explained approximately 69.7% of the variance in the dependent variable, demonstrating strong explanatory power. The adjusted R² was 0.672, with a Durbin–Watson value of 1.618 (within the acceptable range of 1.5–2.5), indicating good independence of residuals and no significant autocorrelation issues. The standard estimation error was 0.43006, supporting the robustness of the model. The confirmatory factor analysis in this study demonstrated good model fit, laying a solid foundation for subsequent research.

4.4.2. Formal Survey

To minimize confounding factors, I conducted the field survey from 1 July to 2 September 2024, targeting individuals in China, Japan, and South Korea who are currently or were previously engaged in agriculture-related work. The survey primarily targeted individuals aged 18 and above, collecting a total of 450 questionnaires, of which 409 were valid responses.

Therefore, after eliminating unreliable data entries, the final valid sample size was n = 409. This sample size is significantly larger than the sample size recommendation by Maxwell [74]. Thus, the sample size exceeded initial expectations, enhancing the reliability of data analysis.

Additionally, SPSS Statistics 25.0 and AMOS 23.0 were used for analysis. SPSS Statistics 25.0 is an open-source statistical tool with a relatively simple user interface, making regression analysis easy to perform. It was chosen for its ability to generate tables directly in APA format. AMOS 23.0 is a tool for Structural Equation Modeling (SEM) analysis, which assists researchers in constructing and testing complex theoretical models through path analysis, causal modeling, and Confirmatory Factor Analysis (CFA), thereby facilitating a deeper understanding of relationships between variables. Descriptive statistics illustrate the demographic characteristics of the data, while inferential statistics are used to test hypotheses.

The results of the formal survey indicate that males accounted for 55%, with the majority aged between 26 and 40 years (57.4%). Respondents with a bachelor’s degree or higher comprised 77.5% of the sample. The primary occupations were students (22.5%) and freelancers (12.4%). The majority of respondents (57.2%) resided in third-tier or lower-tier cities. The sample exhibited a relatively high level of education and a certain degree of urbanization, providing reliable data support for subsequent research.

The city classification was based on China’s latest urban scale classification standard, which categorizes cities according to their permanent population and economic development level, dividing them into categories such as super first-tier, first-tier, second-tier, third-tier, and lower-tier cities [75]. This classification method provides a scientific basis for describing the sample distribution and background, ensuring the representativeness and broad applicability of the data.

4.5. Data Analysis Methods

4.5.1. Qualitative Analysis

The study employs the three-stage coding method of grounded theory, including open coding, axial coding, and selective coding, to analyze interview data and extract conceptual themes.

4.5.2. Quantitative Analysis

Reliability and Validity Analysis:

The internal consistency and construct validity of the questionnaire are tested using Cronbach’s Alpha coefficient and factor analysis.

Structural Equation Modeling (SEM):

AMOS 23.0 software is used to analyze the research model and validate the path relationships between hypotheses. Relevant literature indicates that when a study aims to test a theoretical model and assess its overall fit, Covariance-Based Structural Equation Modeling (CB-SEM) is generally the preferred method. Hair et al. [76] noted that CB-SEM is particularly suitable for theory-driven studies that aim to validate causal relationships, whereas Partial Least Squares (PLS) is more appropriate for predictive models lacking strong theoretical foundations.

5. Empirical Analysis

5.1. Three-Level Coding Qualitative Analysis

This study adopted Grounded Theory, proposed by American sociologist Barney Glasser in the 1960s [77], which is an exploratory method for developing theory emerging from data. In qualitative data analysis, this study applied the three-stage coding process of Grounded Theory, including open coding, axial coding, and selective coding, to systematically extract the influencing factors of social and personal norms on farmers’ carbon sink compensation behavior. Grounded theory is a systematic qualitative research method that enables the construction of a theoretical framework from data [78].

Grounded theory is widely used to explore issues of action, interaction, and processes, making it particularly suitable for studying formative pathways and influencing factors. Noble and Mitchell [79] pointed out that grounded theory, through systematic coding methods and theory construction, can deeply uncover the motivations and mechanisms behind complex behaviors, serving as a reliable tool for social science research. Furthermore, Bowers and Creamer [80], in their study within environmental education, demonstrated the applicability of grounded theory in exploring the long-term impacts of educational interventions on behavior, emphasizing its unique value in identifying critical social processes through theoretical modeling. These perspectives provide significant theoretical and methodological support for this study.

To gain a deeper understanding of the motivations and influencing factors behind farmers’ carbon sequestration compensation behaviors, this study selected farmers with extensive agricultural planting experience as subjects, conducting interviews to deeply analyze their specific contexts, to enhance the interpretive strength and normative rigor of the research’s theoretical construction. As shown in Table 1, this study ensures the universality and representativeness of the results through diversified sampling.

For primary data collection, semi-structured one-on-one interviews were conducted to capture the genuine perceptions and attitudes of respondents. Before the interviews, respondents were ensured to be familiar with the interview outline. Each interview lasted approximately one hour and focused on the following five aspects:

What crops do you currently grow? Have there been any changes in these crops over the past ten years?

How do you handle agricultural waste, such as straw, after crop harvesting?

