The Impact of Digitalization on Agricultural Green Development: Evidence from China’s Provinces

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The work is good with a lot of data, well explained, suggestive and well explained figures and tables.

The introduction is good, as is the work methodology, results, discussions and conclusions. From my point of view, this work can remain in this form.

Comments for author File: Comments.pdf

Author Response

Thank you for your valuable comments. In accordance with your comments, we have rephrased the role of dynamic monitoring in promoting green agricultural development in the policy recommendations section to enhance the connection between the two.The updated content is as follows:

For another, an agriculture-oriented intelligent monitoring system with digital capabilities has to be in place to strengthen remote control, dynamic monitoring, and timely response to assist in the realization and optimization of green agricultural production with the help of digital technology such as the IoT, cloud computing, and satellite remote sensing. This helps to ensure that agricultural production strictly follows green standards and provides technological support for safe, green, and sustainable agricultural development.

Reviewer 2 Report

Comments and Suggestions for Authors

This article found that there is a non-linear U-shaped relationship between digitalization and agricultural green development. From the results of this analysis, it is proposed that only when digitalization reaches a certain level can agricultural green development be promoted. Digitalization has a spatial spillover effect on the green development of agriculture, showing a "siphon effect" in the early stage and a "trickle-down effect" in the later stage. Finally, policy recommendations are put forward on how to use effective digital technology to assist the green transformation of agriculture. This article has complete analysis content and provides good evidence for the direction of policy implementation. 

The current research data is relatively similar to the data for the entire region. If future research can obtain more detailed regional data, such as address data, it will be able to provide more detailed research results.

Author Response

Thank you for your valuable feedback. Based on your feedback, we have added the “Research limitations and perspectives” section, which states that future research will require more detailed regional data. Details are shown below.

5.4. Research limitations and perspectives

This paper acknowledges several limitations. First, the current research data is relatively similar to the data for the entire region. Second, the study is confined to  provinces in China, and the results may be influenced by specific factors such as geography, which limits their generalizability. Future research can address these limitations in the following ways. Obtaining more detailed regional data, such as address data, to provide more detailed research results. Expanding the research sample to include more regions will enhance the generalizability and reliability of the findings.

Reviewer 3 Report

Comments and Suggestions for Authors

    It is of high importance to conduct agricultural green development to achieve SDGs by 2030, especially for the largest developing country of China. This manuscript focused on it, and I believe it is of high interest for readers. However, there are some major problems to solve before publication, and it should be major revision. The main issues are as follows:

(1)   I strongly to advise to polish English due to its long sentences and hard reading.

(2)   This manuscript is too long and it should be more concise. Besides, this manuscript should be presented with “Result” section instead of present section 4 and 5. The introduction section should also be written with literature reviews here. For the research outline, I believe it should move the new section of “Data and Method”.

(3)   All the figures are not easy to get the information. Figure 1, what is the meaning of the result of 0.1-0.5? It should be clearer.

(4)   It should improve Figure 2. There are lots of province is overlapped or missing.

(5)   All table captions should be more directly or clear.

 

(6)   I believe that it should add the “Discussion” section.

Comments on the Quality of English Language

 I strongly to advise to polish English due to its long sentences and hard reading.

 

Author Response

Q1: I strongly to advise to polish English due to its long sentences and hard reading.

Response: Thank you for your valuable feedback regarding the clarity of our writing. We have taken your advice seriously and have thoroughly revised the manuscript to improve the readability and conciseness of the language. Specifically, we have broken down lengthy sentences into shorter, clearer ones and simplified complex phrases to enhance overall comprehension. We believe these changes will make the paper more accessible to readers. Thank you again for your constructive suggestion.

 

Q2: This manuscript is too long and it should be more concise. Besides, this manuscript should be presented with “Result” section instead of present section 4 and 5. The introduction section should also be written with literature reviews here. For the research outline, I believe it should move the new section of “Data and Method”.

Response: Thank you for your insightful feedback regarding the structure and length of our manuscript. We appreciate your suggestions and have made the following revisions to address your concerns:

Conciseness: We have carefully reviewed the manuscript to reduce unnecessary length and enhance clarity. We have streamlined sections to ensure a more concise presentation of our findings.

