Does Digital Financial Inclusion Affect Agricultural Eco-Efficiency? A Case Study on China
Round 1
Reviewer 1 Report
I retain a negative opinion on the manuscript. The Authors not clearly explained selection of agriculture indicators on digital financial inclusion and agricultural eco-efficiency.
Specifying and listing the eco-efficiency index as the ratio of inputs to outputs, they listed multiple variables presented in different units. f.ex. for inputs: one 1,000 ha, 10,000 people, 10,000 kW and others while outputs in 100 million RMB or indexes for unexpected ones. The authors did not provide the manner of calculating the inputs for units useful for calculating eco-efficiency index.
The article is overloaded with general and theoretical information (Chapter 1.1., 1.2., 2.1.), and there is no information allowing for understanding the authors' thinking process and achieving the assumed result. How to compare DFI presented in table 3 (ranged from 1.72 to 5.02) while in later (Chapter 3.1) DFI ranged (for DFI width) from 34.2 (2011) to 281.9 (2018). Without no additional information.
I do not understand how data processing going on and how Authors concluded manuscript without of Index of Peking University data presenting i the period 2011-2018.
Also many comments from the previous review of manuscript (agronomy-1317943) were not taken into account.
Author Response
Dear Reviewer 1:
Thank you for your letter and for the Reviewer 1’s comments concerning our manuscript entitled “Does digital financial inclusion affect agricultural eco-efficiency? A case study on China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made corrections which we hope meet with approval. The revised portion is marked in red in the paper. The main corrections in the paper and the response to the Reviewer 1’s comments are as following:
Point 1: I retain a negative opinion on the manuscript. The Authors not clearly explained selection of agriculture indicators on digital financial inclusion and agricultural eco-efficiency.
Response 1: According to Reviewer1’s comments, we have made a more detailed explanation on the selection of agricultural indicators. Agricultural ecological efficiency is calculated by selecting input-output indicators related to agriculture in this paper. The revisions are in P.7, line 311 to line 341.
Point 2: Specifying and listing the eco-efficiency index as the ratio of inputs to outputs, they listed multiple variables presented in different units. f.ex. for inputs: one 1,000 ha, 10,000 people, 10,000 kW and others while outputs in 100 million RMB or indexes for unexpected ones. The authors did not provide the manner of calculating the inputs for units useful for calculating eco-efficiency index.
Response 2: As Reviewer1’s suggested, we have supplemented the relevant explanations for the units of input-output indicators. The units of input-output indicators selected in this paper refer to the statistical yearbook and the academic research of other scholars. The units of input-output indicators do not have much deep meaning. The revisions are P.7 line 338 to line 341, line 353 to line 358.
Secondly, we have put the specific equations of the super efficiency SBM model used to calculate AEE in Appendix A, so that readers can further learn this method. The revisions are P.18, line 657 to P.19, line 688.
Point 3: The article is overloaded with general and theoretical information (Chapter 1.1., 1.2., 2.1.), and there is no information allowing for understanding the authors' thinking process and achieving the assumed result. How to compare DFI presented in table 3 (ranged from 1.72 to 5.02) while in later (Chapter 3.1) DFI ranged (for DFI width) from 34.2 (2011) to 281.9 (2018). Without no additional information.
Response 3: According to Reviewer1’s comments, we have supplemented the description of relevant information and written the relevant research ideas below. The logical framework of this paper and the results of the implementation assumptions can be divided into the following two points.
Firstly, based on the existing research, this paper uses the digital financial inclusion index of Peking University to measure the development level of digital Inclusive Finance. After consulting relevant academic research, this paper selects the input-output indicators commonly used in agriculture, calculates them with super efficiency SBM-DEA method, and the result is agricultural ecological efficiency, so as to measure agricultural sustainable development.
Secondly, on the basis of theoretical analysis, the relationship between the two is tested by econometric methods. The results of econometric test and further cause analysis can verify whether the hypothesis of this paper is tenable. This is the main logical framework of this paper.
For the problem that DFI in Table 3 ranges from 1.72 to 5.02, while in Chapter 3.1, its sub index DFI width ranges from 34.2 to 281.9, we have made the following explanation。As mentioned in the chapter of variable data description in this paper, in order to show clearer empirical results, we appropriately expand the estimation coefficient of DFI by 100 times, but this will not affect the significance of variables. Based on this, we have reduced the digital inclusive financial index by 100 times. The sub index of digital financial inclusion does not participate in empirical regression, so we have not done the same treatment for the sub index, so there will be obvious differences between the two. Thank the reviewers for their valuable comments on this article. We will further explain this data preprocessing link, so that readers can more easily understand this article. The revisions are P.9, line 365 to line 375, P.11, line 414 to line 416.
