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

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Please check the attached document

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Check on the long sentences

Author Response

General comments

Authors developed an integrated theoretical framework that combined the theory of Planned Behavior and the Norm Activation Model to explore determinants and formation process of farmers’s adoption of low carbon technologies. They collected data from 1008 farmers from Shadong Province using a structral questionnaire. They found that awareness of consequences was a driving force of adoption. The focus of this study is topical, relevant and timely. It is suitable to the journal as it discusses an important area in contemporary agriculture in the face of climate change and necessity to reduce greenhouse gas emissions. The results are beneficial in empowering smallscale farmers. The study hypotheses are too many and the model has not been clearly demonstrated. The auhors too need to enhance their methodology to sufficiently achieve their goal.

 

Response 1: Thank you for pointing this out. We fully acknowledge that an overly complex model with too many hypotheses may dilute the clarity and interpretability of the findings. In response, we have carefully streamlined the theoretical model, retaining only the seven most essential and theoretically grounded hypotheses while removing five peripheral ones that contributed limited explanatory power.

Furthermore, we have enhanced the methodological rigor by:

1. Clarifying the model structure with a more concise conceptual diagram and explicitly linking each retained hypothesis to its theoretical foundation.
2. Re-examining the measurement model through confirmatory factor analysis to ensure robustness and validity.
3. Revising the structural equation modeling (SEM) procedure with improved fit indices and a more precise explanation of the estimation strategy.

Specific Revisions:

Figure 1. Theoretical model of farmers' willingness to adopt low-carbon technologies based on the TPB-NAM framework.

This revision has been made in the Theoretical Framework of Normative Activation section on page [5], lines [197-199] of the revised manuscript.

 

 

Specific comments

 

Title

The title is appropriate for the study subject

 

 

Abstract:

Authors have provided a good background information in the abstract. However, theyshould reduce the number of words to atmost 210.

 

Response 2: Thank you for pointing this out. We have carefully revised and condensed the abstract, and it is now within 200 words while retaining all essential information and key findings.

Specific Revisions:

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.

This revision has been made in the Abstract section on page [1], paragraph [1], lines [19–29] of the revised manuscript.

 

Key words

They are sufficient.

 

Introduction

Authors have introduced the paper by highlighting the climate change threats andchallenges to food security, human health and social development.

 

They have mentioned the major drivers of these changes.

 

They have highlighted the significance of emissions coming from small-scale farmers withan emphasis on Chinese farmers and the importance of having them shift to low emissiontechnologies.

 

However, they have indicated that this is challenging to these farmers who are faced withmany difficulties. Thus, the importance of understanding the influencing factorsinfluencing their willingness to adopt these technologies.

 

This is justified by their discussion on the current studies and their deficiency on this topic.Thus, the need to develop an integrative model combining the theory of Theory of PlannedBehavior (TPB) and Norm Activation Theory (NAT).

 

Line 57-59, this understanding can be referenced by Manono et al., 2025. These referencewill not only apply in many instances throughout the text (it will augment reference citingthe factors in lines 111-118 and also lines 122-127). The reference specifically mentionedthese factors on African Smallholder farmers and can be compared to Asian farmers in thiscontext. Thus, it will add an international aspect to the study, as authors have not usedmany citations out of the study region.

 

Response 3: Thank you for pointing this out. We agree that adding the reference by Manono et al. (2025) will enhance the study’s international perspective. In the revised manuscript, we have cited this reference in lines 55 to support the contextual understanding of the factors influencing farmers’ adoption behavior.

Furthermore, we have also incorporated this reference in lines 113 and 123, where similar socio-economic, institutional, and biophysical factors are discussed. By integrating evidence from African smallholder farmers, this citation provides a meaningful cross-regional comparison with Asian farmers and strengthens the global relevance of our findings.

 

Theorethical framework

 

>The authors have provided good background information on the theories involved in thestudy and model development.

 

The study hypotheses are well conceptualized and supported by literature. However, arethey not too many?

 

Figure 1 provides a good conceptualization of the model formulation. However, it iscomplicated and does not reveal how it will be applied. I think it is because of the numberof hypotheses involved. It is beneficial if it can be improved to include application.