How do you utilize modern machinery and technology in your agricultural activities?

Which government support measures have been the most helpful to you?

What are the main challenges you currently face in agricultural production?

Based on the respondents’ answers, the interview content and process were dynamically adjusted. Pinsky et al. [81], in their study on climate finance in Latin America using grounded theory, demonstrated the flexibility and applicability of this method in addressing complex real-world issues, while emphasizing the importance of dynamic adjustments in data collection and analysis for theoretical construction. Therefore, this study adopted this method and utilized NVivo 12 qualitative analysis software to systematically code and analyze primary interview data, thereby completing the construction of the theoretical model.

5.1.1. Open Coding

Open coding involves breaking down raw data to define concepts and extract their meanings, to achieve the categorization of raw concepts. To authentically reflect the information provided by respondents, this study transcribed the interview recordings into formal text and directly extracted categories based on the original statements. As shown in Table 2, a total of 10 categories were extracted from the raw data.

5.1.2. Axial Coding

The primary function of open coding is to extract meaningful initial concepts from raw data and form categories, while axial coding reveals the causal, sequential, and similar relationships between these concepts. This study distilled 3 core categories from 10 categories, as detailed in Table 3 and Table 4.

5.1.3. Selective Coding

Selective coding involves refining the core categories by organizing and synthesizing the main categories and establishing their relationships with the main categories to construct a theoretical model. The resulting theoretical model forms the basis of the study’s conclusions, aiming to concisely explain the key content.

Through data analysis, this study found that the 10 categories and 3 main categories revolve around the transformation process from personal experiences to carbon compensation behavior. Personal norms, subjective norms, social norms, and carbon sequestration compensation behaviors constitute a “motivation-process-behavior” pathway, influenced by internal and external factors. The model reveals the specific pathways and influencing mechanisms of personal norms in green agricultural product carbon sequestration compensation behaviors. The Hypotheses are listed in Table 5.

5.2. Reliability and Validity

To ensure the reliability and validity of the model, this study employed Cronbach’s Alpha to test internal consistency and used KMO and Bartlett’s tests for factor analysis. Referring to Bollen’s [82] recommendations on the reliability and validity of structural equation models, this study calculated the Average Variance Extracted (AVE) and Composite Reliability (CR) for each latent variable, all of which met theoretical standards, indicating good convergent and discriminant validity of the scale.

5.2.1. Reliability

The internal consistency of each dimension was analyzed using Cronbach’s alpha coefficient, with reliability coefficients ranging from 0.813 to 1, indicating that the scale has good internal consistency and excellent reliability. The data quality in this study is reliable, and the scale design is scientifically rigorous (see Table 6).

5.2.2. Validity Analysis

This study measured key factors using scales and conducted reliability analysis on the questionnaire using SPSS Statistics 25.0. The KMO value was 0.872 (>0.8), and Bartlett’s test significance was 0.000 (<0.001), indicating that the data are suitable for factor analysis, supporting further factor extraction and model validation.

According to the study by Ahrens et al. [83], the KMO and Bartlett’s tests are key steps in validating data suitability and construct validity, ensuring the scientific rigor of factor analysis and the robustness of the results (see Table 7).

Using principal component analysis and Kaiser normalization with varimax rotation, four clear components were extracted. Each component exhibited high factor loadings (>0.7) and low cross-loadings, indicating good component independence, a stable model structure, and strong explanatory power (see Table 8).

Under the premise of a good model fit in the exploratory factor analysis results, a further model fit assessment was conducted. According to the model fit indices presented in the table, the chi-square to degrees of freedom ratio (CMIN/DF) is 2.077, which falls within the acceptable range of 1–3. The Root Mean Square Error of Approximation (RMSEA) is 0.051, which is within the recommended threshold of less than 0.08, indicating a good fit.

Additionally, the goodness-of-fit indices, including NFI, TLI, CFI, and GFI, all exceed 0.9, demonstrating excellent model fit. Therefore, based on the comprehensive analysis results, the Confirmatory Factor Analysis (CFA) model exhibits a good fit (Table 9).

The CFA results indicate that all measurement variables have standardized factor loadings greater than 0.70, indicating a high level of convergent validity for the scale. The lowest factor loading value is 0.705 (SCL4), which remains within the acceptable range (>0.50), demonstrating that all measurement variables adequately explain their respective latent constructs (see Table 10 and Figure 2).

Under the condition that the CFA model exhibits good fit, further tests will be conducted to assess the Average Variance Extracted (AVE) and Composite Reliability (CR) of each dimension in the scale. The AVE values of all dimensions exceeded 0.5, and the CR values exceeded 0.7, indicating that all dimensions of the scale demonstrated good convergent validity and Composite Reliability. The formulas for calculating AVE and CR are as follows:

AVE (Average Variance Extracted):

$\mathrm{AVE}={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{i}}^2}{\mathrm{n}}}$

CR (Composite Reliability):

$\mathrm{CR}={\displaystyle \frac{{\left({\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{i}}\right)}^2}{\left({\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{i}}^2\right)+{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\delta }}_{\mathrm{i}}}}$

According to the standards proposed by Kline [84], if the absolute value of the skewness coefficient is within 3 and the absolute value of the kurtosis coefficient is within 8, the data can be considered to meet the requirements for approximate normal distribution. Descriptive statistical analysis showed that the mean values of all variables were between 3 and 4, suggesting that the respondents’ cognitive and behavioral levels regarding the four dimensions were above average. The skewness and kurtosis test results were within standard ranges (skewness < 3, kurtosis < 8), indicating that the data met the approximate normal distribution assumption.

