Can Rural Digitization and the Efficiency of Agricultural Carbon Emissions Be Coupled and Harmonized under the “Dual-Carbon” Goal?

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors,

No doubt, the presented paper is timely, responding to the need to enhance the coordination between carbon efficiency of agriculture and rural digitalization to improve carbon performance. I highly appreciate the research work invested in seeking coordination between the two concepts.

Allow me to make the following comments and recommendations on the paper included in the attachment.

I hope the suggestions I have made will improve the manuscript!

Wishing you further success!

Comments for author File: Comments.pdf

Author Response

Reviewer #1:

1. Introduction

①Adequately provides China's plans to improve the carbon transition.

    China’s plans to improve the carbon transition are described in the introduction to this paper. Please refer to lines 35-42 for details.

②Reports on the inclusion of carbon sinks in the agricultural carbon performance measurement system.

    This approach, which incorporates carbon sinks into the agricultural carbon performance measurement system, primarily draws on the relevant research literature (Tian and Zhang, 2013). Please refer to lines 51-54 for details.

③Highlights the importance of digital transformation for rural development.

    This paper has already added the importance of digital transformation for rural development in the introduction. Please refer to lines 59-66 for details.

    Digital technology, as a core technology for reducing greenhouse gas emissions from agriculture, provides an effective solution for agricultural carbon efficiency. For example, the application of information technology, network technology and automatic control technology to agricultural production can improve its production efficiency and the level of intelligent and precise management, reduce the waste of resources, and lower the input of "high-carbon" factors of production such as chemical fertilizers, pesticides and agricultural films, thereby reducing greenhouse gas emissions.

④References point to the topic.

    Some of the references in this paper have been adapted to make sure that the references fit the topic, as detailed in section References.

⑤It is not sufficiently clear from the introduction what the Dual-carbon goal is. Please supplement the introduction more with this policy (as I understand it is specific to China). This "Dual-carbon" goal appears in the title of the article, but lacks sufficient detail for implementation. Focus on the relationship between ACEE and RDIG in the context of the Dual-carbon goal.

    This paper has already elaborated on the “dual-carbon” goal in the introduction and has reorganized the relationship between ACEE and RDIG in the context of the “dual-carbon” goal. Please refer to lines 35-75 for details.

⑥Provide further evidence for the claims with sources outside China.

    This paper has provided further evidence on the realization of the “dual-carbon” goal based on information from outside China. Please refer to lines 42-45 for details.

⑦Line 80 - provide the full name of DEA (digital envelopment analysis).

    This paper has provided the full name of DEA (data envelopment analysis). Please refer to line 92 for details.

2. Mechanism Analysis – consider whether it would be more appropriate to include this section with the introduction.

    After analysis, we feel that it is more appropriate to present the introduction and mechanism analysis separately.

3. Materials and Methodology

①The methods used adequately reflect the processes analysed. The system of indicators meets the criteria and objectives sought.

    Thank you for recognizing the research methodology and indicator system of this paper, which has been reorganized according to your comments to ensure that it is more reasonable.

②In the model in 3.2.1. indicate in a little more detail what variables can be included. It is a fact that the indicators detailed in the indicator system, but it is not clear how the outcome of the model will support the research in the paper.

    In 3.2.1, this paper has constructed a global Malmquist-Luenberger productivity index to measure ACEE containing undesired outputs by using the relaxation variable model (SBM) based directional distance function. At the same time, the variables have been described in detail in the notes to the formulas, corresponding to the indicator system below. Please refer to lines 236-252 for details.

③Please provide a data source for the carbon coefficients listed on lines 261-263.

    In this paper, the carbon source coefficients have been selected for each carbon source by drawing on the relevant literature (Jin et al., 2024), which has been supplemented. Please refer to line 363 for details.

④Provide a data source for the carbon sequestration factors listed in Table 3.

    In this paper, each carbon sequestration factor has been selected by drawing on relevant literature, which has been supplemented. Please refer to line 351 for details.

4. Spatial-Temporal Differences of Coupling Coordination

①The data in 4.1 are probably the result of an analysis. Please reference the method used and described further. If it is external data, indicate source.

    The data in 4.1 are mainly the results of the previous measurements of RDIG.

