Spatiotemporal Heterogeneity of Eco-Efficiency of Cultivated Land Use and Its Influencing Factors: Evidence from the Yangtze River Economic Belt, China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. The paper summarises findings from a single case (China's Yangtze River Economic Belt) without explaining the context of the summarisation. It is recommended that the context be further described.
2. Although the introduction stresses the importance of arable land resources, the lack of specific data (such as the rate of change of arable land area, carbon emissions and other quantitative indicators) weakens the persuasiveness of the urgency of the issue. It is suggested to add key data such as the proportion of arable land ‘non-food’ area and the growth rate of agricultural carbon emissions in recent years, so as to highlight the necessity of the study through quantitative description.
3. Insufficient depth of the existing literature review, such as the lack of discussion on the controversy of the research methodology (e.g., the difference in the applicable scenarios between DEA and SFA), which fails to highlight the innovativeness of the Super-SBM model selected in this study
4. Although the Yangtze River Economic Belt is mentioned as an ecological civilisation demonstration area, its typicality is not specified. It is suggested that the reasons for choosing the Yangtze River Economic Belt as the study area be elaborated.
5. The article fails to explain the inherent contradiction between the national strategy of ‘ecological priority’ and the sustainable use of arable land, which weakens the policy relevance of the selected topic.
6. It is recommended that the breakthrough of the three-dimensional evaluation system (socio-economic-ecological) from the traditional one-dimensional efficiency assessment be clearly articulated
7. Suggests discussing the specific reasons why upstream efficiency is higher than that of the middle and lower reaches (e.g., policy support, ecological compensation mechanisms), and the lack of characterisation of the economic characteristics of the river basin (e.g., the impacts of differences in upstream and downstream development gradients on the use of arable land).
8. Suggests further discussion of the reasons for the spatial clustering and migration of centres of eco-efficiency of arable land.
9. It is recommended that specific technical pathways and policy tools be supplemented with differentiated measures for ‘high and low value clustering areas’.
10. Figure numbering needs to be consistent with in-text citations (e.g., subfigures a, b, and c of Figure 7 do not correspond exactly in the text).
11. Suggest considering the role of additional influences on the eco-efficiency of arable land use, such as land transfer (scale operations), digital agriculture and policy interventions?

 

Comments on the Quality of English Language

need further improvement

Author Response

 

We appreciate your suggestions and consider them to be very professional and valuable. We believe that these comments and recommendations will greatly improve our manuscript. We have made revisions according to your feedback, with the modified sections highlighted in red.

 

Comment 1: The paper summarises findings from a single case (China’s Yangtze River Economic Belt) without explaining the context of the summarisation. It is recommended that the context be further described.

Response: In the discussion section (Section 4.1, lines 520-524) of the newly submitted manuscript, the following background explanation has been added:

“In the context of strategies such as food security, ecological protection, and rural revitalization, the deepening implementation of new urbanization will undoubtedly pose more severe challenges to cultivated land protection. As a crucial agricultural industrial belt in the country, the Yangtze River Economic Belt faces significant importance in ensuring and enhancing the ecological efficiency of cultivated land use, which is vital for achieving high-quality regional development.”

 

Comment 2: Although the introduction stresses the importance of cultivated land resources, the lack of specific data (such as the rate of change of cultivated land area, carbon emissions and other quantitative indicators) weakens the persuasiveness of the urgency of the issue. It is suggested to add key data such as the proportion of cultivated land ‘non-food’ area and the growth rate of agricultural carbon emissions in recent years, so as to highlight the necessity of the study through quantitative description.

Response: In lines 46-51 of the text, data quantification indicators such as changes in cultivated land area and the proportion of “non-grain” cultivated land have been added. Additionally, in lines 57-58, data on the average annual growth rate of agricultural carbon emissions from 1995 to 2022 has been included. The specific modifications are as follows:

Lines 22-25: According to the China Natural Resources Bulletin (https://www.gov.cn/, accessed on 28 Nov 2024), the cultivated land area decreased from 135.385 million hectares in the second national land survey to 127.58 million hectares in 2022. Meanwhile, the research team at China Agricultural University estimates that as of 2022, the “non-grain” rate of cultivated land in China is approximately 27% (http://www.moa.gov.cn/, accessed on 28 Nov 2024).

