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Article

Farmers’ Willingness to Adopt Low-Carbon Technologies: Exploring Key Determinants Using an Integrated Theory of Planned Behavior and the Norm Activation Theory Framework

by
Yanmei Yuan
1,2,
Le Sun
2,*,
Zongyun She
2,
Hao Niu
2 and
Shengwei Chen
2,3
1
Faculty of General Education, Taishan College of Science and Technology, Tai’an 271038, China
2
College of Economics and Management, Shandong Agricultural University, Tai’an 271018, China
3
School of Economics, Shandong University of Technology, Zibo 255000, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(16), 7399; https://doi.org/10.3390/su17167399
Submission received: 1 July 2025 / Revised: 21 July 2025 / Accepted: 5 August 2025 / Published: 15 August 2025
(This article belongs to the Special Issue Agriculture, Food, and Resources for Sustainable Economic Development)
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Abstract

Encouraging farmers to adopt low-carbon agricultural technologies is a vital strategy for addressing climate change and fostering a harmonious relationship between humans and nature. An initial step is understanding the formation of farmers’ willingness to adopt them. This study adopts an integrated theoretical framework combining the Theory of Planned Behavior and the Norm Activation Model to explore the determinants and formation process of farmers’ adoption intentions. Using survey data from 1008 farmers in Shandong Province, the study employs Structural Equation Modeling to empirically examine the influencing factors and mechanisms underlying farmers’ willingness to adopt low-carbon agricultural technologies. The results reveal that perceived behavioral control, subjective norm, and attitudes toward behavior serve as the critical external driving forces for the formation of adoption intention, whereas personal norms act as the core intrinsic motivation by fostering farmers’ sense of ecological responsibility. Multi-group analysis reveals socio-demographic heterogeneity: perceived control drives males and wealthier, less-educated farmers; subjective norms influence younger, educated groups; attitudes affect females and low-income farmers, while personal norms dominate among older farmers. Therefore, policy design should enhance farmers’ resource accessibility, strengthen social demonstration, and cultivate ecological ethics with tailored incentives, thereby promoting the widespread adoption of low-carbon agricultural technologies.

1. Introduction

Global climate change poses a significant threat to food security, human health, and sustainable economic and social development, making it a shared challenge for all countries [1]. Carbon emissions from human activities are a major driver of global climate change [2]. While industrial sectors are often the primary focus of carbon reduction efforts, the role of agriculture in carbon emission should not be overlooked. According to Intergovernmental Panel on Climate Change’s Fourth Assessment Report, 13.5% of global carbon emissions come from modern agricultural practices [3]. Notably, smallholder farmers are responsible for more than one-third of agricultural emissions [4], underscoring their critical role in reducing emissions and promoting green, low-carbon, and high-quality development. Research has shown that the widespread adoption of low-carbon agricultural technologies could eliminate 80% of greenhouse gas emissions from this sector [5]. Therefore, encouraging small farmers to actively adopt these technologies is essential to achieving emission reduction and advancing sustainable development.
In China, where the agricultural landscape is characterized by numerous small-scale farmers, these individuals serve as both key implementers and beneficiaries of low-carbon technologies. However, due to the externalities associated with such technologies [6], some farmers exhibit weak responses and a lack of willingness to adopt them, posing a major challenge to their implementation. Despite a series of national policies aimed at encouraging adoption, participation rates remain low, limiting the effectiveness of these initiatives [7,8]. Understanding the factors that influence farmers’ willingness to adopt low-carbon technologies is crucial for enhancing the efficiency of agricultural technology dissemination. Investigating the formation mechanisms behind this willingness holds significant theoretical and practical importance. Such insights can help improve the promotion and governance of low-carbon agriculture, support green and low-carbon transformation, and contribute to rural revitalization.
Currently, from the perspective of research content, both domestic and international scholars have examined the factors influencing farmers’ adoption of low-carbon production practices from various perspectives, including individual characteristics [9,10,11], capital endowment [12,13,14,15], socioeconomic factors [7,16,17,18], cognitive and psychological traits [19,20,21], digital literacy [22,23] and environmental regulation [22,23,24,25,26]. At the same time, from a methodological perspective, most research adopts regression-based models (e.g., Logit, Probit), while fewer studies have developed integrated behavioral frameworks or employed multi-group structural equation modeling to reveal the heterogeneity in decision-making mechanisms [6,9,11,15,16]. Therefore, several gaps remain that warrant further attention. First, most existing research focuses on the influence of external, objective factors on farmers’ adoption behavior, while the internal psychological motivations driving small-scale farmers’ responses to low-carbon technologies have been relatively underexplored. Second, many studies rely on a single theoretical framework or isolate specific influencing factors, resulting in a lack of comprehensive, systematic evaluations of low-carbon technology adoption. Third, research on farmers’ willingness to adopt such technologies often takes a holistic approach, overlooking the differences in decision-making mechanisms among various demographic groups.
In response to these gaps, this paper develops an integrative model of the Theory of Planned Behavior (TPB) and the Norm Activation Theory (NAM), referred to as the TPB-NAM model. This integrated approach overcomes the limitations of using a single theoretical lens by incorporating both rational decision-making and moral constraints. This allows for a more systematic analysis of the factors influencing farmers’ willingness to adopt low-carbon agricultural technologies and offers practical theoretical guidance for promoting low-carbon agriculture. Furthermore, this study applies multi-group structural equation modeling to examine the heterogeneous response patterns across different farmer groups—such as by gender, age, education level, and income level. This approach provides empirical support for developing targeted strategies that account for group-specific differences, thereby enhancing the effectiveness of low-carbon agricultural extension initiatives.
Taken together, this study contributes to the literature by (1) advancing a more comprehensive theoretical framework that integrates rational and moral determinants of low-carbon adoption, (2) introducing a robust methodological approach that captures heterogeneous behavioral mechanisms, and (3) generating policy-relevant empirical evidence to inform differentiated extension strategies.

