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Ecological Security Pattern Construction for Carbon Sink Capacity Enhancement: The Case of Chengdu Metropolitan Area

Sustainability 2025, 17(10), 4483; https://doi.org/10.3390/su17104483

by Langong Hou¹

, Huanhuan Hu^1,*, Tao Liu² and Che Ma¹

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Reviewer 3:

Tianpeng Gao

Reviewer 4: Anonymous

Sustainability 2025, 17(10), 4483; https://doi.org/10.3390/su17104483

Submission received: 25 February 2025 / Revised: 3 May 2025 / Accepted: 7 May 2025 / Published: 14 May 2025

(This article belongs to the Special Issue Sustainable Application of Land Surveying and Mapping in Land Resource Development and Management)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Review comments for the manuscript“Ecological Security Pattern Construction for Carbon Sink Capacity Enhancement: The Case of Chengdu Metropolitan Area ”

This article studies the construction of an ecological security pattern in the Chengdu metropolitan area to enhance carbon sequestration capacity, providing feasible strategies for improving ecological networks and promoting regional carbon neutrality. However, there are serious issues with the content of the article:

The research framework (MSPA-MCR+complex network models,) is a conventional method for ecological network analysis, without proposing new models or theoretical breakthroughs, and only optimizing corridor layout through case applications, lacking innovation.
Although the ecological network changes from 2000 to 2020 were analyzed in the two articles, only three time points (2000, 2010, 2020) were selected, with a data interval of up to 10 years, without considering the dynamic fluctuations in the middle years, which may mask key turning points or nonlinear changes.

3.In line 225, it is mentioned in the article that "Forests, shrublands, grasslands, lakes, wetlands, and other land use types have the capacity to sequester CO₂", but in Table S4, the author only listed "Forest, Grassland, Watershed" three land types, which can lead to serious deviations in carbon sink estimation results.

4.The loss of figures and tables is severe, for example, Figure 2, Table S3, Table S6.

Therefore, this article is not suitable for publication in this journal. We hope the author will carefully revise it and submit it separately.

Comments on the Quality of English Language

The overall quality of the English language in the manuscript is poor. Sentences are often overly complex or lack clarity, especially in the description of methods and interpretation of results.

Author Response

please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study employs morphological spatial pattern analysis (MSPA) and multiple models to analyze the structural dynamics of Chengdu’s ecological network and the spatiotemporal evolution of its carbon sequestration capacity from 2000 to 2020. The findings are crucial for optimizing the ecological network and advancing regional carbon neutrality. However, several key challenges remain to be addressed.

The title refers to the construction of an ecological security pattern, while the abstract and keywords primarily focus on the ecological network. Please clarify the connection between the two and briefly explain their relationship in the abstract.
The introduction does not clearly identify the gaps in existing research or explain how this study addresses them. It is recommended to add this discussion in lines 70–83. Additionally, based on the review of existing studies, highlight the unresolved scientific questions and emphasize the novelty and practical significance of this research to enhance its scientific value.
Currently, the second section appears somewhat brief, causing an imbalance in the overall structure of the paper. It is recommended to merge the current second section, "2. Study Area and Data Sources," with the third section, "3. Methods," into a single section titled "2. Materials and Methods."
Figure 2, mentioned in line 123, is missing from the submitted manuscript. Please check if it was omitted and include the necessary content. Additionally, provide a brief explanation of the process used to standardize socioeconomic data to a 30×30 m resolution.
Administrative divisions are labeled in Figures 5–7, but this information is missing in Figure 4. Please add the administrative names to ensure consistency and completeness in the figure annotations.
Line 262, "4.1.1. Identification and Analysis of Ecological Source Sites," is completely missing content. Please check and provide the necessary information to ensure completeness. It is also possible that this is a formatting error. Please check.
Please review the manuscript for language accuracy and formatting issues, such as "Table S5. ecological network topology indicators." Ensure proper capitalization and consistency throughout the text.
The results section is overly detailed. Please consider summarizing and refining the key findings rather than simply listing raw data.
5.1 "Optimize regional ecological networks to enhance regional carbon sequestration": It is recommended to further broaden the applicability of the strategic insights to enhance their generalizability and the representativeness of the research. 5.2 "Strengths and Recommendations" and 5.3 "Limitations and Challenges" both address strengths and weaknesses, leading to some content overlap. It is suggested to optimize the phrasing to make the structure clearer and the distinctions between the sections more defined.
In the conclusion, (2) does not fully summarize and condense the key points of Section 4.2. It is recommended to further refine and highlight the core findings. Additionally, (3) and (4) are combined in the same paragraph, and it is suggested to split them for better clarity and structure.

Author Response

Subject: Revision Notes (Manuscript ID: sustainability-3524391)

Dear reviewer，

We sincerely appreciate your valuable and constructive comments on our manuscript entitled “Ecological Security Pattern Construction for Carbon Sink Capacity Enhancement: The Case of Chengdu Metropolitan Area”(Manuscript ID: sustainability-3524391). Your insightful suggestions have greatly improved the overall structure, logical coherence, and academic rigor of our study.We have carefully addressed each of your comments and revised the manuscript accordingly. All modifications have been clearly marked in red font in the revised version for your convenience.Below, we provide a detailed point-by-point response to your comments. We truly appreciate your time and effort in reviewing our work and hope that the revisions meet your expectations.

