Identification and Optimization of County-Level Ecological Spaces under the Dual-Carbon Target: A Case Study of Shaanxi Province, China
Round 1
Reviewer 1 Report
1. The PDF version of the manuscript has some formatting issues that need improvement. It contains empty pages and incomplete content in several tables, which impacts the overall quality of the document.
2. The paper mentions the selection of ecological space indicators from three perspectives: natural ecosystem attributes, artificial ecosystem attributes, and natural-artificial interaction ecosystem attributes. This approach allows for a comprehensive evaluation of ecological conditions. However, it would be helpful if the paper provided more justification or reasoning behind the selection of specific indicators from each perspective.
3. The estimation of carbon sink in different land uses, excluding cultivated land which acts as a carbon source. While the expression of the carbon sink estimation model is provided, it would be valuable if the paper elaborated on the specific variables and equations used in the model to calculate the carbon sink.
Author Response
We would like to thank you for your constructive and insightful comments and suggestions. We have revised the manuscript accordingly. To facilitate the visibility of the changes made to all the comments, the changes are marked in blue in the revised manuscript. Below we provide a point-to-point response to all the comments.
Question 1: The PDF version of the manuscript has some formatting issues that need improvement. It contains empty pages and incomplete content in several tables, which impacts the overall quality of the document. PDF
Response: Thanks for your suggestion. We have made the necessary improvements to the PDF version of the manuscript following your suggestion. We have carefully rectified these problems and ensured that all the tables are now complete and accurate. Additionally, we have removed the empty pages to enhance the overall quality and readability of the document.
Question 2: The paper mentions the selection of ecological space indicators from three perspectives: natural ecosystem attributes, artificial ecosystem attributes, and natural-artificial interaction ecosystem attributes. This approach allows for a comprehensive evaluation of ecological conditions. However, it would be helpful if the paper provided more justification or reasoning behind the selection of specific indicators from each perspective.
Response: The suggestion was taken. We have supplemented the manuscript by providing a detailed explanation of the rationale behind selecting specific indicators from different perspectives. The characteristic indicators of natural ecosystems included elevation, slope, relief degree, and aspect. Elevation, as a fundamental natural attribute, exerts an influence on climatic conditions, species distribution, and ecological processes. Areas at higher elevations are more likely to undertake ecological functions such as water conservation and providing animal habitat. Slope and relief degree are positively correlated with ecological space, and areas with larger slopes and relief degree are more likely to be part of the ecological space. Additionally, the aspect determines the orientation solar radiation, which affects the microclimatic conditions of the local ecological spatial distribution. For artificial ecosystems, the characteristic indicators encompassed population density, nighttime light intensity, traffic network density, and surface temperature. Population density represents the distribution and concentration of the population, directly influencing land use patterns and habitat fragmentation. The nighttime lighting data provide better insights into the level of urbanization, economic status, energy consumption, and other human activity factors, reflecting the intensity and scope of human activities. Moreover, traffic network density indicates the intensity of urban traffic infrastructure construction and influences landscape connectivity. Surface temperature can reflect the intensity of the urban heat island effect, affecting the local climate and ecological conditions of the region. As for natural-artificial interaction ecosystems, the characteristic indicators consisted of NDVI, land use intensity, geological disaster point density, and habitat quality. NDVI effectively reflects the health degree and density of vegetation, where higher values suggest a higher likelihood of becoming ecological space. Meanwhile, land use intensity refers to the degree and intensity of human utilization of existing land, and it can reflect potential ecological disturbance areas. Geological disasters are influenced by both natural geological factors and human activities and are prone to potential destruction of ecological processes. Lastly, habitat quality indicates the relationship between patches and their surrounding environment, reflecting the degree of disturbance they experience. Areas with better habitat quality often hold higher ecological value and have greater significance for protection.
Question 3: The estimation of carbon sink in different land uses, excluding cultivated land which acts as a carbon source. While the expression of the carbon sink estimation model is provided, it would be valuable if the paper elaborated on the specific variables and equations used in the model to calculate the carbon sink.
Response: The suggestion was taken. We appreciate your attention to the carbon sink estimation in different land uses. In the revised manuscript, we have supplemented detailed explanations of the carbon sink calculation process.
Reviewer 2 Report
The authors take 107 districts and 18 counties in Shaanxi Province as the research objects, and construct an ecological-spatial comprehensive multi-dimensional identification system under the dual carbon objectives based on the analysis of the spatial and temporal distribution characteristics and driving factors of county carbon sinks. The manuscript is methodologically sound, with detailed analysis and credible conclusions, and is recommended for publication.