Results Section: In response to your suggestion, we have restructured the manuscript to include a dedicated “4. Empirical Results and Analyses” section. Sections 4 and 5 have been merged and revised to fit into this new section, providing a clearer presentation of our findings.

Introduction and Literature Review: We have restructured the original “Literature Review and Research Hypotheses” section by integrating the literature review into the introduction. Additionally, we have separated the theoretical analysis and hypotheses from the literature review to present them as a distinct section “2. Theoretical Analysis and Research Hypotheses”.

Data and Method Section: We have restructured the original “Research Design” section to create two distinct parts: “3.1. Data, Method, and Model” and “3.2. Indicator Measurement”.

 

Q3: All the figures are not easy to get the information. Figure 1, what is the meaning of the result of 0.1-0.5? It should be clearer.

Response: Thank you for your feedback regarding the clarity of the figures. In Figure 1, the vertical axis represents the values of the Agricultural Green Development Index that we calculated. To enhance understanding, we have added a note to Figure 1 to clarify this information. Details are shown below.

Note: The figure shows the agricultural green development index for China’s 30 provinces from 2012 to 2022. Note: The figure shows the agricultural green development index for China’s 30 provinces from 2012 to 2022. The square-shaped line represents the overall level of Agricultural Green Development. The diamond-shaped line represents the dimension of Agricultural Production Efficiency. The circular-shaped line represents the dimension of  Reduction in Resource Input and Emissions. The triangular-shaped line represents the dimension of Resource Recycling. The cross-shaped line represents the dimension of Resource and Environmental Safety.

 

Q4: It should improve Figure 2. There are lots of province is overlapped or missing.

Response: Thank you for your valuable feedback regarding Figure 2. Upon reviewing the data, we identified that three provinces were inadvertently omitted during the plotting process. We have updated Figure 2 to ensure that all provinces are now included and have optimized the visualization to enhance clarity, allowing each province to be clearly visible. We appreciate your suggestion, which has contributed to improving the figure.

 

Q5: All table captions should be more directly or clear.

Response: Thank you for your suggestion regarding the clarity of the table captions. In response, we have revised the captions for the tables as follows:

Table 6 has been changed from “Benchmark Regression” to “Table 6. Benchmark Regression of the Impact of Digitalization on Agricultural Green Development.”

Table 7 has been updated from “Regression Results of Spatial Econometric Models” to “Table 7. Regression Results of the Spatial Effects of Digitalization on Agricultural Green Development.”

Table 8 has been modified from “Direct, Indirect, and Total Effects of Variables” to “Table 8. Direct, Indirect, and Total Effects of Digitalization on Agricultural Green Development.”

Q6: I believe that it should add the “Discussion” section.

Response: Thank you for your valuable feedback. Based on your feedback, we have added the “Discussion” section. Details are shown below.

5.1. Discussion

This paper explores the impact of digitalization on the green development of agriculture. By exploring the spatiotemporal evolution characteristics of the green development level of agriculture at the provincial level in China and examining the impact of digital development on the green development of agriculture in each province, we can understand how digitalization can promote the development of agriculture towards a greener track. The focus of this discussion is on the significance, contribution, limitations, and future research directions of our findings. The research results reveal a non-linear positive U-shaped relationship between digitalization level and agricultural green development, that is, only when the digitalization level reaches a certain level can it play a driving role in promoting agricultural green development. The impact of digitalization level on agricultural green development has spatial spillover effects, and the improvement of digitalization level in adjacent provinces has a positive U-shaped relationship with the local agricultural green development. This paper is of great significance to both the academic and practical communities. Academically, on the one hand, this paper quantitatively explores the spatiotemporal variation characteristics of the level of agricultural green development at the provincial level in China from 2012 to 2022, which has certain extensions in both time and spatial dimensions; On the other hand, this paper constructs a more scientific index system for digital development, which reflects more deeply and comprehensively the impact of digitalization on agricultural green development, and to some extent compensates for the lack of empirical analysis on the impact of digitalization on agricultural green development in existing literature.Practically, this paper proposes policy recommendations to better leverage the role of digital technology in empowering agricultural green transformation, providing decision-making references for relevant departments.