Comments 1:The introduction does not clearly identify the gaps in existing research or explain how this study addresses them. It is recommended to add this discussion in lines 70–83. Additionally, based on the review of existing studies, highlight the unresolved scientific questions and emphasize the novelty and practical significance of this research to enhance its scientific value.

Response 1:

Thank you very much for your thoughtful comment. We sincerely acknowledge that our initial abstract and keywords overly emphasized ecological networks, which may have caused a mismatch with the paper’s title and central theme.

We have carefully revised the abstract and keywords to better align with the ecological security pattern (ESP) as the overarching research framework. In this study, ecological networks are considered a key operational component of ESP. Specifically, we analyzed the structural characteristics and spatial dynamics of ecological space networks from a topological perspective. We then evaluated their relationship with carbon sink capacity. Based on this, we propose optimization strategies to enhance both network connectivity and carbon sequestration potential, thereby supporting the improvement of the ecological security pattern.

In our manuscript, repeated references to “ecological networks” are framed within the context of the broader ESP framework. Furthermore, “ecological space networks”represent a conceptual abstraction using complex network theory, where ecological sources and corridors are modeled as nodes and edges. Topological metrics such as node degree and betweenness centrality are used to characterize the structure and function of the ecosystem, serving as a basis for targeted spatial optimizations (e.g., adding stepping stones, repairing broken corridors).

We have revised the manuscript accordingly to clarify this conceptual relationship and harmonize the terminology across the title, abstract, keywords, and main text. All modifications are marked in red.

Revisions 1:

Abstract: Constructing regional ecological security patterns and enhancing carbon sequestration are essential for achieving China’s dual-carbon goals. However, rapid urbanization has intensified landscape fragmentation, disrupted energy and information flows within ecosystems, and significantly altered urban ecological network structures, thereby weakening their carbon sink capacity. This study takes the Chengdu metropolitan area as a representative case and develops a time-series ecological security pattern (ESP) from 2000 to 2020. To assess the spatiotemporal evolution and carbon sink performance of the ecological network, we integrate Morphological Spatial Pattern Analysis (MSPA), the Minimum Cumulative Resistance (MCR) model, the gravity model, and complex network theory. Results show continuous decline in ecological sources and corridors, increased and then stabilized ecological resistance, and declining connectivity, particularly in central and western subregions. The network structure demonstrates reduced modularity and topological metrics, indicating lower ecological stability and increased vulnerability to external disturbance. Forest land is identified as the dominant carbon sink, significantly correlated with multiple indicators. Grassland sequestration is influenced by clustering coefficients, while water bodies correlate positively with centrality indicators. Adding 10 stepping-stone nodes and 45 corridors could increase carbon sequestration by 4,156Mg C yr⁻¹,with forests contributing 94.8%.by 2020,This study supports regional ESP development and dual-carbon strategy implementation.

Keywords: ecological security patterns;chengdu metropolitan area; MSPA-MCR model; carbon sequestration; complex networks;ecospatial networksarbon strategy implementation.

Comments 2:Currently, the second section appears somewhat brief, causing an imbalance in the overall structure of the paper. It is recommended to merge the current second section, "2. Study Area and Data Sources," with the third section, "3. Methods," into a single section titled "2. Materials and Methods."

Response 2:

We sincerely thank you for your insightful and constructive comment. We fully agree that a clear articulation of research gaps and how our study addresses them is essential for demonstrating the novelty and relevance of the work. In response, we have thoroughly revised the introduction section (particularly lines 70–83) to address the following key aspects:

Identification of research gaps: We now explicitly point out that existing studies on ecological security patterns often focus on either spatial connectivity or carbon sink capacity in isolation, lacking an integrated framework that examines their coupling mechanisms—especially at the urban agglomeration or metropolitan scale.

Scientific problem: We emphasize that previous studies rarely explore the trade-offs or synergies between ecological network topology and carbon sequestration functionality. Moreover, the dynamic evolution of ecological patterns over time remains under-investigated in rapidly urbanizing areas.

Research contribution and novelty: This study proposes an integrated “structure – function – carbon benefit” framework that leverages complex network theory to quantify the spatial – functional relationship between ecological connectivity and carbon sink capacity. By constructing time-series ecological security patterns (2000 – 2020) for the Chengdu metropolitan area and correlating topological indicators with carbon sink metrics, our study provides a novel empirical basis for spatial optimization under the dual-carbon policy context.

We have added this content to the revised manuscript and marked the changes in red for your reference. Once again, we are grateful for your valuable suggestions, which helped improve the scientific depth and clarity of our introduction.