Lines 57-58: Statistics show that over the past 30 years, the average annual growth rate of agricultural carbon emissions in China has reached 1.85% [21].

 

Comment 3: Insufficient depth of the existing literature review, such as the lack of discussion on the controversy of the research methodology (e.g., the difference in the applicable scenarios between DEA and SFA), which fails to highlight the innovativeness of the Super-SBM model selected in this study.

Response: In lines 234-245 of the text, a discussion on the super-efficiency SBM and DEA models has been added. The specific content is as follows:

“The Super-SBM model, proposed by Tone [64], is a derivative of the DEA model. Traditional DEA models assume a monotonic linear relationship between inputs and outputs when analyzing efficiency, serving as a linear programming technique to determine the relative efficiency of decision-making units (DMUs). However, these traditional models struggle to effectively address issues related to undesirable outputs during efficiency measurement. The Super-SBM model not only avoids biases caused by radial and angular measurements but also considers the impact of undesirable output factors in the production process. This model more accurately reflects the essence of efficiency evaluation and effectively addresses problems associated with slack variables and undesirable outputs in the context of cultivated land use [65, 66]. Additionally, it can resolve comparison issues among evaluation units with an efficiency score of 1, allowing for a precise ranking of efficiency values among DMUs.”

 

Comment 4: Although the Yangtze River Economic Belt is mentioned as an ecological civilisation demonstration area, its typicality is not specified. It is suggested that the reasons for choosing the Yangtze River Economic Belt as the study area be elaborated.

Response: In lines 113-116 of the text, the reasons for selecting the Yangtze River Economic Belt as the research area have been added and elaborated. The specific content is as follows:

“The YREB is one of the most dynamic economic regions in China and is also a crucial area for ensuring food security. However, rapid urbanization and industrialization have placed significant pressure on cultivated land resources. Therefore, studying the ECLU in this region is of great practical significance.”

 

Comment 5: The article fails to explain the inherent contradiction between the national strategy of ‘ecological priority’ and the sustainable use of cultivated land, which weakens the policy relevance of the selected topic.

Response: This is an excellent suggestion that will greatly improve our article. We have added clarifications in the introduction section, lines 58-64, as follows:

“At the same time, an ecological priority requires reducing the use of fertilizers and pesticides, limiting cultivated land use in certain areas, and promoting more eco-friendly and sustainable farming practices. However, in the process of sustainable land use, it is essential to ensure food production and stabilize national food security on one hand. On the other hand, farmers aim to achieve economic benefits and agricultural output, which inevitably leads to a conflict between ‘ecological priority’ and sustainable land use.”

 

Comment 6: It is recommended that the breakthrough of the three-dimensional evaluation system (socio-economic-ecological) from the traditional one-dimensional efficiency assessment be clearly articulated.

Response: Thank you for your suggestions. A clear explanation of the three-dimensional evaluation system (social, economic, and ecological) and its breakthrough over traditional one-dimensional efficiency assessments has been added. The specific content is as follows (lines 97-103):

“Therefore, this study will adopt a three-dimensional perspective–social, economic, and ecological–to consider expected outputs. This approach effectively breaks through the limitations of traditional evaluations that only consider single-dimensional economic output or a dual-dimensional perspective of economic and social factors. Additionally, by considering non-expected outputs from the perspectives of carbon emissions and non-point source pollution, the accuracy of measuring the ECLU will be significantly enhanced.”

 

 

Comment 7: Suggests discussing the specific reasons why upstream efficiency is higher than that of the middle and lower reaches (e.g., policy support, ecological compensation mechanisms), and the lack of characterisation of the economic characteristics of the river basin (e.g., the impacts of differences in upstream and downstream development gradients on the use of cultivated land).

