Review Reports - Impact of Digital Infrastructure on Farm Households’ Scale Management

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Acronyms in the Abstract
The acronyms “OLS” (Ordinary Least Squares) and “CRRS” (China Rural Revitalization Survey) are used in the abstract without prior definition. Please ensure that all acronyms are spelled out in full upon first use to aid reader comprehension, especially for interdisciplinary audiences.

Section 2.2 – Moderating Effects of Digital Literacy
This section requires significant revision. First, the authors should provide a clear, conceptually grounded definition of digital literacy. At present, it is operationalized in an overly simplistic binary form (i.e., whether a respondent can access information online), which does not reflect the multifaceted nature of the concept.
I strongly recommend that the authors refer to established frameworks such as the European Commission’s Digital Competence Framework for Citizens (DigComp). Incorporating such a framework would significantly strengthen the theoretical foundation of the moderation analysis and help readers better understand the construct being measured.

Clarification of the Digital Literacy Variable (Lines 322–325)
The manuscript defines digital literacy as “the ability to use digital technology to access, manage, understand, integrate, present, evaluate, and create information securely and rationally,” which is appropriate. However, the operationalisation of this construct—based on a single question regarding the ability to access information online—is overly narrow.
Furthermore, the cited study (Zhang et al., 2024) does not adequately capture the full range of competencies encompassed by digital literacy. Given that the survey question focuses solely on basic information access, I strongly recommend that the authors relabel this variable, as its current form does not align with the broader definition provided. Maintaining the label "digital literacy" is misleading and undermines construct validity.

Lack of Diagnostic Checks for Statistical Models
Although the manuscript applies OLS, moderation, and 2SLS regression models, it lacks essential discussion of the underlying statistical assumptions. I encourage the authors to address the following:

OLS Regression Diagnostics: There is no evidence of tests for linearity, normality of residuals, or homoscedasticity. These diagnostics are necessary to assess the appropriateness and robustness of the regression models.
Moderation Analysis: The interaction terms used in the moderation analysis may be affected by multicollinearity. It is unclear whether the predictor and moderator variables were mean-centered prior to creating the interaction terms—a standard practice. The authors should clarify this and report Variance Inflation Factors (VIFs) or similar diagnostics.
Instrumental Variable (2SLS) Estimation: This is the only component of the statistical analysis where diagnostic reporting is handled appropriately. The use of the Cragg–Donald F-statistic to assess weak instruments is commendable and enhances the credibility of the findings.

Template Text Left in Results Section
The Results section includes unedited template language:
“This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.” This suggests poor manuscript preparation and insufficient attention to detail. Such oversights reduce the professionalism of the submission and should be addressed prior to publication.

Discussion and Conclusion Section
The section titled “Conclusion and Discussion” should be renamed “Discussion and Conclusion” to align with conventional structure and logical flow. More importantly, this section needs to be substantially revised to engage with relevant digital literacy literature. Specifically, the authors should compare and contrast their findings with previous studies to demonstrate whether their results confirm, extend, or contradict existing knowledge. This would enhance the theoretical contribution and show how the study informs or refines policy and practice in rural digital transformation. A more reflective and dialogic conclusion would also provide clearer guidance for targeted interventions.

Author Response

Dear editors and reviewers:

We appreciate your careful work on our submission of "Impact of digital infrastructure on farm households' scale management" (Manuscript ID:sustainability-3746651) on (July 11th). We are also grateful to the reviewers for their constructive comments. We have revised the manuscript following the editor's and reviewers' comments, and carefully proofread the manuscript to minimize typographical, grammatical, and bibliographical errors.

Revised portions are marked in revision mode in the paper. The main corrections in the paper and respondse to the reviewer’s comments are as follows:

Reviewer 1：

1.The acronyms "OLS" (Ordinary Least Squares) and "CRRS" (China Rural Revitalization Survey) are used in the abstract without prior definition. Please ensure that all acronyms are spelled out in full upon first use to aid reader comprehension, especially for interdisciplinary audiences.

RESPONSE 1

Thank you very much for your valuable comments and feedback on this paper. We have defined OLS and CRRS when they first appear in the article to facilitate understanding by interdisciplinary readers.