Point 4: I do not understand how data processing going on and how Authors concluded manuscript without of Index of Peking University data presenting i the period 2011-2018.
Response 4: According to Reviewer1’s comments, we have made the following explanation. As mentioned in the third point, the data processing in this paper mainly focuses on two aspects. First, the value of the digital inclusive financial index is too large, and the calculated agricultural ecological efficiency is about 1. In order for readers to better understand the empirical results, this paper reduces the value of the digital inclusive financial index by 100 times, which will not affect the relationship between the digital inclusive financial index and agricultural ecological efficiency.
Secondly, the non-relative variables are logarithm in order to reduce the influence of data heteroscedasticity. We use the digital financial inclusion data of Peking University from 2011 to 2018 as a measure of the level of digital Inclusive Finance, and infer that the conclusion of this paper needs to be based on sufficient theoretical research and combined with relevant empirical analysis to better reflect the conclusion of this paper.
Point 5: Also many comments from the previous review of manuscript (agronomy-1317943) were not taken into account.
Response 5: We attach great importance to the review comments put forward by each reviewer and fully consider the modification opinions put forward by three reviewers. Every comment of the reviewer is very important for the promotion and improvement of this paper. We have made serious consideration and modification. For many comments from the previous review of manuscript, we have made corresponding changes to most of the review comments in the paper, and some other relevant review comments have been explained. We have seriously considered the importance of each comment, so that the handling of these comments is mentioned in the paper and the reply letter. We would not easily give up the comments of every reviewer。It may be that our revision level is insufficient, which makes you have this kind of trouble. We feel so sorry about that.
Author Response File: 
Author Response.docx
Reviewer 2 Report
The article is, as I said, very interesting.
It has been rewrited and it is much clearer now.
Just some minor questions:
- Line 260: Therefore, Hypothesis 3 and Hypothesis 4 are proposed as follows.
I do not see Hypothesis 4.
- Line 319 : the data including analysis (DEA) can not only carry out dimensionless data processing
Are the authors sure of this word?
Author Response
Dear Reviewer 2:
Thank you for your letter and for the Reviewer 2’s comments concerning our manuscript entitled “Does digital financial inclusion affect agricultural eco-efficiency? A case study on China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made corrections which we hope meet with approval. The revised portion is marked in red in the paper. The main corrections in the paper and the response to the Reviewer 2’s comments are as following:
Point 1: Line 260: Therefore, Hypothesis 3 and Hypothesis 4 are proposed as follows. I do not see Hypothesis 4.
Response 1: As Reviewer 2’s suggested, we have deleted the redundant sentences. Hypothesis 4 is not in this article. Thank you very much for your careful review. This revision is in P.6, line 259 to line 260.
Point 2: Line 319: the data including analysis (DEA) can not only carry out dimensionless data processing Are the authors sure of this word?
Response 2: According to Reviewer2’s comments, we have revised this description. Thanks for your suggestions. In order to make readers understand the AEE calculation method in this paper, we have supplemented some contents about super efficiency SBM-DEA, which are placed in Appendix A. The revisions are in P.18 line 657 to P.19 line 688
Author Response File: Author Response.docx
Reviewer 3 Report
The authors addressed most of the flaws in the first MS.
However, I still would like for a more graphical presentation of results. There is a lot of different points of view on the study that a numerical approach may not suffice. E.g. on lines 450 to 454 the authors state that the impact of digital financial inclusion lies on the right side of a "U"-shaped curve, while digital financial inclusion has a "U”-shaped nonlinear relationship with agricultural eco-efficiency. A graph would help ot support such statement.
Author Response
Dear Reviewer 3:
Thank you for your letter and for the Reviewer 3’s comments concerning our manuscript entitled “Does digital financial inclusion affect agricultural eco-efficiency? A case study on China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made corrections which we hope meet with approval. The revised portion is marked in red in the paper. The main corrections in the paper and the response to the Reviewer 3’s comments are as following:
Point 1: The authors addressed most of the flaws in the first MS.