②Table 5 - indicate the notations in the abbreviations used (ML, EC, TC) because it is not clear enough for those reading the paper.

    The abbreviation symbols (ML, EC, TC) used in Table 5 have been explained in this paper. ML=EC×TC, where ML refers to ACEE, EC represents technological efficiency, and TC represents technological advancement.

③Lines 292-294 - report cited - please provide reference to references at end.

    This paper has added references to this cited report, see section References [36] lines 805-807 for details.

④In lines 321-322 - a document is cited - please provide a reference to the references at the end.

    This paper has added references to this cited document, see section References [37] lines 808-810 for details.

⑤Table 6 refers to Level of Coordination based on the classification criteria listed in Table 1 where "code" is mentioned - please standardize the presentation of the information.

    In this paper, the relevant codes in Table 6 have been modified based on Table 1, as detailed in Table 6.

⑥Tables 5 and 6 need to improve format by not carrying information on two lines. If it is a matter of consideration for the authors, please reflect. It may be a consequence of proofreading the paper.

    After analysis, we feel that the formatting of Tables 5 and 6 is relatively reasonable, and if the layout needs to be corrected, we can make adjustments accordingly.

5. Influencing Factors and Discussion

①The indicators used adequately reflect the coordination between ACEE and RDIG.

    Thank you for recognizing the indicators used in this paper. The indicators used adequately reflect the coordination between ACEE and RDIG.

②In 5.2, the SDM model is commented. I assume it is based on the representations in 3.2.3. Harmonize the presentation in the text.

    This paper has harmonized the formulation of the SDM model in 3.2.4 and 5.2. Please refer to lines 302-306 for details.

③Table 7 comments on "direct effect" and "indirect effect" -please elaborate on what is included.

    The direct effect represents the impact of local factors on the local coupling coordination. The indirect effect represents the impact of local factors on the degree of coupling coordination in the neighborhood. Please refer to the footnote 4 for details.

④In 5.3 the conclusions adequately present the results of the analysis.

    The results of the study have been analyzed in detail in 5.3 and the link between these results and the “dual-carbon” goal has been explained. Reducing agricultural carbon emissions and improving the efficiency of agricultural carbon emissions are concrete manifestations of the realization of the “dual-carbon” goal.

6. Conclusions and Implications

①In 6.2, state what is included in the terms “green mountains” and “silver mountains”.

    “Green mountains” are as good as “silver mountains”, that is, improving the ecological environment is key to developing productivity. Please refer to the footnote 11 for details.

②Improve the link between the results of the analysis and the "Dual- carbon" goal. Given that the      "Dual-carbon" goal is not commented on in detail, one is left with the impression that the results would automatically affect the "Dual-carbon" goal.

    The results of the study have been analyzed in detail in 5.3 and the link between these results and the “dual-carbon” goal has been explained. Reducing agricultural carbon emissions and improving the efficiency of agricultural carbon emissions are concrete manifestations of the realization of the “dual-carbon” goal.

③I would recommend more specificity in the conclusions.

    We have already described the results of this paper in terms of the spatial and temporal evolution characteristics of the degree of rural digitization, the efficiency of agricultural carbon emissions, and the degree of coupling coordination and the influencing factors. We believe that this formulation is more specific and detailed.

④What policy measures can be proposed to governments and local authorities from different regions to improve coordination between ACEE and RDIG.

    This paper has made targeted development proposals for different regional governments to improve coordination between ACEE and RDIG. Please refer to lines 710-716 for details.

⑤Briefly indicate how the article can support future research.

    This paper has studied the spatial differences between the degree of RDIG and ACEE in China at the national and provincial levels, grasped the influencing factors of the degree of coordination between the two from a macroscopic point of view as a whole, and produced robust and detailed empirical results, which have laid a certain foundation for deepening the relevant research.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear editors and authors,

The manuscript is very interesting, current and follows contemporary problems. The authors have invested a lot of effort to collect a lot of data for the entire area of ​​their country.

A few corrections will add to the clarity of the manuscript. Namely, it is not clear whether the four regions are statistical regions, but the authors have determined the regions? The regions are very different in area, so it is not clear what was used to separate them.

Another thing that is unclear is the methodology. Namely, in the explanation of the methodology, formulas are given for calculating numerous variables that do not appear later. Analyzing the degree of rural  digitization, based on what, what was used for the calculation?