2. Theoretical Framework and Research Hypothesis

2.1. Theory of Planned Behavior

The Theory of Planned Behavior (TPB), proposed by Ajzen [27], has been extensively applied to explain the formation of behavioral intentions across a wide range of disciplines [28,29,30,31]. Within this framework, behavior intention is primarily shaped by three core constructs: Attitude Toward the Behavior (ATB), Subjective Norm (SN), and Perceived Behavioral Control (PBC). Given its structured nature, farmers’ engagement in low-carbon agricultural practices can be conceptualized as a form of planned behavior, thus aligning well with the TPB framework.
Attitude Toward the Behavior (ATB) reflects an individual’s overall evaluation—positive or negative—of performing a particular behavior. In the context of agriculture, ATB pertains to farmers’ perceptions and judgments regarding low-carbon production practices. A stronger understanding of low-carbon agriculture, heightened awareness of low-carbon technologies, and a clearer recognition of their environmental and economic benefits are all positively associated with a farmer’s likelihood of adopting such practices [32,33,34,35,36]. Accordingly, this study proposes the following research hypothesis:
H1. 
Attitude Toward the Behavior (ATB) positively affects farmers’ willingness to adopt low-carbon technologies.
Subjective Norm (SN) refers to the social pressure perceived by individuals to perform or refrain from a specific behavior and captures the influence of external expectations on behavioral intentions [37]. In the study context, SN reflects the extent to which farmers perceive that important others, such as peers, authorities, or community members, expect them to engage in environmentally sustainable practices. The influence of SN on farmers may occur in three primary aspects: First, government-led policy publicity and educational initiatives can significantly shape farmers’ production behavior; more effective policy communication and training efforts have been shown to enhance farmers’ willingness to adopt new technologies [7,38]. Second, peer effects, namely, the influence of surrounding individuals, such as neighbors, play a significant role [39,40,41]. Third, pressure from opinion leaders, agricultural cooperatives, or village committees can strongly influence individual decision-making processes [42]. The stronger the SN, the greater the likelihood that an individual will engage in the corresponding behavior [43,44]. Therefore, the following hypothesis is proposed:
H2. 
Subjective Norm (SN) has a positive impact on farmers’ willingness to adopt low-carbon technologies.
Perceived Behavioral Control (PBC) refers to farmers’ perception of the difficulty of low-carbon production behavior. Factors such as capital cost [45], relevant knowledge and technical skills [46], information access [7,47], and risk control ability [48] have direct and indirect influences on the perception degree. The more control an individual believes they have over these factors, the higher their PBC, and consequently, the stronger their willingness to adopt low-carbon technologies [49,50,51].
H3. 
Perceived Behavioral Control (PBC) positively affects farmers’ willingness to adopt low-carbon technologies.

2.2. Theoretical Framework of Normative Activation

The Norm Activation Theory (NAM), developed by Schwartz in 1977, has been widely used to explain altruistic and environmental behavior/intention. According to the theory, the motivation of individuals to engage in environmental behavior stems from an internalized sense of moral obligation, referred to as Personal Norms (PNs). These PNs are activated by two key antecedents: Awareness of Consequences (AC) and Ascription of Responsibility (AR) [52]. In the context of agriculture, AC reflects farmers’ understanding of the negative environmental consequences resulting from the failure to adopt low-carbon agricultural practices. AR, on the other hand, refers to the extent to which farmers perceive themselves as personally accountable for issues such as climate change, environmental degradation, and resource depletion caused by traditional farming methods. PNs are the internalized expectations farmers hold about their responsibility to engage in low-carbon agricultural behaviors, and it is the internalized social norm and self-moral obligation. Prior to taking action, when farmers become aware of the environmental harm associated with conventional practices and accept responsibility for these consequences, this combination triggers their moral obligation to act. Once activated, these personal norms become a significant determinant of behavioral intention [53,54,55].
With the extensive promotion of rural environmental awareness and the growing efforts to disseminate low-carbon agricultural technologies, farmers’ environmental consciousness and sense of responsibility have gradually increased. As farmers realize the ecological damage associated with conventional farming and the potential of low-carbon technologies to mitigate these effects, their PNs will be activated, thereby enhancing their willingness to adopt low-carbon agricultural technologies. The following research hypotheses are therefore proposed:
H4. 
Personal Norms (PNs) have a positive impact on farmers’ willingness to adopt low-carbon technologies.
H5. 
Awareness of Consequences (AC) has a positive impact on Personal Norms (PNs).
H6. 
Awareness of Consequences (AC) has a positive impact on Ascription of Responsibility (AR).
H7. 
Ascription of Responsibility (AR) has a positive impact on Personal Norms (PNs).
In summary, TPB primarily focuses on individual rational decision-making processes, focusing on the key determinants of behavioral intention. However, it pays less attention to internalized moral obligations and their motivational role. In contrast, NAM centers on the activation of internal moral norms while ignoring the influence of external environment and social pressure on decision-making. Recognizing the limitations of each model in isolation, some scholars have suggested that integrating TPB and NAM can enhance the predictive and explanatory power of models addressing pro-environmental behavior and willingness [56]. In agriculture, farmers’ willingness to adopt low-carbon technologies, a specific form of pro-environmental behavior intention, can also be more comprehensively understood through an integrated TPB-NAM framework (see Figure 1).