Response: In the discussion section, lines 532-545, we have added the reasons why the upstream region is higher than the midstream and downstream areas, along with a description of the economic characteristics of the watershed. The specific modifications are as follows:

“This is primarily due to the significant economic differences between the upstream, midstream, and downstream regions. The upstream economy is relatively underdeveloped, but its unique geographical location, abundant sunlight and heat, and important ecological functions, combined with policies for the western development, fiscal transfer payments, and ecological compensation funding, have improved agricultural production conditions and ecological environments, thereby enhancing the ecological efficiency of cultivated land utilization. The downstream regions are economically developed and have high technical levels, enabling them to invest more resources into the efficient use of cultivated land and ecological protection. In contrast, the midstream region has a large agricultural development scale, but with the acceleration of industrial structure adjustment and urbanization, the dilemma of high consumption and high investment to secure food production is difficult to improve, resulting in lower ecological efficiency of cultivated land utilization compared to other areas.”

 

 

Comment 8: Suggests further discussion of the reasons for the spatial clustering and migration of centres of eco-efficiency of cultivated land.

Response: In the discussion section, lines 549-560 have been supplemented with the reasons for the spatial agglomeration of ecological benefits from cultivated land, and lines 564-572 have been added to explain the reasons for the shift in focus. The specific content is as follows:

Lines 549-560: High-high clustering is mainly observed in the upstream region, which stems from two factors: first, its unique geographical location has led to a prominent phenomenon of large-scale concentrated development of cash crops, with relatively high agricultural technology investment, while the usage rates of fertilizers, pesticides, and other sources that can generate carbon emissions and non-point source pollution are low; second, strong support from policies such as the Western Development Strategy and ecological compensation. In contrast, low-low clustering is primarily concentrated in urban areas of the midstream region. With the acceleration of industrial structure adjustment and urbanization, the cultivated land area in these cities is continuously decreasing. To ensure food production, high-consumption and high-investment cultivation methods have to be adopted, resulting in lower ecological efficiency of cultivated land utilization.

Lines 564-572: The shift is attributed to advantages in policy support and resource investment in the southwest direction, which enhance the ecological efficiency of cultivated land utilization. In the northeast direction, economic development and advanced technology support efficient use of cultivated land and ecological protection. Conversely, the midstream region experiences reduced cultivated land and high-consumption production methods, leading to lower ecological efficiency. This regional disparity drives the high-value areas to migrate towards the “southwest-northeast” direction.

 

Comment 9: It is recommended that specific technical pathways and policy tools be supplemented with differentiated measures for ‘high and low value clustering areas’.

Response: In lines 626-636 of the text, we have supplemented specific technical and policy tools for "high-value and low-value agglomeration areas" with differentiated measures. The specific content is as follows:

For high-high clustering areas, implement a farmland protection compensation mechanism that expands the compensation scope, conducts special compensation, and integrates project compensation. This aims to incentivize farmers to protect farmland and promote green utilization. Additionally, through regional cooperation, facilitate technological exchange and resource sharing to drive joint development in surrounding areas. For low-low clustering areas, actively promote water-saving agricultural technologies, implement initiatives for creating green, high-yield, and efficient farming practices, and pursue zero growth in fertilizer and pesticide usage. Reasonable land use planning should be established, alongside strengthened farmland protection. Furthermore, through policy guidance and financial support, promote agricultural industrial upgrading and transformation.

 

Comment 10: Figure numbering needs to be consistent with in-text citations (e.g., subfigures a, b, and c of Figure 7 do not correspond exactly in the text).

Response: We apologize for our oversight and appreciate your thorough review. We have checked the entire text and ensured that similar errors will not occur in the future. Thank you for your understanding.

 

Comment 11: Suggest considering the role of additional influences on the eco-efficiency of cultivated land use, such as land transfer (scale operations), digital agriculture and policy interventions?