However, I still would like for a more graphical presentation of results. There is a lot of different points of view on the study that a numerical approach may not suffice. E.g. on lines 450 to 454 the authors state that the impact of digital financial inclusion lies on the right side of a "U"-shaped curve, while digital financial inclusion has a "U”-shaped nonlinear relationship with agricultural eco-efficiency. A graph would help ot support such statement.
Response 1: This comment is very important for the improvement and promotion of this paper. According to Reviewer3’s comments, we have used a graphical way to explain the empirical results of this paper more intuitively. We added the scatter graph of AEE and DFI after descriptive statistics, and added the corresponding graph after Table 3 as the description of the "U”-shaped nonlinear relationship. The revisions are in P.11, line 422 to P.12 line 431, P.13 line 446 to line 450, P.14 line 460 to line 463
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
The authors sent to the editorial office of Agronomy journal three versions of the manuscript: agronomy-1317943, agronomy-1358019 and agronomy-1385854.
In the last version answered on all remarks from 1358019 version and they addressed some comments from the first review (agronomy-1317943).
Still Authors unanswered on remarks from end of July 2021:
The authors undertook to assess the impact of digital financial inclusion on the eco-efficiency of agriculture by defining eco-efficiency as the ratio of inputs to outputs. Table 1 presents a description of the expenditure for the obtained products, dividing the outputs into expected and unexpected. In my opinion, unexpected outputs have been widely and commonly knowns since the introduce of industrial/chemical production goods into agriculture. The pollution on the environment is one of the most important negative effects of agriculture activity.
In the same table (table 1) Authors presented input-output indicators variables f.ex. in one thousand hectares (Land), ten thousand people (Labor), ton (Pesticides), in currency - RMB 100 million (Total output) or indexes (Agricultural pollution). While in table 2 used different units f.ex. DAM given 10,000 wats/per ha, FSA in %, PS as a ratio and ER total amount of income from pollutant. The results presented in this way are difficult to compare and understand what they were used for. Authors answered: The units of input-output indicators do not have much deep meaning. If so why used unit 1000 hectares not thousand hectares, why used 10,000 people not thousand people, why used 100 million RMB nor million RMB etc.
Author Response
Dear Reviewer 1:
Thank you for your letter and for the Reviewer 1’s comments concerning our manuscript entitled “Does digital financial inclusion affect agricultural eco-efficiency? A case study on China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made corrections which we hope meet with approval. The revised portion is marked in red in the paper. The main corrections in the paper and the response to the Reviewer 1’s comments are as following:
Point 1: The authors undertook to assess the impact of digital financial inclusion on the eco-efficiency of agriculture by defining eco-efficiency as the ratio of inputs to outputs. Table 1 presents a description of the expenditure for the obtained products, dividing the outputs into expected and unexpected. In my opinion, unexpected outputs have been widely and commonly knowns since the introduce of industrial/chemical production goods into agriculture. The pollution on the environment is one of the most important negative effects of agriculture activity.
Response 1: According to Reviewer1’s comments, we have made a more detailed explanation on the selection of unexpected outputs. When considering the unexpected output, referring to the academic research of other scholars, this paper focuses on the environmental pollutant emission and carbon emission in agricultural activities as the unexpected output. In the research of other scholars [1-4], environmental pollution and carbon emission are often regarded as representative indicators of unexpected output. From the perspective of the government, the Chinese government has attached great importance to environmental governance in recent years. Pollutant emission and carbon emission are the main objects of environmental governance. These two indicators have the important negative impact on agricultural activities, so these two indicators are appropriate as the choice of unexpected output. In addition, because the data of environmental pollution emissions and carbon emissions in agricultural production activities are difficult to measure directly, this paper uses the indirect measurement method to measure the data of unexpected output.