Revisions 2:

Against this backdrop, the ESP has emerged as a critical spatial approach to enhance carbon sequestration potential and mitigate urban carbon emissions[7]. Currently, ESP studies have established a relatively mature technical framework of “ecological source identification - resistance surface construction - ecological corridor extraction[8]. In the early stages, nature reserves, ecological redlines, or large ecological patches were often designated as ecological sources[9].With further research developments, methodologies such as MSPA and ecosystem service value assessments have been incorporated to build a more integrated ecological source system[10,11]. Ecological resistance surfaces, which describe the landscape resistance to species migration and dispersal, are typically constructed using expert scoring or parametric models[12].Ecological corridors - ribbon-like regions that connect ecological sources - facilitate the flow of energy, materials and information and can be extracted using models such as MCR, circuit theory and gravity models[12,13]. ESP has been widely applied in the construction of ecological networks for regions such as forests and watersheds[14], play a key role in improving landscape connectivity, preserving biodiversity and mitigating habitat fragmentation[15].With the deepening of ecological network research, carbon emission reduction and carbon storage have become emerging focal areas[16,17]. The research focus has gradually shifted toward multi-objective optimization, seeking to improve regional carbon storage capacity through corridor construction and spatial optimization of land.[18]. For example, Guo et al. [17] achieved carbon-neutral landscape reconstruction by optimizing the spatial layout of desert mining areas[19].Qiu et al.[20] Strategies were proposed, grounded in ecological network theory, to improve the carbon sequestration capacity of forest ecosystems.and Fang et al.[21] found a significant correlation between ecological network topology and vegetation carbon sink capacity. These findings indicate that optimizing ecological networks can not only enhance the efficiency of carbon sinks but also improve the overall stability of ecosystems.

Despite these advances, several challenges remain. First, carbon sink functionality has not been systematically incorporated into the indicator systems used to identify ecological sources and corridors, resulting in a bias toward structural connectivity while neglecting spatial differences in carbon benefits[22].Second, studies on ecological networks in regions with high human activity intensity such as metropolitan areas remain limited, and optimization pathways that adapt to complex urban spatial structures and land-use conflicts are still lacking[17,23].Third, most current ESP constructions do not consider multi-objective synergy, particularly the trade-off mechanisms between ecological connectivity and carbon sink capacity[24]. Furthermore, issues such as redundant corridors, broken nodes, and insufficient connectivity are common in ecological networks, seriously constraining the realization of carbon sink functions[18,25].Therefore, creating an ecological network optimization system that integrate“structure-function-carbon benefits is essential to improve the carbon sequestration potential and sustainability of megacity ecosystems.[26,27].To address these challenges, complex network theory provides new insights into the topological optimization of ecological security models[28].Topological metrics, including degree centrality, betweenness centrality, and clustering coefficients, were then incorporated to analyze network characteristics[14], The ecological cycling capacity of critical nodes can be quantitatively evaluated. This evaluation offers structural support to strengthen carbon sequestration[29]. In topologically optimized ecological networks, species migration is smoother, vegetation restoration potential is greater, and carbon storage capacity is stronger[30,31]. Therefore,exploring how ecological networks affect carbon sequestration capacity in metropolitan regions can offer a scientific foundation for ecological network optimization.This will support the goal of carbon neutrality and facilitate sustainable ecological development in the region.

Comments 3:Currently, the second section appears somewhat brief, causing an imbalance in the overall structure of the paper. It is recommended to merge the current second section, "2. Study Area and Data Sources," with the third section, "3. Methods," into a single section titled "2. Materials and Methods."

Response 3:

We sincerely appreciate your thoughtful suggestion. We fully agree that the original Section 2 are relatively short and contributed to an imbalance in the overall manuscript structure. In response to your advice, we have merged the previous Section 2 (Study Area and Data Sources) and Section 3 (Methods) into a unified Section 2 titled “Materials and Methods”. All revisions are highlighted in red in the updated manuscript.

Revisions 3:

Materials and Methods

2.1 Study Area

2.2 Data Sources and Processing

2.3 Methods

2.3.1 Ecological Security Pattern Construction

(1) Identification of Ecological Sources Based on MSPA

(2) Construction of Resistance Surfaces

(3) Extraction of Potential Ecological Corridors

(4) Corridor Screening Using the Gravity Model

2.3.2 Ecospatial Network Topology Analysis

2.3.3 Calculation of Carbon Sequestration Capacity

Results

3.1 Ecological Security Pattern Construction and Analysis

3.1.1 Identification and Analysis of Ecological Source Sites

3.1.2 Resistance Surface Analysis

3.1.3 Ecological Corridor Analysis

3.2 Topological Characteristics of the Ecospatial Network

3.2.1 Overall Topological Structure

3.2.2 Degree and Betweenness Centrality

3.2.3 Closeness Centrality and Clustering Coefficient

3.2.4 Eigenvector Centrality and PageRank

3.3 Correlation Between Carbon Sequestration and Network Structure

3.3.1 Carbon Sequestration Estimation

3.3.2 Correlation Analysis Between Topology and Carbon Sequestration

Discussion

4.1 Optimizing Ecological Security Pattern for Carbon Sequestration Enhancement

4.2 Strengths and Recommendations

4.3 Limitations and Challenges

Conclusion

Comments 4:Figure 2, mentioned in line 123, is missing from the submitted manuscript. Please check if it was omitted and include the necessary content. Additionally, provide a brief explanation of the process used to standardize socioeconomic data to a 30×30 m resolution.

Response 4:

Thank you very much for your careful review and valuable suggestions. After thorough verification, we confirmed that Figure 2 was unintentionally omitted in the original submission. We sincerely apologized for this oversight. The figure has now been properly included in the revised manuscript, and all associated references and descriptions have been carefully checked and updated to ensure accuracy and clarity.