Response: We appreciate the expert's suggestions, which have provided us with new perspectives and ideas regarding the factors influencing ecological efficiency in cultivated land utilization. However, since the basic framework of the article is already established, incorporating these factors would necessitate a complete revision of the entire section on influencing factors. Therefore, in future research, we will implement the expert's recommendations by considering factors such as land transfer (scale operation), digital agriculture, and policy intervention. Since these factors were not included in this study, I will address them in the future outlook as part of my next research direction.

The specific modification for lines 667-670 is as follows:

“Additionally, the indicator system for influencing factors requires further refinement. In the next phase, factors such as land transfer (scale operation), digital agriculture, and policy intervention will be incorporated into the indicator system to address the current limitations of this study.”

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript provides valuable insights into the spatio-temporal heterogeneity of eco-efficiency in cultivated land use in the Yangtze River Economic Belt. However, several areas require further elaboration and refinement to enhance the study's methodological rigor. My comments:

1. While the study employs advanced models like Super-SBM and geographically weighted regression (GWR), the explanations of these methods are somewhat superficial. For instance, the Super-SBM model is introduced as a solution to slack variables and undesirable outputs, but the mathematical formulation and its advantages over traditional DEA models are not thoroughly discussed. These references are helpful: 10.1007/s11356-021-16969-7, 10.3390/su9091664.
2. Similarly, the GWR model is mentioned, but the spatial weighting function and bandwidth selection process are not detailed. A more in-depth explanation would help readers understand the robustness of the methodology. The authors should also clarify how they handled potential multicollinearity among the influencing factors in the GWR model. While they mention using VIF (Variance Inflation Factor) to diagnose multicollinearity, the threshold used (VIF < 7.5) is relatively high compared to the commonly accepted threshold of 5 or 10. This could raise concerns about the reliability of the regression results.
3. The study excludes five regions due to data gaps, which could introduce bias, especially if these regions have unique characteristics that differ from the included areas. The authors should discuss how these exclusions might affect the generalizability of the findings and suggest ways to address these gaps in future research.
4. Additionally, the study relies on municipal-level data, which may mask significant intra-city variations. The authors should acknowledge this limitation and consider exploring finer spatial scales in future studies.
5. The study identifies several influencing factors (e.g., natural conditions, socio-economic factors, cultivation intensity, and resource endowments) but does not establish causal relationships. For example, while the study finds that higher elevation negatively impacts ECLU, it does not explain why this is the case. Is it due to limited access to resources, lower agricultural productivity, or other factors? A deeper discussion of the underlying mechanisms would strengthen the study.
6. This study highlights significant regional differences in ECLU, with the upstream of YREB showing higher efficiency than the mid- and downstream. However, the discussion of these differences is somewhat superficial. Authors should explore the underlying causes of these differences, such as changes in natural resource endowments, levels of economic development, or policy implementation. For example, higher efficiency in upstream areas may be due to favorable natural conditions (e.g., sufficient sunlight and heat) or effective policy intervention (e.g., western development policy). A more detailed analysis would provide valuable insights for policymakers.
7. The study effectively integrates ecological and economic perspectives by considering desirable outputs (e.g., agricultural output, carbon sequestration) and undesirable outputs (e.g., carbon emissions, non-point source pollution). However, the discussion could be expanded to explore the trade-offs between economic growth and ecological sustainability. For example, authors could discuss how increased agricultural productivity (desirable output) leads to higher carbon emissions (undesirable output) and suggest strategies for balancing these competing goals.

Author Response

 

We appreciate your suggestions and consider them to be very professional and valuable. We believe that these comments and recommendations will greatly improve our manuscript. We have made revisions according to your feedback, with the modified sections highlighted in red.

 

Comment 1: While the study employs advanced models like Super-SBM and geographically weighted regression (GWR), the explanations of these methods are somewhat superficial. For instance, the Super-SBM model is introduced as a solution to slack variables and undesirable outputs, but the mathematical formulation and its advantages over traditional DEA models are not thoroughly discussed. These references are helpful: 10.1007/s11356-021-16969-7, 10.3390/su9091664.

