Spatial–Temporal Characteristics and Influencing Factors of Eco-Efficiency of Cultivated Land Use in the Yangtze River Delta Region

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper is a valuable contribution to the field of land use efficiency, particularly in the context of the Yangtze River Delta region. Its methodological robustness and detailed analysis provide deep insights into the spatial-temporal patterns of ECLU. However, the paper could be improved by enhancing readability for a broader audience and providing more comparative and contextual analysis. Additionally, expanding on the broader implications of the findings could increase the paper's relevance to a global audience.

INTRODUCTION

The authors should integrate more extensive discussions on the theoretical models or frameworks underpinning the ECLU study. This approach will enhance the understanding of the academic foundations of the research. Additionally, the authors should present explicit research questions to guide the reader more effectively through the study. For instance, questions like "What are the spatio-temporal patterns of ECLU in the YRD region from 2000 to 2020?" or "What significant factors influence ECLU in this region?" are recommended. It is also advisable for the authors to formulate hypotheses regarding potential outcomes or relationships. For example, hypothesizing about the potential impacts of urbanization or economic development on ECLU.

DATA AND METHODS

Improvements in the methodology section can be achieved by including simplified explanations or summaries of these models, making them more accessible to lay readers. Additionally, incorporating diagrams or workflow charts to illustrate how these models function would enhance reader comprehension. A discussion comparing the chosen methods with alternative approaches is necessary, explaining why these models are more suitable for this research than other methods. For example, it elucidates why a super-efficient SBM model is more apt for analyzing ECLU in the YRD region. Articulating the connection between the research design and objectives would also strengthen the study. The authors need to address potential limitations or biases of these methods and how they minimize them.

RESULTS AND DISCUSSION

The authors should provide summaries or highlight key points at the end of each major finding. This will help readers grasp the essence of the complex data in this analysis. Comparing the results with findings from other relevant studies is necessary to place this study within a broader knowledge context.

CONCLUSION

 

Policy recommendations should be expanded beyond the YRD to include potential policy implications for similar regions. This could involve discussing how policymakers in other areas might adapt YRD strategies to their unique contexts.

Comments on the Quality of English Language

The paper demonstrates a reasonable command of English with coherent and clear expression. However, notable phrasing, grammar, and syntax issues necessitate correction to achieve the fluency and clarity expected in academic publications. The abstract and sections of the paper reveal occasional awkward or unnatural phrasing, suggesting that the text may have been composed by a non-native speaker or translated from another language​​. For example, sentences are sometimes structured in a way that can impede smooth reading, and there are instances of missing or incorrectly used articles ("the," "a," "an"). Additionally, the paper exhibits occasional grammatical errors and inconsistencies in verb tense and subject-verb agreement​​. Furthermore, the paper occasionally uses terminology and constructs sentences in a manner that could be refined for better clarity and academic tone. For example, while used correctly, technical terms and concepts are sometimes presented in a way that could be streamlined or better explained for the target academic audience​​.

Here are examples from the article that demonstrate awkward or unnatural phrasing, suggesting the need for improvement in English language usage:

1. In the section on hotspot analysis, the sentence structure and phrasing are cumbersome, making it difficult to follow: "Hotspot analysis is a method of calculating the Getis-Ord Gi* statistic for each element in a dataset. The Getis-Ord Gi* index proposed by British scholars Getis and Ord [35] is a statistic used to measure whether there is a local correlation between an observation and a neighboring element and can accurately identify the location where high or low value elements are spatially clustered"​​.
2. The description of the super-efficiency of non-expected output is convoluted and lacks clarity: "super-efficiency of non-expected output proposed by Japanese scholar Tone in 2002 [34] takes into account the influence of factor 'slack' overcomes the problem that it is difficult to compare the effective decision-making units and is able to further compare the decision-making units by the super-efficiency value greater than 1 derived from the frontier"​​.
3. The explanation of the term "eco-efficiency" is presented in a way that disrupts the flow and readability: "The term 'eco-efficiency' was firstly proposed by German scientists Schaltegger and Sturm in 1990[29] defined as the ratio of negative environmental value caused by the growth of economy. In the field of agriculture eco-efficiency research mostly focuses on macro-level agricultural ecological efficiency [30]"​​.

