Comprehensive Assessment of Sustainable Development Goal 11 at the Sub-City Scale: A Case Study of Guilin City

Round 1

Reviewer 1 Report

See the attachment.

Comments for author File: Comments.pdf

Author Response

Dear Editors and Reviewer:

Thank you very much for your constructive and positive comments on our manuscript entitled “Comprehensive assessment of Sustainable Development Goal 11 at the sub-city scale: A case study of Guilin City” submitted to Remote Sensing.

We have revised our paper along the lines outlined by the reviewers. Our detailed responses follow. Note also that changes in the manuscript are in yellow colour.

Response to Reviewer 1 Comments

Point 1: Can you provide more international research progress and case studies, as well as advancements in the research of comprehensive assessment methods for sustainable development?

 

Response: Thank you for your comments. We greatly value international research advancements and case studies, as well as the latest research in comprehensive sustainable development assessment methods. Your input is highly valuable to our study. We have incorporated international research advancements and case studies into the Introduction section to better support and enrich our research.

 

Point 2: The statement is repetitive.

 

Response: Thank you for your comments. The redundant statements have been removed.

 

Point 3: What role does the POI data play in indicator construction? Why were NDVI and SAVI, two indicators reflecting vegetation status, chosen? What are their respective roles in indicator construction?

 

Response: Thank you for your comments. Regarding POI data, our primary selections include public transportation facilities such as bus stations, high-speed rail stations, and long-distance bus stations. We utilize this data to assess the development status of SDG 11.2, specifically evaluating the coverage and accessibility of public transportation infrastructure, which is a core component of SDG 11.2.

The reason for choosing NDVI and SAVI as vegetation indices is that SDG 11.7.1 selects public green spaces as localized data. NDVI and SAVI are vegetation indices extracted from remote sensing data, and they can reflect the extent and growth conditions of surface vegetation. Additionally, they can account for the influence of soil types on vegetation indices, enhancing the reliability of the extracted vegetation information. Therefore, we chose to use NDVI and SAVI as these two indicators to more accurately represent the state and changes of public green spaces, in support of the localized data requirements of SDG 11.7.1. These two indicators play a significant role in our study, contributing to a more comprehensive assessment of relevant indicators for urban sustainable development.

 

Point 4:“If a smaller value of the sub-indicator represents a higher level of sustainability, the formula would be as follows:”

The formula under this condition is missing.

 

Response: Thank you for your comments. We have added formula 2 to clarify that "smaller sub-indicator values represent higher levels of sustainability."

 

Point 5:“We calculate the average value of all indicators for Guilin city each year using equal weights.”

I think the use of equal weights in scoring lacks rationale.

 

Response: Thank you for your comments. In our study, we chose to use equal weights to calculate the annual averages of various indicators for Guilin City. This decision was made to maintain simplicity and transparency in the assessment. Since the development priorities may vary for each city, using equal weights allows for a more equitable consideration of different aspects of the indicators. It prevents assigning excessive weight to certain indicators, which could lead to an imbalance in the scoring. This approach helps mitigate the influence of subjective factors on weight allocation and makes comparisons between different indicators clearer.

 

Point 6: It is recommended to provide a detailed explanation of the classification levels used in the traffic light system in this study. This will enable readers to better understand the criteria and thresholds used to categorize the indicators into different levels, such as green, yellow, and red. Including this information will enhance the transparency and clarity of the methodology and allow readers to interpret the results accurately.

 

Response: Thank you for your comments. We have provided a detailed explanation of the classification levels used for the traffic light system in Section 4.1 of our research. Including this information will enhance the methodological transparency and clarity, enabling readers to interpret our study results more accurately.

 

Point 7: In Figure 2, it is suggested to provide the full names corresponding to the abbreviations of the country areas.

 

Response: Thank you for your comments. We very much agree. In Figure 2, the full names corresponding to the national and regional abbreviations have been added.

 

 

Point 8: It is important to clarify how the changing trends of various indicators in Figure 3 were calculated and categorized.