Section 2.2 – Moderating Effects of Digital Literacy

2.This section requires significant revision. First, the authors should provide a clear, conceptually grounded definition of digital literacy. At present, it is operationalized in an overly simplistic binary form (i.e., whether a respondent can access information online), which does not reflect the multifaceted nature of the concept. I strongly recommend that the authors refer to established frameworks such as the European Commission’s Digital Competence Framework for Citizens (DigComp). Incorporating such a framework would significantly strengthen the theoretical foundation of the moderation analysis and help readers better understand the construct being measured.

RESPONSE 2

Thank you very much for your valuable comments. To ensure readers fully understand, we have clarified the definition of digital literacy. Considering the limitations of using binary variables to measure digital literacy, we have re-constructed the indicator system for digital literacy based on existing research, thereby making the measurement method more scientific and reasonable.

Clarification of the Digital Literacy Variable (Lines 322–325)

The manuscript defines digital literacy as "the ability to use digital technology to access, manage, understand, integrate, present, evaluate, and create information securely and rationally," which is appropriate. However, the operationalisation of this construct—based on a single question regarding the ability to access information online—is overly narrow.

Furthermore, the cited study (Zhang et al., 2024) does not adequately capture the full range of competencies encompassed by digital literacy. Given that the survey question focuses solely on basic information access, I strongly recommend that the authors relabel this variable, as its current form does not align with the broader definition provided. Maintaining the label "digital literacy" is misleading and undermines construct validity.

RESPONSE 3

Thank you for your thorough review and valuable feedback. We have revised the definition of digital literacy to provide a more scientific interpretation of digital literacy.

Lack of Diagnostic Checks for Statistical Models

Although the manuscript applies OLS, moderation, and 2SLS regression models, it lacks essential discussion of the underlying statistical assumptions. I encourage the authors to address the following:

4.OLS Regression Diagnostics: There is no evidence of tests for linearity, normality of residuals, or homoscedasticity. These diagnostics are necessary to assess the appropriateness and robustness of the regression models.

RESPONSE 4

Thank you very much for your feedback. To ensure the rigor of the empirical analysis, we have tested for linearity, normality of residuals and homoscedasticity. Due to space constraints, we have provided a textual description in the main body of the paper.

5.Moderation Analysis: The interaction terms used in the moderation analysis may be affected by multicollinearity. It is unclear whether the predictor and moderator variables were mean-centered prior to creating the interaction terms—a standard practice. The authors should clarify this and report Variance Inflation Factors (VIFs) or similar diagnostics.

RESPONSE 5

Thank you very much for your comments on our statistical models. We have already performed centering before conducting the moderation effect test to avoid the influence of multicollinearity, and we have described this in the revised manuscript.

6.Instrumental Variable (2SLS) Estimation: This is the only component of the statistical analysis where diagnostic reporting is handled appropriately. The use of the Cragg–Donald F-statistic to assess weak instruments is commendable and enhances the credibility of the findings.

RESPONSE 6

We are very grateful for your kind appraisal. Thank you for your time and dedication in reviewing our manuscript. We are honored to have had the opportunity to receive feedback from someone with your level of expertise.

Template Text Left in Results Section

The Results section includes unedited template language:

Results

This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn." This suggests poor manuscript preparation and insufficient attention to detail. Such oversights reduce the professionalism of the submission and should be addressed prior to publication.

RESPONSE 7

Thank you very much for your feedback. In the revised manuscript, we have deleted this sentence from the template.

Discussion and Conclusion Section

The section titled "Conclusion and Discussion" should be renamed "Discussion and Conclusion" to align with conventional structure and logical flow. More importantly, this section needs to be substantially revised to engage with relevant digital literacy literature. Specifically, the authors should compare and contrast their findings with previous studies to demonstrate whether their results confirm, extend, or contradict existing knowledge. This would enhance the theoretical contribution and show how the study informs or refines policy and practice in rural digital transformation. A more reflective and dialogic conclusion would also provide clearer guidance for targeted interventions.

RESPONSE 8

Thank you very much for your comments on our "Conclusion and Discussion". In the revised manuscript, we changed "Conclusion and Discussion" to "Discussion and Conclusion" and extensively rewrote this section. We have included comparisons with existing research in this section and highlighted the theoretical contributions and practical significance of the conclusions.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The study explored the Impact of digital infrastructure on farm households' scale manageme has certain significance, but there are still the following issues that need to be modified.

1.The research lacks a discussion section and needs to be refined based on the research results.