[1] Qiuying, L., Longwu, L., & Zhenbo, W. Spatiotemporal Differentiation and the Factors Influencing Eco-Efficiency in China. Journal of Resources and Ecology, 2021,12(2). doi:10.5814/j.issn.1674-764x.2021.02.003
[2] Pan, W.-T., Zhuang, M.-E., Zhou, Y.-Y., & Yang, J.-J. Research on sustainable development and efficiency of China's E-Agriculture based on a data envelopment analysis-Malmquist model. Technological Forecasting and Social Change, 2021,162. doi:10.1016/j.techfore.2020.120298
[3] Yasmeen, H., Tan, Q., Zameer, H., Tan, J., & Nawaz, K. Exploring the impact of technological innovation, environmental regulations and urbanization on ecological efficiency of China in the context of COP21. J Environ Manage, 2020,274, 111210. doi:10.1016/j.jenvman.2020.111210
[4] Zhang, X., Sun, D., Zhang, X., & Yang, H. Regional ecological efficiency and future sustainable development of marine ranch in China: An empirical research using DEA and system dynamics. Aquaculture, 2021,534. doi:10.1016/j.aquaculture.2021.736339
Point 2: In the same table (table 1) Authors presented input-output indicators variables f.ex. in one thousand hectares (Land), ten thousand people (Labor), ton (Pesticides), in currency - RMB 100 million (Total output) or indexes (Agricultural pollution). While in table 2 used different units f.ex. DAM given 10,000 wats/per ha, FSA in %, PS as a ratio and ER total amount of income from pollutant. The results presented in this way are difficult to compare and understand what they were used for. Authors answered: The units of input-output indicators do not have much deep meaning. If so why used unit 1000 hectares not thousand hectares, why used 10,000 people not thousand people, why used 100 million RMB nor million RMB etc.
Response 2: As Reviewer1’s suggested, we have supplemented the relevant explanations for the units of input-output indicators. Firstly, the meanings of the units of variables in Table 1 and table 2 are different. In Table 1, the unit of each input-output indicator variable mainly refers to the unit adopted in the relevant statistical yearbook. We also make some relevant explanations on the unit selection of input-output indicators. For example, as Reviewer 1 said, we use 10,000 people or 1000 people as units, or 100 million RMB or 1 million RMB. The AEE results calculated by the super efficiency SBM-DEA method are consistent. We have tried this decimal variable data unit transformation, which will not change the value of AEE in super efficiency SBM-DEA method. The variables measured by DEA method are not affected by the dimension of input-output variables. Secondly, the conversion of variable data units in Table 2 would affect the size of variable estimation coefficient in empirical regression. For example, we have reduced the DFI data by 100 times, which would expand the estimation coefficient of DFI by 100 times in this paper. We may have many problems in the unit description of input-output indicators, so we are deeply sorry that you have not received a satisfactory modification reply in this regard.
Author Response File: Author Response.docx
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Manuscript entitled "Does digital financial inclusion affect agricultural eco-efficiency? A case study on China" is not suitable for publication in Agronomy MPDPI in current version.
The authors undertaken an important issue from rural areas development point of view. Unfortunately, the study is incomprehensible, a lot of information is redundant and not described, and others are insufficiently explained. The authors did not explain and clearly state how the impact of the digital financial inclusion on selected eco-efficiency indicators in agriculture was assessed. Moreover, it is difficult to define some of the agricultural production indicators as positively influencing on the reduction of impact in the environment.
The authors undertook to assess the impact of digital financial inclusion on the eco-efficiency of agriculture by defining eco-efficiency as the ratio of inputs to outputs. Table 1 presents a description of the expenditure for the obtained products, dividing the outputs into expected and unexpected. In my opinion, unexpected outputs have been widely and commonly knowns since the introduce of industrial/chemical production goods into agriculture. The pollution on the environment is one of the most important negative effects of agriculture activity.
In the same table (table 1) Authors presented input-output indicators variables f.ex. in one thousand hectares (Land), ten thousand people (Labor), ton (Pesticides), in currency - RMB 100 million (Total output) or indexes (Agricultural pollution). While in table 2 used different units f.ex. DAM given 10,000 wats/per ha, FSA in %, PS as a ratio and ER total amount of income from pollutant. The results presented in this way are difficult to compare and understand what they were used for.
Others variables presented in table 3 they haven't characteristic and Authors should explain what does mean ARD, AIL, and in what units they are presented. The same question refers to other indicators like AEEit, IFI, ER and how and how to understand DAM presented in 10,000 wats per ha, FSA in %, PS as a ratio of grain crop acreage/crop sown area - grain crop acreage. Additionally later on manuscript only AEE and IFI variables was included and detailed described. Why Authors represented DAM, PS, FSA, ARD, AIL and ER variables?
Authors used different factors for agricultural eco-efficiency analysis (Equation 1-3). Unfortunately the same symbols was used (AEEit) for different equations.