 

Response: Thank you for your comments. We have added Formula 5 in Section 3.2.1 to explain the calculation method for indicator trends.

 

Point 9: Sections 4.2.1, 4.2.2, 4.2.4, and 4.2.6:

It is suggested to provide figures or tables to support the analysis in these sections, allowing readers to better understand your analysis.

 

Response: Thank you for your comments. The sections you mentioned, 4.2.1, 4.2.2, 4.2.4, and 4.2.6, provide detailed analyses of individual sub-indicators in our paper to offer a more comprehensive understanding of various aspects. While these sections are not the primary focus of the entire paper, they provide readers with deeper insights and specific data support to enhance our analysis and conclusions in the comprehensive assessment of SDG11. However, the main emphasis of the entire paper remains on the holistic evaluation of SDG11, aiming to provide a comprehensive overview of the sustainable development goals.

 

Point 10: Additional information is needed regarding the satellite data source, temporal coverage, and processing methods for the AOD product based on remote sensing. This will help readers assess the reliability of conclusions drawn using these data.

 

Response: Thank you for your comments. Section 4.2.5, as an analysis of individual sub-indicators, is not a focal chapter of the paper but serves as supplementary support for the main content. We have also mentioned reference methods and data sources to provide readers with additional context and background information for better understanding.

 

Point 11: Lingshan District cannot be found in the figure.

 

Response: Thank you for your comments. We have taken note of and corrected the errors in the paper. Your feedback is highly valuable in improving the quality and accuracy of the paper.

 

Point 12: What does the legend in the figure represent? Is it the scores?

 

Response: Thank you for your comments. The legend in Figure 7 represents the overall scores for SDG11 in Guilin City and has been further explained in Figure 7.

 

Point 13: In the discussion section, the applicability and limitations of this method, as well as the comparison with similar methods, were not adequately addressed. It is recommended to strengthen these aspects.

 

Response: Thank you for your comments. We have incorporated your feedback and added content regarding the applicability, limitations, and comparisons with similar methods in the discussion section. This information is crucial for assessing the effectiveness and generalizability of our research method.

 

Reviewer 2 Report

The study selected multiple influencing factors such as "housing, public transportation, land consumption rate, disasters, solid waste, aerosol emissions and public space area" to analyze their impact on urban sustainable development from the perspective of SDG11. The research has some novelty for publication in the journal. Before that happens, though, there are a lot of issues that need to be fixed.

 

(1)    Throughout the article, there are objections to the time scale of the research, some of which are the results of node years such as 2010, 2015, and 2020, and some of which are consecutive years. If the node year is not a key year, is the analysis here meaningful?

 

(2)    The raising of scientific questions is not obvious. Does this article want to express the sustainable development goals to guide the sustainable development evaluation indicators of Guilin City? The lack of an obvious scientific question leads to a weak purpose in solving the problem, which is limited to result analysis without further discussion and analysis.

 

(3)    In this line 224-233, we did not directly see the development and dispersion in the research, and the suggestions made are also very broad. This way of writing is not rigorous. This article does not show spatial agglomeration and dispersion, but only shows the horizontal comparison of the same indicator in different cities. For example, in line 224, Lingshan District is an agricultural area, Quanzhou is an industrial area, and Yangshuo is a tourist attraction. This seems to be common sense, but it does not directly reflect the industrial structure of the region in the study. It is necessary to cite papers or list the proportions of local three industries to prove the leading industries in the region, so that readers can better understand the overview of the region.

 

(4)    The indicators of SDGs guide the sustainable development of the region from 17 perspectives. The author chose SDG11 as the evaluation indicator of the sub-city, which is a very good focus. However, SDG11 is also reflected in one perspective, which cannot fully reflect the entire study area and can only express part of the urban sustainable development indicator system. The highlight of this article is how to monitor some sustainable development indicators under the guidance of SDG11 and optimize the deficiencies in this part. It is suggested that this article strengthen the description of this section.

 

(5)    Please avoid repeating the same expression. The meanings expressed in Figures 8 and 9 are repeated. If the authors can mark the values on the Synergies and Trade-offs lines in Figure 9. It can use a picture to express the meaning, which will be more concise.

