Trade-Offs and Synergies and Ecosystem Service Bundles of Long-Term Ecosystem Services in Xiong’an New Area, China
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsI have enclosed a pdf file with my comments.
Comments for author File: 
Comments.pdf
Although the paper is readable as it stands, the written English must be improved. Some hard-to-understand idiomatic expressions could be better expressed if they were avoided.
Author Response
Comments 1
The paper Trade-offs and synergies and ecosystem service bundles of long-term ecosystem services in Xiong'an New Area, China describes changes in Ecosystem Services in the aforementioned Xiong'an New Area, China.
The paper employs several analytical techniques to track the changes in these services between 1990 and 2023, with special emphasis on 2018, the year the New Area concept was implemented. Comparisons emphasize differences before and after this year. The paper uses different spatial analysis techniques to illustrate measured changes and discusses the implications of these on the study area.
I found that the lack of clear definitions, writing style and methodological mistakes result in a paper that is short of expectations. There is a general lack of care in the preparation and submission.
Response 1: Thank you sincerely for your meticulous review and valuable feedback on our manuscript entitled "Trade-offs and synergies and ecosystem service bundles of long-term ecosystem services in Xiong'an New Area, China". We fully acknowledge your points regarding the lack of clear definitions, suboptimal writing style, methodological errors, and insufficient rigor in preparation and submission, and we apologize for these shortcomings.
We comprehensively revised the manuscript to address all these issues, we supplemented clear definitions of core concepts (e.g., ES, ESBs) with reference to authoritative literature; optimized the logical structure and academic expression to enhance coherence and readability; corrected methodological flaws, verified model parameters, and supplemented data processing details and reproducibility materials; and conducted rigorous proofreading to standardize formatting, citations, and language. We took your comments seriously to significantly improve the manuscript’s quality and rigor.
Comments 2
Specific comments
Figures should be independent of the text. It is impossible to understand the figures as they stand. The size of many of the legends make it impossible to read. If different axis values are used, as is the case, this should be pointed out in the figure’s description.
Response 2: Thanks for your suggestion. We adjusted the font size and format of all legends to ensure they are clearly legible. For figures with different axis values, we added explicit annotations in the figure captions to highlight these differences. These changes ensure that each figure can be fully understood independently of the main text.
Comments 3
line 36. Develop and reference the concepts of ecological priority and green development.
Response 3: Thanks for your suggestion. We developed the concepts and referred to the relevant references of ecological priority and green development in the revised manuscript.
(Page 2 line 40-45)
“Xiong'an aims to build a high-level modern city characterized by green and low-carbon development, which emphasizes giving priority to ecological and environmental protection in the process of economic development, so as to achieve sustainable development and the harmonious coexistence between humans and nature, with the harmonious coexistence of human beings and nature realized in 2035 [1].
[1] Dong, L., ChenH., 2025. China’s path to a global ecological civilization: Concepts and practices for sustainable development. Chin. J. Popul. Resour. Environ. 23(3), 295-300. https://doi.org/10.1016/j.cjpre.2025.07.001.”
Comments 4
lines 42-43. What is ecological civilization?
Response 4: Thanks for your question. “ecological civilization” refers to a holistic concept centered on the harmonious coexistence of humanity and nature, emphasizing respect for natural laws and the establishment of green development models, and covering the sum of material, spiritual and institutional achievements. We added the concepts in the revised manuscript.
(Page 2 line 49-53)
“This work is of irreplaceable significance for maintaining the ecological balance of Xiong'an and promoting ecological civilization, which takes the harmonious coexistence between humans and nature as its core, emphasizes respecting the laws of nature, building a green development model, and covers the sum of material, spiritual and institutional achievements [1].”
Comments 5
line 47 Babí Almenar et al. 2018 needs a number.
Response 5: Thanks for your reminder. We added the number for Babí Almenar et al. (2018) in the revised manuscript.
(Page 2 line 55-57)
“ESs are essential for maintaining human health, well-being, and sustainable development by provisioning, regulating, supporting, and cultural functions [3], and act as a vital link between natural systems and human society [4].”
[4] Elliot, T., Babí Almenar, J., Niza, S., Proença, V., Rugani, B., 2019. Pathways to modelling ecosystem services within an urban metabolism framework. Sustainability 11(10), 2766. https://doi.org/10.3390/su11102766.
Comments 6
line 57 Bacani et al.
Response 6: Thanks for your reminder. We added the "et al" in the revised manuscript.
(Page 2 line 75-76)
“Bacani et al. [8] employed the InVEST model for evaluating the spatio-temporal alterations of carbon storage in Três Lagoas.”
Comments 7
line 70 combine→combines
Response 7: Thanks for your suggestion. We revised the word "combine" to "combines" as suggested.
(Page 3 line 98-100)
“Thus, this paper, for the first time, combines the InVEST model, correlation analysis, SOM method, and OPGD model to analyze a comprehensive and long-term ESs.”
Comments 8
line 85 Xiongxian, Rongcheng, Anxin Xiongxian, Rongcheng, and Anxin
Response 8: Thanks for your suggestion. We adjusted the content from "Xiongxian, Rongcheng, Anxin" to "Xiongxian, Rongcheng, and Anxin" as suggested.
(Page 3 line 110-112)
“Xiong'an (38°43′N–39°02′N, 115°38′E–116°07′E) locates in central Hebei Province, covering Xiongxian, Rongcheng, and Anxin counties, and surrounding areas, with a total area of approximately 1770 km2 (Figure 1).”
Comments 9
Fig 1 Needs a map of China, so we can locate the study area.
Response 9: Thanks for your suggestion. We added the China map as requested.
(Page 4 line 125-126)
Figure 1 Location map of Xiong'an New Area.
Comments 10
line 86 What are vertical and horizontal rivers and canals?
Response 10: Thank you for pointing out the vague expression. Our original intention was to describe that Xiong'an is crisscrossed by rivers and canals, forming a sophisticated water network. We rewrote this sentence in the revised manuscript.
(Page 3 line 114-115)
“Xiong'an features an interlaced system of rivers and canals, a sophisticated water network, and is classified under the Daqing River water system.”
Comments 11
line 97 What is a pioneering ecological-priority model?
Response 11: Thank you for your careful review and pointing out the vague expression. Our original intention was to emphasize that as a demonstration for China's urbanization development, Xiong’an has taken ecological protection as the core premise, with the restoration of Baiyangdian Lake as a key focus, and constructed a development model that integrates ecological conservation with urban construction. We rewrote this sentence in the revised manuscript.
(Page 3 line 121-124)
“As a model for China's urbanization development, Xiong'an has established an eco-first development model centered on the ecological restoration of Baiyangdian Lake, achieving the integration of ecological conservation and urban development.”