2. Studying lines 267 to 269 might be redundant. Please place them in appropriate positions.

3. The research table does not explain the meanings of the numbers in parentheses and *.

4.From the regression results, the study presents the basic regression results in Table 2, which are in line with expectations. However, the model may have omitted variables. From the perspective of the dependent variable, the main factor influencing the scale of operation (or efficiency) is the level of mechanization, but it is not controlled in the research model.

5. It is mentioned in Table 3 that "80% sample regression, but the number of samples has not changed; In Table 4, DI and Digital infrastructure may be the same, but they are not consistent. Under Model 19 in Table 5, it should be Unit output.

6. Please explain clearly what "The coefficient of the seemingly unrelated regression" in line 455 means.

7.It seems inappropriate for the author to divide the study area into "East" and "Midwest" in Table 6. Because within the study area, the eastern part is relatively developed but most areas are not major agricultural production areas. The central part is a major agricultural production area but the author did not discuss it separately. However, the author used the description of "western region" in line 524.

8.The author's description in the conclusion section 527-537 is consistent with the previous text. Please add a discussion.

Author Response

Dear editors and reviewers:

Revised portions are marked in revision mode in the paper. The main corrections in the paper and respondse to the reviewer’s comments are as follows:

Reviewer 2：

The study explored the Impact of digital infrastructure on farm households' scale management has certain significance, but there are still the following issues that need to be modified.

1.The research lacks a discussion section and needs to be refined based on the research results.

RESPONSE 1

Thank you very much for pointing out the issue of lacking discussion section. We have supplemented the "Discussion" in 5.1 to make our research more relevant to reality.

Studying lines 267 to 269 might be redundant. Please place them in appropriate positions.

RESPONSE 2

Thank you very much for your valuable comments. As suggested by the reviewer, we have deleted the redundant content in lines 267–269 of the manuscript to improve conciseness.

The research table does not explain the meanings of the numbers in parentheses and *.

RESPONSE 3

Thank you very much for your valuable comments. In the revised manuscript, we have added the meanings of the numbers in parentheses and *.

RESPONSE 4

Thank you for your thorough review and valuable feedback. We have included mechanization levels into the model as a control variable and performed regression analysis again. The empirical results confirm the robustness of the conclusions in this paper.

It is mentioned in Table 3 that "80% sample regression, but the number of samples has not changed; In Table 4, DI and Digital infrastructure may be the same, but they are not consistent. Under Model 19 in Table 5, it should be Unit output.

RESPONSE 5

Thank you very much for your feedback. In the revised manuscript, we have rechecked the sample number in Table 3; in Table 4, it has been unified as digital infrastructure; in addition, we have modified the variable names in Table 5 to make them correct.

Please explain clearly what "The coefficient of the seemingly unrelated regression" in line 455 means.

RESPONSE 6

Thank you very much for pointing out the inaccuracy in our translation. In the revised manuscript, we have changed "The coefficient of the seemingly unrelated regression" to "The permutation test " to avoid ambiguity.

RESPONSE 7

Thank you for your thorough review and valuable feedback. Considering that the division between the eastern and central-western regions may not be reasonable, we re-conducted the heterogeneity analysis using terrain as the basis for division in revised manuscript. The specific results are shown in 4.5.1.

8.The author's description in the conclusion section 527-537 is consistent with the previous text. Please add a discussion.

RESPONSE 8

Thank you very much for your valuable comments. In the revised manuscript, we have supplemented "5.1 Discussion".

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Interesting study, but I suggest some improvements as detailed below:

Title: Good.
Abstract: Good.
Keywords: Good.

Introduction
Although the study presents relevant theoretical and empirical background, the writing in the introduction (lines 39 to 156) is overly lengthy and lacks conciseness. Ideas are repeated, and there is insufficient clarity in linking previous findings to the identified research gap. It is recommended to reorganize the content around key themes and reduce redundancy. For example, the phrase “Existing studies on digital infrastructure and agricultural scale management...” could be summarized as: “Previous studies show positive effects of digital infrastructure, but often lack empirical validation and ignore regional and moderating factors.” This would clarify the study’s rationale.

End of the Introduction; Materials and Methods
Although the study presents a solid methodology, it does not clearly and directly state the research questions, and the hypotheses are embedded in the text without numbering or formatting that would aid their identification. While the use of the OLS regression model, moderating effects model, and other statistical techniques is adequately described, the writing is overly technical and lengthy, making it difficult for readers to grasp the overall design of the research.