 

(6)    The trade-offs and synergies in Section 4.5 can only be used to describe the results. The authors do not indicate the optimization path in the discussion or conclusion and are therefore unable to offer practical advice. The author is recommended for further in-depth discussion or constructive conclusions.

The study selected multiple influencing factors such as "housing, public transportation, land consumption rate, disasters, solid waste, aerosol emissions and public space area" to analyze their impact on urban sustainable development from the perspective of SDG11. The research has some novelty for publication in the journal. Before that happens, though, there are a lot of issues that need to be fixed.

 

(1)    Throughout the article, there are objections to the time scale of the research, some of which are the results of node years such as 2010, 2015, and 2020, and some of which are consecutive years. If the node year is not a key year, is the analysis here meaningful?

 

(2)    The raising of scientific questions is not obvious. Does this article want to express the sustainable development goals to guide the sustainable development evaluation indicators of Guilin City? The lack of an obvious scientific question leads to a weak purpose in solving the problem, which is limited to result analysis without further discussion and analysis.

 

(3)    In this line 224-233, we did not directly see the development and dispersion in the research, and the suggestions made are also very broad. This way of writing is not rigorous. This article does not show spatial agglomeration and dispersion, but only shows the horizontal comparison of the same indicator in different cities. For example, in line 224, Lingshan District is an agricultural area, Quanzhou is an industrial area, and Yangshuo is a tourist attraction. This seems to be common sense, but it does not directly reflect the industrial structure of the region in the study. It is necessary to cite papers or list the proportions of local three industries to prove the leading industries in the region, so that readers can better understand the overview of the region.

 

(4)    The indicators of SDGs guide the sustainable development of the region from 17 perspectives. The author chose SDG11 as the evaluation indicator of the sub-city, which is a very good focus. However, SDG11 is also reflected in one perspective, which cannot fully reflect the entire study area and can only express part of the urban sustainable development indicator system. The highlight of this article is how to monitor some sustainable development indicators under the guidance of SDG11 and optimize the deficiencies in this part. It is suggested that this article strengthen the description of this section.

 

(5)    Please avoid repeating the same expression. The meanings expressed in Figures 8 and 9 are repeated. If the authors can mark the values on the Synergies and Trade-offs lines in Figure 9. It can use a picture to express the meaning, which will be more concise.

 

(6)    The trade-offs and synergies in Section 4.5 can only be used to describe the results. The authors do not indicate the optimization path in the discussion or conclusion and are therefore unable to offer practical advice. The author is recommended for further in-depth discussion or constructive conclusions.

Author Response

Dear Editors and Reviewer:

Thank you very much for your constructive and positive comments on our manuscript entitled “Comprehensive assessment of Sustainable Development Goal 11 at the sub-city scale: A case study of Guilin City” submitted to Remote Sensing.

We have revised our paper along the lines outlined by the reviewers. Our detailed responses follow. Note also that changes in the manuscript are in green colour.

Response to Reviewer 2 Comments

Point 1: Throughout the article, there are objections to the time scale of the research, some of which are the results of node years such as 2010, 2015, and 2020, and some of which are consecutive years. If the node year is not a key year, is the analysis here meaningful?

 

Response: Thank you for your concern regarding the temporal scale of the study. The choice of node years (such as 2010, 2015, and 2020) represents interval years within the long time series selected for this research. These years align with the monitoring and reporting cycles of the United Nations' Sustainable Development Goals (SDGs), allowing our study to be comparable with international standards of sustainable development progress. This alignment is crucial for assessing sustainability progress.

However, during the research process, there were some missing data for the selected node years. To address this, we incorporated continuous year data to better track the short-term development trends and trajectories of Guilin City. This approach allows us to identify issues that may require more timely intervention.

 

Point 2: The raising of scientific questions is not obvious. Does this article want to express the sustainable development goals to guide the sustainable development evaluation indicators of Guilin City? The lack of an obvious scientific question leads to a weak purpose in solving the problem, which is limited to result analysis without further discussion and analysis.