These metrics ensure that the scale dimensions are both reliable and valid for measuring the intended constructs (see Table 11).

Through Pearson correlation analysis, it was found that there were significant positive correlations among all variables (p < 0.01), and the correlation coefficients (r) were all greater than 0, indicating that the variables had significant positive associations in this analysis (see Table 12).

5.3. Comparison of Hypothesis Testing Results

During the quantitative analysis phase, this study used Structural Equation Modeling (SEM) to validate the path relationships among the hypotheses. SEM is a statistical tool capable of simultaneously handling complex relationships among multiple variables, making it suitable for both exploratory and confirmatory research [85]. Referring to the SEM and fit index standards proposed by Hair et al., this study used AMOS 23.0 software for model estimation to ensure the significance of path coefficients and the overall fit of the model [76].

5.3.1. SEM Model Fit Test

The model fit test results showed that CMIN/DF was 2.077 (within the range of 1–3), RMSEA was 0.051 (<0.08), and NFI, TLI, CFI, and GFI all exceeded 0.9, indicating that the SEM model had good fit.

The study employed structural equation modeling (SEM) methods to comprehensively quantify the influence pathways of personal norms, subjective norms, and social norms on carbon sequestration compensation behaviors. This method’s wide application and effectiveness in environmental behavior research have been supported by numerous studies. Mardani et al. [85] emphasized the importance of SEM in addressing environmental sustainability issues, particularly in handling causal relationships among complex variables, where its fit and explanatory power are especially notable (see Table 13).

5.3.2. SEM Model Path Relationship Hypothesis Testing Results

The results of the SEM path analysis indicate that all hypotheses passed the significance test (p < 0.01), with path coefficients ranging from 0.218 to 0.470, suggesting that the relationships between normative variables and their influence on carbon compensation behavior are well supported.

Among these, the impact of personal norms on subjective norms is the most significant (β = 0.456, p < 0.001), confirming the Norm Activation Model and the Theory of Planned Behavior’s assertion that personal responsibility drives behavioral intentions. Subjective norms and social norms both exhibit positive effects on carbon compensation behavior (β = 0.218 and β = 0.324), highlighting the critical role of external expectations and group atmosphere in promoting low-carbon behavior.

Additionally, the reinforcing effect of social norms on subjective norms is particularly pronounced (β = 0.470, p < 0.001), confirming the importance of the social environment in individual behavioral choices. These findings further support the applicability of the integrated multi-theory model in explaining multi-level norm interactions and behavioral decisions, providing a theoretical foundation for optimizing carbon compensation mechanisms.

Evermann and Tate [86], through an evaluation of SEM model estimation methods, further corroborated the predictive performance and robustness of this method in behavior path modeling, supporting the validity of the path hypotheses in this study (see Table 14 and Figure 3).

6. Discussion

This study employed a combination of questionnaire surveys and in-depth interviews, using Structural Equation Modeling (SEM) to validate the multi-level influence of social norms, subjective norms, and personal norms on farmers’ carbon sink compensation behavior, and constructed a “Motivation-Process-Behavior” pathway model.

In the empirical analysis, the following key points are worth discussing:

6.1. Personal Norms as an Important Driver of Farmers’ Low-Carbon Behavior, Primarily Driven by Economic Considerations

The SEM model path analysis shows that personal norms have a significant direct impact on carbon sink compensation behavior (path coefficient = 0.231, p < 0.01). However, the results of in-depth interviews indicate that farmers’ motivations for adopting low-carbon behaviors (e.g., straw incorporation and reduced fertilizer use) are not purely based on moral responsibility or environmental awareness but are more driven by cost reduction and risk mitigation. For example, several respondents mentioned, “Now someone is watching, and burning it will result in a fine.” This suggests that although personal norms appear as a primary driving factor in the model, the core driving force in farmers’ actual decision making comes more from external policy constraints and economic rationality.

Farmers pay more attention to the economic benefits of implementing low-carbon behaviors. For instance, some farmers choose to bundle and sell straw or sell it to mushroom factories, which not only reduces the environmental problems caused by straw burning but also generates additional income for the farmers. This indicates that the economic rationality within personal norms plays a crucial role in practice.

Although farmers’ low-carbon behaviors appear highly consistent with carbon sink compensation goals on the surface, the underlying driving force mainly stems from economic rationality rather than traditional moral norms. Therefore, the manifestation of personal norms among farmers should be understood as behavior choices driven by economic rationality rather than purely by moral responsibility.

This finding suggests that policymakers, when promoting carbon sink compensation policies, should focus on further reducing farmers’ actual costs through economic incentives and technical support to more effectively stimulate their willingness to adopt low-carbon behaviors.