2.3. Model Development Process

To develop the structural model, we first reviewed the core theoretical constructs of the TPB (ATB, SN, and PBC) and the NAM (AC, AR, and PN) in the context of pro-environmental and agricultural behaviors. Based on this review, we proposed 12 research hypotheses linking seven latent variables relevant to farmers’ low-carbon adoption intention.
To ensure theoretical parsimony and empirical feasibility, the hypotheses were refined by excluding redundant or weakly supported relationships after an iterative review of previous empirical studies and expert consultations. As a result, the final model retained seven key hypotheses, which balance explanatory power with model simplicity.
The integrated TPB-NAM framework (Figure 1) thus captures both the rational decision-making process emphasized in TPB and the moral obligation mechanism central to NAM, enabling a more comprehensive explanation of farmers’ willingness to adopt low-carbon agricultural technologies.

3. Survey Design, Data Description, and Model Validation

3.1. Questionnaire Design

The survey questionnaire comprises two sections. The first section collects information on the individual and household production characteristics of the surveyed farmers, including gender, age, education level, health status, farming experience (in years), number of family laborers, proportion of agricultural income, cultivated land area, status as a village cadre, and other socio-demographic variables. The second section contains measurement items corresponding to the theoretical framework underpinning the study.
The survey was conducted between 15 July and 20 August 2024. Specifically, the 1100 farmers were selected using a multistage stratified random sampling approach. First, sampling points were established in all 16 prefecture-level cities of Shandong Province to ensure full provincial coverage and regional representativeness. The sample size in each city was proportionally allocated based on the agricultural population. Second, each city, county, township, and village was stratified and randomly selected according to their economic development level and agricultural structure. Finally, eligible farming households were randomly drawn from village registers.
All participants were adult farmers who voluntarily agreed to take part in the study. Prior to completing the questionnaire, each participant was presented with a written explanation outlining the purpose of the study, the anonymity and confidentiality of the data collection process, and the voluntary nature of participation. In accordance with the Ethical Review Methods for Life Sciences and Medical Research Involving Humans issued by the National Health Commission of China in February 2023, ethics approval and written consent are not required for studies involving anonymized data and minimal risk. Therefore, no signed informed consent forms were required.
Before data collection, the research team conducted special training sessions covering the objectives of the study, survey content, methodologies, and key procedural considerations. Subsequently, trained investigators conducted face-to-face interviews with farmers and administered the questionnaires individually. To ensure that respondents possessed a comprehensive and systematic understanding of green and low-carbon agricultural practices, investigators first provided an explanation of the concepts and critical components of low-carbon production. In total, 1100 questionnaires were distributed during the survey, of which 1008 were deemed valid after excluding incomplete or inconsistent responses, resulting in an effective response rate of 91.64%.
Drawing on TPB and NAM, and informed by prior research on index selection and scale design [30,37,40,49,57], the questionnaire includes 27 measurement items representing seven latent variables relevant to farmers’ low-carbon production practices. A five-point Likert scale was employed, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Specific items are assigned according to scores, and relevant measurement indicators and descriptive statistical characteristics are shown in Table 1.

3.2. Data Description

Farmers’ characteristics are shown in Table 2. In total, 57.94% of the respondents are male, and 42.06% are female. The farmers interviewed are mainly middle-aged and older adults, of which farmers aged 51 and above account for 64.58% of the total sample. About 82% of the farmers have reached an education level up to junior high school. In terms of health status, most respondents think that their health status is “average” or “good”. Regarding the number of family laborers, the proportion of farmers with two laborers is the highest, at 68.15%. A total of 72.61% of farmers have worked for over 20 years.
In total, 56.94% of farmers’ annual household income is less than 50,000 yuan. The average cultivated land of the surveyed farmers is 8.7 mu (1 mu equals 1/15 hectare). This shows that the farmers interviewed are generally characterized by aging, poor education, low income, small-scale production, and rich experience in farming, which is nationally representative.