In response to your suggestion, we have also revised Section 2.2: Data Sources and Processing to improve both transparency and scientific rigor. Specifically, we have added a detailed explanation of how the socio-economic datasets—including road networks, water bodies, and population density—were preprocessed and standardized to match the 30 × 30 m spatial resolution of the land use data. This step ensures consistency and alignment across all geospatial datasets used in the analysis. All relevant revisions have been clearly marked in red in the updated manuscript. We sincerely appreciate your kind reminder, which has helped us improve the quality and completeness of the manuscript.

Revisions 4:

This study employed datasets from 2000 to 2020, all standardized to a 30 m × 30 m resolution to ensure consistency and comparability. The data included Land use and land cover change (LUCC), Digital Elevation Model (DEM), slope, topographic relief, Normalized Difference Vegetation Index (NDVI), water bodies, roads, and population (Figure 2). LUCC was derived from Landsat imagery and classified into six categories. slope, topographic relief data came from DEM, and NDVI was calculated using spectral bands. Vector data for roads and water bodies were rasterized, and population data were interpolated. All datasets were resampled to match the land use resolution, ensuring spatial precision and analytical reliability (Table S1).

Comments5:Administrative divisions are labeled in Figures 5–7, but this information is missing in Figure 4. Please add the administrative names to ensure consistency and completeness in the figure annotations.

Response 5:

Thank you very much for your detailed and constructive feedback. We have carefully reviewed all figures and added administrative boundary labels to Figure 4 to maintain consistency with Figures 5–7. In addition, we conducted a thorough inspection of all figure annotations to ensure that legends, scales, and geographic references are standardized and coherent throughout the manuscript.The revised version of Figure 4 has been included in the updated manuscript, and a before-and-after comparison of the original and modified figures has been provided in the supplementary materials for your reference.

Revisions 5:

Figure 4. Original diagram

Figure 4.Modified diagram

Comments 6 :Line 262, "4.1.1. Identification and Analysis of Ecological Source Sites," is completely missing content. Please check and provide the necessary information to ensure completeness. It is also possible that this is a formatting error. Please check.

Response 6:

Thank you very much for your careful review and for pointing out this issue. We sincerely apologize for the oversight. Upon thorough inspection, we found that this was a formatting error caused during the manuscript restructuring. In the revised version, we have repositioned and restored the missing section, which now appears as Section 3.1.1 “Identification and analysis of ecological source sites” under the Results section.

Comments 7:Please review the manuscript for language accuracy and formatting issues, such as "Table S5. ecological network topology indicators." Ensure proper capitalization and consistency throughout the text.

Response7:

Thank you for your insightful comment. We have carefully reviewed and corrected the capitalization and formatting inconsistencies in Table S5: Ecological Network Topological Indicators, ensuring clarity and consistency with the MDPI’s formatting standards. All necessary modifications have been highlighted in red in the revised manuscript and supplementary material. We also rechecked the entire manuscript to ensure uniformity in capitalization and style, particularly in section titles, figure/table labels, and specialized terms. All revisions are highlighted in red in the updated manuscript.

We truly appreciate your attention to detail, which helped us further enhance the clarity and presentation quality of our work.

Revisions 7:

Table S5. Ecological network topology indicators.

Indicators/Years	2000	2010	2020
Degree	7.5	7.067	6.286
Pagerank	0.063	0.066	0.071
Closeness centrality	0.676	0.679	0.658
Betweenness centrality	3.75	3.51	3.50
Eigenvector centrality	0.601	0.671	0.662
Clustering coefficient	0.579	0.563	0.50

Comments 8:The results section is overly detailed. Please consider summarizing and refining the key findings rather than simply listing raw data.

Response 8:

Thank you very much for your pertinent suggestions. We fully agree that too much detail in the “Results” section may obscure the core conclusions of the study. In this regard, we have comprehensively reorganized and rewritten the Results section, focusing on key findings such as important trends in the evolution of ecological security patterns, changes in the topology of ecological networks and their impacts on carbon sink capacity. The relevant revisions have been marked in red in the manuscript.

Revised 8:

Results

3.1. Ecological security pattern construction and analysis

3.1.1. Identification and analysis of ecological source site

The core area, identified as potential ecological source land through MSPA analysis, covered 6,762.18 km² in 2000, 6,780.32 km² in 2010, and 6,740.11 km² in 2020, reflecting a slight increase followed by a decline over the 20-year period. This trend indicates localized degradation of ecological patterns under continued urbanization. These core ecological sources are mainly distributed in the Longmen, Qionglai, and Longquan mountain ranges, as well as in the Minjiang and Tuojiang river basins. They significantly overlap with ecological redline areas and serve as critical zones for safeguarding regional ecological security and supporting carbon sequestration. In contrast, ecological patches in the central and eastern parts of the Chengdu Metropolitan Area (CMA) are highly fragmented. Some ecological sources lie outside the redline boundaries and are primarily located at the urban fringe, where severe patch fragmentation creates potential blind spots in ecological protection. Rapid urban expansion has disrupted ecological corridors, weakened landscape connectivity, constrained ecosystem carbon sink capacity, and created spatial ecological gaps—leading to stronger ecological performance in the west and weaker in the east (Figure 4).