6.2. The Mediating and Amplifying Role of Subjective Norms

Subjective norms play a significant mediating role between personal norms and carbon sink compensation behavior (path effect = 0.218, p < 0.001). The study results indicate that when farmers perceive the behaviors of others and group expectations in their surrounding environment, their willingness to act is significantly enhanced.

In the interviews, several farmers mentioned that seeing neighbors and other villagers adopting low-carbon measures such as straw incorporation motivated them to follow suit: “The village has a unified biogas pool, so everything is collected centrally”; “Now there are combined harvesters and bundlers, which are very convenient”; “There is a mushroom factory near the village that buys straw, so we can sell it for money.”

The technological innovations and community cooperation models mentioned by farmers effectively reduced the difficulty and cost of straw processing. For example, the use of bundlers and combined harvesters improved the efficiency of straw collection, while unified biogas pool projects provided centralized solutions for straw processing. These measures significantly reduced technical barriers and psychological burdens for farmers.

This phenomenon indicates that subjective norms influence farmers’ behavior mainly through group pressure and social expectations. This “follow-up effect” not only strengthens the guiding role of personal norms in behavior but also further reduces farmers’ psychological resistance during the behavior adoption process.

Therefore, in policy promotion, in addition to providing direct economic incentives, community demonstrations and group promotion activities should be employed to enhance farmers’ subjective norms, guiding more farmers to develop spontaneous willingness to adopt low-carbon behaviors.

6.3. The Guiding and Constraining Role of Social Norms

Social norms, as a constraining force from the external environment, play a significant guiding role in farmers’ carbon sink compensation behavior (path coefficient = 0.324, p < 0.01). Interview results showed that some farmers mentioned, “Now someone is watching, and burning it will result in a fine”, indicating that the combination of policies and market mechanisms has significantly reduced farmers’ burden in straw handling and increased their willingness to participate.

Several farmers in the interviews mentioned that policy constraints and penalty mechanisms significantly influenced their decision-making, particularly the enforcement of the straw burning ban. Under the compulsory effect of these external norms, farmers gradually developed a habit of not burning straw, indicating that policy constraints play a crucial role in shaping social norms.

Therefore, although the direct influence of social norms on farmers’ behavior is less significant than that of personal norms, their indirect effects and lasting impacts should not be overlooked. This finding suggests that policymakers, when promoting carbon sink compensation mechanisms, should focus on continuously strengthening social norms by creating a low-carbon social atmosphere through policy constraints and public guidance, so that external pressure is gradually internalized into farmers’ long-term behavioral habits.

6.4. Applicability of the Multi-Layer Norm Interaction Pathway

The “Social Norms—Subjective Norms—Personal Norms—Carbon Sink Compensation Behavior” pathway model constructed in this study demonstrated good fit among agricultural practitioners in China, Japan, and South Korea (RMSEA = 0.051, CFI = 0.967). This result indicates that the multi-layer norm interaction pathway is not only applicable to a single country or specific cultural context but also has broad applicability in regions with similar agricultural backgrounds and policy environments.

This finding provides empirical evidence for promoting low-carbon agricultural policies across regions. In particular, regions with similar agricultural structures and policy environments can adopt this norm interaction pathway model to enhance the effectiveness of carbon sink compensation mechanisms through differentiated policy incentives and community cooperation models.

7. Conclusions

7.1. Theoretical Contributions

This study extends the application scenarios of existing norm theories by proposing a multi-layer norm interaction mechanism driven by economic rationality. Unlike previous literature that emphasizes personal norms being primarily driven by moral responsibility and environmental awareness, the empirical results of this study indicate that in farmers’ low-carbon behaviors, the driving force of personal norms is more a result of the combined effect of economic rationality and policy trust.

This finding fills a gap in the existing literature regarding the applicability of norm-driven mechanisms in specific economic contexts and provides theoretical support for the further application of the NAM and TPB theories. Moreover, this study validates the key mediating role of subjective norms and the indirect guiding effect of social norms, indicating that under the combined influence of community cooperation and policy incentives, the multi-layer norm interaction mechanism can effectively promote farmers’ adoption of low-carbon behaviors.

This theoretical model demonstrated good fit among agricultural practitioners in China, Japan, and South Korea, indicating that the pathway model has strong generalizability.

7.2. Practical Implications

Based on the empirical analysis results of this study, specific strategies for policymakers to promote low-carbon agricultural practices were proposed, including economic incentives and cost reduction strategies, community cooperation and demonstration guidance strategies, and policy implementation and social atmosphere creation strategies.

First, unlike existing literature that primarily emphasizes the intrinsic driving role of personal norms [34,37], this study found that farmers’ decisions regarding low-carbon behaviors are more driven by economic rationality. Therefore, policy design should focus on directly reducing farmers’ actual costs by providing subsidies and lowering the costs of low-carbon technologies to enhance their willingness to adopt. Moreover, this economic incentive is also supported by existing literature. For example, Zeweld et al. [43] pointed out that the combination of economic benefits and policy support significantly increased small farmers’ willingness to engage in environmental behaviors, while Wei et al. [41] emphasized the amplifying effect of combining economic incentives with personal values on behavioral participation.