Here are examples from the article that demonstrate the use of terminology and sentence construction that could be refined for better clarity and academic tone:

1. In the description of the super-efficient SBM model: "The super-efficient SBM (Slacks-based model) model based on undesired output is constructed based on the super-efficient DEA (Data envelopment analysis) model and the advantages of SBM. The traditional DEA model measures the efficiency with the advantages of no need to make a priori assumptions on the production function no need for price information and relative objectivity [33] but the process of calculation will be"​​. This sentence is overly complex and could be simplified for clarity.
2. Reference to a previous study: "asset-capital attributes and its spatial mechanism. Applied Geography. 2020 125 102284-102297. 25. Xiao L.; YiQ.; Piling S.; Wei Y.; WenlongP.Green transition of cultivated land use in the Yellow River Basin A perspective of green utilization efficiency evaluation. Land. 2020 9(12)475-497"​​. The citation style here is inconsistent and disrupts the flow of the text.
3. Explanation of the panel Tobit models: "The panel Tobit regression model was proposed by the American scholar Tobin in 1958 in order to solve the econometric model [39] for the restricted situation of the explanatory variables which can effectively make up for the biased and inconsistent parameter estimation of the least squares method [40,41]"​​. This sentence is cumbersome and could be more succinctly structured.

Author Response

Response to Reviewer 1 Comments

 

Ⅰ Summary

Dear Editor and Reviewer：

 

Thank you for your letter and the reviewers' valuable comments on our manuscript entitled “Spatial-temporal characteristics and influencing factors of eco-efficiency of cultivated land use in the Yangtze River Delta Region” (land-2830497). All of these comments are valuable and helpful for us to revise and improve the paper, and also have important guiding significance for our research.

We have carefully studied these comments and revised them, and we hope to get your approval. Our point-to-point revisions are presented below, along with an explanation of how we have addressed these comments in the paper. Detailed responses to reviewers' comments are highlighted in red below. Revisions in the manuscript are also marked in red. Meantime, we are submitting the Revision in simple markup mode in an attachment.

 

Ⅱ Point by Point response to Comments and Suggestions for Authors

 

Comments 1:

This paper is a valuable contribution to the field of land use efficiency, particularly in the context of the Yangtze River Delta region. Its methodological robustness and detailed analysis provide deep insights into the spatial-temporal patterns of ECLU. However, the paper could be improved by enhancing readability for a broader audience and providing more comparative and contextual analysis. Additionally, expanding on the broader implications of the findings could increase the paper's relevance to a global audience.

 

Response 1:

Thank you very much for your meticulous reading and the positive comments. Your suggestion to enhance readability for a broader audience and provide more comparative and contextual analysis would be very beneficial to our article. At the same time, your suggestion to expand on the broader implications of the findings has further optimized the findings, conclusions and policy recommendations of our study. We will detail the specific changes below.

 

 

Comments 2:

INTRODUCTION

The authors should integrate more extensive discussions on the theoretical models or frameworks underpinning the ECLU study. This approach will enhance the understanding of the academic foundations of the research. Additionally, the authors should present explicit research questions to guide the reader more effectively through the study. For instance, questions like "What are the spatio-temporal patterns of ECLU in the YRD region from 2000 to 2020?" or "What significant factors influence ECLU in this region?" are recommended. It is also advisable for the authors to formulate hypotheses regarding potential outcomes or relationships. For example, hypothesizing about the potential impacts of urbanization or economic development on ECLU.

                                                                             

Response 2:

Thank you for pointing out these issues. Firstly, regarding a broader discussion on the theoretical models or frameworks supporting ECLU research, we have optimized the concept of ECLU from lines 55-60 (modified, the same below), and modified the theoretical sources of ECLU from lines 84-90. Secondly, we fully agree with your point that clear research questions should be raised, so we have added research questions related to the research content in lines 104-105. The specific content is as follows:

 

“Lines 55-60: ECLU refers to the degree to which a certain input of production factors can maximize social and economic output and minimize environmental pollution during the utilization of farmland. ECLU refers to the degree to which certain production factors such as farmland, labor, and pesticides, are invested in the utilization of arable land, aiming to maximize expected output (agricultural output value, gross grain output) and minimize unexpected output (carbon emissions) [6-7].”