 

Response: Thank you for your comments. This article aims to provide a comprehensive assessment of Guilin City's sustainable development indicators under the Sustainable Development Goals (SDG) framework, specifically focusing on SDG 11. Your point about clarity is well-taken. In the fifth section of the discussion, we will further elaborate on the analysis, delving into the practical implications of our research findings to address the challenges faced by urban sustainability.

 

Point 3: In this line 224-233, we did not directly see the development and dispersion in the research, and the suggestions made are also very broad. This way of writing is not rigorous. This article does not show spatial agglomeration and dispersion, but only shows the horizontal comparison of the same indicator in different cities. For example, in line 224, Lingshan District is an agricultural area, Quanzhou is an industrial area, and Yangshuo is a tourist attraction. This seems to be common sense, but it does not directly reflect the industrial structure of the region in the study. It is necessary to cite papers or list the proportions of local three industries to prove the leading industries in the region, so that readers can better understand the overview of the region.

 

Response: Thank you for your comments. We have already supplemented the proportions of Guilin City's three major industries in Section 4.1, and we have presented this information through the use of Figure 3. This addition will help readers gain a more comprehensive understanding of the region's industrial structure, further supporting our research findings. This contributes to enhancing the scientific credibility of the article. In Section 4.3, specifically in Figure 7, we have provided visualizations of spatial clustering and dispersion.

 

Point 4: The indicators of SDGs guide the sustainable development of the region from 17 perspectives. The author chose SDG11 as the evaluation indicator of the sub-city, which is a very good focus. However, SDG11 is also reflected in one perspective, which cannot fully reflect the entire study area and can only express part of the urban sustainable development indicator system. The highlight of this article is how to monitor some sustainable development indicators under the guidance of SDG11 and optimize the deficiencies in this part. It is suggested that this article strengthen the description of this section.

 

Response: Thank you for your assessment of our choice of SDG11 as the evaluation indicator in our study. Indeed, SDG11 represents just one perspective among the 17 Sustainable Development Goals. While SDG11 is crucial for addressing urban sustainability, it may not encompass all the indicators required for a comprehensive assessment of Guilin City's sustainable development landscape. The title of our study is "Comprehensive assessment of Sustainable Development Goal 11 at the sub-city scale: A case study of Guilin City." Through the localization of SDG11 indicators, we conducted a comprehensive evaluation of the development status of SDG11 in Guilin City. This assessment allowed us to understand the performance of various city-counties in Guilin in terms of sustainable development and analyze the trends in urban sustainable development. At the same time, we also identified some issues in the urban development process and the reasons behind these problems.

 

Point 5: Please avoid repeating the same expression. The meanings expressed in Figures 8 and 9 are repeated. If the authors can mark the values on the Synergies and Trade-offs lines in Figure 9. It can use a picture to express the meaning, which will be more concise.

 

Response: Thank you for your comments. Figure 10 (formerly Figure 9) focuses on illustrating the connectivity and interactions between SDG11 indicators. We have now added numerical labels to the Synergies and Trade-offs lines, which will make the image more concise in conveying this information.

 

Point 6: The trade-offs and synergies in Section 4.5 can only be used to describe the results. The authors do not indicate the optimization path in te discussion or conclusion and are therefore unable to offer practical advice. The author is recommended for further in-depth discussion or constructive conclusions.

 

Response: Thank you for your comments. You mentioned that the section on trade-offs and synergies is primarily used to describe the results, and there is no mention of optimization pathways in the discussion and conclusion. Your suggestion is very valid, and indeed, determining optimization pathways can be a complex task that often requires more in-depth research and refinement. In the current study, our focus has been on assessing and understanding the current state of sustainable development and identifying potential issues. However, for specific optimization measures and pathways, it would require more specialized research and consideration for implementation.

 

Reviewer 3 Report

The study introduced a framework method for assessing sustainable development at the city-county scale. The logic of the paper is clear, while some points should be explained in deatail.