3.3. Reliability and Validity of the Model

To ensure the reliability and validity of the research findings, this study uses SPSS 26.0 to conduct comprehensive tests on the overall scale. The results are shown in Table 3. In the reliability test, Cronbach’s α coefficients of all latent variables range from 0.815 to 0.941, exceeding the commonly accepted threshold of 0.7, thereby indicating strong internal consistency. The Composite Reliability (CR) values are consistent with Cronbach’s α coefficients, further confirming the high reliability of each measurement index.
In the validity test, the Kaiser–Meyer–Olkin Measure of Sampling Adequacy (KMO) values of latent variables are all between 0.689 and 0.874, which is greater than 0.5, the rule of thumb, and the p-values of the Bartlett Spherical Test are all less than 0.001, suggesting the appropriateness of factor analysis, which is then used to extract the load coefficients of all observables on their latent variables, and the standardized factor load coefficients are between 0.695 and 0.944, greater than the threshold of 0.5. In addition, the Average Variance Extraction (AVE) of all latent variables is greater than 0.5, further suggesting high validity of the survey data.

3.4. Model Fitness Test

AMOS 26.0 was used to test model adaptability. The fitness index of the Structural Equation Model usually includes an absolute fitness index, such as Chi-Square/Degrees of Freedom Ratio (χ2/df), Root-Mean-Square Error of Approximation (RMSEA), Goodness-of-Fit Index (GFI), and Adjusted Goodness-of-Fit Index (AGFI); value-added fitness index, including Normed Fit Index (NFI), Relative Fit Index (RFI), Incremental Fit Index (IFI), Tucker–Lewis Index (TLI), and Comparative Fit Index (CFI); and reduced fitness index, covering Parsimony Goodness-of-Fit Index (PGFI) and Parsimony Normed Fit Index (PNFI). The test results in Table 4 show that the adaptability of each index has passed their respective threshold, indicating that the constructed theoretical model is well-fitted with the sample survey data.

4. Analysis of Empirical Results

4.1. Model Hypothesis Test

Structural Equation Modeling (SEM) was conducted using AMOS 26.0 to analyze farmers’ willingness to adopt low-carbon technologies and the influencing factors. The results indicate that hypotheses H1 through H7 are supported. The outcomes of the model hypothesis testing are presented in Table 5.
Within the TPB framework, the standardized path coefficients for ATB, SN and PBC are 0.156, 0.172, and 0.223, respectively—all statistically significant at the 1% level. These findings support Hypotheses H1, H2, and H3, indicating that farmers’ affective evaluations of low-carbon agricultural practices, perceived social pressure, and perceived control over low-carbon production exert significant positive influences on their willingness to adopt such technologies [58]. Notably, the standardized path coefficient for PBC is higher than those for ATB and SN, suggesting that farmers’ perceived control—over financial resources, time, risk management, knowledge, skills, and access to information—plays a particularly critical role in shaping their adoption intentions [19,59,60].
Currently, smallholder farmers in China generally face severe financial constraints and a lack of technical support. Even if they hold a positive attitude toward low-carbon technologies, their adoption is often hindered by limitations in economic and technical capacities. Conversely, when farmers face internal constraints (e.g., insufficient knowledge or skills) or external barriers (e.g., limited access to time or financial support), their willingness to engage in low-carbon production diminishes accordingly. This highlights the practical importance of alleviating financial pressures and providing technical support in the promotion of low-carbon agricultural technologies.
Within the NAM framework, PNs have a significant impact on farmers’ willingness to adopt low-carbon technologies (β = 0.501, H4 supported) and are stronger than ATB (β = 0.156), SN (β = 0.172), and PBC (β = 0.223) in TPB, indicating that farmers’ moral obligation plays a more important role in low-carbon production decision-making than the influence of external social norms, and farmers’ moral obligation is a key driver of farmers’ willingness to adopt low-carbon technologies [32,39]. The reason may lie in the collective values and intergenerational sense of responsibility embedded in rural Chinese society, which fosters a widespread moral obligation among farmers to protect arable land and maintain ecological balance.
Although AC does not have a direct effect on farmers’ willingness to adopt low-carbon technologies, it plays a critical mediating role through the indirect pathway. AC enhances PNs through AR, further promotes the formation of willingness (WILL) to adopt low-carbon technologies, and facilitates the chain transmission: AC→AR→PN→WILL. Specifically, AC directly affects farmers’ PNs (β = 0.682, H5 holds); at the same time, it can indirectly influence PNs by influencing AR (H6 and H7 supported), which shows that farmers can develop a certain degree of moral responsibility for low-carbon production by their recognition of the negative impacts of conventional agriculture. This indicates that AR plays a critical mediating role in the process through which AC influences the formation of farmers’ PNs. When farmers realize the negative impact of conventional high-carbon agriculture, they are more responsible for their behavior consequences, thus making them more likely to stimulate their sense of moral obligation to low-carbon production [17]. This implies that, within the new development philosophy of innovative, coordinated, green, open, and shared development, enhancing farmers’ awareness of environmental risks through policy publicity and village-level ecological initiatives can effectively activate their personal norms and promote voluntary behavioral change.
Moreover, the integrated TPB-NAM framework reveals that the normative pathway is critical in shaping farmers’ intentions to adopt low-carbon agricultural technologies. Both SN and PNs exhibit significant and positive effects on adoption intentions. On one hand, in rural contexts, the opinions and behaviors of significant others—such as relatives, neighbors, and village cadres—often serve as behavioral referents, exerting substantial social pressure. Farmers tend to align their actions with perceived collective expectations to maintain social cohesion and legitimacy, reinforcing conformity-driven behavioral intentions.
On the other hand, in agricultural production, low-carbon practices are characterized by strong positive externalities and delayed environmental returns. As such, personal norms become particularly salient when farmers internalize the long-term ecological implications of their actions. This internalization fosters a sense of moral obligation that motivates voluntary behavioral commitment, even without external incentives. As evidenced in Table 5, personal norms demonstrate the most pronounced positive influence on behavioral intention among all examined constructs. This underscores the importance of fostering ecological ethics and cultivating shared environmental values in disseminating and promoting low-carbon technologies.