Table 7 shows numerous variables, coefficients that were not previously explained. It is necessary to explain the indicators in table 7, how were they calculated?

In the manuscript, it is necessary to make a better connection between what is explained in the methodology and what is presented and analyzed in the results (Rho/λ, sigma2_e, Log-likelihood, Moran’s I, LM-error,  LM-lag, Robust LM-lag, Wald-spatial lag, LR-spatial lag, Wald-spatial error, LR-spatial error, Hausman. Some of the variables are in the table, but not analyzed. An analysis should be given, what are the results for?

Best regards to all

 

Author Response

Reviewer #2:

1. A few corrections will add to the clarity of the manuscript. Namely, it is not clear whether the four regions are statistical regions, but the authors have determined the regions? The regions are very different in area, so it is not clear what was used to separate them.

    For the purpose of regional heterogeneity analysis, we also separated 30 provinces in China into four major regions according to the divisions of the National Bureau of Statistics: The Eastern region, Central region, Western region, and Northeast region. Please refer to lines 218-221 for details.

2. Another thing that is unclear is the methodology. Namely, in the explanation of the methodology, formulas are given for calculating numerous variables that do not appear later. Analyzing the degree of rural digitization, based on what, what was used for the calculation?

    In the methodology, 3.2.1 Super-Efficient Non-Expected Output SBM-ML Model is mainly used for the measurement of ACEE; 3.2.2 The Entropy Method is mainly used for the measurement of the degree of RDIG; 3.2.3 Coupling Coordination Model is mainly used for the calculation of the degree of coupling coordination between the two; while 3.2.4 Spatial Econometric Model is mainly used for the analysis of the influencing factors in the later section. For the measurement of the degree of RDIG, please see in section 3.2.2 lines 253-278.

3. Table 7 shows numerous variables, coefficients that were not previously explained. It is necessary to explain the indicators in table 7, how were they calculated?

    This paper has provided a detailed explanation of each coefficient and indicator in Table 7. Please refer to lines 537-538, lines 555-557 for details.

4. In the manuscript, it is necessary to make a better connection between what is explained in the methodology and what is presented and analyzed in the results (Rho/λ, sigma2_e, Log-likelihood, Moran’s I, LM-error, LM-lag, Robust LM-lag, Wald-spatial lag, LR-spatial lag, Wald-spatial error, LR-spatial error, Hausman. Some of the variables are in the table, but not analyzed. An analysis should be given, what are the results for?

    ①Rho/λ reflects the magnitude and direction of the spatial hysteresis effect, with a value between -1 and 1. When Rho/λ is equal to 0, the spatial lag value of each factor has no effect on the degree of coupling coordination; when Rho/λ is positive, the increase of each factor in neighboring regions has a positive effect on the increase of coupling coordination; when Rho/λ is negative, the increase of each factor in neighboring regions has a negative effect on the improvement of coupling coordination.

    ②In the regression results of the SDM model, sigma2_e represents the variance of the spatial error term, which is the degree of spatial autocorrelation error. If sigma2_e is small, then the spatial autocorrelation error is small and the SDM model fits well. If sigma2_e is large, then the spatial autocorrelation error is large and the fitting effect of SDM model is poor.

    ③The Moran’s I index is commonly used to measure the spatial correlation between variables. Its value range is [-1,1]. A closer value to 1 or -1 indicates a stronger spatial correlation in the sample space. When closer to 0, this index indicates lower spatial autocorrelation among sample enterprises.

    ④Lagrange Multiplier (LM) and Robust Lagrange Multiplier (Robust LM) tests are used to determine the necessity of using spatial econometric models. The LM test passes significance, indicating that there is a certain spatial correlation between the explained variable and the explanatory variable; it was necessary to introduce a spatial econometric model.

    ⑤LR and Wald tests are used to diagnose whether SDM model will be simplified into SAR model and SEM model. LR and Wald tests pass significance, indicating that the fitting effect of SDM was superior to SAR and SEM in our case.

    ⑥The Hausman test is used to determine whether the study applies to fixed or random effects. The Hausman test passes significance, indicating that the null hypothesis should be rejected and fixed effects should be selected.

Author Response File: Author Response.pdf