Second, regarding community cooperation and demonstration guidance strategies, this study further extended the research of Kaushal and Rosendahl [7] and Francavilla et al. [1], who emphasized the importance of community norms and social expectations in promoting pro-environmental behaviors. Through empirical analysis and interviews, this study validated the key role of community cooperation models in reducing the risks associated with farmers’ adoption of new technologies. Therefore, it is recommended to establish low-carbon agricultural demonstration zones, organize mutual aid groups for farmers, and conduct technical training to strengthen mutual assistance and trust among farmers, thereby improving policy implementation. In addition, the promotion of successful cases within the community can help create positive incentives and group effects, further amplifying the guiding role of subjective norms.

Finally, the strategy of policy implementation and social atmosphere creation was proposed based on this study’s validation of the external constraining effect of social norms. Consistent with the conclusions of Morris et al. [58] and Sieverding et al. [59] that social norms enhance personal behavioral intentions through cultural dynamics and localized feedback, this study found that the strength of policy enforcement and positive guidance from public opinion are important pathways for gradually internalizing external pressure into long-term behavioral habits. Therefore, policymakers should continuously strengthen policy enforcement and public education, creating a supportive social atmosphere to promote the sustainability and generalization of farmers’ low-carbon behaviors.

This integrated strategy combining economic incentives, community cooperation, and policy implementation provides systematic guidance for further optimizing low-carbon agricultural policies and offers empirical evidence for enhancing farmers’ willingness to participate in carbon sink compensation.

7.3. Policy Recommendations

7.3.1. Strengthening the Behavior Guidance of Personal Norms

To enhance farmers’ willingness to participate in green agriculture and carbon sink compensation behaviors, policymakers should focus on strengthening personal norms through economic incentives and promotional guidance. To address the high initial costs of promoting green agricultural technologies, direct economic subsidies and tax reductions should be provided to ensure that farmers’ additional costs are effectively covered. Additionally, a market-based carbon credit trading mechanism should be gradually introduced in the later stages, allowing farmers to gain long-term economic benefits by participating in carbon sink projects.

Targeted promotional activities should be conducted based on local characteristics, showcasing the economic benefits and successful cases of low-carbon agriculture to improve farmers’ awareness of the returns from green agriculture. For example, “model farmers” can be invited to share their practical experiences, and farmers can be organized to visit demonstration sites to strengthen their willingness to adopt through tangible results.

7.3.2. Enhancing the External Constraints and Support of Social Norms

The community, as an important carrier of social norms, plays a key role in promoting farmers’ adoption of low-carbon behaviors. Therefore, in areas with promotion conditions, “low-carbon agriculture demonstration zones” should be established, with special funding support from the government to encourage pioneers to lead the practice of green agricultural technologies and guide other farmers to follow through a “demonstration effect”. Leveraging existing agricultural cooperatives and local organizations, mutual aid groups and technical training activities should be regularly organized to encourage experience sharing and cooperation among farmers, reducing the risks and psychological barriers in the adoption of technology.

Reward mechanisms should be provided for farmers and communities participating in low-carbon agriculture and carbon sink compensation behaviors, such as awarding the title of “Green Agriculture Pioneer” or establishing special subsidies to further motivate participation.

7.3.3. Improving the Economic Incentive and Technical Support System

The effectiveness of policy incentives not only depends on the level of subsidies but also closely relates to the continuity of policy implementation and technical support. Therefore, it is recommended to improve the economic incentive and technical support system by providing long-term stable subsidy policies, especially during the initial promotion stage of green agricultural technologies, to ensure that farmers can benefit continuously.

At the same time, incentive standards should be flexibly adjusted according to different regions and crop types to enhance the targeting and fairness of the policies. The government should form professional agricultural technical guidance teams to regularly visit rural areas, providing technical training and on-site guidance to ensure that farmers can smoothly implement green agricultural technologies.

In addition, promoting low-carbon agricultural equipment and tools can further reduce the difficulty for farmers in applying these technologies. Through information disclosure and policy feedback platforms, policymakers should promptly convey policy details and implementation progress to farmers, enhancing their trust in policies. Meanwhile, farmers’ feedback should be collected regularly to enable timely policy adjustments and optimizations.

7.4. Limitations and Future Prospects

This study, using agricultural practitioners from China, Japan, and South Korea as samples, revealed the decision-making mechanisms of farmers regarding carbon sink compensation behaviors in the context of East Asian culture. However, the regional and cultural limitations may result in the study’s conclusions lacking generalizability in other cultural or economic contexts. Altdorff et al. [87] pointed out that differences in the socio-cultural and policy environments of various agricultural regions significantly affect the implementation of sustainable development strategies. This indicates that future research should further expand the sample scope to include countries with different cultural and economic backgrounds, especially developing countries and agricultural regions with high carbon emissions, to validate the broad applicability of the study’s conclusions.

In addition, this study mainly adopted questionnaire surveys and interviews, which, although effective in revealing farmers’ subjective intentions and behavioral tendencies, may involve self-reporting bias, affecting the authenticity and reliability of the data. Therefore, future research can combine field experiments and longitudinal studies to dynamically analyze the long-term effects of policy interventions on behavior and the mechanism of habit formation. For example, the cultural multilevel selection framework proposed by Hillis et al. [88] emphasizes that combining field experiments with cultural perspectives can more deeply reveal the causal mechanisms of group behavior change, providing an important reference for future research methods.