Response 4: Thank you for pointing this out. We agree with this comment. An excessive number of hypotheses may increase the complexity of the model and make its application less intuitive. In response, we have carefully streamlined the theoretical framework by retaining only the seven most essential hypotheses strongly supported by theory and empirical relevance, while removing five peripheral hypotheses that contributed limited explanatory value.

Moreover, we have revised Figure 1 to improve clarity and enhance the framework’s practical applicability. The updated figure simplifies the conceptual structure and explicitly illustrates how the model will be applied in the empirical analysis.

These revisions significantly improve the parsimony and interpretability of the theoretical framework, making it easier for readers to understand both the conceptual logic and its empirical implementation.

Figure 1. Theoretical model of farmers’ willingness to adopt low-carbon technologies based on the TPB-NAM framework.

This revision has been made in the Theoretical Framework of Normative Activation section on page [5], paragraph [1], lines [197-199] of the revised manuscript.

 

Survey design and data description

Authors have described the information collected by their questionnaires.

 

They have also provided the region where data was collected from and when.

 

However, authors have not indicated how the 1100 participants were identified.

 

Authors should provide a methodology used to identify these participants. What criteriadid the authors use to identify them? What determined their inclusion or exclusion? Thisinformation is important to help determine the viability of the data collected

 

Response 5: Thank you for pointing this out. We fully agree that clarifying the participant identification process is essential for ensuring the transparency and reliability of the data. In the revised manuscript, we have added a detailed description of the sampling methodology and inclusion/exclusion criteria.

Specific Revisions:

Specifically, the 1,100 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 town 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.

This revision has been added to the Questionnaire Design section on pages [5], lines [210-216] of the revised manuscript.

To ensure the validity of responses, the inclusion criteria required participants to (1) be the primary decision-maker or co-decision-maker in agricultural production within the household, (2) have engaged in crop cultivation for at least the past three years, and (3) be aged between 18 and 75 to ensure cognitive ability for survey participation. Households were excluded if they had recently migrated, abandoned agricultural production, or were unwilling to provide informed consent.

 

 

Results and Discussion

The analysis procedures have been provided. However, some of this informationinvolving the methodological analysis should be taken to the methods section (theprevious section).

 

Response 6: Thank you for pointing this out. We agree that some methodological details were inappropriately placed within the Results and Discussion section. We have reorganized the content in the revised manuscript by moving the relevant methodological descriptions to the Methods section to ensure a more precise separation between methodology and results. This adjustment improves the logical flow and readability of the manuscript.

 

Authors have provided detailed results.

 

Tables are well presented and captioned.

 

It is however not clear how the model was developed and at what stage. This should beclearly presented by the authors. I suggest that they indicate this before testing its fitnessin section 4.3 and the hypotheses test and conclusions presented in Table 5.

 

Response 7: Thank you for pointing this out. We agree that the original manuscript did not clearly describe the model development process. In the revised version, we have added a concise section explaining how the integrated TPB–NAM framework was developed, including the theoretical basis, initial hypothesis formulation, iterative refinement, and the rationale for retaining the final seven hypotheses.

Specific Revisions:

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.

This additional explanation has been inserted to the Model Development Process section on pages [4-5], lines [182-196] of the revised manuscript.

 

The discussion in section 4.3 and 4.4 is well presented. Authors have discussed the observations and their implications. However, authors can enhance this section by addingmore references especially those from outside the study region.

Response 8: Thank you for pointing this out. We agree that incorporating additional references, particularly those from outside the study region, will strengthen the discussion and enhance the international relevance of the findings. In the revised manuscript, we have enriched Sections 4.3 and 4.4 by adding several studies from other regions, including research on smallholder farmers’ adoption of sustainable agricultural practices in Sub-Saharan Africa. These references provide broader comparative insights and place our findings in a wider global context, thereby improving the depth and generalizability of the discussion.

 

 

Conclusions and policy implications

 

These sections are well written and supported by data. However, authors should not

repeat reporting of results or discussing them in the conclusion.

Response 9: Thank you for pointing this out. We agree that the conclusion should avoid repeating detailed results or discussions. In the revised manuscript, we have streamlined the conclusion by removing redundant descriptions of the results and focused instead on summarizing the key findings, highlighting their theoretical and practical implications, and suggesting directions for future research. This adjustment improves the conciseness and clarity of the conclusion section.