English language flow field, easy to read.
Author Response
Thank you for your positive feedback and recommendation for publication. We are delighted to know that you found our manuscript methodologically sound and appreciated the detailed analysis and credible conclusions presented in the paper. Your feedback reinforces our confidence in the significance and validity of our research. If you have any further suggestions or inquiries, please do not hesitate to let us know.
Reviewer 3 Report
I thought this manuscript was well written and the research design and writing were systematic and logical.
My problem is that in the discussion section, the author should further discuss the issue of carbon sinks and further explain the mechanism of action and planning from carbon sinks to dual carbon targets.
Also the sample of the author's study is Shaanxi Province, China. Why study Shaanxi Province and what are the unique findings compared to other regions?
In addition, the authors should make further policy proposals to highlight the research significance of this research.
Minor editing of English language required
Author Response
We would like to thank you for your constructive and insightful comments and suggestions. We have revised the manuscript accordingly. To facilitate the visibility of the changes made to all the comments, the changes are marked in blue in the revised manuscript. Below we provide a point-to-point response to all the comments.
Question 1: My problem is that in the discussion section, the author should further discuss the issue of carbon sinks and further explain the mechanism of action and planning from carbon sinks to dual carbon targets.
Response: The suggestion was taken. We appreciate your valuable suggestion regarding the discussion section. In the revised manuscript, we have supplemented the discussion section with a discussion on the issue of carbon sink, and further explored the pathways and strategies for achieving the dual carbon targets in different county-level regions.
Question 2: Also the sample of the author's study is Shaanxi Province, China. Why study Shaanxi Province and what are the unique findings compared to other regions?
Response: Thank you for the suggestion. We have supplemented the reasons for selecting Shaanxi Province as the research area in the revised manuscript. The choice of Shaanxi Province as the study area was based on the following factors including ecological diversity, regional importance, and data availability.
Ecological Diversity: Shaanxi Province encompasses a wide range of ecological landscapes, including mountains, plateaus, and plains, resulting in diverse ecosystems and habitats. This ecological diversity makes Shaanxi an interesting and representative area to examine ecological space dynamics and carbon sequestration patterns in a region with varying environmental conditions.
Regional Importance: Shaanxi Province holds significant ecological and economic importance within China. It is an important industrial and agricultural region with abundant energy reserves, currently undergoing rapid urbanization and development. Understanding the dynamics of ecological spaces and carbon sink in Shaanxi can provide valuable insights into the challenges and opportunities for sustainable development and carbon management in similar regions.
Data Availability: The availability of comprehensive and reliable data for Shaanxi Province allowed us to conduct a detailed analysis of ecological space and carbon sequestration. This enabled us to derive meaningful conclusions and propose relevant optimization strategies based on real data.
The uniqueness of this study lies in the selection of ecological space identification indicators related to county-level carbon sink differentiation and the incorporation of carbon sink elements into the ecological space identification system. Subsequently, the ecological space distribution characteristics and optimization strategies under the dual-carbon target were investigated. Shaanxi Province exhibits diverse ecological and geomorphic types, encompassing various city types such as industrial, agricultural, and ecological areas, making it highly representative. Tailored optimization strategies for different county-level ecological spaces were proposed in this research, providing valuable insights for carbon reduction, carbon sequestration, and ecological space optimization in other regions.
Question 3: In addition, the authors should make further policy proposals to highlight the research significance of this research.
Response: The suggestion was taken. In the revised manuscript, we have addressed your suggestion by providing tailored policy proposals for different types of county-level ecological spaces. These policy recommendations aim to highlight the research significance of our study and offer practical guidance for carbon reduction, carbon sequestration, and ecological space optimization in various regions.
Reviewer 4 Report
I think the biggest problem in the article is that a simple large scale ecological suitability assessment job applies an additional carbon reduction approach. The authors may not be sure if this is scientific or not. I think this article is not suitable to be published in a remote sensing , unless significant revisions can be made.
1. The second part could specify the rationale for the choice of data indicators. Why not add forest and water related indicators?
2. When introducing the carbon sink coefficient method, can the carbon sink efficiency be interpreted as a weighting of ecological protection? What is the impact of using the results of the GeoDetector analyze on the conclusions of the article?