 

“Lines 84-90: The notion of "eco-efficiency" was defined in 1990 by the German scientists Schaltegger and Sturm [36] as the ratio of negative environmental values caused by economic growth. Since then, eco-efficiency has been commonly used in various fields such as industry, agriculture and tourism. ECLU is an innovative application and advancement of farmland use efficiency. Achieving coordination and unity of the "resource, socio-economic, environment" and achieving efficiency and effectiveness are its goals, which are to maximize desirable output and reduce unpleasant output [37].”

 

“Lines 104-105 Therefore, what are the spatio-temporal patterns, areal differentiation and the driving factors of ECLU in the YRD region from 2000 to 2020?”

 

 

Comments 3:

DATA AND METHODS

Improvements in the methodology section can be achieved by including simplified explanations or summaries of these models, making them more accessible to lay readers. Additionally, incorporating diagrams or workflow charts to illustrate how these models' function would enhance reader comprehension. A discussion comparing the chosen methods with alternative approaches is necessary, explaining why these models are more suitable for this research than other methods. For example, it elucidates why a super-efficient SBM model is more apt for analyzing ECLU in the YRD region. Articulating the connection between the research design and objectives would also strengthen the study. The authors need to address potential limitations or biases of these methods and how they minimize them.

 

Response 3:

Thank you very much for your suggestions on the presentation of our research methods, which are very helpful for us to optimize the presentation. Firstly, we supplemented the framework flowchart at the beginning of the research method (Figure 2), We hope to effectively enhance the reader's understanding. Secondly, in lines 177-185, it was added to explain why the SBM model is more suitable for this study than other methods, while in the original manuscript, lines 220-226 and 267-270 have already explained the advantages of the Dagum Gini coefficient and the panel TOBIT model, respectively. The details are as follows.

Figure 2. Research methodology framework diagram.

 

“Lines 177-185: The conventional DEA model calculates efficiency using a flawed calculation procedure that prevents further comparisons when the DEA efficiency results from different measurement units add up to one [41]. The SBM model based on super-efficiency of non-expected output constructed by Japanese scholar Tone in 2002 considering non-expectation can well address the shortcomings of the traditional DEA model. The model fully takes into account the impact of input and output slack variables on the efficiency level, thus accurately measuring the input-output efficiency of decision-making units, and at the same time, it can be compared for multiple effective decision-making units.”

 

“Lines 220-226: Unlike the Thiel index [44] and the traditional Gini coefficient, the Dagum Gini coefficient not only has the advantage of overcoming the problem of data overlap among samples and revealing the causes of overall differences, but also can further decom-pose the sources of differences in a particular indicator, decompose the overall regional differences into the three parts of intra-regional differences Gw, inter-regional differences Gnb, the inter-regional hypervariable densities Gt [45].”

 

“Lines 267-270: The value domain of the ECLU obtained is in the truncated state, the estimation of the ordinary least squares method will have a large bias, so this study adopts the Tobit re-gression model which has a small bias and a high degree of accuracy, uses the stata17 software for evaluation.”

 

 

Comments 4:

RESULTS AND DISCUSSION

The authors should provide summaries or highlight key points at the end of each major finding. This will help readers grasp the essence of the complex data in this analysis. Comparing the results with findings from other relevant studies is necessary to place this study within a broader knowledge context.

 

Response 4:

We appreciate your suggestions, so we have provided summaries, highlighted key points or results from other relevant studies for comparison in each section of the results, specifically in lines293-296, 325-329, 360-363, and 407-411. The details are shown below.

 

“Lines 293-296: In summary, there are large differences in the time variation characteristics of ECLU among the provinces in the YRD region, with Jiangsu province having the highest and growing ECLU values, while Shanghai and Anhui have a decreasing trend, and Zhejiang's ECLUs remain stable over the study period.”

 

“Lines 325-329: Overall, the temporal variation of ECLU in different sized cities is characterized by the fact that the ECLU of Supercity, Type I large-sized city and Type I small-sized City show an increasing trend during the study period, while the ECLU values of Megacity, Type II large-sized city and Medium-sized city show a decreasing trend.”

 

“Lines 360-363: The high values and hot spots of ECLU in the YRD region were mainly concentrated in Shanghai and central Jiangsu, while the low values and cold spots were distributed in Anhui Province and southern Zhejiang Province during the study period.”