Specific Revisions:

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.

This revision has been added to the Research Conclusions section on pages [13-14], lines [404-435] of the revised manuscript.

 

References

Authors should enhance the manuscript quality by adding more international referencesespecially in the discussion section.

I have suggested one reference below to cite a statement inline 57-59 that is not referenced and can be applied in several other statements within the text.

1. anono, B.O.; Khan, S., Kithaka, K.M. A Review of the Socio-Economic, Institutional, andBiophysical Factors Influencing Smallholder Farmers’ Adoption of Climate SmartAgricultural Practices in Sub-Saharan Africa. Earth 2025, 6, 48.https://doi.org/10.3390/earth6020048

Response 10: Thank you for this valuable suggestion. We agree that incorporating more international references will improve the academic rigor and enhance the manuscript’s global relevance. In the revised version, we have cited the suggested reference by Manono et al. (2025) in lines 55 and other relevant sections of the discussion where similar socio-economic and institutional factors are addressed.

In addition, we have enriched the discussion section by adding several international studies on smallholder farmers’ adoption of climate-smart and low-carbon agricultural practices. These references provide a broader comparative perspective, strengthening our findings’ international applicability and contextual depth. The additional references are as follows:

1. Musafiri, C.M.; Kiboi, M.; Macharia, J.; Ng’etich, O.K.; Kosgei, D.K.; Mulianga, B.; Okoti, M.; Ngetich, F.K. Adoption of climate-smart agricultural practices among smallholder farmers in Western Kenya: do socioeconomic, institutional, and biophysical factors matter? Heliyon 2022, 8.
2. Atta-Aidoo, J.; Antwi-Agyei, P.; Dougill, A.J.; Ogbanje, C.E.; Akoto-Danso, E.K.; Eze, S. Adoption of climate-smart agricultural practices by smallholder farmers in rural Ghana: An application of the theory of planned behavior. PLoS Climate 2022, 1, e0000082.
3. Autio, A.; Johansson, T.; Motaroki, L.; Minoia, P.; Pellikka, P. Constraints for adopting climate-smart agricultural practices among smallholder farmers in Southeast Kenya. Agricultural Systems 2021, 194, 103284.
4. Shani, F.K.; Joshua, M.; Ngongondo, C. Determinants of Smallholder Farmers’ Adoption of Climate-Smart Agricultural Practices in Zomba, Eastern Malawi. Sustainability 2024, 16, 3782.
5. Abegunde, V.O.; Sibanda, M.; Obi, A. Determinants of the adoption of climate-smart agricultural practices by small-scale farming households in King Cetshwayo District Municipality, South Africa. Sustainability 2019, 12, 195.
6. Tran N L D, Rañola Jr R F, Ole Sander B, et al. Determinants of adoption of climate-smart agriculture technologies in rice production in Vietnam[J]. International journal of climate change strategies and management, 2020, 12(2): 238-256.
7. Ma W, Rahut D B. Climate-smart agriculture: adoption, impacts, and implications for sustainable development[J]. Mitigation and Adaptation Strategies for Global Change, 2024, 29(5): 44.
8. Tadjiev A, Djanibekov N, Soviadan MK, Herzfeld T. Participation in informal cooperation in water management and adoption of sustainable agricultural practices: Empirical evidence from Uzbekistan. Australian Journal of Agricultural and Resource Economics. 2025 Jul 8.
9. Dessart FJ, Barreiro-Hurlé J, Van Bavel R. Behavioural factors affecting the adoption of sustainable farming practices: a policy-oriented review. European Review of Agricultural Economics. 2019 Jul 1;46(3):417-71.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This paper investigates the factors that influence Chinese farmers’ willingness to adopt low-carbon agricultural technologies. The authors build an integrated analytical framework combining the TPB and NAM, aiming to address gaps in the literature related to psychological motivation and heterogeneity across farmer groups.

The sample is drawn solely from Shandong Province, which though typical in some respects, limits the generalizability of the conclusions to all of rural China.

While the integrated TPB-NAM framework is a strength, the paper does not delve deeply into the mechanisms by which social norms or moral obligations translate into actual behavioral change. The discussion of indirect and mediating effects, though statistically significant, remains at a theoretical level without rich contextualization.