In table 5 presented regression result from two different model (Tobit and OLS) for the same sample and sub-samples. Please explain significant effect of InAIL with Eastern China for Tobit model (0.0165**) while for the same variables in OLS model for the same value (0.0165) value was not significant. The same dependence was found for InER vs Central China (0.0512**) of the Tobit model while for OLS model was not significant (0.0512). Please explain differences.
What parameters of IFI presented (width or depth). Average value ranged from 1.46 (mean for Eastern China) to 2.71 (Western China) while Peking University Digital Financial Inclusion Index presented at lines 440-442 has much different value (Peking University Digital Financial Inclusion Index that the average values of these two indicators in 2011 are 34.28 and 46.93 respectively, while in 2018 the values of these two indicators are 281.92 and 287.50 respectively).
Comments from line 480-483 not correspond with value presented in table 5. IFI data from table 5 for Eastern -0.563, Central China -0.1345 (for Tobit model) and for Eastern -0.563, Central China -0.1345 (for OLS model) while for IFL2 for the same dependence value 0.0175 (Eastern) and 0.0404 (Central) for both models.
Comments in line 524-525 need correction. Basic on information from Peking University Digital Financial Inclusion Index increased from 2011 to 2018 year. What should be understood as a positive trend when the value is higher. In the assessed work, the IFI index for Western China was 2.71, while in the East it was 1.46 and in the Central China 1.49
Author Response
Dear Reviewer 1:
Thank you for your letter and for the Reviewer 1’s comments concerning our manuscript entitled “Does digital financial inclusion affect agricultural eco-efficiency? A case study on China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made corrections which we hope meet with approval. The revised portion is marked in red in the paper. The main corrections in the paper and the response to the Reviewer 1’s comments are as following:
Point 1: The authors undertaken an important issue from rural areas development point of view. Unfortunately, the study is incomprehensible, a lot of information is redundant and not described, and others are insufficiently explained. The authors did not explain and clearly state how the impact of the digital financial inclusion on selected eco-efficiency indicators in agriculture was assessed. Moreover, it is difficult to define some of the agricultural production indicators as positively influencing on the reduction of impact in the environment. The authors undertook to assess the impact of digital financial inclusion on the eco-efficiency of agriculture by defining eco-efficiency as the ratio of inputs to outputs. Table 1 presents a description of the expenditure for the obtained products, dividing the outputs into expected and unexpected. In my opinion, unexpected outputs have been widely and commonly knowns since the introduce of industrial/chemical production goods into agriculture. The pollution on the environment is one of the most important negative effects of agriculture activity.
Response 1: According to Reviewer1’s comments, we have deleted the redundant information in this article and supplemented the interpretation of other relevant variables. Through empirical analysis, this paper finds that there is a nonlinear relationship between digital financial inclusion and agricultural ecological efficiency, and expounds the impact relationship in detail. As for“Moreover, it is difficult to define some of the agricultural production indicators as positively influencing on the reduction of impact in the environment.”, We agree with reviewer 1 that environmental pollution is one of the most important negative effects in agricultural activities. When calculating agricultural ecological efficiency, because the data of environmental pollution is difficult to measure directly, this paper considers using indirect measurement to measure the environmental pollution of unexpected output.
Point 2: In the same table (table 1) Authors presented input-output indicators variables f.ex. in one thousand hectares (Land), ten thousand people (Labor), ton (Pesticides), in currency - RMB 100 million (Total output) or indexes (Agricultural pollution). While in table 2 used different units f.ex. DAM given 10,000 wats/per ha, FSA in %, PS as a ratio and ER total amount of income from pollutant. The results presented in this way are difficult to compare and understand what they were used for.
Response 2: As Reviewer1’s suggested, we have explained the unit problem and purpose of input-output index variables in Table 1. For the unit problems of these input-output index variables, this paper uses super efficiency SBM-DEA to calculate AEE, which can avoid the inconsistency of unit dimension of these input-output indicators. Therefore, these input-output indicators used in this paper should represent the measurement values of all aspects of AEE, and their units will not affect the measured AEE values. This revision is in P.7, line 323 to line 328.
Point 3: Others variables presented in table 3 they haven't characteristic and Authors should explain what does mean ARD, AIL, and in what units they are presented. The same question refers to other indicators like AEEit, IFI, ER and how and how to understand DAM presented in 10,000 wats per ha, FSA in %, PS as a ratio of grain crop acreage/crop sown area - grain crop acreage. Additionally later on manuscript only AEE and IFI variables was included and detailed described. Why Authors represented DAM, PS, FSA, ARD, AIL and ER variables?