 

“Lines 407-411: Overall, the Gini coefficient within Jiangsu region is the smallest, indicating the smallest difference in ECLU within the region, followed by Anhui and Zhejiang. The Gini coefficient between Jiangsu and Shanghai is the smallest, indicating that the efficiency difference between these two regions is relatively small, while the difference between other regions is large.”

 

“Lines 456-459: In conclusion, it is evident that the degree of urbanization and per capita GDP positively affect the ECLU of the research region, whereas the degree of industrial structure and agriculture scale negatively affect the ECLU's improvement in the YRD region, which is consistent with earlier research.”

 

 

Comments 5:

CONCLUSION

Policy recommendations should be expanded beyond the YRD to include potential policy implications for similar regions. This could involve discussing how policymakers in other areas might adapt YRD strategies to their unique contexts.

Response 5:

We strongly agree with your recommendations on conclusions, so we add the importance of the integrated development of agriculture in the Yangtze River Delta to national agriculture on lines 539-551, while expanding the policy recommendations to other regions similar to the Yangtze River Delta area. The details are as follows.

 

“Lines 539-551: The YRD is the region with the most complete modern agricultural industry system, the strongest modern agricultural innovation ability, and the richest rural business formats in China. It is also one of the regions where rural development is most restricted by factors such as land and environmental carrying resources, and the distribution of resources is most uneven. If the integrated development pattern of rural revitalization can be formed as soon as possible, it will undoubtedly achieve the synergistic development effect of rural revitalization in the YRD, improve the efficiency of regional integration development, and provide momentum for the high-quality implementation of national strategies, make greater contributions to the comprehensive construction of a new pattern of integrated development of rural revitalization. The policy recommendations for sustainable agricultural development in the YRD based on the ECLU measurements and analyses are also applicable to several other economically developed regions in China, such as the Beijing-Tianjin-Hebei Urban Agglomeration (BTHA) and the Pearl River Delta (PRD).”

 

 

Comments 6:

The paper demonstrates a reasonable command of English with coherent and clear expression. However, notable phrasing, grammar, and syntax issues necessitate correction to achieve the fluency and clarity expected in academic publications. The abstract and sections of the paper reveal occasional awkward or unnatural phrasing, suggesting that the text may have been composed by a non-native speaker or translated from another language. For example, sentences are sometimes structured in a way that can impede smooth reading, and there are instances of missing or incorrectly used articles ("the," "a," "an"). Additionally, the paper exhibits occasional grammatical errors and inconsistencies in verb tense and subject-verb agreement. Furthermore, the paper occasionally uses terminology and constructs sentences in a manner that could be refined for better clarity and academic tone. For example, while used correctly, technical terms and concepts are sometimes presented in a way that could be streamlined or better explained for the target academic audience.

 

Here are examples from the article that demonstrate awkward or unnatural phrasing, suggesting the need for improvement in English language usage:

 

1. In the section on hotspot analysis, the sentence structure and phrasing are cumbersome, making it difficult to follow: "Hotspot analysis is a method of calculating the Getis-Ord Gi* statistic for each element in a dataset. The Getis-Ord Gi* index proposed by British scholars Getis and Ord [35] is a statistic used to measure whether there is a local correlation between an observation and a neighboring element and can accurately identify the location where high or low value elements are spatially clustered".
2. The description of the super-efficiency of non-expected output is convoluted and lacks clarity: "super-efficiency of non-expected output proposed by Japanese scholar Tone in 2002 [34] takes into account the influence of factor 'slack' overcomes the problem that it is difficult to compare the effective decision-making units and is able to further compare the decision-making units by the super-efficiency value greater than 1 derived from the frontier".
3. The explanation of the term "eco-efficiency" is presented in a way that disrupts the flow and readability: "The term 'eco-efficiency' was firstly proposed by German scientists Schaltegger and Sturm in 1990[29] defined as the ratio of negative environmental value caused by the growth of economy. In the field of agriculture eco-efficiency research mostly focuses on macro-level agricultural ecological efficiency [30]".