Furthermore, this study did not delve into the regional adaptability of technology and policy and their synergistic effects. Mukherjee et al. [89] emphasized that significant differences in technological adaptability and policy implementation capacity across regions directly affect the promotion of carbon sink compensation behaviors. Therefore, future research could compare the promotion effects of technological and policy tools across different agricultural regions and further explore strategies for optimizing technological adaptability and policy implementation capacity.

Finally, this study mainly focused on the individual-level carbon sink compensation behaviors of farmers and did not fully consider the synergistic effects of the upstream and downstream segments of the supply chain. The processing, transportation, and sales segments of the supply chain also play an important role in carbon reduction. Future research could comprehensively consider the carbon footprint of the entire supply chain and explore how multi-stakeholder collaboration mechanisms can enhance the overall effectiveness of carbon sink compensation behaviors. Spiegel et al. [90] proposed in their agricultural ecosystem research that multi-stakeholder collaboration and policy integration can significantly enhance carbon reduction efficiency, providing practical guidance for future research on supply chain collaboration.

In summary, future research should further expand in four aspects—sample diversity, methodological innovation, regional adaptability, and supply chain collaboration—to optimize the carbon sink compensation mechanism and improve the scientific and practical effectiveness of policy design and implementation.

Author Contributions

Conceptualization, Y.F. (Yi Feng); Methodology, Y.F. (Yi Feng); Software, Y.F. (Yi Feng); Validation, Y.F. (Yi Feng); Formal analysis, Y.F. (Yi Feng); Investigation, Y.F. (Yi Feng); Resources, Y.F. (Yi Feng); Data curation, Y.F. (Yu Feng); Writing—original draft, Y.F. (Yu Feng); Writing—review & editing, Y.F. (Yu Feng); Visualization, Y.F. (Yu Feng); Supervision, Y.F. (Yu Feng) and Z.L.; Project administration, Y.F. (Yu Feng); Funding acquisition, Y.F. (Yu Feng). All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

According to China’s regulations, ethical approval can be exempted since this research involves de-identified, anonymous data and poses minimal risk to participants, in accordance with the Ethical Review Methods for Life Sciences and Medical Research Involving Humans, issued by the National Health Commission in February 2023. The full text of the regulation can be accessed at: https://www.gov.cn/zhengce/2023-02/28/content_5743660.htm (accessed on 9 February 2025). According to Korea’s regulations, ethical approval can be exempted since this research is non-interventional and relies solely on anonymous or publicly available data, in accordance with Article 15(1), Chapter 2 of the Bioethics and Safety Act (effective 1 December 2016). More details can be found at: https://www.law.go.kr/법령/생명윤리및안전에관한법률 (accessed on 9 February 2025).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:

SEM	Structural Equation Modeling
SPSS	Statistical Package for the Social Sciences
AMOS	Analysis of Moment Structures
GCO	Green Consumer Orientation
CR	Composite Reliability
AVE	Average Variance Extracted
PSL	Personal Norms
SBJ	Subjective Norms
SCL	Social Norms
COB	Carbon Offset Behavior

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Model The “Social Norms—Subjective Norms—Personal Norms—Carbon Sink Compensation Behavior” pathway model. (PSL = Personal Norms; SBJ = Subjective Norms; SCL = Social Norms; COB = Carbon Offset Behavior).

Figure 2. Confirmatory Factor Analysis (CFA) Model. (PSL = Personal Norms; SBJ = Subjective Norms; SCL = Social Norms; COB = Carbon Offset Behavior).

Figure 3. SEM Analysis Model Diagram. (PSL = Personal Norms; SBJ = Subjective Norms; SCL = Social Norms; COB = Carbon Offset Behavior).

Table 1

Descriptive Statistics of Population Sample.

Category	Category Values	Frequency	Percentage
Gender	Male	225	55
	Female	184	45
Age	18–25 years	81	19.8
	26–30 years	143	34.9
	31–40 years	92	22.5
	41–50 years	31	7.5
	51–60 years	41	10
	61–70 years	21	5.1
Education Level	Primary School	20	4.8
	Junior High	10	2.4
	High School	21	5
	College Diploma	41	10
	Bachelor’s Degree	225	55
	Master’s Degree or Above	92	22.5
Occupation	Agricultural Worker	50	12.2
	Administration	30	7.3
	Product/Operations Personnel	21	5
	Self-employed	21	5
	Finance/Accounting/Cashier/Audit	31	7.5
	Technical Developer/Engineer	21	5
	Technical Developer/Engineer	31	7.5
	Freelancer	51	12.4
	Retired	10	2.4
	Student	92	22.5
	Teacher	10	2.4
	Government Staff	41	10
City Tier of Residence	Ultra-tier-1 Cities	31	7.5
	Tier-1 Cities	92	22.5
	Tier-2 Cities	41	10
	Tier-3 Cities	112	27.3
	Tier-4 Cities	61	14.9
	Tier-5 Cities	41	10
	Township	21	5
	Village	10	2.4
Total		409	100

Table 2

Basic Information of Research Participants.