Response: Through reading the literature you recommended, we have gained a deeper understanding of the relevant models and made the following modifications (lines 234-245):

“The Super-SBM model, proposed by Tone [64], is a derivative of the DEA model. Traditional DEA models assume a monotonic linear relationship between inputs and outputs when analyzing efficiency, serving as a linear programming technique to determine the relative efficiency of decision-making units (DMUs). However, these traditional models struggle to effectively address issues related to undesirable outputs during efficiency measurement. The Super-SBM model not only avoids biases caused by radial and angular measurements but also considers the impact of undesirable output factors in the production process. This model more accurately reflects the essence of efficiency evaluation and effectively addresses problems associated with slack variables and undesirable outputs in the context of cultivated land use [65, 66]. Additionally, it can resolve comparison issues among evaluation units with an efficiency score of 1, allowing for a precise ranking of efficiency values among DMUs.”

 

Comment 2: Similarly, the GWR model is mentioned, but the spatial weighting function and bandwidth selection process are not detailed. A more in-depth explanation would help readers understand the robustness of the methodology. The authors should also clarify how they handled potential multicollinearity among the influencing factors in the GWR model. While they mention using VIF (Variance Inflation Factor) to diagnose multicollinearity, the threshold used (VIF < 7.5) is relatively high compared to the commonly accepted threshold of 5 or 10. This could raise concerns about the reliability of the regression results.

Response: Thank you very much for your comments and suggestions. In the newly submitted manuscript, we provide a detailed description of the spatial weighting function and bandwidth selection (lines 444–455):

 

“Furthermore, to investigate multicollinearity among factors, we set the Variance Inflation Factor (VIF) threshold to less than 7.5, considering the characteristics of the data and research objectives. We employed IBM SPSS Statistics 22 (https://www.onlinedown.net/soft/1227737.htm, accessed on 28 Nov 2024) for Ordinary Least Squares (OLS) linear regression analysis to diagnose factors with VIF values below 7.5, thereby eliminating multicollinearity among factors. Ultimately, we established nine influencing factors for geographical weighted regression. Using GWR4 software (https://gwrtools.github.io/, accessed on 28 Nov 2024), we took the ecological efficiency of cultivated land use in the Yangtze River Economic Belt as the dependent variable. The spatial weighting function was selected as Gaussian with AICc bandwidth methods, and modeling was conducted on an annual basis. The residual squares, Sigma, and AICc values were all relatively small (Table 4), indicating that the model fit was satisfactory.”

 

Additionally, as you mentioned, “While they mention using VIF (Variance Inflation Factor) to diagnose multicollinearity, the threshold used (VIF < 7.5) is relatively high compared to the commonly accepted threshold of 5 or 10. This could raise concerns about the reliability of the regression results.” Indeed, many scholars utilize a threshold of 5 or 10; however, some researchers do use 7.5 (doi: 10.11870/cjlyzyyhj202411009; 10.11867/j.issn.1001-8166.2021.040). From our experimental results, we found that a threshold of 7.5 also yields a good model fit.

 

Comment 3: The study excludes five regions due to data gaps, which could introduce bias, especially if these regions have unique characteristics that differ from the included areas. The authors should discuss how these exclusions might affect the generalizability of the findings and suggest ways to address these gaps in future research.

Response: This study comprehensively identifies the spatial heterogeneity of cultivated land use ecological efficiency across various cities (or prefectures) in the Yangtze River Economic Belt. However, due to significant data omissions in five areas—Enshi Tujia and Miao Autonomous Prefecture, Xiantao City, Qianjiang City, Tianmen City, and Shennongjia Forest District—these regions were not included in the overall analysis. Additionally, except for Enshi, the other four areas are county-level administrative units directly under Hubei Province, while the remaining cities are at the prefecture-level administrative unit. Thus, even with these data omissions, this study still provides a good reflection of the overall situation and trends in cultivated land use ecological efficiency within the Yangtze River Economic Belt. In future research, we plan to strengthen collaboration with local statistical departments and research institutions to obtain more comprehensive and accurate data, as well as expand the study scope to include as many uncovered regions as possible, further enhancing the generalizability and representativeness of our findings.