Comments 12
Table S1 How do you deal with the differences in scale that go from 101 to 103 m when you incorporate them into the maps?
Response 12: Thanks for your question. We standardized all data to a unified coordinate system and resampled it to a 250 m resolution, ensuring consistency in both coordinate systems and accuracy across all datasets. We have supplemented the standard accordingly.
(Page 4 line 132-133)
“All raster datasets have been unified to the WGS_1984_Albers projected coordinate system and resampled to 250 m resolution.”
Comments 13
Fig 2 and so on→etc.
Response 13: Thanks for your suggestion. We revised "and so on" to "etc." in the figure as suggested.
Figure 2 Technical framework of this study.
Comments 14
lines 134-137 Number the OPGD variables one by one, not only mention that they are variables X1…X12, maybe convert to a Table. They are hard to track in the later Figures.
Response 14: Thanks for your suggestion. We fully understand your concern that the OPGD variables are hard to track in the later figures. Due to space constraints, we did not convert them into a table, but instead listed the specific names of variables X1-X12 in detail below Figure 4. This arrangement allows readers to conveniently check and correspond to the variables when viewing Figure 4, ensuring the clarity and traceability of the variables.
Figure 4 Drivers of ecosystem services in Xiong'an. (a) factor importance ranking, (b) interaction detection and correlation intensity range, and (c) frequency function. X1 is the elevation; X2 is the Distance from the city's secondary road; X3 is the Distance from expressway; X4 is the Distance from railway; X5 is the Rainfall; X6 is the slope; X7is the Population; X8 is the Distance from the city's tertiary road; X9 is the distance from the city's township road; X10 is the Gross Domestic Product; X11 is the Distance from the first-grade highway; X12 is the Distance from the government.
Comments 15
line 155 Why use ρ for a correlation instead of using r, as you are calculating a statistic whose parameter is unknown.
Response 15: Thanks for your question. This choice follows the established convention in statistics to distinguish between different types of correlation coefficients. The symbol ρ (or sometimes rs) is specifically designated for the Spearman rank correlation coefficient, a nonparametric measure that assesses the monotonic relationship between variables based on their ranks. In contrast, r is reserved for the Pearson product-moment correlation coefficient, which quantifies the linear relationship between two continuous variables using their original values. The use of ρ here aligns with this standard practice, ensuring clarity about the statistical approach employed (Spearman rank correlation) and maintaining consistency with field conventions-regardless of whether the population parameter is known.
Comments 16
line 181 expect→except
Response 16: Thanks for your suggestion. We revised "expect" to "except" in the corresponding text as recommended.
(Page 7 line 214-216)
“The data ranged from 1990-2023 and were divided in 5-year gaps except for 2018. Xiong'an New area was established in 2017 and entered the key stage of planning and construction in 2018; thus, 2018 was selected as the turning node.”
Comments 17
line 185 delineate? Maybe a better term is limited, but I fail to understand.
Response 17: Thanks for your suggestion. We fully agree with your suggestion and appreciate your help in improving the expression clarity. Considering the context that 2024 data is incomplete, we revised "delineated" to "limited" to more accurately convey the constraint on the data range.
(Page 8 line 217-218)
“To ensure the reliability of data and the scientific rigor of analysis results, the data range was limited to 2023.”
Comments 18
line 198 What is a mega wetland? Is there a classifying scheme that would allow me to compare it to other wetlands?
Response 18: Thanks for your questions. Our original intent was to emphasize Baiyangdian’s large scale and important ecological status among wetlands in northern China. Since "mega wetland" lacks a universally recognized classification scheme and may hinder comparison with other wetlands, we replaced “mega wetland” with the more precise description “the largest freshwater wetland on the north plain of China.”
(Page 8 line 231-234)
“As the largest freshwater wetland on the north plain of China, Baiyangdian not only has rich ecological resources but also provides extensive spatial resources and strategic advantages, improving the long-term high-quality maintenance of habitat quality.”
Comments 19
line 200 I don’t understand what enabling the high-value layout of headquarters. Should it be headquarters→habitat quality?
Response 19: Thanks for your suggestion. Our core intention was to emphasize that Baiyangdian as the largest freshwater wetland on the North China Plain, relies on its rich ecological resources, extensive spatial resources, and unique strategic advantages to effectively support the long-term and high-quality maintenance of local habitat quality that aligns with the study’s focus on ecosystem service dynamics in Xiong’an.
(Page 8 line 231-234)
“As the largest freshwater wetland on the North China Plain, Baiyangdian not only has rich ecological resources but also provides extensive spatial resources and strategic advantages, improving the long-term high-quality maintenance of habitat quality.”
Comments 20
Fig 3 How do we read the values? Are they arithmetic means for the region?
Response 20: Thanks for your questions. The values in each subgraph of Fig 3 represent the arithmetic mean of the overall ecosystem services (ES) indicators for the Xiong’an region in the corresponding year. In the data below (line 240-241), Net Primary Productivity (NPP) and Habitat Quality (HQ) are arithmetic means used directly, while soil conservation (SC), Carbon Storage (CS), and Water Yield (WY) are total values calculated based on regional area.
Comments 21
Fig. 4(a) There should be a list that is easy to read of the Xi variable names, not the written-out list of lines 134-137.
Response 21: Thanks for your suggestion. Due to space constraints, we did not convert them into a table, but instead listed the specific names of variables X1-X12 in detail below Figure 4. This arrangement allows readers to conveniently check and correspond to the variables when viewing Figure 4, ensuring the clarity and traceability of the variables.
Figure 4 Drivers of ecosystem services in Xiong'an. (a) factor importance ranking, (b) interaction detection and correlation intensity range, and (c) frequency function. X1 is the elevation; X2 is the Distance from the city's secondary road; X3 is the Distance from expressway; X4 is the Distance from railway; X5 is the Rainfall; X6 is the slope; X7is the Population; X8 is the Distance from the city's tertiary road; X9 is the distance from the city's township road; X10 is the Gross Domestic Product; X11 is the Distance from the first-grade highway; X12 is the Distance from the government.
Comments 22
Fig. 4(b) Confusing without a y axis. As placed, it seems a continuation of Figure 4a.
Response 22:
Thanks for your suggestion. We have added the y axis to Figure 4b. Figure 4b employs a heatmap to visualize the internal interactions among the 12 drivers.