4.2. Multi-Group SEM Analysis

As farmers often manifest different behavior patterns [61], their intentions to make certain decisions are usually highly heterogeneous due to individual differences. Therefore, under the TPB-NAM framework, this study further discusses the differences in low-carbon technology adoption willingness associated with varying observables, including gender, age, education level, and family income level, and reveals the differential response paths of different groups’ low-carbon technology adoption willingness through multi-group SEM analysis.
The multi-group analysis also served to test the model’s applicability across demographic segments. The results indicate that the model meets measurement invariance requirements and is suitable for multi-group comparisons. As presented in Table 6, the extended TPB-NAM model demonstrates variations across gender, age, education, and income groups in explaining farmers’ willingness to adopt low-carbon technologies.
There are multiple important findings from the above modeling procedure. Regarding gender discrepancies, men are mainly influenced by PBC and SN, suggesting men are more inclined to strengthen decision-making through external social norm and ability perception. However, due to the influence of ecologism [62], women are more inclined to be driven by ATB and PN. This gender difference might have stemmed from different family gender roles and social division of labor.
Regarding age differences, younger people are more dependent on SN, suggesting that they are more inclined to get behavioral guidance from social and group norms. On the contrary, older people are more influenced by PN and PBC, possibly due to richer experience, stronger financial standing, and higher sense of responsibility for environmental protection.
The difference in educational level shows that PBC and ATB mainly influence lower-educated groups. Farmers with lower educational attainment are more likely to rely on emotional responses and direct experiential judgments, placing greater emphasis on evaluating their perceived ability and the intuitive feasibility of adopting low-carbon production when making decisions. However, better educated groups tend to be driven by SN and PN. Likely, they are more easily driven by external social norm and personal ethics.
In addition, low-income groups are more susceptible to ATB. However, PBC and PN exert a more pronounced influence on high-income groups. Hence, the stronger the ability perception and the sense of responsibility for low-carbon production, the stronger the willingness of high-income farmers to adopt low-carbon technologies.

5. Conclusions and Policy Recommendations

5.1. Research Conclusions

Using an integrated TPB-NAM model, this study empirically explores the factors influencing farmers’ willingness to adopt low-carbon technologies by applying SEM to primary survey data in China. The results support the applicability of TPB-NAM theory in the context, and reveal the mechanism of ATB, SN, PBC, AC, AR and PN in affecting farmers’ willingness to adopt these technologies. Through multi-group analysis, this paper discusses the different response paths of varying subgroups classified by gender, age, education level, and income level.
The main findings are summarized as follows. First, PBC, SN, and ATB constitute key psychological mechanisms shaping farmers’ willingness to adopt low-carbon technologies. Farmers’ perceptions of their resource availability—such as financial capital, knowledge and skills, and time—and their perceived ability to manage potential risks in low-carbon production, are critical external conditions influencing the formation of adoption willingness. Given the prevailing characteristics of Chinese farmers—low educational attainment, advanced age, low income, and small-scale farming—the cognitive cost of making autonomous production decisions tends to be relatively high. Consequently, the low-cost “temptation of conformity” makes farmers more susceptible to the influence of external authorities and peer groups. When farmers recognize and positively evaluate low-carbon production, such affective judgments can further facilitate the formation of willingness to adopt.
Second, PN strongly influences farmers’ willingness to adopt low-carbon technologies and serves as the core internal driving force of behavioral intention. When farmers become aware of the negative ecological consequences of conventional high-carbon agriculture and develop a sense of responsibility, this awareness is further internalized into a moral obligation to engage in low-carbon agricultural practices. This finding underscores that fostering ecological ethics and cultivating shared environmental values are essential to generating long-term voluntary behavioral commitment in promoting low-carbon technologies.
Finally, multi-group analysis reveals significant heterogeneity in the formation pathways of adoption willingness across farmers with different sociodemographic characteristics, including gender, age, educational attainment, and income levels. Specifically, PBC exerts a more pronounced effect on male, higher-income, and lower-educated groups; SN has a more substantial impact among younger and better-educated groups; ATB demonstrates greater motivational power for female and lower-income groups; while PN shows the most significant influence among older farmers. These findings suggest that policy design should fully account for heterogeneous farmer groups’ distinct characteristics and decision-making logics and adopt differentiated incentive measures to enhance the precision and effectiveness of low-carbon technology promotion.