To address this, improvements should focus primarily on Section 2, “Materials and Methods,” as well as the end of the introduction, where the research questions should be explicitly stated. These questions should be clearly connected to the theoretical gap identified earlier. Likewise, the hypotheses should be presented in a separate, numbered, and highlighted format for easier identification. This would help readers understand the study's focus from the outset and follow the logic of the empirical analysis more easily. Additionally, the methodological section could be reorganized to begin with a concise description of the general research design, followed by the statistical methods used, and finally the detailed explanation of the variables. Example of improved writing:
At the end of the introduction, the following paragraph could be included:
“Based on the above, this study aims to answer the following questions: How does digital infrastructure influence the expansion of agricultural scale management among rural households? What role do digital literacy and digital skills training play in this relationship? To answer these questions, the following hypotheses are proposed:
H1: Digital infrastructure promotes the expansion of land scale management.
H2: Digital literacy positively moderates the relationship between digital infrastructure and land unit output.
H3: Digital skills training positively moderates the relationship between digital infrastructure and land management area.”

This reformulation would allow the reader to clearly identify the study’s objective, questions, hypotheses, and methodological approach from the beginning.

Section 4. Empirical Analysis (especially 4.1 Benchmark Regression and 4.4 Mechanism Test)
Clear narrative explanations should be added after each table to explain what the coefficients mean and how they relate to the hypotheses. The effects should also be interpreted in real-world, not just statistical, terms.

Example: After presenting the results in Table 2, the following could be added:
“The coefficient of 0.386 in Model 2 indicates that having digital infrastructure increases production per hectare by approximately 38.6%. This effect is statistically significant and reflects a substantial improvement in agricultural production efficiency associated with digital access.”

This would make the results more understandable to non-specialist readers and reinforce the study’s empirical clarity.

Conclusion and Discussion
The discussion is clear and based on solid empirical evidence; however, it tends to be repetitive and descriptive. While the conclusions are mostly supported by the results, they lack deeper critical interpretation, as well as a direct and explicit connection to the data presented and the prior literature. Moreover, the theoretical, practical, and policy implications derived from the findings are insufficiently developed. Suggestions for improvement:
a) Add a more critical interpretation of the results, contrasting findings with prior studies not only in terms of confirmation but also by highlighting divergences or novel contributions.
b) Explicitly link each conclusion to key quantitative results (e.g., coefficients and significance levels from the models presented).
c) Connect each finding to the research gap identified in the introduction, showing how the study helps to address previous limitations.
d) Develop the theoretical, practical, and policy implications in greater depth, considering the regional and demographic heterogeneity identified.
e) Write in a more analytical style.
Example: Instead of stating:
"This is consistent with previous studies, and the findings of this study confirm the robustness of Hypothesis 1...",
consider:
"While our findings align with previous studies on the positive influence of digital infrastructure, they diverge in highlighting significant heterogeneity by region and age group. This suggests that future agricultural digitalization strategies should be tailored not only by geographic context but also by demographic profiles—an aspect overlooked in earlier works."

These improvements would make the discussion more critical, persuasive, and useful for researchers, policymakers, and other stakeholders.

References within the study
The article includes references, but in several sections (particularly the “Introduction,” “Materials and Methods,” and “Discussion”), important claims are not adequately supported by citations, or the references used are generic, outdated, or insufficient for the level of academic depth expected in a high-impact journal article. Where to improve: In the Introduction and the theoretical development in Section 2 (especially 2.1, 2.2, and 2.3), as well as in the Discussion and in parts of the Results Analysis where causal relationships are interpreted without specific support.

How to improve:
– Include more recent and specific references to support key claims.
– Strengthen the link between theoretical and empirical statements by citing updated and relevant authors in the field.
– Avoid general phrases such as “studies show that…” without citing concrete sources.

Example:
In line 83, it states: “some studies have also argued that the application of digital infrastructure may also give rise to production-related waste and energy consumption…” but only references [10-11], without a critical discussion or contextualization. A more robust citation could be:
“Recent research by Zhang et al. (2023) emphasizes that the rapid digitalization in rural agriculture has unintended environmental consequences, particularly energy-intensive server operations in smart farming platforms.”

This would provide greater depth and credibility to the statement.