Figure 4 Drivers of ecosystem services in Xiong'an. (a) factor importance ranking, (b) interaction detection and correlation intensity range, and (c) frequency function. X1 is the elevation; X2 is the Distance from the city's secondary road; X3 is the Distance from expressway; X4 is the Distance from railway; X5 is the Rainfall; X6 is the slope; X7is the Population; X8 is the Distance from the city's tertiary road; X9 is the distance from the city's township road; X10 is the Gross Domestic Product; X11 is the Distance from the first-grade highway; X12 is the Distance from the government.
Comments 23
Fig. 4(c) The frequency distributions have very different y values. Using a log scale on the axis will homogenize the axes and make it easier to compare. What are the red vertical lines?
Response 23: Thanks for your suggestion and question. The red vertical lines in Fig. 4(c) represent the median values of each variable. They serve as a visual reference to indicate the central tendency of each variable’s frequency distribution, helping readers quickly grasp where most values cluster. For example, in the subplot of "Elevation (X1)", the red lines highlight the median elevation, reflecting the typical elevation level in the study area. We also understand your concern about the differing y-axis ranges. However, we prefer to retain the linear scale for the following reasons. The large differences in y-axis values arise from the intrinsic variability of the variables themselves (e.g., GDP and population have much wider value ranges than elevation or slope). A linear scale directly reflects the actual frequency distribution of each variable, ensuring the visibility of both high-frequency and low-frequency intervals. As described in the text, we have already provided detailed interpretations for each variable’s distribution characteristics (e.g., “Distance from the city's secondary road showed a dense frequency distribution” and “GDP exhibited a dispersed frequency distribution”). This textual explanation complements the visual information, allowing readers to understand the differences without needing a log scale. Switching to a log scale might obscure subtle patterns in low-frequency ranges, which are still informative for interpreting variable distributions (e.g., the sparse but meaningful occurrences in certain value intervals for GDP or population). We hope this clarification addresses your questions. We sincerely appreciate your input, which helps us ensure the clarity and rigor of our work.
Comments 24
Fig 5 Which correlations are statistically significant? Are the paired correlations significantly different?
Response 24: Thanks for your questions. Statistically significant correlations are marked with asterisks (*p<0.05, **p<0.01, ***p<0.001). We added these comments in the revised manuscript. All paired variables labeled with asterisks in the figure passed the correlation test. The results have been integrated into the figure, allowing for an intuitive assessment of the statistical significance of differences between different pairing groups.
Figure 5 Correlation analysis of ESs in Xiong'an. (a) 1990-2018 and (b) 2018-2023. (*p<0.05; **p<0.01, ***p<0.001). NPP: net primary productivity, SC: soil conservation, HQ: habitat quality, CS: carbon storage, and WY: water yield.
Comments 25
lines 285-286 How can a r = 0.20 association, a weak association, have synergistic effects? Is there evidence in the literature that the r value presented here is large enough to claim synergistic effects?
Response 25: Thanks for your questions. Trade-offs and synergies in ecosystem services refer to the interactions among different services provided by ecosystems, encompassing both trade-off relationships where gains in one service correspond to losses in another, and synergistic relationships where multiple services increase or decrease simultaneously. Although the positive correlation of r=0.20 in this study is relatively weak, it is statistically significant. The magnitude of the correlation coefficient merely reflects the strength of this relationship and does not imply the absence of synergistic effects. This methodology is widely adopted in existing literatures. Song et al. (2025) confirmed in their study of Xinjiang that even extremely weak correlation coefficients as low as 0.039 reflected synergistic relationships in regional ecosystem service research, supporting the validity of this methodology. Meanwhile, Lyu et al. (2025) confirmed through their study of Changbai Mountain that even an extremely weak correlation coefficient as low as 0.009 demonstrated a synergistic relationship between two ecosystem service factors in the region. Li et al. (2025) study on ecosystem service trade-offs and synergies in the transition zone between China's Loess Plateau and North China Plain demonstrated that synergistic effects between two ecosystem service factors could be demonstrated even when the synergy coefficient was only 0.057. Therefore, 0.20 can be interpreted as a synergistic.
Ning, S., Alimujiang, K., Lina, T., Jia, C., Yan, Z., Xue, A., Xue, Z., 2025. A network perspective on ecosystem service trade-offs and synergies relationships and their spatial pattern optimization in arid regions[J], Environmental Technology & Innovation, https://doi.org/10.1016/j.eti.2025.104584
Lyu, R.; Li, S.; Yuan, M.; Fu, X.; Qu, L.; Tang, M.; Zhu, Y.; Wu, G., 2025. Trade-offs and synergies between ecosystem services under the mountain-river project: A case study from Changbai Mountain. Ecological Frontiers, https://doi.org/10.1016/j.ecofro.2025.09.003.
Li, L.; Feng, R.; Hou, G.; Xi, J., 2025. Trade-offs, synergies and driving pathways between tourism cultural ecosystem service and multiple ecosystem services in ecological functional zone. Habitat International 165, 103558. https://doi.org/10.1016/j.habitatint.2025.103558.
Comments 26
lines 291-293 The problem is the opposite of the comment above. Can a r =-0.33 be enough evidence for a trade-off?
Response 26: Thanks for your question. Similarly, in ecosystem service (ES) research, a trade-off is defined by a statistically significant negative correlation between two ESs. The magnitude of the correlation coefficient quantifies the reflecting their opposing change trends of this relationship, not whether the trade-off exists. Even with a moderate negative correlation, as long as it is statistically significant and aligns with ecological mechanisms, it constitutes valid evidence of a trade-off. This methodological approach is consistent with mainstream ES research, where the direction of the correlation is the core criterion, regardless of the coefficient’s absolute value. Xiao et al. (2025) study on ecosystem services in China's Manas River basin, even when the negative correlation between two ecosystem services was -0.157, trade-offs still existed between the two ecosystem service factors. Li et al. (2025) study in Aksu, Xinjiang, revealed that the correlation between the two ecosystem service factors also exhibited a moderate negative correlation of -0.312, yet it still indicated the presence of trade-offs. Zhang et al. (2025) study in Canberra, Australia, revealed that even when the correlation between two ecosystem service factors was as low as -0.091, it still indicated a trade-off relationship. Therefore, -0.33 can be interpreted as a trade-off.
Xiao, R.; Dong, X.; Wang, X.; Xiao, X.; Yan, Y.; Liu, R.; Qi, Y., 2025. Spatial association and trade-off/synergy relationships between ecosystem health and human well-being: a case study of the Manas River Basin. Ecological Indicators 178, 114060. https://doi.org/10.1016/j.ecolind.2025.114060.
Li, Y., Gao, F., He, B., Li, H., Wang, L., 2025. Trade-offs and synergies of ecosystem services and their driving factors in the Aksu River Basin[J]. Research in Cold and Arid Regions. https://doi.org/10.1016/j.rcar.2025.09.004.