3. The explanation of Equation 4 is garbled.
4. The result 4.1 section shows the characteristics of spatial and temporal changes in the county-level carbon sinks in Shaanxi Province, but there is no further analysis of temporal changes, and they are in fact not helpful to the later article.
5. What year of data is used in Figure 4d, and can the data years used in Section 4.3 and 4.2 correspond?
6. What is the reason for discussing Shaanxi Province? It is not a closed space.
7. The flowchart in Figure 2 is not consistent with the article. Can Section 3.2 be reordered to 3.1?
8. The method of calculating many indicators in Table 2 is very unclear, e.g. III2.
9. Section 4.4 and 5.1 contents can be merged.
Author Response
We would like to thank you for your constructive and insightful comments and suggestions. We have revised the manuscript accordingly. To facilitate the visibility of the changes made to all the comments, the changes are marked in blue in the revised manuscript. Below we provide a point-to-point response to all the comments.
Question 1: The second part could specify the rationale for the choice of data indicators. Why not add forest and water related indicators?
Response: The suggestion was taken. We have supplemented the manuscript by providing a detailed explanation of the rationale behind selecting specific indicators from different perspectives. The characteristic indicators of natural ecosystems included elevation, slope, relief degree, and aspect. Elevation, as a fundamental natural attribute, exerts an influence on climatic conditions, species distribution, and ecological processes. Areas at higher elevations are more likely to undertake ecological functions such as water conservation and providing animal habitats. Slope and relief degree are positively correlated with ecological space, and areas with larger slopes and relief degree are more likely to be part of the ecological space. Additionally, the aspect determines the orientation of solar radiation, which affects the microclimatic conditions of the local ecological spatial distribution.
For artificial ecosystems, the characteristic indicators encompassed population density, nighttime light intensity, traffic network density, and surface temperature. Population density represents the distribution and concentration of the population, directly influencing land use patterns and habitat fragmentation. The nighttime lighting data provide better insights into the level of urbanization, economic status, energy consumption, and other human activity factors, reflecting the intensity and scope of human activities. Moreover, traffic network density indicates the intensity of urban traffic infrastructure construction and influences landscape connectivity. Surface temperature can reflect the intensity of the urban heat island effect, affecting the local climate and ecological conditions of the region.
As for natural-artificial interaction ecosystems, the characteristic indicators consisted of NDVI, land use intensity, geological disaster point density, and habitat quality. NDVI effectively reflects the health degree and density of vegetation, where higher values suggest a higher likelihood of becoming an ecological space. Meanwhile, land use intensity refers to the degree and intensity of human utilization of existing land, and it can reflect potential ecological disturbance areas. Geological disasters are influenced by both natural geological factors and human activities and are prone to the potential destruction of ecological processes. Lastly, habitat quality indicates the relationship between patches and their surrounding environment, reflecting the degree of disturbance they experience. Areas with better habitat quality often hold higher ecological value and have greater significance for protection.
This study examined the inherent functions of ecological patches and considered their relationship with the landscape environment. Existing research foundations were referenced to determine ecological spatial identification indicators from three perspectives: natural ecosystem attributes, artificial ecosystem attributes, and natural-artificial interaction ecosystem attributes. We focused on indicators that have been widely recognized in the literature and have demonstrated strong correlations with ecological conditions and carbon sink potential. While we acknowledge the importance of forest and water-related indicators in assessing ecological conditions, our decision to exclude them in this particular study was based on the availability of relevant and reliable data for the chosen indicators. The indicators selected were considered to be comprehensive, representative and informative in characterizing the ecological characteristics of the study area. However, we appreciate your suggestion to include forest and water-related indicators in this study. These indicators indeed play crucial roles in ecosystem functioning and carbon sink. In future studies, we will make efforts to include additional indicators, like forest and water-related metrics, provided that comprehensive and reliable data sources are available.
Question 2: When introducing the carbon sink coefficient method, can the carbon sink efficiency be interpreted as a weighting of ecological protection? What is the impact of using the results of the GeoDetector analyze on the conclusions of the article?
Response: Thank you for your suggestion. When introducing the carbon sink coefficient method, the carbon sink efficiency can be interpreted as a weighting of ecological protection to some extent. The higher the carbon sink efficiency, the more effective the ecosystem is in sequestering carbon, indicating its potential for ecological protection and contribution to carbon sink. The analysis results of GeoDetector have a significant impact on the conclusions of this study, as they determine the selection of ecological spatial identification indicators under the dual-carbon target. The factor detector and interaction detector results of GeoDetector help identify the driving factors behind the spatial variations in county-level carbon sinks. Understanding these driving factors allows for more scientifically and rationally selecting ecological spatial identification indicators relevant to carbon sink spatial variations, thereby aligning with the construction of an ecological spatial identification system under the dual-carbon target. This, in turn, contributes to a comprehensive understanding of the distribution of ecological spaces in Shaanxi Province at the county level and facilitates the proposal of more targeted and effective optimization strategies.