The research relies exclusively on self-reported questionnaire data, which may be susceptible to social desirability or recall bias, especially in a context where “low-carbon” behaviors are politically and socially encouraged.

There is limited discussion about implementation challenges or how the findings could inform more innovative or tailored policy tools.

The overall language of the manuscript is generally fluent, but there are instances where grammatical structures and logical coherence could be improved, and some academic terms are not used precisely, occasionally resulting in informal expressions that affect professionalism. For example, in the abstract, there is repetitive and imprecise phrasing, such as “awareness of consequences is a significant driving force , indirectly influencing adoption willingness through intermediary variables such as ascription of responsibility, personal norms, subjective norm, and attitude toward the behavior.” This sentence contains an unnecessary space and is overly long, which can hinder clarity. Moreover, some paragraphs lack logical connectors when introducing reasoning or arguments.

Author Response

Comments 1: This paper investigates the factors that influence Chinese farmers’ willingness to adopt low-carbon agricultural technologies. The authors build an integrated analytical framework combining the TPB and NAM, aiming to address gaps in the literature related to psychological motivation and heterogeneity across farmer groups. The sample is drawn solely from Shandong Province, which though typical in some respects, limits the generalizability of the conclusions to all of rural China.

 

Response 1: Thank you for pointing this out. We agree with this comment. To address this concern, we have explicitly acknowledged this limitation and added a detailed discussion in the revised manuscript's Limitations and Future Research section. Specifically, we state that Shandong Province, as a typical central grain-producing region with diverse topography and heterogeneous farm structures, provides an appropriate context for testing the applicability and robustness of the TPB–NAM framework. However, regional differences in development levels, policy environments, and climatic conditions may affect the psychological mechanisms driving farmers' willingness to adopt.

Specific Revisions:

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. Although 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, regional disparities in development levels, policy environments, and climatic conditions may limit the external generalizability of how farmers’ psychological mechanisms influence their willingness to adopt low-carbon technologies. Future studies could adopt a stratified sampling approach covering multiple provinces and different types of agricultural regions to further 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.

This revision has been added to the Limitations and Future Research section on pages [14-15], paragraphs [10-14], lines [460-457] of the revised manuscript.

 

 

 

Comments 2:While the integrated TPB-NAM framework is a strength, the paper does not delve deeply into the mechanisms by which social norms or moral obligations translate into actual behavioral change. The discussion of indirect and mediating effects, though statistically significant, remains at a theoretical level without rich contextualization.

Response 2: Thank you for pointing this out. We agree with this comment. We fully acknowledge this limitation and have addressed it in Section 4.3 by refining our analysis based on the revised TPB-NAM integrated framework and the regression results in Table 5. Specifically, we have:

Enhanced the mediation analysis of the AC→AR→PN→WILL pathway to elucidate the underlying mechanisms.

Examined the normative pathways in greater depth, particularly focusing on how subjective norm (SN) and personal norms (PN) translate into actual behavioral intentions in the context of low-carbon agricultural technology adoption.

Specific Revisions:

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 PN through AR, further promotes the formation of willingness (WILL) to adopt low-carbon technologies, and facilitates the chain transmission: AC→AR→PN→WILL. In specific, AC directly affects farmers’ PN (β= 0.682, H5 holds), at the same time, it can indirectly influence PN 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' PN. When farmers realize the negative impact of conventional high-carbon agriculture, they are more responsible for their behavior consequences, thus more likely to stimulate their sense of moral obligation to low-carbon production.

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 PN 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.

This revision has been added to the Model Hypothesis Test on pages [11-12], paragraphs [4-5], lines [305-333] of the revised manuscript.

Comments 3:The research relies exclusively on self-reported questionnaire data, which may be susceptible to social desirability or recall bias, especially in a context where “low-carbon” behaviors are politically and socially encouraged.

 

Response 3: Thank you for pointing this out. We agree with this comment. To minimize social desirability bias, this study adopted an anonymous survey to reduce social desirability bias and explicitly informed respondents that the data would be used solely for academic research and not linked to government supervision or subsidies. In addition, all questions were neutrally worded to avoid leading respondents toward socially desirable answers.