Using Conefor 2.6, woodlands, grasslands, and water bodies larger than 0.25 km² were selected, resulting in 16, 15, and 14 key patches (dPC > 0.2) across the three periods. These patches are primarily located in the southwestern part of the metropolitan area and serve as core ecological nodes that make the greatest contributions to landscape connectivity and carbon sink functionality. Based on dPC values and spatial characteristics, ecological sources were categorized into three levels: important, general, and secondary.

3.1.2. Analysis of resistance surfaces

The ecological resistance surface of the CMA for 2000, 2010, and 2020 was modeled using eight factors, including land use, elevation, and slope (Figure 5). Results indicated that overall ecological resistance increased between 2000 and 2010 due to rapid urban expansion and intensified human activities. From 2010 to 2020, resistance changes stabilized, suggesting a saturation of development intensity. Ecological resistance exhibited a distinct “high-center, low-edge” spatial pattern. High ecological resistance is primarily observed in the urban core of Chengdu and adjacent cities, including southern Deyang and central Ziyang. In these areas, high population density, extensive transportation infrastructure, and concentrated industrial activities collectively contribute to elevated resistance levels.Medium-resistance areas were located at urban-rural transition zones, including northeastern Deyang and southeastern Meishan, where development and natural landscapes are intertwined, and ecological risk remains uncertain. Low-resistance zones were primarily found in the forested Longmen, Qionglai, and Longquan Mountains, and in wetland and river valley regions around the Minjiang and Tuojiang Rivers, where ecological connectivity remains strong.

3.1.3. Ecological corridor analysis

Using the cost-path model and the iterative tool in ArcGIS, 120 potential ecological corridors were identified in 2000, 105 in 2010, and 91 in 2020, indicating a significant downward trend（Figure 6).This decline is primarily attributed to the reduction in ecological source areas and increased landscape fragmentation driven by urban expansion.Ecological corridors are primarily distributed in the Longmen and Qionglai Mountains to the west, and in Longquanshan Forest Park to the east. This forms a spatial pattern characterized by edge concentration and central sparsity.The central city of Chengdu, eastern Deyang, Ziyang, and southeastern Meishan had sparse corridor distributions and fragmented networks with weak connectivity. The significance of each ecological corridor was assessed using the GM.Corridors were categorized into primary, secondary, and general levels (Table S6). The number of primary corridors remained relatively stable (8–10), while secondary and general corridors decreased, reflecting a trend of increasing simplification and reduced redundancy. Priority should be given to restoring ruptured areas and establishing “stepping-stone” nodes to enhance network connectivity and carbon sink capacity.

3.2. Topological characteristics of the ecospatial network

3.2.1. Topological network structure overview

Ecological sources identified for the period from 2000 to 2020 were represented as nodes, while the ecological corridors linking these sources were modeled as edges, thereby constructing the topological structure of the ecological network.The resulting topology was analyzed and visualized using Gephi 0.10.0. The results indicated that the ecological network in the study area underwent substantial structural changes from 2000 to 2020 (Figure7). At a standard resolution of 1.0, the network comprised three ecological communities in 2000 and 2010, which reduced to two in 2020, suggesting reduced landscape heterogeneity and weakened ecological connectivity. This transformation is likely attributed to the loss of ecological corridors due to construction land expansion, thereby reducing regional carbon sink capacity. Further analysis revealed that the spatial distribution of ecological communities closely corresponds to the ecological resilience pattern of the Chengdu metropolitan area: networks are more stable in the central and western regions, while the ecological foundations in the east and southeast are degraded, leading to disrupted energy and material flows and diminished restoration potential.

3.2.2. Degree and betweenness centrality

Degree and meso-centrality metrics were calculated using Gephi 0.10.0, and results showed that the average centrality value exhibited a declining trend from 2000 to 2020 (Table S5), indicating reduced network connectivity, diminished redundancy, and increased ecological vulnerability (Figure 8a). Further analysis revealed that nodes with higher centrality such as nodes 5, 10, and 14 in 2000, and nodes 9 and 12 in 2020 served as key bridges for ecological flow and carbon sequestration. If these nodes degrade or disappear, the carbon sequestration capacity of nearby source areas may decline significantly.Therefore, priority should be placed on protecting nodes with high and medium centrality, and ecological compensation and incentives should be provided to areas with high carbon sequestration potential in line with the “Carbonwise Tianfu” credit system to enhance overall network resilience.

3.2.3. Proximity centrality and clustering coefficients

The study found that both closeness centrality and clustering coefficient generally declined from 2000 to 2020, with only slight fluctuations (Table S4). This trend indicates increasing average distances between nodes in the ecological network and reduced accessibility, likely due to fragmentation of ecological sources and corridor loss (Figure 8b). Nodes such as 2, 4, 5, 11, and 13 in 2000; 7, 8, 9, 11, and 15 in 2010; and 6, 10, and 11 in 2020 showed the lowest clustering coefficients, suggesting weak connectivity and low carbon sequestration efficiency. To improve stability, corridors should be added in these low-coefficient areas to enhance connectivity and carbon sink function. These key nodes should also be prioritized in the“Carbonwise Tianfu”credit system to encourage ecological restoration and carbon trading through market incentives.