 

Here are examples from the article that demonstrate the use of terminology and sentence construction that could be refined for better clarity and academic tone:

 

1. In the description of the super-efficient SBM model: "The super-efficient SBM (Slacks-based model) model based on undesired output is constructed based on the super-efficient DEA (Data envelopment analysis) model and the advantages of SBM. The traditional DEA model measures the efficiency with the advantages of no need to make a priori assumptions on the production function no need for price information and relative objectivity [33] but the process of calculation will be"​​. This sentence is overly complex and could be simplified for clarity.
2. Reference to a previous study: "asset-capital attributes and its spatial mechanism. Applied Geography. 2020 125 102284-102297. 25. Xiao L.; YiQ.; Piling S.; Wei Y.; WenlongP.Green transition of cultivated land use in the Yellow River Basin A perspective of green utilization efficiency evaluation. Land. 2020 9(12)475-497"​​. The citation style here is inconsistent and disrupts the flow of the text.
3. Explanation of the panel Tobit models: "The panel Tobit regression model was proposed by the American scholar Tobin in 1958 in order to solve the econometric model [39] for the restricted situation of the explanatory variables which can effectively make up for the biased and inconsistent parameter estimation of the least squares method [40,41]". This sentence is cumbersome and could be more succinctly structured.

 

Response 6:

We apologize for the poor language of our manuscript. We worked on the manuscript for a long time and the repeated addition and removal of sentences and sections obviously led to poor readability. We have now worked on both language and readability and have also involved native English speakers for language corrections. We really hope that the flow and language level have been substantially improved.

 

Firstly, the use of the English language needs to be improved in terms of sentences, and we have made changes to optimize the statements in lines 200-203, lines 179-182, lines 84-86. Secondly, for sentences that needed to improve clarity and academic tone, we carried out rewriting and optimization of sentence structure and content in lines 212-215, 264-267, And we are very sorry that the expression in lines 643-646 was unclear and caused you to misunderstand. The details are shown below.

 

“Lines 200-203: Hotspot analysis is a method in spatial correlation analysis, which focuses on calculating the Getis-Gi* statistic (called Gi*) for each element in the dataset that needs to be analyzed [42], returning Gi* and counting the Z scores, and determining whether or not there is a local correlation between the observations and neighboring elements.”

 

“Lines 179-182: The SBM model based on super-efficiency of non-expected output constructed by Japanese scholar Tone in 2002 considering non-expectation can well address the short-comings of the traditional DEA model.”

 

“Lines 84-86: The notion of "eco-efficiency" was defined in 1990 by the German scientists Schaltegger and Sturm [36] as the ratio of negative environmental values caused by economic growth.”

 

“Lines 175-179: The super-efficient SBM (Slacks-based model) model based on undesired output is constructed by combining the advantages of SBM with the super-efficient DEA (Data envelopment analysis) [40]. The conventional DEA model calculates efficiency using a flawed calculation procedure that prevents further comparisons when the DEA efficiency results from different measurement units add up to one [41].”

 

“Line 643-646:

23. Yunyang, S.; Wenkai, D.; Luuk, F.; Mu, L.; Jinmin, H. Study on evaluation of regional cultivated land quality based on re-source-asset-capital attributes and its spatial mechanism. Applied Geography. 2020, 125, 102284-102297.

24. Xiao, L.; Yi,Q.; Piling, S.; Wei, Y.; Wenlong,P.Green transition of cultivated land use in the Yellow River Basin, A perspec-tive of green utilization efficiency evaluation. Land. 2020, 9(12),475-497.”

 

“Lines 264-267: The panel Tobit regression model was proposed by the American scholar Tobin in 1958 to solve the econometric model with restricted explanatory variables [50], which can effectively compensate for the biased and inconsistent parameter estimation of the least squares method [51,52].”

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Author Response

Response to Reviewer 2 Comments

 

Ⅰ Summary

Dear Editor and Reviewer：

 

Thank you for your letter and the reviewers' valuable comments on our manuscript entitled “Spatial-temporal characteristics and influencing factors of eco-efficiency of cultivated land use in the Yangtze River Delta Region” (land-2830497). All of these comments are valuable and helpful for us to revise and improve the paper, and also have important guiding significance for our research.

 

We have carefully studied these comments and revised them, and we hope to get your approval. Our point-to-point revisions are presented below, along with an explanation of how we have addressed these comments in the paper. Detailed responses to reviewers' comments are highlighted in red below. Revisions in the manuscript are also marked in red. Meantime, we are submitting the Revision in simple markup mode in an attachment.