To mitigate recall bias, the questionnaire focused on recent and specific low-carbon fertilization practices, and cross-validation questions (e.g., fertilizer purchase channels and frequency) were included to improve response consistency.

While self-reported data have inherent limitations, they remain a widely used and practical method in rural agricultural research, where farms are scattered and systematic records are lacking. In future research, we plan to complement self-reports with objective data (e.g., village-level fertilizer sales records, remote sensing monitoring) and field experiments to validate and enhance the accuracy of farmers’ reported behaviors.

Notably, the robustness of our empirical results suggests that potential biases are unlikely to overturn the key findings.

In addition, this limitation and the proposed future improvements have been explicitly discussed in the “Limitations and Future Research” section on page [15], paragraph [2], lines [471-476] of the revised manuscript.

 

 

Comments 4:There is limited discussion about implementation challenges or how the findings could inform more innovative or tailored policy tools.

 

Response 4: Thank you for pointing this out. We agree with this comment. In the revised manuscript, we have expanded the Policy Implications section in two ways:

1.Implementation challenges

We now explicitly discuss the contextual barriers that may constrain the real-world applicability of TPB-NAM behavioral mechanisms. These include the short-term nature of social norm effects without institutional guarantees, the heterogeneity of farmers’ resource endowments and risk perceptions that limits perceived behavioral control, the weakening of moral responsibility under short-term economic pressures, and the difficulty of applying one-size-fits-all policies due to diverse behavioral response pathways across age, education, and income groups.

2.Innovative and tailored policy tools

Building on the theoretical and empirical findings, we propose a combinatorial policy strategy that integrates social norms, economic incentives, and technical support. Specifically, we suggest differentiated policy packages for distinct farmer groups, using digital platforms to reduce information asymmetry, behavioral “nudge” tools such as carbon footprint visualization and peer benchmarking, and linking green certification to premium market mechanisms to generate sustainable incentives.

These additions provide a more balanced discussion that bridges the behavioral mechanisms identified by the TPB-NAM model with actionable, innovative, and context-specific policy recommendations.

This revision has been added to the Policy Implications section on pages [14], paragraphs [4-5], lines [440-457] of the revised manuscript.

 

Comments 5:The overall language of the manuscript is generally fluent, but there are instances where grammatical structures and logical coherence could be improved, and some academic terms are not used precisely, occasionally resulting in informal expressions that affect professionalism. For example, in the abstract, there is repetitive and imprecise phrasing, such as “awareness of consequences is a significant driving force , indirectly influencing adoption willingness through intermediary variables such as ascription of responsibility, personal norms, subjective norm, and attitude toward the behavior.” This sentence contains an unnecessary space and is overly long, which can hinder clarity. Moreover, some paragraphs lack logical connectors when introducing reasoning or arguments.

 

Response 5: Thank you for pointing this out. We agree with this comment. We have taken the following steps in the revised version:

1.Language refinement and grammar correction

We carefully reviewed the entire manuscript and corrected minor grammatical errors, such as unnecessary spaces and redundant phrasing. Overly long or complex sentences (including those cited in the abstract) were rewritten for greater clarity and readability.

2.Improvement of logical coherence

We added appropriate logical connectors and transition phrases to improve the flow of reasoning within and between paragraphs, ensuring clearer argumentation.

3.Precision in academic terminology

We rechecked all key concepts (e.g., awareness of consequences, ascription of responsibility, subjective norms) to ensure consistency with established TPB-NAM literature, avoiding informal or imprecise expressions.

 

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

This study integrates the Theory of Planned Behavior and the Norm Activation Model to empirically analyze the factors influencing farmers’ willingness to adopt low-carbon agricultural technologies in Shandong Province, revealing heterogeneity across different demographic and socioeconomic groups. While the research offers theoretical and policy value, the current version still has several areas that require improvement:

1.The articulation of the study’s contributions is currently unclear. The authors are advised to reframe and clearly highlight the innovative aspects of this research—whether in theoretical construction, methodological approach, or empirical findings—to strengthen its academic value.

2.The literature review is insufficient. It is recommended that the authors conduct a more systematic analysis of existing domestic and international studies on low-carbon agricultural technology adoption, to better position the study within the existing body of research and clarify its unique contributions.