Zhang, B.; Brookhouse, M., 2025. Microclimatic benefits of urban shading trees: Synergies and trade-offs in Canberra, Australia. Building and Environment 285, Part A, 113584. https://doi.org/10.1016/j.buildenv.2025.113584.
Comments 27
line 316 The Davis-Bouldin Index should be introduced and explained in the Materials and Methods section.
Response 27: Thanks for your suggestion. We added the introduction and explain about DBI in the Materials and Methods section.
(Page 7 line 200-205)
“The Davis-Bouldin Index (DBI) is a clustering validity metric used to evaluate the quality of clustering results. Its core principle is to assess the balance between intracluster compactness and inter-cluster separation. In this study, DBI was used to verify the rationality of the ecosystem service bundle (ESB) classification results. A lower DBI value indicates better clustering performance, helping to determine the optimal number of ecosystem service bundles (ESBs).”
Comments 28
Fig 8 Keep legends consistent through the three parts of the figure.
Response 28: Thanks for your suggestion. We note that the legend in Figure 8 originally applied only to Figure 8(c). We sincerely apologize for any confusion this may have caused. To clearly distinguish the scope of each subfigure, we have enlarged the font size of the subfigure labels (a), (b), and (c). Additionally, we revised the legend of Figure 8(c) to ensure it accurately corresponds to the spatial distribution map, enhancing the clarity and interpretability of the figure.
Figure 8 Identification of ecosystem service bundles in Xiong'an. (a) the ideal quantity of ecosystem service bundles, (b) petals of each bundle, and (c) spatial and temporal distribution of ecosystem service bundles. NPP: net primary productivity, SC: soil conservation, HQ: habitat quality, CS: carbon storage, and WY: water yield.
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe paper addresses a current and relevant topic, analyzing ecosystem services in a region of China that is to be transformed into a model eco-city. The theme is of interest, and the intention to correlate urban transformation with maintaining ecological balance is commendable. However, the article raises several difficulties in understanding and critical evaluation.
First, the excessive and sometimes abusive use of abbreviations makes reading significantly more difficult. Numerous acronyms are introduced without adequate clarification or without being repeated in later sections, which makes the text difficult to follow, especially for a reader unfamiliar with the local context or the terminology specific to the study.
Second, a major problem concerns access to essential information on data and methodology. These are mentioned as being available only in supplementary materials, which were not accessible. The absence of these details makes it difficult to understand how the authors collected, analyzed, and interpreted the data. Consequently, the validation of the results and the assessment of the scientific rigor of the approach are limited.
Although the subject of the paper is valuable and timely, the article suffers from an overly technical writing style and a structure that does not facilitate methodological transparency. I recommend clarifying the abbreviations used, integrating a summary description of the main methodology into the main text, and ensuring the accessibility of additional materials to allow for a complete and accurate evaluation of the study.
Author Response
Comments 1
The paper addresses a current and relevant topic, analyzing ecosystem services in a region of China that is to be transformed into a model eco-city. The theme is of interest, and the intention to correlate urban transformation with maintaining ecological balance is commendable. However, the article raises several difficulties in understanding and critical evaluation.
First, the excessive and sometimes abusive use of abbreviations makes reading significantly more difficult. Numerous acronyms are introduced without adequate clarification or without being repeated in later sections, which makes the text difficult to follow, especially for a reader unfamiliar with the local context or the terminology specific to the study.
Response 1: We sincerely appreciate your valuable suggestions. We optimized the use of abbreviations in the manuscript. Key terms—NPP→Net Primary Productivity, SC→Soil Conservation, HQ→Habitat Quality, WY→Water Yield, and CS→Carbon Storage, SOM→Self-organizing maps, which are now fully spelled out throughout the main text, with their abbreviated forms noted upon first mention.
(Page 8 line 220-222)
From a spatial distribution perspective, the long-term analysis revealed that net primary productivity in Xiong'an was predominantly low, indicating that Xiong'an maintained a generally moderate to low level.
(Page 8 line 222-224)
High productivity values were sporadically distributed in Xiong'an, signifying that these localized areas had significantly higher vegetation net primary productivity than their surroundings, resulting in a patchy high-value distribution.
(Page 11 line 306-312)
Net primary productivity showed weak negative correlations with water yield, carbon storage, habitat quality, and soil conservation, indicating a certain trade-off relationship within these factors. Water yield exhibited moderate positive correlations with carbon storage and soil conservation, and had a weak positive correlation with habitat quality. There was a strong positive correlation between carbon storage and habitat quality, while carbon storage was moderately positively correlated with soil conservation. Additionally, a moderate positive correlation existed between habitat quality and soil conservation.
(Page 12 line 320-328)
From 2018-2023, the correlations among net primary productivity and other factors remained largely unchanged overall, but its correlation with carbon storage slightly strengthened (Figure 5b). The correlation between water yield and carbon storage significantly weakened, and shifted from a weak positive to a weak negative correlation with habitat quality. Water yield 's correlation with soil conservation also weakened. The correlation between carbon storage-habitat quality and carbon storage-soil conservation weakened. Similarly, the correlation between habitat quality and soil conservation diminished.
(Page 7 line 206-210)
ESBs refer to spatially or temporally recurring collections of ESs, and self-organizing maps serves as a method for delineating ESBs. The “kohonen” package in Rstudio was used to compute self-organizing maps for ecosystem function zoning. Self-organizing maps can generate low-dimensional discrete representations by learning patterns in the input data to provide a basis for ecosystem functional zoning.
Comments 2
Second, a major problem concerns access to essential information on data and methodology. These are mentioned as being available only in supplementary materials, which were not accessible. The absence of these details makes it difficult to understand how the authors collected, analyzed, and interpreted the data. Consequently, the validation of the results and the assessment of the scientific rigor of the approach are limited.
Response 2: Thanks for your question and suggestion. To address this issue, we moved the key data source summary (previously Table S1 in supplementary materials) into the “Materials and Methods” section of the main text (now Table 1), which details data types, applications, sources, and resolutions.
(Page 4 line 128-131)
“Various data sources were utilized in this study, including the boundaries of the study area, land use data, rainfall, potential evapotranspiration, population density, soil data, climate, roads, and road network data (Table 1). And Table 1 lists the applications, sources, and accuracy of the data.”