3.2.4. Feature Vector Centrality and PageRank

According to eigenvector centrality and PageRank values from 2000 to 2020 (Table S5), the ecological network's overall importance peaked in 2010 but declined by 2020 (Figure 8c), reflecting reduced structural stability under external disturbances and potentially affecting carbon sequestration efficiency. Nodes 5, 10, 13, and 15 (2000) and nodes 5, 9, 11, and 12 (2020) had the highest PageRank scores, suggesting their substantial contribution to ecological connectivity and carbon sinks. In contrast, nodes such as 8 and 14 in 2020 exhibited low centrality, likely due to peripheral locations or weak connectivity, resulting in limited carbon sink functionality.

3.3. Correlation analysis between carbon sequestration calculations and ecological network structure

3.3.1. Calculation of carbon sequestration

Ecological source areas were categorized into forest, grassland, and water bodies based on their corresponding land use types. Carbon sequestration was estimated for 16, 15, and 14 source sites within the CMA in the years 2000 to 2020, respectively, by applying land-use-specific carbon coefficients (Table S2), and corresponding proportions are shown in (Table S7).The results indicate that both the total number of ecological source sites and the overall carbon sequestration capacity have declined over the past two decades. This decline is primarily attributed to the reduction in the number of source sites.Despite some variation in carbon uptake by grassland and aquatic systems, forests consistently contributed over 87% of total sequestration, maintaining a dominant role. Spatially, ecological assets were concentrated in forested and grass-covered zones such as Longmen Mountain, Qionglai Range, Longquanshan Forest Park, and the Tuojiang River Basin. In contrast, central Chengdu, southeastern Deyang, and Ziyang exhibited limited ecological land, thus constraining regional sequestration potential. Strengthening the quantity and quality of ecological source areas is critical for enhancing the metropolitan area’s carbon sink capacity.

3.3.2. Analysis of the relationship between ecological network structure and carbon sequestration capacity

To investigate the association between carbon sink performance and ecological network structure, this study employed Spearman’s rank correlation to evaluate how total and per-unit-area carbon sequestration values relate to six topological metrics across various ecological land types (Figure 9). In 2000, water-related sources demonstrated a positive correlation with the clustering coefficient (p < 0.05, r = 0.50), while all other indicators showed negative correlations. Per-unit carbon sequestration in forested areas showed positive associations with node degree, closeness centrality, and eigenvector centrality (p < 0.05), with a mean r of 0.51. By 2010, clustering coefficients remained significantly positively correlated with total carbon in both forest (p < 0.05, r = 0.62) and grassland (p < 0.01, r = 0.70) areas. However, forest carbon was negatively correlated with betweenness, closeness, PageRank, and eigenvector centrality. Grassland per-unit carbon showed a positive correlation with clustering (p < 0.05, r = 0.63), but negative correlations with betweenness and PageRank were not statistically significant. In water ecosystems, per-unit carbon was positively associated with degree, betweenness, closeness, and PageRank (p < 0.05), though the average r value of 0.54 suggested moderate rather than strong correlations.By 2020, forest areas continued to exhibit a positive correlation with the clustering coefficient (p < 0.05, r = 0.49), and their per-unit carbon sequestration also showed a positive association with eigenvector centrality (p < 0.05, r = 0.48).

Forest ecosystems represent a dominant contributor to carbon sequestration and demonstrate significant correlations with various network topology indicators. In comparison, grassland ecosystems exhibit strong positive correlations primarily with clustering coefficients; however, their negative correlations with centrality indicators imply a relatively decentralized role within the ecological network. Conversely, carbon sequestration intensity in aquatic ecosystems positively correlates with multiple centrality indices, underscoring their local significance within network structures.These results emphasize that carbon sequestration capacities differ substantially depending on ecological network topology. Therefore, enhancing network structures particularly by increasing corridor quantity and quality connected to lower-efficiency ecological sources, such as certain grasslands and aquatic areas near urban expansion zones can improve both ecological connectivity and carbon sequestration performance. Strengthening corridor linkages among key ecological sources can further create positive externalities, benefiting adjacent economic activities. This integrated approach is essential not only for enhancing carbon sink functionality in metropolitan contexts but also provides a robust foundation for comprehensive land-use planning and maximizes regional land-resource value.

Comments 9 :5.1 "Optimize regional ecological networks to enhance regional carbon sequestration": It is recommended to further broaden the applicability of the strategic insights to enhance their generalizability and the representativeness of the research. 5.2 "Strengths and Recommendations" and 5.3 "Limitations and Challenges" both address strengths and weaknesses, leading to some content overlap. It is suggested to optimize the phrasing to make the structure clearer and the distinctions between the sections more defined.

Response 9:

Thank you for your valuable comments. Based on your suggestions, we have completely revised the discussion section (Section 4). In section 4.1 “Optimizing Regional Ecological Networks to Enhance Regional Carbon Sequestration”, we have introduced the case studies of optimizing the ecological networks in Jiaodong Peninsula and Yunnan Province, which further enhance the representativeness and universal value of the strategy.

To address the second point, we have reorganized and delineated the content boundaries of 4.2 “Strengths and Recommendations”and 4.3 “Limitations and Challenges”. Section 4.2 focuses on methodological strengths, research highlights and policy recommendations, while Section 4.3 independently discusses limitations and future research directions. Relevant additions have been highlighted in red in the text.