5.2. Policy Implications

In order to increase farmers’ willingness to adopt low-carbon technologies, it is necessary to navigate promotional strategies with available resources, social standard guidance, and environmental responsibility training. Multifold policy lessons are drawn from the above empirical findings. First, enhance farmers’ resource control capacity, strengthen social demonstration effects, and improve their ability to manage production risks. Government subsidies, policy-based credit, and technical guidance should be provided to reduce the financial costs and learning burdens associated with low-carbon agricultural production. In addition, learning effects should be reinforced through the demonstration and guidance of village cadres, cooperatives, and leading farmers engaged in large-scale planting and breeding.
Second, foster ecological ethics and a shared consensus on low-carbon development, leveraging the positive guiding role of attitudes and the moral constraint of personal norms. Policy publicity and opinion guidance should be strengthened by utilizing multiple channels, such as village broadcasting, social media, and short videos, to disseminate low-carbon practices and scientific knowledge. Green development concepts should be integrated into village regulations and rural governance systems to enhance social recognition of low-carbon agriculture, stimulate farmers’ environmental responsibility, and cultivate their moral obligation to adopt low-carbon production.
Finally, differentiated strategies tailored to heterogeneous farmer groups should be developed. Targeted technical training, financial support, and risk protection should be provided to help male, higher-income, and lower-educated groups overcome resource and technological barriers. Promotional activities should enhance the value perception of low-carbon production among female and lower-income farmers. Village committees, cooperatives, and leading farmers should be mobilized to strengthen peer influence and demonstration effects for younger and better-educated groups. Meanwhile, personal norms should be emphasized to guide older farmers in transforming low-carbon production into a moral obligation and social responsibility.
While these policy measures can effectively enhance farmers’ willingness to adopt low-carbon technologies, their implementation may still encounter several practical challenges that must be carefully addressed. In addition, subjective norm’ demonstration and guidance effects can generate a short-term “herd effect,” but without institutionalized guarantees and long-term benefit expectations, their influence is difficult to sustain. Moreover, there are significant differences in farmers’ resource endowments and risk perceptions; groups with low levels of perceived behavioral control cannot often learn and apply low-carbon technologies independently. Furthermore, farmers’ sense of moral responsibility is often undermined by pursuing short-term economic interests, leading to a potential gap between pro-environmental attitudes and actual behavior. Also, the pronounced heterogeneity among farmer groups across age, education, and income levels results in differentiated behavioral response pathways, making one-size-fits-all policies ineffective.
Therefore, policy design should explore a combinatorial strategy integrating social norms, economic incentives, and technical support. The incentive structure should achieve a dual linkage between financial compensation and moral responsibility, while leveraging digital platforms, demonstration-led approaches, and green market mechanisms to enhance the sustainability of policy support.

5.3. Limitations and Future Research

Despite the theoretical and empirical contributions of this study, several limitations remain that warrant further investigation in future research.
First, the research sample was drawn exclusively from Shandong Province. Shandong, as a typical major grain-producing region with diverse topography—including plains, hills, and coastal areas—and heterogeneous farm sizes and cropping structures, provides an appropriate context for validating the applicability of the TPB-NAM model. At the same time, regional disparities in development levels, policy environments, and climatic conditions may limit the external generalizability of the findings. Future studies could adopt a stratified sampling approach that covers multiple provinces and different types of agricultural regions further to examine the robustness and universality of the proposed model.
Second, this study employed cross-sectional, self-reported survey data. Although anonymity, cross-validation questions, and careful questionnaire design were used to reduce social desirability and recall bias, such biases cannot be entirely eliminated. Future research may combine longitudinal tracking data or choice experiment methods to capture the dynamic evolution of farmers’ willingness to adopt and their actual low-carbon behaviors.
Third, this study, based on the integrated TPB-NAM framework, primarily examined the influence of psychological motivations on behavioral intention. However, the willingness to adopt low-carbon practices is also shaped by external structural factors such as environmental regulations and market dynamics. Future research could integrate the TPB-NAM framework with external contextual factors to further reveal the joint effects of internal psychological mechanisms and external structural conditions.

Author Contributions

Y.Y.: conceptualization, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, visualization. L.S.: conceptualization, methodology, formal analysis, resources, writing—review and editing. Z.S.: investigation, resources, data curation, visualization. H.N.: investigation, data curation, writing—review and editing, project administration. S.C.: resources, supervision, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Foundation Key Project, Research on the Optimization of China’s Agricultural Insurance Policy Based on the “Trinity” of Scale, Structure, and Efficiency, grant number 23AGL026.