Table 1 Summary of data sources. NPP: net primary productivity, SC: soil conservation, HQ: habitat quality, CS: carbon storage, WY: water yield and OPGD: optimized parameter geodetector.
|
Data |
Application |
Data source |
Resolution |
|
Study area boundary
Land use data
Evapotranspiration data Population density data Soil data |
NPP, WY, CS, SC, HQ NPP, WY, CS, SC, HQ WY
OPGD
SC |
Resource and Environmental Science and Data Platform Resource and Environmental Science and Data Platform Resource and Environmental Science and Data Platform Resource and Environmental Science and Data Platform Resource and Environmental Science and Data Platform |
30 m
500 m
1 km
1 km
|
|
Precipitation data |
WY |
National Tibetan Plateau/Third Pole Environment Data Center |
1 km
|
|
Slope |
OPGD |
Derived using the ArcGIS Slope tool |
30 m |
|
Digital elevation model |
SC |
PIE- Engine Studio |
30 m |
|
Temperature
Rainfall erosivity factor Soil erodibility |
OPGD
WY
SC |
National Earth System Science Data Center National Earth System Science Data Center National Earth System Science Data Center |
1 km
1 km
|
|
Net Primary Productivity Vegetation cover factor Soil and water conservation measures factor |
SC
SC
|
Geographic remote sensing ecological network platform InVEST Model guidelines
InVEST Model guidelines
|
500 m |
|
Distance to primary urban roads
Distance to secondary urban roads Distance to tertiary urban roads
Distance to residential areas
Distance to railways Distance to government facilities |
OPGD
OPGD
OPGD
OPGD
OPGD OPGD |
Calculated using the euclidean distance tool in ArcGIS
Calculated using the euclidean distance tool in ArcGIS
Calculated using the euclidean distance tool in ArcGIS
Calculated using the euclidean distance tool in ArcGIS
Calculated using the euclidean distance tool in ArcGIS Calculated using the euclidean distance tool in ArcGIS |
|
Comments 3
Although the subject of the paper is valuable and timely, the article suffers from an overly technical writing style and a structure that does not facilitate methodological transparency. I recommend clarifying the abbreviations used, integrating a summary description of the main methodology into the main text, and ensuring the accessibility of additional materials to allow for a complete and accurate evaluation of the study.
Response 3: Thanks for your suggestions. We fully understand your concerns and have implemented the following revisions to enhance the paper's readability, methodological transparency, and accessibility.
Abbreviation clarification: We have systematically reviewed and minimized the use of abbreviations. Key terms (e.g., NPP, SC, HQ, WY, CS) are now spelled out in full and labeled with their abbreviations upon first appearance in the text, with subsequent references maintaining the full spelling. We have optimized the use of abbreviations in the manuscript. Key terms—NPP→Net Primary Productivity, SC→Soil Conservation, HQ→Habitat Quality, WY→Water Yield, and CS→Carbon Storage, SOM→Self-organizing maps, which are now fully spelled out throughout the main text, with their abbreviated forms noted upon first mention.
Methodological integration: Data collection sources, purposes, processing methods, and precision have been consolidated into the “Materials and Methods” section to ensure methodological transparency and traceability.
(Page 4 line 128-131)
“Various data sources were utilized in this study, including the boundaries of the study area, land use data, rainfall, potential evapotranspiration, population density, soil data, climate, roads, and road network data (Table 1). And Table 1 lists the applications, sources, and accuracy of the data.”
(Page 4 line 134-135)
Table 1 Summary of data sources. NPP: net primary productivity, SC: soil conservation, HQ: habitat quality, CS: carbon storage, WY: water yield and OPGD: optimized parameter geodetector.
|
Data |
Application |
Data source |
Resolution |
|
Study area boundary
Land use data
Evapotranspiration data Population density data Soil data |
NPP, WY, CS, SC, HQ NPP, WY, CS, SC, HQ WY
OPGD
SC |
Resource and Environmental Science and Data Platform Resource and Environmental Science and Data Platform Resource and Environmental Science and Data Platform Resource and Environmental Science and Data Platform Resource and Environmental Science and Data Platform |
30 m
500 m
1 km
1 km
|
|
Precipitation data |
WY |
National Tibetan Plateau/Third Pole Environment Data Center |
1 km
|
|
Slope |
OPGD |
Derived using the ArcGIS Slope tool |
30 m |
|
Digital elevation model |
SC |
PIE- Engine Studio |
30 m |
|
Temperature
Rainfall erosivity factor Soil erodibility |
OPGD
WY
SC |
National Earth System Science Data Center National Earth System Science Data Center National Earth System Science Data Center |
1 km
1 km
|
|
Net Primary Productivity Vegetation cover factor Soil and water conservation measures factor |
SC
SC
|
Geographic remote sensing ecological network platform InVEST Model guidelines
InVEST Model guidelines
|
500 m |
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Distance to primary urban roads
Distance to secondary urban roads Distance to tertiary urban roads
Distance to residential areas
Distance to railways Distance to government facilities |
OPGD
OPGD
OPGD
OPGD
OPGD OPGD |
Calculated using the euclidean distance tool in ArcGIS
Calculated using the euclidean distance tool in ArcGIS
Calculated using the euclidean distance tool in ArcGIS
Calculated using the euclidean distance tool in ArcGIS
Calculated using the euclidean distance tool in ArcGIS Calculated using the euclidean distance tool in ArcGIS |
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Supplementary material accessibility: We have confirmed that all supplementary materials have been correctly uploaded to the submission system and are fully accessible.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsFirst of all, this is an interesting study.
This research is relatively systematic in its methodology, integrating the InVEST model, OPGD, spatial autocorrelation analysis, and SOM, among others. It conducts a long-term and multi-dimensional analysis of the ecosystem services in Xiongan New Area from 1990 to 2023, which holds significant practical and scientific value.
1.The introduction part is currently the most prominent weak link in the article. It failed to lay an effective foundation for the research and had obvious deficiencies in the depth of logical advancement and literature review.
The current literature review is more like a "tool list" (listing models such as InVEST, OPGD, and SOM) rather than a discussion on the "evolution of scientific issues". It does not clearly state: What is the knowledge context of the current field's research on the interaction relationship of ecosystem services (ES)? What progress has been made in the existing research? More importantly, what are the key Research gaps that have not yet been addressed? This absence greatly reduces the innovation and necessity of your research.
2. In key charts such as Figures 3 and 5, the legends, axis labels or color distinctions are not clear enough (please provide high-resolution images), which affects readers' understanding of spatial distribution and changing trends.
3. Although key drivers such as rainfall, GDP, and road density have been identified, the explanations for how these factors specifically affect various ecosystem services are rather general. It is suggested to analyze the causal relationship between the driving factors and the changes in ES to enhance the persuasiveness.
4. Although the discussion section cited some literature, it lacked comparisons with ES studies in similar new areas at home and abroad (such as Shenzhen and Pudong) or wetland cities (such as Wuhan and Hangzhou). It is suggested to increase horizontal comparisons to highlight the particularity or universality of ES changes in Xiongan New Area and enhance the breadth and depth of the research.