 

Comment 4: Additionally, the study relies on municipal-level data, which may mask significant intra-city variations. The authors should acknowledge this limitation and consider exploring finer spatial scales in future studies.

Response: Thank you for your valuable suggestions, which have illuminated the direction for our future research. In section 4.5 (lines 660–666), we clearly outline our next steps, which involve conducting a more detailed analysis at a micro scale to detect spatial differences. Specifically, we state:

“However, given the large number of cities involved and the lengthy time span, and considering data availability, only municipal statistical data, cultivated land use data, and geographic spatial data were used for calculations. Future research will adopt a micro perspective through field surveys, focusing on farmers’ own cultivation behaviors, collecting data, establishing models, and conducting in-depth studies to provide more scientific evidence for sustainable agricultural development.”

 

Comment 5: The study identifies several influencing factors (e.g., natural conditions, socio-economic factors, cultivation intensity, and resource endowments) but does not establish causal relationships. For example, while the study finds that higher elevation negatively impacts ECLU, it does not explain why this is the case. Is it due to limited access to resources, lower agricultural productivity, or other factors? A deeper discussion of the underlying mechanisms would strengthen the study.

Response: Thank you for your suggestions. In section 3.3, "Analysis of Influencing Factors," we conducted a causal relationship analysis of the regression coefficients for each factor. For detailed information, please refer to section 3.3.

 

Comment 6: This study highlights significant regional differences in ECLU, with the upstream of YREB showing higher efficiency than the mid- and downstream. However, the discussion of these differences is somewhat superficial. Authors should explore the underlying causes of these differences, such as changes in natural resource endowments, levels of economic development, or policy implementation. For example, higher efficiency in upstream areas may be due to favorable natural conditions (e.g., sufficient sunlight and heat) or effective policy intervention (e.g., western development policy). A more detailed analysis would provide valuable insights for policymakers.

Response: Thank you for your valuable comments and suggestions. Based on your feedback, we have made the following revisions (lines 531–544):

“This is primarily due to the significant economic differences between the upstream, midstream, and downstream regions. The upstream economy is relatively underdeveloped, but its unique geographical location, abundant sunlight and heat, and important ecological functions, combined with policies for the western development, fiscal transfer payments, and ecological compensation funding, have improved agricultural production conditions and ecological environments, thereby enhancing the ecological efficiency of cultivated land utilization. The downstream regions are economically developed and have high technical levels, enabling them to invest more resources into the efficient use of cultivated land and ecological protection. In contrast, the midstream region has a large agricultural development scale, but with the acceleration of industrial structure adjustment and urbanization, the dilemma of high consumption and high investment to secure food production is difficult to improve, resulting in lower ecological efficiency of cultivated land utilization compared to other areas.”

 

 

Comment 7: The study effectively integrates ecological and economic perspectives by considering desirable outputs (e.g., agricultural output, carbon sequestration) and undesirable outputs (e.g., carbon emissions, non-point source pollution). However, the discussion could be expanded to explore the trade-offs between economic growth and ecological sustainability. For example, authors could discuss how increased agricultural productivity (desirable output) leads to higher carbon emissions (undesirable output) and suggest strategies for balancing these competing goals.

Response: This is a very useful suggestion, and we believe it will enhance the quality of our article. Therefore, we have added a section titled "How Increased Agricultural Productivity Leads to Higher Carbon Emissions" (Section 4.3) in the discussion part:

 