Institutional Review Board Statement

According to China’s regulations, ethical approval can be exempted for this research, as it uses anonymized data. This exemption aligns 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 1 January 2025).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. According to ethical guidelines, studies using anonymized data do not require obtaining written informed consent. Therefore, no informed consent document was signed for this survey. We conducted the questionnaire survey by verbally inquiring and obtaining the participants’ consent. The following information should be communicated to each respondent before the survey begins: This survey is anonymous, and no personally identifiable information will be collected. There are no right or wrong answers, and respondents are encouraged to answer based on their actual situation and genuine thoughts. All collected data will be used solely for scientific research and will be processed in a way that ensures participant privacy and confidentiality. If you agree, please feel free to participate in our questionnaire survey.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Sustainability 17 07399 g001
Figure 1. Theoretical model of farmers’ willingness to adopt low-carbon technologies based on the TPB-NAM framework.
Figure 1. Theoretical model of farmers’ willingness to adopt low-carbon technologies based on the TPB-NAM framework.
Sustainability 17 07399 g001
Table 1. Definition and descriptive statistics.
Table 1. Definition and descriptive statistics.
Variable
Dimension		Serial NumberMeasurement ItemMean ValueStandard DeviationCronbach’s α CoefficientCronbach’s α After Item DeletionDelete Item?
Adoption
Willingness (WILL)		WILL1I am willing to learn low-carbon agricultural technology3.2781.0060.9100.880no
WILL2I am willing to adopt low-carbon agricultural technology3.3180.9820.870no
WILL3I am willing to overcome all kinds of difficulties encountered in the process of adopting low-carbon technologies3.4391.0080.899no
WILL4I am willing to recommend low-carbon agricultural technology to my relatives and friends3.4370.9840.883no
Attitude
Toward the Behavior (ATB)		ATB1I think developing low-carbon agriculture is conducive to increasing crop yield3.3180.9470.9180.902no
ATB2I think developing low-carbon agriculture is conducive to increasing income3.2190.9880.904no
ATB3I think developing low-carbon agriculture is conducive to providing high-quality and safe agricultural products3.5030.9440.984no
ATB4I think developing low-carbon agriculture is conducive to protecting the ecological environment of farmland3.6660.9140.901no
ATB5I think developing low-carbon agriculture is conducive to ensuring the long-term high output capacity of land3.4750.9360.896no
Subjective Norm (SN)SN1My family encourages and supports me to adopt low-carbon agricultural technology3.4371.0510.9120.872no
SN2My friends encourage and support me to adopt low-carbon agricultural technology3.4011.0620.857no
SN3Farmers adjacent to your plot encourage and support me to adopt low-carbon agricultural technologies3.3661.060.867no
SN4The village committee or township government encourages and supports me to adopt low-carbon agricultural technology3.691.010.941yes
Perceived
Behavioral Control (PBC)		PBC1I have time to learn low-carbon agricultural technology2.7261.1140.8150.801no
PBC2I have the ability to bear the cost of low-carbon agriculture3.0681.090.682no
PBC3I have the ability to bear the operational risks in low-carbon agricultural management2.8541.0110.735no
Personal Norms (PNs)PN1We should change the traditional agricultural production habits and reduce the negative impact on the environment3.7270.9630.8480.795no
PN2We should learn from other farmers’ low-carbon agricultural practices3.6810.9390.771no
PN3Excessive or irrational use of water resources, pesticides, fertilizers and other high-carbon agricultural behaviors makes you feel guilty3.1411.0650.872yes
PN4Engage in green and low-carbon agricultural production activities, in line with your principles, values and beliefs3.5250.9620.785no
Awareness of Consequences (AC)AC1High-carbon agricultural production will aggravate abnormal situations such as climate warming3.541.0110.9160.910no
AC2High-carbon agricultural production will affect soil, water source and biodiversity3.6780.9670.876no
AC3High-carbon agricultural production will bring about waste of agricultural resources3.6091.0060.881no
AC4High-carbon agricultural production will affect the quality and safety of agricultural products, and then affect people’s health3.6130.9810.895no
Ascription of Responsibility (AR)AR1Do you think you have some responsibility for the climate warming caused by some high-carbon behaviors you have taken3.6970.9790.9300.908no
AR2Do you think you are responsible for the environmental pollution caused by some high-carbon behaviors you have taken3.7010.9790.873no
AR3Do you think you are responsible for the waste of resources caused by some high-carbon behaviors you have taken3.6930.9670.913no