Response 3: According to Reviewer1’s comments, we have completed the details of the variables. In this paper, most variables exist in the form of relative numbers such as ratio, and the unit has no specific meaning. Therefore, the unit meaning of each variable is not explained in detail in this paper. The revisions are in P.7, line 318 to P.10, line 376.
Point 4: Authors used different factors for agricultural eco-efficiency analysis (Equation 1-3). Unfortunately the same symbols was used (AEEit) for different equations.
Response 4: According to Reviewer1’s comments, we have revised the form of equation 1-3.The revisions are in P.6, line 276 to line 290.
Point 5: In table 5 presented regression result from two different model (Tobit and OLS) for the same sample and sub-samples. Please explain significant effect of InAIL with Eastern China for Tobit model (0.0165**) while for the same variables in OLS model for the same value (0.0165) value was not significant. The same dependence was found for InER vs Central China (0.0512**) of the Tobit model while for OLS model was not significant (0.0512). Please explain differences.
Response 5: As Reviewer1’s suggested, we have reselected the estimation method to solve the problem of inconsistent estimation of these variables. Generally speaking, different estimation methods may have different t values for the estimation coefficients of the same variable. In order to simplify the empirical process, reduce unnecessary empirical analysis and solve the endogeneity between variables, this paper decides to use the differential GMM estimation method. The revisions are in P.11, line 392 to P.12, line 417.
Point 6: What parameters of IFI presented (width or depth). Average value ranged from 1.46 (mean for Eastern China) to 2.71 (Western China) while Peking University Digital Financial Inclusion Index presented at lines 440-442 has much different value (Peking University Digital Financial Inclusion Index that the average values of these two indicators in 2011 are 34.28 and 46.93 respectively, while in 2018 the values of these two indicators are 281.92 and 287.50 respectively).
Response 6: According to Reviewer1’s comments, we have added some detailed information. At the beginning, we pre-processed the data of digital financial inclusion, reduced the digital financial inclusion index of various regions by 100 times, and did not deal with the sub-indexes of other dimensions of digital financial inclusion accordingly. The data pre-processing will not change the significance of the empirical results of this paper, but will only change the estimation coefficient of digital financial inclusion. The revisions are in P.8, line 335 to line 339.
Point 7: Comments from line 480-483 not correspond with value presented in table 5. IFI data from table 5 for Eastern -0.563, Central China -0.1345 (for Tobit model) and for Eastern -0.563, Central China -0.1345 (for OLS model) while for IFL2 for the same dependence value 0.0175 (Eastern) and 0.0404 (Central) for both models.
Response 7: As Reviewer1’s suggested, we have readjusted the estimation method to solve the above problems. The revisions are in P.11, line 388 to P.12, line 417.
Point 8: Comments in line 524-525 need correction. Basic on information from Peking University Digital Financial Inclusion Index increased from 2011 to 2018 year. What should be understood as a positive trend when the value is higher. In the assessed work, the IFI index for Western China was 2.71, while in the East it was 1.46 and in the Central China 1.49
Response 8: As Reviewer1’s suggested, we have readjusted the structure and estimation method of the article, reorganized the data, and solved the above problems.
Author Response File: Author Response.docx
Reviewer 2 Report
The effect of adequate finance to business development is well-known. This is especially true in agricultural business located in rural areas. So, research about this is always sound and necessary.
However, I cannot understand the purpose of this paper. I read it five times.
I cannot see the purpose of including lines 81-I27.
Also, I cannot see how it "explores the impact of digital financing inclusion on agricultural eco-efficiency" as the authors quote 312-313.
I am not able to see what authors want to do.
I do not understand how they collect and process data and how they get conclusions from them.
May be it is my fault.
I am sorry.
The only thing I can suggest is that authors rewrite the paper in a more clear way.
Author Response
Dear Reviewer 2:
Thank you for your letter and for the Reviewer 2’s comments concerning our manuscript entitled “Does digital financial inclusion affect agricultural eco-efficiency? A case study on China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made corrections which we hope meet with approval. The revised portion is marked in red in the paper. The main corrections in the paper and the response to the Reviewer 2’s comments are as following:
Point 1: However, I cannot understand the purpose of this paper. I read it five times.
I cannot see the purpose of including lines 81-I27.