5. The "Discussion" section shows a mechanical listing and lacks the guidance of a core argument. Please rewrite the discussion section with the goal of "telling a profound story about ecological governance in Xiongan". Break free from the stereotype of result description, delve deeply into the reasons behind the mechanism, and answer the question "why". Engage in dialogue with critical literature to enhance academic value.
Author Response
Comments 1
First of all, this is an interesting study. This research is relatively systematic in its methodology, integrating the InVEST model, OPGD, spatial autocorrelation analysis, and SOM, among others. It conducts a long-term and multi-dimensional analysis of the ecosystem services in Xiong'an New Area from 1990 to 2023, which holds significant practical and scientific value.
1.The introduction part is currently the most prominent weak link in the article. It failed to lay an effective foundation for the research and had obvious deficiencies in the depth of logical advancement and literature review. The current literature review is more like a "tool list" (listing models such as InVEST, OPGD, and SOM) rather than a discussion on the "evolution of scientific issues". It does not clearly state: What is the knowledge context of the current field's research on the interaction relationship of ecosystem services (ES)? What progress has been made in the existing research? More importantly, what are the key Research gaps that have not yet been addressed? This absence greatly reduces the innovation and necessity of your research.
Response 1: Thanks a lot for your valuable suggestions and questions. We have supplemented three key aspects as you suggested. First, we have clarified the knowledge context of current research on ecosystem service (ES) interaction relationships. Second, we have systematically summarized the progress achieved in existing studies. Third, we have explicitly identified the key unresolved research gaps relevant to policy-driven rapidly urbanizing regions on the Xiong’an. We rewrote the Introduction in the revised manuscript.
(Page1-2 line35-107)
“1. Introduction
The Xiong'an New Area was established in 2017. As the third state-level new area of China, it is recognized as another development plateau of great national strategic significance following Shenzhen and Shanghai Pudong New Areas. The historical mission of creating a national model for high quality development is shouldered by Xiong'an, adhering to the concept of ecological priority and green development. Xiong'an aims to build a high-level modern city characterized by green and low-carbon development, which emphasizes giving priority to ecological and environmental protection in the process of economic development, so as to achieve sustainable development and the harmonious coexistence between humans and nature, with the harmonious coexistence of human beings and nature realized in 2035 [1]. However, there is a lack of long-term, comprehensive research on ES in Xiong’an, which hinders evidence-based decision-making for its eco-city construction. In order to make up for these deficiencies and help Xiong'an eco-city construction, it is necessary to conduct a comprehensive and long-term analysis of ESs in Xiong'an. This work is of irreplaceable significance for maintaining the ecological balance of Xiong'an and promoting ecological civilization, which takes the harmonious coexistence between humans and nature as its core, emphasizes respecting the laws of nature, building a green development model, and covers the sum of material, spiritual and institutional achievements [1].
ESs represent benefits that humans acquire directly or indirectly from natural ecosystems and their species [2]. ESs are essential for maintaining human health, well-being, and sustainable development by provisioning, regulating, supporting, and cultural functions [3], and act as a vital link between natural systems and human society [4]. However, global urbanization and economic growth have led to excessive resource exploitation, intensifying the imbalance between ES supply and demand, leading to a decline around 60% of the global ESs [5]. This circumstance emphasizes the pressing need to comprehend the intricate relationships among ESs [6, 7]. Thus, clearly comprehending the relationships among ESs is fundamental for achieving a sustainable balance between ecological preservation and urban advancement. The field of ES trade-offs and synergies emerged following the formalization of the ES concept. Early research focused on defining ES categories and quantifying their individual contributions to human well-being, but scholars soon recognized that ES do not operate in isolation-spurring a shift to studying their interdependencies. Trade-offs and synergies were subsequently introduced as core concepts. Exploring these interaction mechanisms is now a core scientific issue in ES research, as it provides critical basis for balancing ecological conservation and regional development. However, there is a gap in research on ES in Xiong'an which necessitating further investigation.
In the early stage, the primary challenge was to accurately quantify ES spatial-temporal patterns. The ESs and trade-offs (InVEST) model emerged as a key tool due to its ability to integrate geospatial data for ES assessment, and its combination with ArcGIS enables intuitive visualization of ES dynamics [2]. Bacani et al. [8] employed the InVEST model for evaluating the spatio-temporal alterations of carbon storage in Três Lagoas. However, as research deepened, scholars found that traditional single-scale or single-service analysis failed to capture the multi-dimensional coupling relationships between ES [9], traditional single-scale analyses are insufficient to capture their multi-dimensional coupling mechanisms [10]. InVEST model and correlation analysis can be used to identify the trade-offs and synergies between ESs, marking the transition from "quantification" to "interaction identification [11]. With the increasing demand for targeted ecological management, the focus of scientific issues shifted to "revealing driving mechanisms" and "supporting spatial zoning." Single model limitations became prominent, prompting the integration of multi-method frameworks. Gao et al. [12] used the InVEST and optimized parameter geodetector (OPGD) models in the karst coastal coupling zone of southwest Guangxi and Beibu Gulf, and determined that the key driving factors affecting ESs, addressing the key question regarding the driving mechanisms of ES interaction changes. Meanwhile, overlaying self-organizing maps (SOM) with ecosystem service bundles (ESBs) can classify homogeneous service bundles according to ES correlations, enabling precise ecological zoning [13]. Zhang et al. [14] divided the Danjiangkou River Basin into ecological conservation, agricultural production, and balanced zones based on ES spatial-temporal patterns, trade-offs and synergies, and drivers, bridging the gap between theoretical analysis and practical application.
Despite these advances in ecosystem services research, gaps remain in understanding ESs of Xiong'an. This knowledge deficit limits comprehensive insights into ESs of Xiong'an and interactions of ESs, making it imperative to conduct the first eco-system services study of Xiong'an using a multi-model framework. Thus, this paper, for the first time, combines the InVEST model, correlation analysis, self-organizing maps method, and OPGD model to analyze a comprehensive and long-term ESs. This paper aims to make a comprehensive and long-term ESs evaluation in Xiong'an. Specifically, (1) to calculate and analyze five ES factors in Xiong'an from 1990 to 2023; (2) to conduct a correlation analysis of these factors to study trade-offs and synergies, comparing changes before and after the New Area construction; (3) to perform a spatial correlation analysis to explore spatial clustering; (4) to identify drivers of ES changes; (5) to divide Xiong'an into ESBs to identify dominant ESs. These findings will fill the research gap in the long-term sequence aspects of ecosystem services in Xiong'an.”