“In this study, we measure the ecological efficiency of cultivated land use by considering expected and unexpected outputs, effectively integrating ecological and economic factors. However, the question of how increased agricultural productivity leads to higher carbon emissions warrants reflection. On one hand, while advancements in agricultural productivity often rely on scientific and technological means, many regions still depend on the increased use of agricultural inputs such as fertilizers and pesticides. The rise in these inputs inevitably releases more greenhouse gases. On the other hand, with the acceleration of urbanization, the scale of construction land continues to expand, cities absorb more rural population, and the issue of rural hollowing becomes severe. To ensure grain production, large-scale mechanized farming is necessary, leading to increased consumption of diesel, electricity, and other resources, thereby increasing carbon emissions. Additionally, farmers’ pursuit of higher economic returns will inevitably alter crop structures, posing significant challenges for carbon emissions. To effectively balance these relationships, we propose the following strategies: First, actively promote green agricultural technologies to improve resource utilization efficiency and reduce carbon emissions and non-point source pollution. Second, optimize agricultural industry structure, strengthen policy support and guidance, advocate for green agricultural technologies, and raise farmers' awareness of environmental protection. Third, establish a sound ecological compensation mechanism to provide precise compensation for farmers who incur economic losses due to ecological protection, promoting a positive interaction between ecological conservation and agricultural production.”

 

Reviewer 3 Report

Comments and Suggestions for Authors

This paper is related to the field of sustainable development of territories and focuses on agriculture and its role in this process. The paper is important for assessing the ecological efficiency of agricultural land use (ECLU) in China. In general, the work is of regional nature.

The methods and data used are adequate and applicable to this type of work.

The paper has a good structure and is quite well filled with data and cartographic materials.

The list of references is mainly represented by modern sources and contains the necessary references.

There are a number of comments and remarks that need to be corrected to improve the manuscript:
1. All formulas in the work should be referenced, unless they are your new contribution. In the latter case, they should be justified.
2. Autocorrelation is not discussed enough in the methods section. How were the parameters set:
1. Distance Band or Threshold Distance
2. Standardization
3. Conceptualization of Spatial Relationships
4. and others.
Disclose this in the methods if possible.
3. Was stepwise spatial autocorrelation used, which is necessary to assess the manifestation of autocorrelations at different distances?
4. Was the ordinary least squares (OLS) method used before applying geographically weighted regression (GWR)? is this necessary to find significant variables? It is imperative to exclude multicollinearity in the data.
5. The reason for the sharp decline in ECLU in 2020-2022 is not discussed. It seems obvious, but still a few words need to be said about the reason.

Author Response

 

We appreciate your suggestions and consider them to be very professional and valuable. We believe that these comments and recommendations will greatly improve our manuscript. We have made revisions according to your feedback, with the modified sections highlighted in red.

 

Comment 1: All formulas in the work should be referenced, unless they are your new contribution. In the latter case, they should be justified.

Response: In the revised manuscript, we have cited the sources for all formulas as per the suggestions. The specific references are as follows:

1) Super-SBM Model: The formula is referenced from “ Tone, K. A slacks-based measure of super-efficiency in data envelopment analysis." European Journal of Operational Research 143 (2002): 32-41. https://doi.org/10.1016/S0377-2217(01)00324-1. ”

2) Spatial Autocorrelation Model:

Moran’s I Index: The formula is cited from “Moran, P.A.P. Notes on Continuous Stochastic Phenomena. Biometrika 1950, 37, 17-23. ”

Global Moran’s I: The formula is referenced from " Anselin, L. Local Indicators of Spatial Association—LISA. Geographical Analysis 1995, 27, 93-115. "

3) Standard Deviational Ellipse: The formula is cited from " Lefever, D. W. "Measuring geographic concentration by means of the standard deviational ellipse." American Journal of Sociology 32 (1926): 88-94. http://www.jstor.org/stable/2765249. "

4) Geographically Weighted Regression: The formula is referenced from " Brunsdon, C., A. S. Fotheringham and M. E. Charlton. "Geographically weighted regression: A method for exploring spatial nonstationarity.Geographical Analysis 28 (1996): 281-98. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x. "

These citations ensure proper attribution and enhance the credibility of our research.

 

Comment 2: Autocorrelation is not discussed enough in the methods section. How were the parameters set:

1. Distance Band or Threshold Distance
2. Standardization
3. Conceptualization of Spatial Relationships
4. and others.

Disclose this in the methods if possible.