No.	Gender	Age	Education Level	City Tier	Occupation
1	Male	55	Junior High	Tier-4 City	Worker
2	Male	67	Primary School	Township	Agricultural Worker
3	Male	58	Technical School	Township	Self-employed
4	Male	40	High School	Township	Self-employed
5	Female	44	Junior High	Tier-5 City	Service Worker
6	Female	62	Primary School	Tier-3 City	Agricultural Worker
7	Male	63	Junior High	Tier-3 City	Agricultural Worker
8	Male	81	Junior High	Tier-3 City	Retired
9	Female	79	High School	Tier-3 City	Retired
10	Male	70	Primary School	Township	Agricultural Worker
11	Female	69	Primary School	Township	Agricultural Worker
12	Male	31	Technical School	Township	Self-employed
13	Female	61	Primary School	Tier-3 City	Agricultural Worker

Table 3

Open Coding.

NO.	Category	Example Original Statements
1	Straw Reutilization	“Bury it in the soil, and when the time is right, plant the next crop”
2	Impact of Environmental Regulations	“Burning is not allowed now, so we basically bury it in the soil”
3	Commercial Recycling	“There’s a company in town that collects straw and offers free pick-up”
4	Legal Compliance and Economic Penalties	“There are people monitoring now, and burning it will result in fines”
5	Costs and Environmental Risks	“Burying it isn’t great either; pests grow later, and pesticides cost more”
6	Traditional Utilization	“When it gets cooler, we go to the fields to collect straw and use it as firewood at home”
7	Economic Value of Straw	“There’s a mushroom factory near the village that buys straw; you can sell it”
8	Efficiency and Profitability	“We have a lot of land, so we call a baling machine to bundle it, and we can sell it”
9	Community Collective Use	“The village has a shared biogas tank, and they collect straw together, so we don’t have to worry about it”
10	Technological Innovation	“Now there’s a harvester and baler in one machine; it’s very convenient”

Table 4

Axial Coding.

Main Category	Category	Example Original Statements
Personal Norms	Straw Reutilization	“Bury it in the soil, and when the time is right, plant the next crop”
	Costs and Environmental Risks	“Burying it isn’t great either; pests grow later, and pesticides cost more”
	Traditional Utilization	“When it gets cooler, we go to the fields to collect straw and use it as firewood at home”
	Efficiency and Profitability	“We have a lot of land, so we call a baling machine to bundle it, and we can sell it”
Social Norms	Impact of Environmental	“Burning is not allowed now, so we basically bury it in the soil”
	Legal Compliance and Economic Penalties	“There are people monitoring now and burning it will result in fines”
Subjective Norms	Commercial Recycling	“There’s a company in town that collects straw and offers free pick-up”
	Economic Value of Straw	“There’s a mushroom factory near the village that buys straw; you can sell it”
	Community Collective Use	“The village has a shared biogas tank, and they collect straw together, so we don’t have to worry about it”
	Technological Innovation	“Now there’s a harvester and baler in one machine; it’s very convenient”

Table 5

Research Hypotheses.

Hypothesis	Statement
H1	Personal norms positively influence subjective norms
H2	Personal norms positively influence social norms
H3	Personal norms positively influence Carbon Offset Behavior
H4	Subjective norms positively influence Carbon Offset Behavior
H5	Social norms positively influence subjective norms
H6	Social norms positively influence Carbon Offset Behavior

Table 6

Reliability Statistics Analysis.

Dimension	Cronbach’s Alpha	Number of Items
PSL	0.860	4
SBJ	0.876	5
SCL	0.840	4
COB	0.813	3
Overall	0.887	16

PSL = Personal Norms; SBJ = Subjective Norms; SCL = Social Norms; COB = Carbon Offset Behavior.

Table 7

KMO and Bartlett’s Test.

KMO Measure of Sampling Adequacy		0.872
Bartlett’s Test of Sphericity	Approx. Chi-Square	3246.759
	Degrees of Freedom	120
	Significance	0.000

Table 8

Rotated Component Matrix.

Item	Component
	1	2	3	4
PSL1		0.868
PSL2		0.752
PSL3		0.790
PSL4		0.780
SBJ1	0.896
SBJ2	0.741
SBJ3	0.763
SBJ4	0.805
SBJ5	0.736
SCL1			0.877
SCL2			0.737
SCL3			0.713
SCL4			0.762
COB1				0.882
COB2				0.813
COB3				0.757

Extraction Method: Principal Component Analysis. Rotation Method: Kaiser-normalized varimax rotation; converged after 5 iterations. PSL = Personal Norms; SBJ = Subjective Norms; SCL = Social Norms; COB = Carbon Offset Behavior.

Table 9

CFA Model Fit Test Results.

Metric	CMIN/DF	NFI	TLI	CFI	RMSEA	GFI
Excellent Value	>1, <3	>0.9	>0.9	>0.9	<0.05	>0.9
Good Value	>3, <5	>0.8	>0.8	>0.8	<0.08	>0.8
Result	2.077	0.938	0.959	0.967	0.051	0.945

Table 10

CFA Factor Loadings.