Reviewer 4 Report

Comments and Suggestions for Authors

Summary

This paper presents a comprehensive analysis of the interplay between digitalization and agricultural green development across China’s provinces, utilizing a robust methodological framework. The authors employ panel data spanning a decade (2012-2022) to evaluate and correlate digitalization with agricultural practices aimed at sustainability. Through advanced statistical techniques, including entropy weight, TOPSIS, Moran’s I index, OLS, and spatial Durbin models, the findings delineate the nuanced relationship between these two critical areas, offering insightful policy implications for advancing green agricultural practices.

Originality

The research contributes original insights to the field of sustainable development by addressing the relatively underexplored nexus between digitalization and agricultural green practices in a Chinese context. The novel application of spatial analysis to elucidate both direct and spillover effects of digitalization marks a significant advancement in understanding regional disparities and dynamics in agricultural transformation.

Accuracy and Completeness

The methodology employed is largely sound, with appropriate statistical techniques that enhance the robustness of the findings. However, some areas would benefit from additional clarity. For instance, a more detailed explanation of the entropy weight and TOPSIS method's application in assessing digitalization and green development would bolster comprehensibility. Additionally, while the U-shaped relationship is intriguing, a deeper exploration of potential confounding variables influencing this relationship could enhance the completeness of the analysis.

Structure and Organization

The structure of the paper is generally coherent, leading the reader through a logical progression of ideas. Nonetheless, the flow could be improved by ensuring smoother transitions between sections. For example, linking the discussion of the methodological framework more explicitly to the results would enhance cohesion. A clearer delineation of subsections within the results section could also aid reader navigation.

Readability and Length

The writing is predominantly clear and scholarly; however, certain passages are densely packed with information, which may impede readability. Simplifying complex sentences and employing more straightforward language in some sections could enhance accessibility without sacrificing academic rigor. The length of the paper is appropriate for the depth of analysis, but some redundancy could be eliminated, particularly in the introduction and conclusion.

Detailed Suggestions for Improvement

1. Methodological Clarity: Elaborate on the entropy weight and TOPSIS methodologies in the methodology section to elucidate their relevance and application in the context of digitalization and agricultural green development.

2. Confounding Variables: Consider discussing potential confounding factors that could influence the U-shaped relationship between digitalization and agricultural green development, such as regional economic conditions or variations in policy implementation.

3. Transitions and Cohesion: Enhance the transitions between sections to create a more fluid reading experience. Explicitly connect the methodology to the results to reinforce the rationale behind the findings.

4. Subsection Organization: Within the results section, consider subdividing findings into clearer thematic categories, facilitating easier navigation and comprehension of key results.

5. Sentence Structure: Revise sections with dense technical language or complex sentence structures. Strive for clarity by breaking down lengthy sentences and avoiding jargon where possible.

6. The literature review section is relatively brief and would benefit from an expansion. By incorporating a broader range of studies on related topics, the authors can establish a more comprehensive understanding of the existing research, enabling a thorough comparison of findings. For example:https://doi.org/10.1002/bse.3584; https://doi.org/10.1016/j.jenvman.2024.120271; https://doi.org/10.1016/j.jretconser.2024.104006;

7. Conclusion Strengthening: In the conclusion, summarize key findings more succinctly and emphasize their implications for policymakers. Highlight potential avenues for future research to enhance the paper's contribution to the field.

Author Response

Q1: Methodological Clarity: Elaborate on the entropy weight and TOPSIS methodologies in the methodology section to elucidate their relevance and application in the context of digitalization and agricultural green development.

Response: Thank you for your valuable comments.We have elaborated on the entropy weight and TOPSIS methods in the methodology section, and elucidated their relevance and application in the context of digitalization and green agricultural development.Details are shown below.