Response:

In the submitted manuscript, we have added a discussion on the autocorrelation method. The revised content is as follows (lines 264–267):

“ω_ij represents the spatial weight matrix of the study units, indicating the spatial adjacency relationships among the evaluation units. Since the data in this study is based on the ecological efficiency of cultivated land use at the city level, we constructed the spatial weight matrix using geographic adjacency.”

 

Comment 3: Was stepwise spatial autocorrelation used, which is necessary to assess the manifestation of autocorrelations at different distances?

Response: We sincerely appreciate your suggestions regarding the manuscript. Your recommendation to use a stepwise spatial autocorrelation method is highly valuable and has greatly benefited us. In this study, we focus on the overall patterns and trends of spatial autocorrelation rather than the specific manifestations at different distances. Therefore, we opted for a global spatial autocorrelation analysis method. This approach effectively assesses spatial autocorrelation across the entire study area, providing us with a macro-level understanding. We recognize that the stepwise spatial autocorrelation method has unique advantages in evaluating autocorrelation at varying distances. In future related research, we will seriously consider adopting this method to explore spatial autocorrelation more comprehensively and in detail.

 

 

Comment 4: Was the ordinary least squares (OLS) method used before applying geographically weighted regression (GWR)? is this necessary to find significant variables? It is imperative to exclude multicollinearity in the data.

Response: Before using Geographically Weighted Regression (GWR), we employed Ordinary Least Squares (OLS) and conducted a multicollinearity diagnosis. For detailed information, please refer to lines 4440-455 of the manuscript.

“Furthermore, to investigate multicollinearity among factors, we set the Variance Inflation Factor (VIF) threshold to less than 7.5, considering the characteristics of the data and research objectives. We employed IBM SPSS Statistics 22 (https://www.onlinedown.net/soft/1227737.htm, accessed on 28 Nov 2024) for Ordinary Least Squares (OLS) linear regression analysis to diagnose factors with VIF values below 7.5, thereby eliminating multicollinearity among factors. Ultimately, we established nine influencing factors for geographical weighted regression. Using GWR4 software (https://gwrtools.github.io/, accessed on 28 Nov 2024), we took the ecological efficiency of cultivated land use in the Yangtze River Economic Belt as the dependent variable. The spatial weighting function was selected as Gaussian with AICc bandwidth methods, and modeling was conducted on an annual basis. The residual squares, Sigma, and AICc values were all relatively small (Table 4), indicating that the model fit was satisfactory.”

 

 

Comment 5: The reason for the sharp decline in ECLU in 2020-2022 is not discussed. It seems obvious, but still a few words need to be said about the reason.

Response: Thank you very much for your attention to our paper and for your valuable comments.

Regarding your mention of the “lack of discussion on the reasons for the sharp decline in ECLU from 2020 to 2022,” we conducted a thorough review and analysis. In Section 3.1 of the paper, we state, “Overall, from 2005 to 2022, the ECLU in the YREB has been on the rise, increasing from 0.882 in 2005 to 0.934 in 2022, a growth of 5.81%. The trend shows a “rise-fall-rise” pattern, exhibiting an “N”-shaped change.” We have provided appropriate analysis and discussion of the reasons behind both the overall increase and the ‘rise-fall-rise’ phenomenon to make the study more comprehensive and rigorous. The specific modifications are as follows:

“The overall increase is primarily driven by the combined effects of policy support, technological advancements, and resource optimization that enhance efficiency. The mid-term decline is mainly attributed to rapid urbanization and industrialization, exacerbated agricultural non-point source pollution, and excessive resource development and utilization leading to reduced efficiency. The subsequent rise is due to intensified ecological restoration and governance efforts, the promotion of green agricultural development models, and improved resource management and optimization that contribute to efficiency recovery.”

 

 

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have addressed my concerns. Thanks!

Reviewer 3 Report

Comments and Suggestions for Authors

The article submitted for correction was well corrected, and in those questions that required clarification, there were comprehensive answers.
In addition, the authors added important recommendations to the discussion section. The research methods were clarified, including in the area of ​​data verification requested by the reviewer.
I believe that after the revision, the article can be accepted for publication in the journal.