Comments for author File: Comments.pdf

Author Response

Dear editors and reviewers:

Revised portions are marked in revision mode in the paper. The main corrections in the paper and respondse to the reviewer’s comments are as follows:

Reviewer 3：

Interesting study, but I suggest some improvements as detailed below:

Title: Good.

Abstract: Good.

Keywords: Good.

Introduction.

Although the study presents relevant theoretical and empirical background, the writing in the introduction (lines 39 to 156) is overly lengthy and lacks conciseness. Ideas are repeated, and there is insufficient clarity in linking previous findings to the identified research gap. It is recommended to reorganize the content around key themes and reduce redundancy. For example, the phrase "Existing studies on digital infrastructure and agricultural scale management..." could be summarized as: "Previous studies show positive effects of digital infrastructure, but often lack empirical validation and ignore regional and moderating factors." This would clarify the study’s rationale.

RESPONSE 1

We appreciate your critical feedback on the introduction. We have comprehensively revised lines 39–156 by:

Removing repetitive statements.
Rewording the target phrase to: "Prior studies suggest digital infrastructure has positive impacts, but often lack empirical validation and overlook regional moderators."

2.End of the Introduction; Materials and Methods

Although the study presents a solid methodology, it does not clearly and directly state the research questions, and the hypotheses are embedded in the text without numbering or formatting that would aid their identification. While the use of the OLS regression model, moderating effects model, and other statistical techniques is adequately described, the writing is overly technical and lengthy, making it difficult for readers to grasp the overall design of the research.

To address this, improvements should focus primarily on Section 2, "Materials and Methods," as well as the end of the introduction, where the research questions should be explicitly stated. These questions should be clearly connected to the theoretical gap identified earlier. Likewise, the hypotheses should be presented in a separate, numbered, and highlighted format for easier identification. This would help readers understand the study's focus from the outset and follow the logic of the empirical analysis more easily. Additionally, the methodological section could be reorganized to begin with a concise description of the general research design, followed by the statistical methods used, and finally the detailed explanation of the variables. Example of improved writing:

At the end of the introduction, the following paragraph could be included:

"Based on the above, this study aims to answer the following questions: How does digital infrastructure influence the expansion of agricultural scale management among rural households? What role do digital literacy and digital skills training play in this relationship? To answer these questions, the following hypotheses are proposed:

H1: Digital infrastructure promotes the expansion of land scale management.

H2: Digital literacy positively moderates the relationship between digital infrastructure and land unit output.

H3: Digital skills training positively moderates the relationship between digital infrastructure and land management area."

This reformulation would allow the reader to clearly identify the study’s objective, questions, hypotheses, and methodological approach from the beginning.

RESPONSE 2

Thank you for your thorough review and valuable feedback. In the revised manuscript, we fully implemented the recommendations as follows:

End of Introduction (lines 148–156): Added a dedicated paragraph with bolded and numbered hypotheses (H1-H3) to explicitly link research questions to identified gaps.
Section 2 (Theoretical analysis and research hypotheses): All hypotheses are now formatted as " H1: [text] " and highlighted.

Section 4. Empirical Analysis (especially 4.1 Benchmark Regression and 4.4 Mechanism Test)

Clear narrative explanations should be added after each table to explain what the coefficients mean and how they relate to the hypotheses. The effects should also be interpreted in real-world, not just statistical, terms.

Example: After presenting the results in Table 2, the following could be added:
"The coefficient of 0.386 in Model 2 indicates that having digital infrastructure increases production per hectare by approximately 38.6%. This effect is statistically significant and reflects a substantial improvement in agricultural production efficiency associated with digital access."

This would make the results more understandable to non-specialist readers and reinforce the study’s empirical clarity.

RESPONSE 3

Thank you very much for your feedback. In the revised manuscript, we have included descriptive explanations so that readers can clearly understand our empirical results.

4.Conclusion and Discussion

The discussion is clear and based on solid empirical evidence; however, it tends to be repetitive and descriptive. While the conclusions are mostly supported by the results, they lack deeper critical interpretation, as well as a direct and explicit connection to the data presented and the prior literature. Moreover, the theoretical, practical, and policy implications derived from the findings are insufficiently developed. Suggestions for improvement:

a) Add a more critical interpretation of the results, contrasting findings with prior studies not only in terms of confirmation but also by highlighting divergences or novel contributions.
b) Explicitly link each conclusion to key quantitative results (e.g., coefficients and significance levels from the models presented).
c) Connect each finding to the research gap identified in the introduction, showing how the study helps to address previous limitations.
d) Develop the theoretical, practical, and policy implications in greater depth, considering the regional and demographic heterogeneity identified.
e) Write in a more analytical style.