 

Ⅱ Point by Point response to Comments and Suggestions for Authors

 

Comments 1:

Line 17 in the abstract reports that this paper aims to explore the spatio-temporal characteristics of the ECLU and its influencing mechanisms in the YRD region. However, this paper only analyses its influencing factors and does not deal with the mechanism. There is a big difference between factors and mechanisms, and it is suggested to correct the language presentation.

 

Response 1:

WE sincerely thank you for careful reading. In response to your reminder and suggestion, we have amended "mechanism" to "factor" in lines 16-19 (modified, the same below). The details are as follows.

 

“Line 16-19: The measurement, spatial-temporal features and influence factors of ECLU in the YRD are investigated by various methods, such as super-efficient SBM model, hot spot analysis, Dagum Gini coefficient and panel Tobit model.”

 

 

Comments 2:

In general, article keywords should not be too long and proper streamlining is

recommended.

 

Response 2:

Thanks to your suggestion, we have modified the keywords of the article as shown in lines 31-32 according to the research objectives and research content.

 

“Lines 31-32: Keywords: Eco-efficiency of cultivated land use; cultivated land utilization; Spatio-temporal evolution; Influencing factors; the Yangtze River Delta region”

 

 

Comments 3:

Line 66 in the introduction explains that ECLU-related studies have mainly explored the measurement methods, spatio-temporal evolution characteristics and influencing factors of ECLU. Therefore, it is suggested that the following literature review section follows the measurement methods, spatio-temporal evolution characteristics and influencing factors of ECLU.

 

Response 3:

We strongly agree with your suggestion, so we restructured the paragraph after line 66 to start with a literature review of ECLU measurements, spatio-temporal evolutionary features, and influencing factors, followed by a summary of previous research scales. The details are shown below.

 

“Lines 67-79: In terms of efficiency measurement, the research is mainly based on the differences of research backgrounds and objectives, uses different methods to quantitatively measure the ECLU[14-15], such as the stochastic frontier production function method [16], the DEA model [17], the SBM model [18], the Malmquist model [19], the Bootstrap method of random sampling [20], and the SWAT model [21] et al. Scholars use spatial autocorrelation method [22], kernel density method [23], exploratory spatial data analysis (ESDA) method [18], Gi* index [24] and other methods to detect spatial and temporal evolution characteristics of cultivated land utilization efficiency.  The affecting factors are investigated using a variety of techniques, including the econometric model [25], the Tobit regression model [26,27], geographically weighted regression [16], and others. From a research scale standpoint, it mainly analyzes the cultivated land utilization efficiency at the national level [28-30], provincial (city) level [31,32], and explores the driving factors of cultivated land utilization efficiency [33,34].”

 

 

Comments 4:

In the introduction it is reported that the innovation of this paper lies in the specific study of national development strategy regions and economically developed regions, the essence of which is a renewal of the study region. The authors are advised to sort out the innovations of this paper again.

 

Response 4:

Thank you very much for your comments. We are very sorry for the misrepresentation, the article wanted to express that the Yangtze River Delta, as an important food production base in China and an economically developed region with a relatively large share of agricultural pollution emissions, was better chosen for the study. We have revised and optimized the expression in lines 94-97 according to your suggestion. The details are shown below.

 

“Lines 94-97: Secondly, the YRD region was selected as the object in this paper. As an important food production base and a region with a relatively large proportion of agricultural pollution emissions in China, the YRD region has received relatively little attention in relevant studies, and the selection of this region helps to enrich the relevance of the study on the ECLU.”

 

 

Comments 5:

It is recommended that the Yangtze River be included in Figure 1 to show the location of the study area more clearly.

 

Response 5:

Your suggestions for article maps have been very helpful and we have updated Figure 1 based on your suggestions. It is shown below.

Figure 1. Yangtze River Delta Region Location Map

 

 

Comments 6:

The six research methods are described in detail in section 2.3 of the article, and it is recommended that the excription in the research methods section be streamlined accordingly.

 

Response 6:

Thank you very much for your suggestions regarding the methodology section of our study. Based on your suggestions, we have added the framework flowchart Figure 2 in front of the research methodology, as well as simplified and streamlined it within the research methodology (lines 179-182, lines 200-203, lines 264-267). As shown in the following text.