Response 1: As Reviewer 2’s suggested, we have deleted lines 81-127 and modified it to be more relevant to the topic. We wrote lines 81-127 to make readers better understand the differences and relevance between this paper and previous studies. From the perspective of ecological efficiency, this paper first analyses the urban ecological efficiency, followed by the agricultural ecological efficiency. The progressive form helps readers find the highlights of this paper. Maybe it's because of our poor writing that the reviewers can't understand the content of this paragraph. We're sorry.
Point 2: Also, I cannot see how it "explores the impact of digital financing inclusion on agricultural eco-efficiency" as the authors quote 312-313.
Response 2: According to Reviewer2’s comments, we have deleted the redundant content and added some key information to make readers better understand this article. The research ideas of this paper are as follows. Based on the existing research, this paper measures the development level of digital financial inclusion with the digital inclusive finance index of Peking University, measures the sustainable development of rural areas with Agricultural ecological efficiency, and explores the relationship between the two through empirical analysis. The data collection and collation of this paper is not too detailed, so we would supplement it completely. The data in this paper are basically collected and sorted into panel data through China's Rural Statistical Yearbook and the statistical yearbooks of provinces, autonomous regions and municipalities in mainland China, and then use the panel regression model to explore the relationship between the two. The empirical results show that the estimation coefficients of the primary and secondary terms of digital inclusive finance are significant, which can show that there is a nonlinear U-shaped relationship between digital financial inclusion and Agricultural ecological efficiency.
Point 3: I am not able to see what authors want to do.
I do not understand how they collect and process data and how they get conclusions from them.
May be it is my fault.
I am sorry.
The only thing I can suggest is that authors rewrite the paper in a more clear way.
Response 3: As Reviewer 2’s suggested, we have rewrited the paper in a more clear way. Firstly, we have deleted the redundant literature in order to let readers better understand the highlights of this paper. Secondly, we have modified the model and method to reduce some unnecessary discussion and proof, so as to better highlight the key content of the empirical analysis of this paper, so that readers can understand this paper more directly.
Author Response File: Author Response.docx
Reviewer 3 Report
This study aims to assess the impact of digital financial inclusion on agricultural eco-efficiency in mainland China through panel Tobit and panel OLS regression models. For that panel data of 30 provinces, autonomous regions and municipalities, were used. Despite on how the study is presented, some issues arise as discussed below. Overall, the publication of the MS should be reconsidered after minor revisions.
Comments:
Introduction
This section be more concise, focused and, therefore shortened. The section only need to address properly the literature on the impact of digital financial inclusion on rural development.
Material and Methods
Table 4 seems out of place. The variable thereby described appear first in Table 3. This needs to be revised.
Results
A graphical presentation would help to better discuss the results. Table 5 is hard to read; the discussion helps to better understand the results, but the way they are presented isn’t appealing.
Also, the Tables format should be revised.
Author Response
Dear Reviewer 3:
Thank you for your letter and for the Reviewer 3’s comments concerning our manuscript entitled “Does digital financial inclusion affect agricultural eco-efficiency? A case study on China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made corrections which we hope meet with approval. The revised portion is marked in red in the paper. The main corrections in the paper and the response to the Reviewer 3’s comments are as following:
Point 1: Introduction
This section be more concise, focused and, therefore shortened. The section only need to address properly the literature on the impact of digital financial inclusion on rural development.
Response 1: According to Reviewer3’s comments, we have deleted the redundant literature review. The two parts of literature review we left would help readers better understand the differences and connections between this paper and previous studies. The revisions are in P.2, line 73 to P.3, line 145.
Point 2: Material and Methods
Table 4 seems out of place. The variable thereby described appear first in Table 3. This needs to be revised.


Response 2: As Reviewer 3’s suggested, we have put the description of variables and data sources in Table 2. The revisions are in P.9, line 375 to P.10, line 376.
Point 3: Results
A graphical presentation would help to better discuss the results. Table 5 is hard to read; the discussion helps to better understand the results, but the way they are presented isn’t appealing.
Also, the Tables format should be revised.


Response 3: As Reviewer 3’s suggested, we already readjusted the empirical results of the article. We have revised the form of the table and the redundant empirical results to demonstrate our core views in a more concise empirical way. Readers can understand the empirical results and conclusions of this paper through the significance of the estimated coefficient of the core explanatory variable DFI in Table 4. We have reduced unnecessary discussion as much as possible, so as to make it easier for readers to understand the core content of the paper.