1.       In section 2.2, the paper introduced the remote sensing data were from google earth engine database. But which kinds of rs data? The resolution was important for the result

2.       In Statistical data, there should be the amount of built-up area. Based on RS data, one can withdraw the area of built-up area. There must be difference between them. Which one is more credible?

3.       In line 193, equation (4), the meaning of N?  In line 191 and 192, whether the weight is equal or allocated by scores based on weight distribution[38]?

In line 141 and 142, the description of POI was duplicate.

Author Response

Dear Editors and Reviewer:

Thank you very much for your constructive and positive comments on our manuscript entitled “Comprehensive assessment of Sustainable Development Goal 11 at the sub-city scale: A case study of Guilin City” submitted to Remote Sensing.

We have revised our paper along the lines outlined by the reviewers. Our detailed responses follow. Note also that changes in the manuscript are in red colour.

Response to Reviewer 3 Comments

Point 1: In section 2.2, the paper introduced the remote sensing data were from google earth engine database. But which kinds of rs data? The resolution was important for the result

 

Response: Thank you for your comments. Resolution indeed plays a crucial role in the results. In our study, the remote sensing data we obtained are primarily from the Google Earth Engine database, consisting mainly of two major satellite data sources: Landsat multispectral data and Sentinel-2 multispectral satellite data. For the time-series data, we used Landsat multispectral data from 2010 to 2013, as well as Sentinel-2 multispectral satellite data from 2014 to 2020. These two data sources provide information over a long time span, which helps us gain a more comprehensive understanding of the dynamic changes in urban sustainable development.

During the data selection process, we made sure to choose data of high quality and resolution to enhance the accuracy and credibility of our results. The resolution of remote sensing data is crucial for capturing urban features and changes, and we made every effort to select data that best suited our research purposes.

 

Point 2:  In Statistical data, there should be the amount of built-up area. Based on RS data, one can withdraw the area of built-up area. There must be difference between them. Which one is more credible?

 

Response: Thank you for your comments. In our study, we recognize the importance of having accurate and reliable data, especially when quantifying the built-up area. We acknowledge that there can be differences between statistical data and data derived from remote sensing sources. Statistical data, often collected through surveys and administrative records, provide valuable information but may have limitations, such as reporting errors or delays in data collection and reporting. On the other hand, RS data, which involve the use of satellite imagery or aerial photography, can offer more current and spatially explicit information about land cover, including built-up areas.

To address these potential differences and ensure the reliability of our findings, we have taken the following steps: We have cross-referenced statistical data with RS data to identify any significant disparities. If substantial differences exist, we have conducted further investigations to understand the reasons behind them. In some cases, we have used statistical data to calibrate RS data or vice versa, aiming to align the two data sources as closely as possible. We have assessed the quality and accuracy of both data sources and have highlighted any discrepancies or limitations in our study. Ultimately, the credibility of the data source may vary depending on the specific context and region. Our goal is to provide the most accurate and reliable assessment possible by carefully considering and addressing any differences between statistical and RS data sources.

 

Point 3: In line 193, equation (4), the meaning of N?  In line 191 and 192, whether the weight is equal or allocated by scores based on weight distribution?

 

Response: Thank you for your comments. We have explained in Equation 6 (formerly Equation 4) that N represents the total number of indicators. In our study, we chose to use equal weights to calculate the annual averages of various indicators for Guilin City. This choice was made to maintain simplicity and transparency in the assessment. Given that each city may have different development priorities, the use of equal weights helps to provide a more equitable consideration of different aspects of the indicators. It prevents any single indicator from being overly weighted, thereby avoiding an imbalance in the scoring. This approach is designed to mitigate the influence of subjective factors on weight allocation and enhance the clarity of comparisons between different indicators.

 

Point 4: In line 141 and 142, the description of POI was duplicate.

 

Response: Thank you for your comments. The redundant statements have been removed.

 

Round 2

Reviewer 2 Report

The authors made changes in accordance with the reviewer's recommendations. Accept in present form.

The authors made changes in accordance with the reviewer's recommendations. Accept in present form.