Regarding measurement methods, drawing from the existing research[41,42], this paper ultimately employed a combined entropy weight and TOPSIS method to evaluate the digital economy development of each province. In the evaluation process, the magnitude of entropy mainly depends on the degree of change in the evaluation index values. If the degree of change in a certain evaluation indicator value is greater, the entropy value will be smaller and the weight will be greater. On the contrary, the smaller the degree of change in the indicator value, the larger the entropy value, and the smaller the weight. Therefore, based on the degree of change in each evaluation indicator value, the entropy value can be calculated to determine the weight of each indicator. In further evaluation, TOPSIS method, as a commonly used decision-making technique for limited option multi-objective decision analysis in systems engineering, can be used to comprehensively process and calculate various indicator data, obtain the degree of approaching ideal solutions and moving away from negative ideal solutions, and objectively evaluate the performance of digitalization and agricultural green development. In the context of digitalization and green agricultural development, it is necessary to comprehensively consider numerous influencing factors, so this paper first used the entropy weight method to calculate the weights of each indicator and then ranked the proximity of a limited number of evaluation objects to the ideal solution using the TOPSIS method.

 

Q2: Confounding Variables: Consider discussing potential confounding factors that could influence the U-shaped relationship between digitalization and agricultural green development, such as regional economic conditions or variations in policy implementation.

Response: Thank you for your insightful comment regarding potential confounding factors. In our analysis, we have selected the results presented in the third column of Table 7, which corresponds to the two-way fixed Spatial Durbin Model (SDM). This model effectively controls for both time fixed effects and province fixed effects. Additionally, we have accounted for several relevant control variables, including Policy Support, Industrial Structure, Agricultural Financial Services, and Rural Digital Infrastructure. By incorporating these factors, we aim to minimize the influence of potential confounding variables on the relationship between digitalization and agricultural green development. We appreciate your suggestion, which has helped us strengthen our discussion of these aspects in the manuscript.

 

Q3: Transitions and Cohesion: Enhance the transitions between sections to create a more fluid reading experience. Explicitly connect the methodology to the results to reinforce the rationale behind the findings.

Response: Thank you for your valuable comments.We have enhanced the transitions between sections and explicitly connected the methodology to the results. Some examples are shown below.

Example 1: Based on the previous analysis, this subsection will further explore the level of agricultural green development in 30 provinces in China.

Example 2: With the rapid development of the digital age, green development in agriculture has also ushered in new opportunities and challenges. Overall, it is necessary to further explore the spatial effects of digitalization on agricultural green development through spatial econometric models. In the next section, we will further analyze the impact of digitalization on the green development of agriculture.

Example 3: On the basis of benchmark regression, we build the spatial models.

Example 4: The regression coefficients of spatial econometric models not only capture the impact of explanatory variables on dependent variables, but also reveal complex interactions between variables through direct and indirect effects. Direct effects measure the influence of a region’s explanatory variables on its own dependent variables, including feedback effects—where changes in neighboring regions’ dependent variables indirectly affect the region’s own outcomes. Indirect effects reflect the influence of neighboring regions’ explanatory variables on the region’s dependent variables. The total effect is the sum of these direct and indirect effects.

 

Q4: Subsection Organization: Within the results section, consider subdividing findings into clearer thematic categories, facilitating easier navigation and comprehension of key results.

Response: Thank you for your valuable comments.We have redefined the topic categories in the results section. Thank you for your suggestion to make it easier to navigate and understand the key results.Details are shown below.

4. Empirical Results and Analyses

4.1. Analysis of Agricultural Green Development

4.1.1. Spatiotemporal Characteristics of Agricultural Green Development

4.1.2. Spatial Autocorrelation Analysis of Agricultural Green Development

4.2. Spatial Effect Analysis of the Impact of Digitalization on Agricultural Green Development

4.2.1. Benchmark Regression

4.2.2. Analysis of Spatial Panel Model Results

 

Q5: Sentence Structure: Revise sections with dense technical language or complex sentence structures. Strive for clarity by breaking down lengthy sentences and avoiding jargon where possible.

Response: Thank you for your constructive feedback regarding the sentence structure and clarity of our manuscript. We have carefully reviewed the sections with dense technical language and complex sentences, breaking down lengthy sentences into shorter, clearer statements. Additionally, we have worked to reduce jargon wherever possible to enhance readability and ensure that the content is accessible to a wider audience. We appreciate your suggestions, which have significantly improved the overall clarity of the manuscript.