Question 3: The explanation of Equation 4 is garbled.
Response: Thank you for your suggestion. In the revised manuscript, we have provided a detailed and clear explanation of the mathematical expression in Equation 4.
Questions 4: The result 4.1 section shows the characteristics of spatial and temporal changes in the county-level carbon sinks in Shaanxi Province, but there is no further analysis of temporal changes, and they are in fact not helpful to the later article.
Response: Thank you for your suggestion. Compared to the spatial variation, the temporal changes in county-level carbon sinks in Shaanxi Province show less pronounced characteristics. This finding indirectly indicates that the driving factors of carbon sink spatial variation have a more significant influence on the variations in carbon sinks. The primary focus of this study is to conduct an in-depth analysis of the driving factors of county-level carbon sink spatial variation and subsequently select indicators for identifying ecological spaces under the dual-carbon target. Although this study did not conduct further analysis on the temporal changes in county-level carbon sinks in Shaanxi Province, the temporal variation results of county-level carbon sinks presented in Section 4.1 provide a foundational basis for the subsequent analysis of the characteristics of different types of county-level ecological spaces and the formulation of targeted optimization strategies.
Question 5: What year of data is used in Figure 4d, and can the data years used in Section 4.3 and 4.2 correspond?
Response: Thank you for your suggestions. We have specified the data years for each figure in the revised manuscript. Figure 4d used the data from 2000 to 2020. The spatial distribution of cold and hot spots in total county-level carbon sink in Shaanxi Province from 2000 to 2020 exhibited consistency. To analyze the driving mechanisms of spatial variation in total carbon sinks at the county level in Shaanxi Province and select ecological spatial identification indicators, Section 4.2 conducted an analysis using the GeoDetector data from 2000 to 2020. Figure 5a and Figure 5b respectively presented the average results of the factor detector and the interaction detector for 2000, 2010, and 2020. Additionally, in Section 4.3, we used the data from 2020 to analyze the approximate current distribution of county-level ecological spaces under the dual-carbon target and provide a basis for proposing targeted optimization strategies in the future.
Question 6: What is the reason for discussing Shaanxi Province? It is not a closed space.
Response: Thank you for your suggestions. We have supplemented the reasons for selecting Shaanxi Province as the research area in the revised manuscript. While it is true that Shaanxi Province is not a closed space, the decision to study this specific region was based on factors such as ecological diversity, regional importance, and data availability.
Ecological Diversity: Shaanxi Province encompasses a wide range of ecological landscapes, including mountains, plateaus, and plains, resulting in diverse ecosystems and habitats. This ecological diversity makes Shaanxi an interesting and representative area to examine ecological space dynamics and carbon sequestration patterns in a region with varying environmental conditions.
Regional Importance: Shaanxi Province holds significant ecological and economic importance within China. It is an important industrial and agricultural region with abundant energy reserves, currently undergoing rapid urbanization and development. Understanding the dynamics of ecological spaces and carbon sink in Shaanxi can provide valuable insights into the challenges and opportunities for sustainable development and carbon management in similar regions.
Data Availability: The availability of comprehensive and reliable data for Shaanxi Province allowed us to conduct a detailed analysis of ecological space and carbon sequestration. This enabled us to derive meaningful conclusions and propose relevant optimization strategies based on real data.
Question 7: The flowchart in Figure 2 is not consistent with the article. Can Section 3.2 be reordered to 3.1?
Response: The suggestion was taken. Regarding the flowchart in Figure 2, we have rectified the inconsistency with the article. The revised version of Figure 2 now accurately reflects the content studied in the manuscript. Additionally, we have reordered Section 3.2 to Section 3.1 as per your recommendation.
Question 8: The method of calculating many indicators in Table 2 is very unclear, e.g. III2.
Response: The suggestion was taken. In response to your comment, we have revised the manuscript to provide detailed and comprehensive explanations for the calculation methods of all the indicators listed in Table 2, including III2.
Questions 9: Section 4.4 and 5.1 contents can be merged.