Figure 2. Research methodology framework diagram

 

“Lines 179-182: Secondly, the YRD region was selected as the object in this paper. As an important food production base and a region with a relatively large proportion of agricultural pollution emissions in China, the YRD region has received relatively little attention in relevant studies, and the selection of this region helps to enrich the relevance of the study on the ECLU.”

 

“Lines 200-203: Hotspot analysis is a method in spatial correlation analysis, which focuses on calculating the Getis-Gi* statistic (called Gi*) for each element in the dataset that needs to be analyzed [42], returning Gi* and counting the Z scores, and determining whether or not there is a local correlation between the observations and neighboring elements.”

 

“Lines 264-267: The panel Tobit regression model was proposed by the American scholar Tobin in 1958 to solve the econometric model with restricted explanatory variables [50], which can effectively compensate for the biased and inconsistent parameter estimation of the least squares method [51,52].”

 

 

Comments 7:

The introduction reports that one of the innovations of this paper lies in the study of intra ECLU differences and their influencing factors in economically developed regions.

However, the similarities and differences between economically developed and less developed regions do not seem to have been elaborated in the discussion; are the influencing factors in less developed regions roughly the same as in developed regions?

 

Response 7:

We are very sorry that a misrepresentation led to this problem, and your suggestion is very helpful for our future research. In the article, we select the factors influencing ECLUs in economically developed regions based on previous research in lines 248-261. The details are shown as follows:

 

“Lines 248-261: As for the socio-economic influencing factors, the urban population ratio indicator reflects the proportion of the rural population, the per capita GDP indicator reflects the regional economic development, the agricultural GDP ratio indicator reflects the regional agricultural development level, and the disposable income of farmers express the influence of the main cropland operators on the ECLU. As for the natural environment influencing factors, the rainfall indicator reflecting the regional water resources endowment was selected. Regarding the factors that influence agricultural development, the degree of modernization in agriculture is reflected by the indicator of mechanization density [47], the indicator of agricultural scale level reflects the level of agricultural intensification in the region [48], and the indicator of agricultural industry structure reflects the type of structure of the primary industry development. Referring to the algorithm of Shu et al. [49], the agricultural industrial structure of the region is expressed by dividing the value of gross regional agricultural output by the value of gross agricultural, forestry, livestock and fishery output.”

 

 

Comments 8:

The references do not appear to contain cutting-edge literature on the direction of the research, and it is recommended that the references be updated.

 

Response 8:

Thank you for your suggestion about updating the supplementary frontier references, based on your suggestion, we have carried out an update of the references as shown below.

“Lines 615-628:

9.  Song, G.; Gaofeng, R. Spatial response of cultivated land use efficiency to the maize structural adjustment policy in the "Sickle Bend" region of China: An empirical study from the cold area of northeast. Land Use Policy, 2022, 9, 106421-106433.

10. Guo, B.; Chen, K.; Jin, G. Does multi-goal policy affect agricultural land efficiency? A quasi-natural experiment based on the natural resource conservation and intensification pilot scheme. Appl Geogr, 2023, 163, 103141-103153.

11. Xiao Z.; Di W.; Jiangfeng L.; Jiale L.; Dou Z.; Wanxu C. Cultivated land use efficiency and its driving factors in the Yellow River Basin, China. Ecol. Indic. 2022, 144, 109411-109424.

12. Bing K.; Xinhai L.; Min Z.; Danling C. Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered. Technol Forecast Soc Change. 2020, 151, 119874-119884.

13. Haibin H.; Xiaoyu Z. Static and dynamic cultivated land use efficiency in China: A minimum distance to strong efficient frontier approach. J. Clean. 2020, 246, 119002-119017.

14. Jiayi W.; Dan S.; Qing W.; Guoyu L.; Yu C. Study on eco-efficiency of cultivated land utilization based on the improvement of ecosystem services and emergy analysis. Sci. Total Environ. 2023, 882, 163489-163498.

15. Guo, B.; He, D.; Jin, G. Agricultural production efficiency estimation and spatiotemporal convergence characteristic analy-sis in the Yangtze River Economic Belt: A semi‐parametric metafrontier approach. Land Degrad Dev, 2023, 34, 4635 - 4648.”

Author Response File: Author Response.docx