Comments 2
- In key charts such as Figures 3 and 5, the legends, axis labels or color distinctions are not clear enough (please provide high-resolution images), which affects readers' understanding of spatial distribution and changing trends.
Response 2: Thanks for your suggestion. We updated all figures with high-resolution versions, optimized the font size and readability of legends and axis labels to enhance differentiation.
Figure 3 Long-term spatiotemporal distribution of ES in Xiong'an. NPP: net primary productivity, SC: soil conservation, HQ: habitat quality, CS: carbon storage, WY: water yield and OPGD: optimized parameter geodetector.
Figure 5 Correlation analysis of ESs in Xiong'an. (a) 1990-2018 and (b) 2018-2023. NPP: net primary productivity, SC: soil conservation, HQ: habitat quality, CS: carbon storage, WY: water yield and OPGD: optimized parameter geodetector.
Comments 3
- Although key drivers such as rainfall, GDP, and road density have been identified, the explanations for how these factors specifically affect various ecosystem services are rather general. It is suggested to analyze the causal relationship between the driving factors and the changes in ES to enhance the persuasiveness.
Response 3: Thanks for your suggestions. To address this issue, we have revised the relevant section to clarify the specific causal mechanisms linking each key driver (rainfall, GDP, road network) to changes in individual ESs (NPP, soil conservation, carbon storage, habitat quality, water yield). We detailed the biophysical processes (for rainfall), dual pressure-regulation effects (for GDP), and spatial disturbance-mitigation pathways (for road network) that directly shape ES dynamics, with each causal link supported by corresponding ecological principles and literature citations.
(Page17-18 line 449-503)
“Combining the OPGD model, the top five factors influencing ESs were rainfall, GDP, distance to railways, distance to expressways, and distance to urban main roads, respectively. Rainfall serves as a foundational climatic driver by directly shaping the water-energy balance of ecosystems, with distinct causal pathways for each ES. For net primary productivity, adequate rainfall maintains soil moisture at optimal levels for vegetation root uptake and leaf photosynthesis, promoting biomass accumulation; conversely, uneven or reduced rainfall creates water stress, inhibiting chlorophyll synthesis and limiting plant growth rate [39]. For soil conservation, moderate rainfall infiltrates the soil to enhance aggregate stability, while intense and concentrated rain-fall increases raindrop splash erosion and surface runoff velocity, which directly scouring topsoil and reducing the soil’s anti-erosion capacity [40]. From 1990 to 2018, soil conservation in Xiong'an declined, which was linked to increased soil erosion caused by reduced or unevenly distributed rainfall [41]. After 2018, Xiong'an integrated "sponge city" design into urban construction, using permeable pavements, rain gardens, and ecological ditches to reduce surface runoff velocity, which offsetting in-tense rainfall erosion risks. For habitat quality, adequate rainfall can maintain water balance of ecosystem, and promote vegetation growth, further improving habitat quality. Insufficient rainfall can cause drought, vegetation degradation, and lower habitat quality. Aide et al. also supported this viewpoint, and highlighted the im-portance of climate conditions in changes to habitat quality [42]. But Xiong'an’s wet-land replenishment projects ensured habitat quality stability even during dry years. For carbon storage, rainfall regulates vegetation growth and soil carbon cycles. Moderate rainfall can boost vegetation growth for increasing carbon absorption and storage, and affect soil moisture and microbial activity for influencing soil carbon decomposition and storage. For water yield, rainfall is a direct determinant of water yield; more rainfall areas have abundant water replenishment with higher water yield, and vice versa [43].
GDP reflects the intensity of anthropogenic activities. The unique position of Xiong'an as a model for sustainable urban development makes these impacts on ESs more pronounced. According to the Environmental Kuznets Curve, rapid GDP growth has both positive and negative effects [44]. The negative effects of GDP growth drive urban expansion and industrialization, leading to conversion of ecological land to construction land, which directly reduces vegetation coverage, increases impervious surfaces, and fragments habitats [45]. In Xiong'an, however, this negative effect was constrained by rigid institutional arrangements that 70% of ecological space was de-lineated as "non-development zones" upfront, preventing large-scale ecological land occupation. In turn, economic progress provides the material basis for proactive environmental governance, consistent with green development mission of Xiong'an. Despite the common perception that rapid economic expansion typically leads to ecological degradation, the Xiong’an New Area has defied this assumption through its governance framework. The establishment of non-development zones demonstrates a commitment to sustainable practices, signaling a profound understanding that eco-nomic growth need not come at the expense of ecological integrity. By leveraging enhanced fiscal capacity to invest in green infrastructure, Xiong’an has emerged as a model case for harmonizing economic development with ecological sustainability [46]. This dual ecologic-economic drivers support the ecological sustainability, reflecting the role of Xiong'an as a testing ground for balancing growth and ecological management.
Road network density, often a by-product of economic integration, introduces additional complexity. Road construction involves soil excavation and vegetation clearance, directly reducing local soil conservation capacity and net primary productivity; meanwhile, roads act as ecological barriers—fragmenting habitats and blocking species migration, which reduces habitat connectivity and quality [47]. To mitigate these impacts, Xiong'an prioritizes eco-friendly transportation planning, which aims to reduce soil erosion, protect vegetation, and maintain natural ecological capacity alongside infrastructure development. These measures reflect the commitment to incorporating ecological considerations into urban planning.”
Comments 4
- Although the discussion section cited some literature, it lacked comparisons with ES studies in similar new areas at home and abroad (such as Shenzhen and Pudong) or wetland cities (such as Wuhan and Hangzhou). It is suggested to increase horizontal comparisons to highlight the particularity or universality of ES changes in Xiong’an New Area and enhance the breadth and depth of the research.
Response 4: Thanks for your suggestions. We supplemented the discussion section of the revised draft with a comparative analysis.
(Page16 line 409-415)
“Xiong'an plays a pivotal role in maintaining regional ecological balance. Particularly, Baiyangdian wetland provided critical habitats for biodiversity and significantly influenced the ES functions of the entire region. The spatial heterogeneity of ESs in Xiong'an depended on the intricate distribution patterns of urban ESs, which in turn impact the spatial heterogeneity of ES distribution [26, 27]. ESs in Xiong'an exhibited significant spatial-temporal heterogeneity from 1990 to 2023, which is a common feature across the Beijing-Tianjin-Hebei region, but with distinct distribution drivers and patterns [28].”