Q6: The literature review section is relatively brief and would benefit from an expansion. By incorporating a broader range of studies on related topics, the authors can establish a more comprehensive understanding of the existing research, enabling a thorough comparison of findings. For example:https://doi.org/10.1002/bse.3584; https://doi.org/10.1016/j.jenvman.2024.120271; https://doi.org/10.1016/j.jretconser.2024.104006;

Response: Thank you for your valuable comments.We have expanded the literature review section and included a wider range of related topic studies.The updated references are as follows.

• Tan, L.; Yang, Z.; Irfan, M. Toward low‐carbon sustainable development: Exploring the impact of digital economy development and industrial restructuring.Business Strategy and the Environment. 2024, 33:2159.
• Han, Y.; Li, Z.; Feng, T. Unraveling the impact of digital transformation on green innovation through microdata and machine learning. Journal of Environmental Management. 2024, 354.
• ChW.; Geng F.; Zhang H. A Study on the Impact of Digital Economy Development on Carbon Emission Efficiency in the Plantation Industry——Empirical Tests Based on Mediation and Threshold Effects. Chinese Journal of Eco-Agriculture. 2024, 1–13.
• Jiang,;Zhong, M.; Ma, G. Impact of Digital Economy on Agricultural Green Total Factor Productivity: A Mediation Analysis Based on Land Operation Efficiency. Journal of China Agricultural University. 2024, 29, 27–39.

 

Q7: Conclusion Strengthening: In the conclusion, summarize key findings more succinctly and emphasize their implications for policymakers. Highlight potential avenues for future research to enhance the paper’s contribution to the field.

Response: Thank you for your valuable comments.In the “Conclusion and Outlook” section, we summarize the main findings more concisely and emphasize their impact on policymakers. In addition, we emphasize potential avenues for future research.Details are shown below.

5.3. Policy Implications

For policy makers, the conclusions above have important guiding significance. Firstly, given the overall low level of green development in Chinese agriculture during the sample period, policy makers should attach great importance to the issue of green development in agriculture and increase investment and support for it. At the same time, it is recognized that agricultural production efficiency and resource recycling are key factors in improving the level of green development in agriculture. Relevant policies should be formulated to encourage and guide agricultural production to shift towards high efficiency and resource recycling. Secondly, due to the non-linear positive U-shaped relationship between digitalization level and agricultural green development, policy makers should not rush to achieve results when promoting agricultural digitalization development. They should recognize that only when the digitalization level reaches a certain level can it have a positive impact on agricultural green development. We should continue to increase investment in agricultural digitalization construction, gradually improve the level of agricultural digitalization, in order to better promote green development in agriculture. Finally, considering the spatial spillover effects of digitalization on green agricultural development and the positive U-shaped impact of the improvement of digitalization levels in neighboring provinces on the local area, policy makers should strengthen cooperation and coordination between regions when formulating policies. In the early stages of digital development, we should be vigilant about the potential adverse effects of the "siphon effect" and take measures to alleviate regional development imbalances; In the later stage of digital development, fully leverage the "trickle down effect" to promote the coordinated progress of regional agricultural green development.

5.4. Research limitations and perspectives

This paper acknowledges several limitations. First, the current research data is relatively similar to the data for the entire region. Second, the study is confined to  provinces in China, and the results may be influenced by specific factors such as geography, which limits their generalizability. Future research can address these limitations in the following ways. Obtaining more detailed regional data, such as address data, to provide more detailed research results. Expanding the research sample to include more regions will enhance the generalizability and reliability of the findings.

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

After reading the revised mansucript, I believe that it have been improved a lot, and I think it can be published after minor revision. The main suggestion is as follows:

All the figures can be improved, like more colors. 

Author Response

Thank you for your valuable comment, we  improved the figures.

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The authors didn't addressed my previous comments totally.

Comments on the Quality of English Language

some english errors could be found in the text. 

Author Response

Thank you for your valuable comment.

Please see the attachment.

Author Response File: Author Response.pdf

Round 3

Reviewer 4 Report

Comments and Suggestions for Authors

Authors make a significant efforts to the revision, therefore I recommend publishing it in its current version