Example: Instead of stating:

"This is consistent with previous studies, and the findings of this study confirm the robustness of Hypothesis 1...", consider: "While our findings align with previous studies on the positive influence of digital infrastructure, they diverge in highlighting significant heterogeneity by region and age group. This suggests that future agricultural digitalization strategies should be tailored not only by geographic context but also by demographic profiles—an aspect overlooked in earlier works."

These improvements would make the discussion more critical, persuasive, and useful for researchers, policymakers, and other stakeholders.

RESPONSE 4

Thank you for pointing out the shortcomings of our discussion section. We have rewritten the discussion section of the manuscript according to your suggestions to ensure its standardization:

(1) We have supplemented the discussion section with comparisons to existing research.

(2) Based on the shortcomings of existing research identified in the introduction, we have highlighted the innovative aspects of our study.

(3) Based on the results of the heterogeneity analysis, we have made some recommendations for practice and provided theoretical support for policymakers.

5.References within the study

The article includes references, but in several sections (particularly the "Introduction," "Materials and Methods," and "Discussion"), important claims are not adequately supported by citations, or the references used are generic, outdated, or insufficient for the level of academic depth expected in a high-impact journal article. Where to improve: In the Introduction and the theoretical development in Section 2 (especially 2.1, 2.2, and 2.3), as well as in the Discussion and in parts of the Results Analysis where causal relationships are interpreted without specific support.

How to improve:

– Include more recent and specific references to support key claims.

– Strengthen the link between theoretical and empirical statements by citing updated and relevant authors in the field.

– Avoid general phrases such as "studies show that…" without citing concrete sources.

Example:

In line 83, it states: "some studies have also argued that the application of digital infrastructure may also give rise to production-related waste and energy consumption…" but only references [10-11], without a critical discussion or contextualization. A more robust citation could be:

"Recent research by Zhang et al. (2023) emphasizes that the rapid digitalization in rural agriculture has unintended environmental consequences, particularly energy-intensive server operations in smart farming platforms."

This would provide greater depth and credibility to the statement.

RESPONSE 5

We sincerely appreciate your rigorous feedback on citation support. Key revisions include:

Eliminating vague claims by removed all generic phrases (e.g., "studies show that") .
In the introduction and theoretical analysis and hypothesis sections, seven articles have been added, all of which are the latest research in recent years and come from influential journals, such as:

①Introduction (section 1):

Wang F, Wang H, Xiong L. Does the digital economy exhibit multiplier effects? A case study on the optimization of agricultural production structure in rural digital economy[J]. International Journal of Agricultural Sustainability, 2024, 22(1): 2386821.
Yang C , Ji X , Cheng C ,et al.Digital economy empowers sustainable agriculture: Implications for farmers' adoption of ecological agricultural technologies[J].Ecological Indicators, 2024, 159(000): 111723.
Du Z Y, Wang Q. Digital infrastructure and innovation: Digital divide or digital dividend?[J]. Journal of Innovation & Knowledge, 2024, 9(3): 100542.
Lioutas E D, Charatsari C, De Rosa M. Digitalization of agriculture: A way to solve the food problem or a trolley dilemma?[J]. Technology in Society, 2021, 67: 101744.
Liu S, Zhang X. Digital Infrastructure and Grain Production:Empirical Evidence Based on Deep Learning[J].Journal of Quantitative & Technological Economics, 2024, 41(07): 155-176 (in Chinese).

②Theoretical analysis and research hypotheses (section 2.1):

Balogun A L, Marks D, Sharma R, et al. Assessing the potentials of digitalization as a tool for climate change adaptation and sustainable development in urban centres[J]. Sustainable Cities and Society, 2020, 53: 101888.
Bi J. Can rural areas in China be revitalized by digitization? A dual perspective on digital infrastructure and digital finance[J]. Finance Research Letters, 2024, 67: 105753.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thanks for taking the time to address my comments. The manuscript now reaches a publishable level.

Reviewer 2 Report

Comments and Suggestions for Authors

The title of 5.1 still has formatting errors