Revised 9:

4.Discussion

4.1. Optimizing regional ecological security patterns to enhance regional carbon sequestration

This study reveals the key ecological challenges facing the Chengdu metropolitan area amid rapid urban expansion between 2000 and 2020 (Figure10), including fragmentation of ecological source sites, reduction in ecological corridors, declining spatial connectivity, and weakening carbon sink capacity. These transformations have undermined the structural integrity and ecological functionality of regional systems, thereby constraining the realization of China's national “dual-carbon” strategy. Empirical evidence shows a significant positive relationship between ecological network topology and carbon sequestration performance, indicating that higher spatial connectivity enhances both ecological energy flow and carbon sink efficiency. Therefore, constructing a well-connected ecological spatial network is essential for improving carbon sequestration potential.

Using the 2020 optimization scenario as a case study and drawing on the framework proposed by Qiu et al [47] network enhancement can be achieved by increasing ecological corridors and inserting “stepping stone” nodes to shorten paths between low-efficiency nodes. These stepping stones serve as relay habitats, reducing migration risks and improving species movement success rates. Based on this approach, the present study optimized several structurally weak nodes (e.g., nodes 6, 10, 11, and 15), adding 10 new ecological nodes and 45 ecological corridors, thereby significantly improving network accessibility and topological balance. Compared to the baseline scenario,the total carbon sink increased by approximately 4.16 million tons, accompanied by marked improvements in both the structural stability and operational efficiency of the ecological network.A structural analysis based on carbon sink contributions revealed that forest land accounted for 94.8% of the increase, confirming its dominant role. Grassland and water bodies contributed 1.0% and 4.2%, respectively, further highlighting the importance of forest ecosystems in strengthening connectivity and carbon performance.

Newly added corridors were concentrated in areas previously lacking ecological sources, such as eastern Deyang, northwestern Ziyang, and southern Meishan. The optimization process not only enhanced ecological integrity but also unlocked considerable spatial potential for land value transformation. This paves the way for developing a synergistic model integrating ecology, carbon sinks, and economic growth. These findings align with similar studies conducted in Yunnan and the Jiaodong Peninsula, validating both the theoretical underpinnings and regional adaptability of the methodology[26,27]. Overall, the optimized ecological security pattern in the Chengdu metropolitan area not only improves carbon sink capacity and spatial connectivity but also provides a strategic interface between ecological restoration and the pursuit of carbon neutrality.

4.2. Strengths and recommendations

This study constructs the ecological security pattern of the CMA from a time-series perspective (2000–2020), addressing the previous lack of dynamic analyses on ecosystem evolution in metropolitan regions. Comparative analysis across different periods reveals the interrelated evolution of ecological source fragmentation, declining network connectivity, and the degradation of carbon sink capacity. Methodologically, this study adopts a complex network topology analysis to quantitatively assess the relationship between ecological network structure and regional carbon sequestration capacity through the use of relevant topological indicators[29].

Moreover, ecological source areas were overlaid with ecological redlines to identify both high-value ecological patches and conservation gaps, thereby providing a scientific basis for formulating differentiated protection and restoration strategies. It is recommended that areas already designated within the ecological redline be subject to strict protection; buffer zones should be established for source areas located outside the redline but possessing high ecological value; and regions that are highly fragmented yet exhibit significant restoration potential should be designated as priority zones for ecological restoration[53].At the practical level, the study suggests implementing carbon compensation mechanisms in ecologically fragmented, low-centrality marginal areas through platforms such as “CarbonSmart Tianfu” to incentivize societal participation in restoration. It further recommends developing a carbon sink evaluation framework based on network optimization to foster synergies among policies related to carbon sink evaluation, ecological conservation, and carbon trading. Overall, this research provides scientific support for optimizing ecological security patterns and offers theoretical guidance for institutional design and policy innovation toward carbon neutrality in future metropolitan areas.

4.3. Limitations and challenges

This study applies Spearman correlation analysis to investigate the association between ecological network topology and carbon sink capacity. As a non-parametric method, Spearman’s correlation is well-suited for identifying monotonic relationships between variables and handling non-normally distributed data. However, compared to Pearson or partial correlation, it may be limited in quantifying the precise effects of network structure on carbon stocks. Future research could integrate multiple correlation methods to further validate and refine the relationship between topological metrics and carbon sequestration potential. Additionally, the classification of land use types in this study remains relatively coarse. While major carbon sink ecosystems such as forests, grasslands, and water bodies were included, ecosystems like wetlands, shrublands, and agroforestry systems also possess substantial carbon storage potential. A more refined land classification system could offer a more comprehensive evaluation of the spatiotemporal contributions of various ecosystems to regional carbon balance. Moreover, future studies should incorporate dynamic simulations of carbon sink capacity under different ecological security scenarios urban expansion, ecological restoration, policy interventions, using spatial optimization models PLUS or FLUS integrated with carbon accounting frameworks to support adaptive management and policy decision-making toward carbon neutrality.

Comments 10 :In the conclusion, (2) does not fully summarize and condense the key points of Section 4.2. It is recommended to further refine and highlight the core findings. Additionally, (3) and (4) are combined in the same paragraph, and it is suggested to split them for better clarity and structure.

Response 10:

Thank you for your valuable comments and suggestions. We have carefully revised the conclusion section based on your feedback, addressing the two issues you raised as follows:

First, we refined item (2) in the conclusion by summarizing the key findings from Section 4.2 more concisely and clearly. This revised content has been highlighted in red in the updated manuscript for your review.