3.The paper presents too many research hypotheses, some of which appear redundant. The authors are encouraged to consolidate and streamline the hypotheses to enhance the clarity and coherence of the research framework.

4.The authors should contextualize their empirical findings more deeply by engaging with the specific realities of China’s rural society and agricultural development. This would improve the study’s practical relevance and explanatory power.

5.The rationale for choosing Shandong Province as the research site is not sufficiently explained. Are Shandong’s farmers particularly representative or typical? The authors should elaborate on the sampling rationale and its implications for the generalizability of the findings.

6.The authors are advised to include a discussion of the study’s limitations and suggest directions for future research in the conclusion section to improve the completeness and openness of the study.

Author Response

This study integrates the Theory of Planned Behavior and the Norm Activation Model to empirically analyze the factors influencing farmers’ willingness to adopt low-carbon agricultural technologies in Shandong Province, revealing heterogeneity across different demographic and socioeconomic groups. While the research offers theoretical and policy value, the current version still has several areas that require improvement:

Comments 1: The articulation of the study’s contributions is currently unclear. The authors are advised to reframe and clearly highlight the innovative aspects of this research—whether in theoretical construction, methodological approach, or empirical findings—to strengthen its academic value.

Response 1: Thank you for pointing this out. We agree with this comment. Accordingly, we have revised the end of the Introduction to articulate the theoretical, methodological, and empirical contributions clearly. Specifically, we now expressly emphasize that this study (1) advances a more comprehensive TPB-NAM framework integrating rational and moral determinants of low-carbon adoption, (2) applies a robust multi-group structural equation modeling approach to uncover heterogeneous behavioral mechanisms, and (3) provides policy-relevant empirical evidence to inform differentiated low-carbon agricultural extension strategies.

Specific Revisions:

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. It allows for a more systematically 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.

This revision has been added to the Introduction section on pages [2-3], lines [80-96] of the revised manuscript.

 

Comments 2: The literature review is insufficient. It is recommended that the authors conduct a more systematic analysis of existing domestic and international studies on low-carbon agricultural technology adoption, to better position the study within the existing body of research and clarify its unique contributions.

Response2: Thank you for pointing this out. We agree with this comment. Accordingly, we have revised and expanded the literature review section in two key ways:

From the perspective of research content, we now systematically summarize the factors influencing farmers’ adoption of low-carbon agricultural practices, including individual characteristics, capital endowment, socioeconomic factors, cognitive and psychological traits, digital literacy, and environmental regulation.

From the methodological perspective, we explicitly compare domestic and international approaches, noting that most existing studies rely on regression-based models (e.g., Logit, Probit), while relatively few adopt integrated behavioral frameworks or multi-group structural equation modeling to reveal heterogeneity in decision-making mechanisms.

In addition, we incorporated more international studies to enrich the comparative perspective. For example, we refer to Musafiri et al. (2022) on climate-smart agriculture adoption in Sub-Saharan Africa and Dessart et al. (2019) on behavioral drivers of sustainable farming practices in Europe. These additions allow us to highlight both the common challenges and the context-specific differences between developing and developed countries, thereby clarifying the global relevance of this study.

We also clarified the research gaps more explicitly, emphasizing that (1) internal psychological motivations remain underexplored, (2) many studies rely on a single theoretical framework, lacking comprehensive and systematic evaluations, and (3) heterogeneity across demographic and socioeconomic groups is often overlooked.

Specific Revisions:

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 [9-11], capital endowment [12-15], socioeconomic factors [7,16,17,18], cognitive and psychological traits [19-21], digital literacy [22, 23] and environmental regulation [22-26]. 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. It allows for a more systematically 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.

This revision has been added to the Introduction section on pages [2-3], paragraphs, lines [62-96] of the revised manuscript.

 

Comments 3: The paper presents too many research hypotheses, some of which appear redundant. The authors are encouraged to consolidate and streamline the hypotheses to enhance the clarity and coherence of the research framework.

Response 3: Thank you for pointing this out. We fully acknowledge that an overly complex model with too many hypotheses may dilute the clarity and interpretability of the findings. In response, we have carefully streamlined the theoretical model, retaining only the seven most essential and theoretically grounded hypotheses while removing five peripheral ones that contributed limited explanatory power.