(Page16 line 416-426)
“Spatially, the "mountain-high, plain-low" gradient of soil conservation, carbon storage, and habitat quality aligns with global vertical differentiation rules, but its unique "wetland-centered high-value zone" breaks the regional pattern dominated by mountain ecosystems [29, 30]. In Beijing-Tianjin-Hebei region, carbon storage, habitat quality, and soil conservation also concentrate in mountainous areas [28]. This difference originates from Xiong'an’s planning philosophy, instead of treating wetlands as "reclaimable land", Baiyangdian was designated as the ecological core from the outset, with 70% of ecological space reserved upfront [31, 32]. Following the establishment of Xiong’an New Area in 2018, ecological restoration efforts have led to a recovery in soil and water conservation, carbon storage, habitat quality, and water yield. This demonstrates the significant effectiveness of policy-driven ecological governance [33].”
(Page18 line 519-524)
“Habitat quality-soil conservation also shows synergies, indicating that high-quality habitats usually have good soil conservation, which further maintains and upgrades habitat quality and similar to the ES synergy characteristics of a typical wetland city-Wuhan. Wuhan, with blue-green spaces accounting for over 55% of its total area, exhibits significant positive correlations between carbon sinks and habitat quality, as well as urban cooling capacity [49].”
(Page18 line 525-536)
“The weakening of synergies between 2018 and 2023 reveals the short-term pressure of construction, but Xiong'an served as an emergency wetland replenishment measure, averting irreversible damage. Water yield-habitat quality turned from a weak positive to a weak negative correlation, with a reduction in the correlation with soil conservation. Water changes had more complex and diverse impacts on other ESs. The synergies of carbon storage-habitat quality, carbon storage-soil conservation and habitat quality-soil conservation also weakened, with less tight links during this period due to interference from other factors. This dynamic change of ES relationships is also observed in Wuhan that encroaching on wetlands and cropland, the synergy between carbon sinks and water yield weakened, while the trade-off between carbon emissions and habitat quality intensified [49]. Comparing the trade-offs and synergies of ESs in Xiong'an between 1990-2018 and 2018-2023 shows that ES relationships are highly dynamic and change over time.”
(Page19 line 556-563)
“Xiong'an's governance model employs forward-looking planning and management, integrating ecological and environmental protection into spatial planning from the initial stages. This approach avoids the costly path of “destroy first, restore later” taken by Shenzhen, where mangrove restoration took seven years to achieve microbial functional reconstruction [53]. Integrated coordination bundle (ESB2) forms a multi-service trading network, and previous studies have shown that this type of ESB reflects the overall coordination of various environmental services [51]. Xiong'an should strengthen this synergy to build a more robust ES network.”
Comments 5
- The "Discussion" section shows a mechanical listing and lacks the guidance of a core argument. Please rewrite the discussion section with the goal of "telling a profound story about ecological governance in Xiong’an". Break free from the stereotype of result description, delve deeply into the reasons behind the mechanism, and answer the question "why". Engage in dialogue with critical literature to enhance academic value.
Response 5: Thank you for your suggestion. In accordance with your core requirement to “tell profound stories about Xiong’an's ecological governance,” we have revised the discussion section to focus on how Xiong’an achieves its goals through forward-looking planning and precise governance.
(Page16, line 416-426)
“Spatially, the "mountain-high, plain-low" gradient of soil conservation, carbon storage, and habitat quality aligns with global vertical differentiation rules, but its unique "wetland-centered high-value zone" breaks the regional pattern dominated by mountain ecosystems [29, 30]. In Beijing-Tianjin-Hebei region, carbon storage, habitat quality, and soil conservation also concentrate in mountainous areas [28]. This difference originates from Xiong'an’s planning philosophy that instead of treating wetlands as "reclaimable land", Baiyangdian was designated as the ecological core from the outset, with 70% of ecological space reserved upfront [31, 32]. Following the establishment of Xiong’an New Area in 2018, ecological restoration efforts have led to a recovery in soil and water conservation, carbon storage, habitat quality, and water yield. This demonstrates the significant effectiveness of policy-driven ecological governance [33].”
(Page16, line 427-428)
“Temporally, the stability of net primary productivity from 1990 to 2023 and the "decline-then-rebound" of soil conservation reveal a deliberate governance strategy.”
(Page16, line 433-436)
“The fluctuation of habitat quality, linked to Baiyangdian’s hydrological regulation, further underscores Xiong'an’s adaptive management, which instead of ignoring hydrological changes, it integrated water allocation and wetland restoration, ensuring ecological resilience amid environmental variability [32, 35].”
(Page16, line 441-443)
“The recovery of ESs highlighting a key insight that ecological governance is most effective when embedded in the initial planning, not appended as an afterthought.”
(Page17, line 461-469)
“After 2018, Xiong'an integrated "sponge city" design into urban construction, using permeable pavements, rain gardens, and ecological ditches to reduce surface runoff velocity, which offsetting intense rainfall erosion risks. For habitat quality, adequate rainfall can maintain water balance of ecosystem, and promote vegetation growth, further improving habitat quality. Insufficient rainfall can cause drought, vegetation degradation, and lower habitat quality. Aide et al. also supported this viewpoint, and highlighted the importance of climate conditions in changes to habitat quality [42]. But Xiong'an’s wetland replenishment projects ensured habitat quality stability even during dry years.”
(Page17, line 485-494)
“Despite the common perception that rapid economic expansion typically leads to ecological degradation, the Xiong’an New Area has defied this assumption through its governance framework. The establishment of non-development zones demonstrates a commitment to sustainable practices, signaling a profound understanding that economic growth need not come at the expense of ecological integrity. By leveraging enhanced fiscal capacity to invest in green infrastructure, Xiong’an has emerged as a model case for harmonizing economic development with ecological sustainability [46]. This dual ecologic-economic drivers support the ecological sustainability, reflecting the role of Xiong'an as a testing ground for balancing growth and ecological management.”
(Page18, line 505-516)
“There are complex trade-offs and synergies among ESs in Xiong'an. From 1990 to 2018, net primary productivity had certain trade-offs with other factors. The trade-off between net primary productivity and water yield was mild that because Xiong'an adopted water-saving vegetation and constructed wetland reservoirs, which reduced vegetation water consumption while maintaining productivity. Meanwhile, vegetation expansion can affect the physical structure and nutrient cycling of soil, further influencing the soil conservation and carbon storage in complex ways. The interactions between water yield and carbon storage, water yield and soil conservation, and water yield and habitat quality showed synergies, which was consistent with previous studies [48]. The synergistic relationship arises because Baiyangdian's hydrological regulation not only increases water volume but also enhances soil water retention capacity and vegetation carbon sequestration capacity.”
(Page18, line 525-527)
“The weakening of synergies between 2018 and 2023 revealed the short-term pressure of construction, but Xiong'an served as an emergency wetland replenishment measure, averting irreversible damage.”
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsThe authors clarified all aspects mentioned in the previous report.