Item			Estimate
PSL4	<---	PSL	0.771
PSL3	<---	PSL	0.791
PSL2	<---	PSL	0.709
PSL1	<---	PSL	0.856
COB1	<---	COB	0.868
COB2	<---	COB	0.729
COB3	<---	COB	0.732
SBJ1	<---	SBJ	0.879
SBJ2	<---	SBJ	0.725
SBJ3	<---	SBJ	0.740
SBJ4	<---	SBJ	0.769
SBJ5	<---	SBJ	0.726
SCL4	<---	SCL	0.705
SCL3	<---	SCL	0.714
SCL2	<---	SCL	0.748
SCL1	<---	SCL	0.865

PSL = Personal Norms; SBJ = Subjective Norms; SCL = Social Norms; COB = Carbon Offset Behavior.

Table 11

Convergent Validity (AVE), Composite Reliability (CR), and Descriptive Statistical Analysis.

Item	AVE	CR	Mean	Std. Deviation	Variance	Skewness	Kurtosis	Overall M	Overall SD
PSL4	0.6139	0.8636	3.42	1.333	1.778	−0.437	−0.959	3.2280	0.97838
PSL3			3.22	1.082	1.171	−0.138	−0.601
PSL2			3.14	1.110	1.231	−0.100	−0.614
PSL1			3.14	1.119	1.253	−0.087	−0.690
COB3	0.6069	0.8214	3.39	1.363	1.857	−0.446	−1.005	3.1985	0.94366
COB2			3.14	1.096	1.202	−0.121	−0.574
COB1			3.13	1.098	1.206	−0.025	−0.663
SBJ5	0.5929	0.8786	3.20	1.110	1.231	−0.101	−0.696	3.0905	0.94959
SBJ4			3.13	1.083	1.172	−0.062	−0.528
SBJ3			3.22	1.333	1.778	−0.193	−1.117
SBJ2			3.06	1.105	1.222	−0.018	−0.624
SBJ1			3.08	1.091	1.190	0.004	−0.511
SCL4	0.5786	0.8451	3.00	1.073	1.152	0.012	−0.544	3.2241	1.01461
SCL3			3.39	1.335	1.783	−0.389	−0.999
SCL2			3.14	1.106	1.223	−0.103	−0.588
SCL1			3.14	1.111	1.235	−0.132	−0.613

PSL = Personal Norms; SBJ = Subjective Norms; SCL = Social Norms; COB = Carbon Offset Behavior.

Table 12

Correlation Matrix.

	PSL	SBJ	SCL	COB
PSL	1
SBJ	0.348 **	1
SCL	0.479 **	0.383 **	1
COB	0.339 **	0.343 **	0.363 **	1

** Significant correlation at the 0.01 level (two-tailed). PSL = Personal Norms; SBJ = Subjective Norms; SCL = Social Norms; COB = Carbon Offset Behavior.

Table 13

SEM Model Fit Test Results.

Metric	CMIN/DF	NFI	TLI	CFI	RMSEA	GFI
Excellent Value	>1, <3	>0.9	>0.9	>0.9	<0.05	>0.9
Good Value	>3, <5	>0.8	>0.8	>0.8	<0.08	>0.8
Result	2.077	0.938	0.959	0.967	0.051	0.945

Table 14

SEM Path Analysis Results.

Path Relationship			Estimate	S.E.	C.R.	p	Hypothesis
SCL	<---	PSL	0.456	0.053	8.665	***	Supported
SBJ	<---	PSL	0.279	0.088	3.167	0.002	Supported
SBJ	<---	SCL	0.470	0.105	4.484	***	Supported
COB	<---	PSL	0.231	0.088	2.642	0.008	Supported
COB	<---	SBJ	0.218	0.058	3.793	***	Supported
COB	<---	SCL	0.324	0.106	3.073	0.002	Supported

PSL = Personal Norms; SBJ = Subjective Norms; SCL = Social Norms; COB = Carbon Offset Behavior; *** p < 0.001.

Appendix A. Questionnaire

Variable	Code	Item	Source
Personal Norms	PSL1	I believe straw incorporation should be implemented	Setiawan et al. [66]
	PSL2	I feel responsible for implementing straw incorporation
	PSL3	I would feel guilty if I did not implement straw incorporation
	PSL4	I believe I should be accountable for straw incorporation behavior
Subjective Norms	SBJ1	I feel responsible for resisting straw burning to reduce negative impacts	Kim et al. [67]
	SBJ2	Even if my efforts are small, I insist on implementing straw incorporation
	SBJ3	I should act to prevent the negative effects of straw burning
	SBJ4	I worry that not supporting straw incorporation might mislead others
	SBJ5	I worry that not supporting straw incorporation might negatively affect those around me
Social Norms	SCL1	Everyone has the obligation to implement straw incorporation to combat climate change	Gelfand M J et al. [68]; Cui et al. [35]
	SCL2	Implementing straw incorporation helps mitigate climate change
	SCL3	Everyone should take responsibility for global warming
	SCL4	Implementing straw incorporation helps reduce global warming
Carbon Offset Behavior	COB1	Implementing straw incorporation helps reduce global warming	Hermann et al. [27]; Gruijter et al. [70]
	COB2	I will implement straw incorporation
	COB3	I intend to implement straw incorporation

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