Furthermore, we have enhanced the methodological rigor by:

1. Clarifying the model structure with a more concise conceptual diagram and explicitly linking each retained hypothesis to its theoretical foundation.
2. Re-examining the measurement model through confirmatory factor analysis to ensure robustness and validity.
3. Revising the structural equation modeling (SEM) procedure with improved fit indices and a more precise explanation of the estimation strategy.

Figure 1. Theoretical model of farmers' willingness to adopt low-carbon technologies based on the TPB-NAM framework.

This revision has been made in the Theoretical Framework of Normative Activation section on page [5], lines [197-199] of the revised manuscript.

 

Comments 4: The authors should contextualize their empirical findings more deeply by engaging with the specific realities of China’s rural society and agricultural development. This would improve the study’s practical relevance and explanatory power.

Response 4: Thank you for pointing this out. We agree with this comment. Accordingly, we have revised the Analysis of Empirical Results section to provide richer contextual interpretations grounded in China’s rural socioeconomic conditions.

Specific Revisions:

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 [16,59,60].

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, PN 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 norm, 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 PN through AR, further promotes the formation of willingness (WILL) to adopt low-carbon technologies, and facilitates the chain transmission: AC→AR→PN→WILL. In specific, AC directly affects farmers’ PN (β= 0.682, H5 holds), at the same time, it can indirectly influence PN 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’ PN. When farmers realize the negative impact of conventional high-carbon agriculture, they are more responsible for their behavior consequences, thus 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 PN 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.

This revision has been added to the Analysis of Empirical Results section on pages [11-12], lines [297-357] of the revised manuscript.

 

 

Comments 5: The rationale for choosing Shandong Province as the research site is not sufficiently explained. Are Shandong’s farmers particularly representative or typical? The authors should elaborate on the sampling rationale and its implications for the generalizability of the findings.

Response 5: Thank you for pointing this out. We agree with this comment. In the revised manuscript, we have provided a more detailed explanation in the Limitations and Future Research section.

Specifically, we emphasize that Shandong Province is one of China’s major grain-producing regions, characterized by diverse topography (plains, hills, and coastal areas), heterogeneous farm sizes, and varied cropping structures, making it a suitable context for testing the applicability and robustness of the TPB–NAM framework. At the same time, we acknowledge that regional differences in socioeconomic development, policy environments, and climatic conditions may affect farmers’ adoption mechanisms, which could limit the generalizability of the findings.

To address this limitation, we suggest that future studies adopt stratified sampling across multiple provinces and agricultural zones further to examine the robustness and universality of the proposed model.

Specific Revisions:

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. While 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.

This revision has been added to the Limitations and Future Research section on pages [15], lines [482-493] of the revised manuscript.

 

Comments 6: The authors are advised to include a discussion of the study’s limitations and suggest directions for future research in the conclusion section to improve the completeness and openness of the study.

Response 6: Thank you for pointing this out. We agree with this comment. In the revised manuscript, we have added a new subsection, 5.3 Limitations and Future Research.

In this subsection, we acknowledge three main limitations of the current study: (1) the use of a single-province sample (Shandong) that may limit the external generalizability of the findings, (2) the reliance on cross-sectional self-reported survey data that could be subject to social desirability and recall biases, and (3) the focus on internal psychological mechanisms without incorporating external contextual factors such as environmental regulations and market dynamics.

To address these limitations, we also suggest several directions for future research, including conducting stratified multi-regional sampling, integrating longitudinal or experimental methods to capture the dynamics of adoption willingness and behavior, and combining the TPB–NAM framework with external structural variables to explain farmers’ low-carbon adoption behaviors better.

Specific Revisions:

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. While 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.

This revision has been added to the Limitations and Future Research section on pages [15], lines [482-505] of the revised manuscript.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I have no further comments

Reviewer 2 Report

Comments and Suggestions for Authors

Accept

Reviewer 3 Report

Comments and Suggestions for Authors

After reviewing the second round of revised manuscripts and the author's responses, I believe that the author has responded fully and specifically to all the questions I raised, and the quality of the paper has been significantly improved. I recommend it for acceptance.