Response: The suggestion was taken. In the revised manuscript, we have consolidated the contents of Section 4.4 and 5.1.
Round 2
Reviewer 1 Report
The revision to this paper is good. The changes enhance the clarity and overall quality of the paper.
Author Response
Thank you for your positive feedback on the revised paper. I'm glad to hear that the changes made have improved the clarity and overall quality of the manuscript. We appreciate your time and input in reviewing the paper. If you have any further suggestions or questions, please don't hesitate to let us know.
Reviewer 4 Report
During this round of revisions, there was a significant improvement in the quality of the article. The following issues suggest further thinking:
1.Figure1 China map drawing is not standardized, there is no nine-dash line and scale, which may be the picture is not clear.
2. Highlight the innovation of the article - the factor of carbon sink is added on the basis of ecological suitability. In addition, please explain the difference between this method and the “双评价” of national spatial planning, so as to attract readers' interest.
3. I am most confused by the disconnect between the importance of the factors identified by the geodetectors and the weighting of the factors as determined by the AHP method. How can using a geodetector for factor weight identification help the scientific research question of the article with the topic of the article, just by deleting the aspect and geological disaster point density? From the results, it is possible to identify the ecological space of the county without using a geodetector.
4.Table 5, Calculation of carbon sink element, please give the year.
Author Response
We would like to thank you for your constructive and insightful comments and suggestions. We have revised the manuscript accordingly. To facilitate the visibility of the changes made to all the comments, the changes are marked in blue in the revised manuscript. Below we provide a point-to-point response to all the comments.
Question 1: Figure1 China map drawing is not standardized, there is no nine-dash line and scale, which may be the picture is not clear.
Response: The suggestion was taken. We appreciate your attention to detail in ensuring the standardization of the map. In response to your comments, we have implemented necessary improvements to the Figure 1. Specifically, we have added the nine-dash line, the scale bar and the north arrow. Additionally, we have uploaded high-resolution versions of each figure within the submission system.
Question 2: Highlight the innovation of the article - the factor of carbon sink is added on the basis of ecological suitability. In addition, please explain the difference between this method and the “双评价” of national spatial planning, so as to attract readers' interest.
Response: The suggestion was taken. In the revised manuscript, we highlighted the innovations of this article and provided an explanation of the differentiation between our research methodology and the "double evaluation" approach in national spatial planning. Compared to the "double evaluation" approach in national spatial planning, which primarily focused on a comprehensive assessment of land resources and environmental carrying capacity, the methodology employed in this research delved deeply into the underlying mechanisms of spatial differentiation in carbon sink. It underscored the carbon sink attribute of ecological space identification indicators. Additionally, targeted optimization strategies were proposed for diverse county-level ecological spaces, offering valuable insights for achieving regional dual-carbon goals.
Question 3: I am most confused by the disconnect between the importance of the factors identified by the geodetectors and the weighting of the factors as determined by the AHP method. How can using a geodetector for factor weight identification help the scientific research question of the article with the topic of the article, just by deleting the aspect and geological disaster point density? From the results, it is possible to identify the ecological space of the county without using a geodetector.
Response: Thanks for your suggestion. We greatly appreciate your thoughtful inquiry into the relationship between the application of Geodetector and the AHP method in this study. The utilization of Geodetector in this research aims to explore the driving factors behind county-level carbon sink spatial differentiation, thereby selecting ecological space identification indicators associated with carbon sink spatial differentiation. Through the analysis of the driving factors of county-level carbon sink spatial differentiation in Shaanxi Province from 2000 to 2020, it was determined that geological disaster point density and aspect had minimal influence on carbon sink spatial differentiation. To align with the objective of establishing an ecological space identification framework under the dual-carbon target, these two factors were excluded. The importance of each indicator identified by the Geodetector represents its capability to explain carbon sink spatial differentiation, yet it may not accurately reflect its relevance to ecological space identification. Thus, the weighting of ecological space indicators needs to be determined through the AHP method. While it is possible to identify the ecological space of the county without using Geodetector, this approach lacks the connection to carbon sink dynamics, which is essential for achieving the dual-carbon target.
Question 4: Table 5, Calculation of carbon sink element, please give the year.
Response: The suggestion was taken. In the revised manuscript, we have provided the calculation year for carbon sink element. Throughout the ecological space identification process under the dual-carbon target, the indicators for artificial ecosystem, natural-artificial interaction ecosystem, and carbon sink element need to utilize data from the same year.