Table 2. Sample characteristic distribution.
Table 2. Sample characteristic distribution.
CategoryOptionsNumber of SamplesSpecific Gravity (%)CategoryOptionsNumber of SamplesSpecific Gravity (%)
GenderMale58457.94Village
Cadres
Identity		no95794.94
Female42442.06yes515.06
Age30 years old or below454.46Health
Condition		Very bad161.59
31–40403.97Not so good949.33
41–5027226.98General33533.23
51–6032231.94Better36536.21
61 years old or above32932.64Very good19819.64
Education LevelElementary school or below49849.4Number of Household Labor Force115515.38
Junior high school32732.44268768.15
High school or
vocational school		12112311211.11
Bachelor’s degree or above626.154 persons or above545.36
Farming Years(0, 10 years)11411.31Household Annual
Income		(−∞, 10,000 yuan)17617.46
(10, 20 years)16216.07(10,000 yuan, 30,000 yuan)33733.43
(20, 30 years)24724.5(30,000 yuan, 50,000 yuan)23723.51
(30, ∞)48548.11(50,000 yuan, 100,000 yuan)19118.95
(100,000 yuan, ∞)676.65
Note: Data source: Compiled based on the survey questionnaire.
Table 3. Results of reliability and validity tests.
Table 3. Results of reliability and validity tests.
Latent VariableMeasurement ItemFactor LoadCombination Reliability (CR)Average Variation Extraction (AVE)Cronbach’s α CoefficientKaiser–Meyer–Olkin Measure of Sampling Adequacy (KMO)Bartlett’s Test of Sphere
ARAR10.8870.9310.8180.9300.7542477.767
(p = 0.000)
AR20.94
AR30.885
ATBATB10.8030.9190.6810.9180.8743581.276
(p = 0.000)
ATB20.814
ATB30.857
ATB40.835
ATB50.852
PBCPBC10.6950.8270.6160.8150.6891140.187
(p = 0.000)
PBC20.849
PBC30.803
PNsPN10.8420.8770.7050.8720.7151623.259
(p = 0.000)
PN20.879
PN40.796
ACAC10.8040.9380.7420.9160.8342944.130
(p = 0.000)
AC20.892
AC30.885
AC40.847
SNSN10.9020.9410.8420.9410.7632744.343
(p = 0.000)
SN20.944
SN30.907
WILLWILL10.8630.9110.7150.9100.8452720.912
(p = 0.000)
WILL20.880
WILL30.791
WILL40.856
Note: Data source: AMOS 26.0 analysis.
Table 4. Model fit assessment.
Table 4. Model fit assessment.
Statistical Test IndexAbsolute Fitness IndexValue-Added Fitness IndexStreamlined Fitness Index
χ2/dfRMSEAGFIAGFINFIRFIIFITLICFIPGFIPNFI
Model
esimate		4.3810.0580.9150.8940.9490.9410.9600.9540.9600.7300.819
Judgment Criteria<5<0.08>0.9>0.9>0.9>0.9>0.9>0.9>0.9>0.5>0.5
ResultAcceptableIdealIdealAcceptableIdealIdealIdealIdealIdealIdealIdeal
Note: Data source: AMOS 26.0 analysis.
Table 5. Hypothesis verification and path coefficients.
Table 5. Hypothesis verification and path coefficients.
Path HypothesisStandardized Estimator CoefficientC.R. (t-Value)Conclusion
H1: ATB→WILL0.1564.431 ***support
H2: SN→WILL0.1724.782 ***support
H3: PBC→WILL0.2238.636 ***support
H4: PN→WILL0.50114.352 ***support
H5: AC→PN0.68212.334 ***support
H6: AC→AR0.92721.652 ***support
H7: AR→PN0.5839.278 ***support
Note. *** has passed the significance test at the levels of 1%. Data source: AMOS 26.0 analysis.
Table 6. Estimation results of multi-group analysis.
Table 6. Estimation results of multi-group analysis.
Path
Hypothesis		Classify Farmers by GenderClassify Farmers by AgeClassify Farmers by
Education		Classify Farmers by
Income
MaleFemaleYoungerOlderLess
Educated		Better
Educated		Low
Income		High
Income
H1: ATB→WILL0.150 ***0.172 ***0.133 **0.159 **0.152 ***0.126 **0.173 ***0.143 ***
H2: SN→WILL0.181 **0.152 ***0.275 ***0.099 **0.168 ***0.202 ***0.160 ***0.166 ***
H3: PBC→WILL0.248 ***0.195 ***0.239 ***0.216 ***0.272 ***0.176 ***0.212 ***0.247 ***
H4: PN→WILL0.477 ***0.532 ***0.406 ***0.573 ***0.462 ***0.549 ***0.499 ***0.509 ***
Note. ***, ** have passed the significance test at the levels of 1%, 5%, respectively. Data source: AMOS 26.0 analysis.
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Yuan, Y.; Sun, L.; She, Z.; Niu, H.; Chen, S. Farmers’ Willingness to Adopt Low-Carbon Technologies: Exploring Key Determinants Using an Integrated Theory of Planned Behavior and the Norm Activation Theory Framework. Sustainability 2025, 17, 7399. https://doi.org/10.3390/su17167399

AMA Style

Yuan Y, Sun L, She Z, Niu H, Chen S. Farmers’ Willingness to Adopt Low-Carbon Technologies: Exploring Key Determinants Using an Integrated Theory of Planned Behavior and the Norm Activation Theory Framework. Sustainability. 2025; 17(16):7399. https://doi.org/10.3390/su17167399

Chicago/Turabian Style

Yuan, Yanmei, Le Sun, Zongyun She, Hao Niu, and Shengwei Chen. 2025. "Farmers’ Willingness to Adopt Low-Carbon Technologies: Exploring Key Determinants Using an Integrated Theory of Planned Behavior and the Norm Activation Theory Framework" Sustainability 17, no. 16: 7399. https://doi.org/10.3390/su17167399

APA Style

Yuan, Y., Sun, L., She, Z., Niu, H., & Chen, S. (2025). Farmers’ Willingness to Adopt Low-Carbon Technologies: Exploring Key Determinants Using an Integrated Theory of Planned Behavior and the Norm Activation Theory Framework. Sustainability, 17(16), 7399. https://doi.org/10.3390/su17167399

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