Second, we separated items (3) and (4) into two independent paragraphs in order to enhance the logical clarity and structural hierarchy of the conclusion section, thereby improving its overall readability and expression.

We sincerely appreciate your constructive suggestions on this part, which have significantly improved both the clarity and logical coherence of the conclusion.

Revised 10:

The ecological spatial network showed signs of degradation, with network modules declining from three to two. Although the core remained stable, the southeast was marginalized due to corridor disruptions, and key topological indicators declined, reflecting reduced connectivity.

Yours sincerely,

Langong Hou ¹, Huanhuan HU ¹^*,and Tao Liu ²,Che Ma ¹

1 School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621000, China

2 Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript takes the optimization of ecological networks in Chengdu as the research object, constructs the "MSPA-MCR InVEST" multi model coupling framework, and systematically evaluates the carbon sink function and corridor connectivity of ecological source areas. By adjusting the weights of resistance factors such as NDVI and elevation, quantifying the impact of terrain constraints on ecological network resilience, and combining cases such as the Jiaodong Peninsula and the Wuhan metropolitan area, the synergistic effect of wetland restoration on regional land value enhancement is verified. The research conclusion has guiding significance for enhancing the carbon sequestration capacity of ecological security parks. But there are still many specific issues that need to be supplemented and revised.

The method of dynamic simulation of carbon sink capacity can be further optimized. It is suggested that Integrate the "Carbon Storage and Sequestration" module from the InVEST model into the existing MSPA-MCR framework to predict carbon sink trends post-optimization (e.g., 2030 projections).such as by simulatingcarbon sequestration changes in CMA under scenarios like expanded forest corridors or wetland restoration.
Onlines 160-175, Page 5, Resistance Surface Sensitivity Analysis: It is recommended to adjust the weights of key resistance surface parameters (such as NDVI±20%, altitude±15%) to evaluate their impact on corridor identification and carbon sink results.
In the Results and Discussion section, combined with the case of the Jiaodong Peninsula, supplement the analysis of the correlation between ecological network optimization and regional economic development (such as land appreciation potential) and carbon emission intensity reduction, and quantify the synergistic effect of "ecology economy".
According to the concept of "Living Community of Mountains, Rivers, Forests, Fields, Lakes, and Grasslands", the contribution rate of increasing water bodies and wetland carbon sinks to ecological network connectivity should becalculated.
According to the standards for delineating ecological protection red lines, a superposition analysis of ecological source areas and current red line areas in the Chengdu metropolitan area should be proposed to identify protection gaps and develop graded control strategies.
At line 295, page 10, Section 4.2, drawing on the policy framework of "Urban Ecological Resilience", incorporating the goal of carbon sequestration enhancement into the design of Chengdu's "Carbon Benefiting Tianfu" credit system (such as the reward mechanism for citizens' participation in ecological stepping stone maintenance).
It is suggested toset up three scenarios of "natural evolution", "policy intervention", and "extreme climate", referring to the rain-flood management model, to evaluate the response threshold of ecological network carbon sequestration capacity under different scenarios.
‌Standardize terminology and figures‌:Align terms like "ecological source areas" and "core patches". Add NDVI data sources, and statistical significance markers in figures.
‌Update references‌:Replace outdated citations (pre-2020) with recent studies (e.g., Wuhan Metropolitan Area case , Guangxi Hechi research).

Comments on the Quality of English Language

The grammar expression in the relevant chapters of the manuscript needs improvement, and we hope to polish it again to ensure the fluency of the manuscript.

Author Response

Subject: Revision Notes (Manuscript ID: sustainability-3524391)

Dear reviewer，

Comments 1：The method of dynamic simulation of carbon sink capacity can be further optimized. It is suggested that Integrate the "Carbon Storage and Sequestration" module from the InVEST model into the existing MSPA-MCR framework to predict carbon sink trends post-optimization (e.g., 2030 projections).such as by simulatingcarbon sequestration changes in CMA under scenarios like expanded forest corridors or wetland restoration.

Response 1:

We would like to thank you for your pertinent suggestions on this study. The suggestion of integrating the “carbon storage and sequestration” module of the InVEST model into the MSPA-MCR framework for the prediction of dynamic carbon sinks is indeed very forward-looking and valuable. At the initial stage of the study, we have fully understood and evaluated the model and carefully considered its applicability in this study.

However, the “Carbon Storage and Sequestration” module of the InVEST model is mainly based on the carbon density allocation of land use data, and is used to assess the carbon storage potential of the whole region, which is suitable for carbon estimation at the macro level, but there are some limitations in spatial accuracy and identification of ecological units, and it is difficult to refine the carbon storage situation of multiple land use types within a single ecological source area. However, there are some limitations in spatial accuracy and ecological unit identification, making it difficult to identify the carbon storage situation of multiple land use types within a single ecological source area.

Our study focused on the micro-relationship between the node-edge mechanism and the carbon sequestration capacity of ecological networks, and we need to explicitly extract the carbon storage data of different land-use types (forest, grassland, and water) in the ecological source area, and further correlate the data with the network topological indicators (degrees, clustering coefficients, and intermediary centrality, etc.) to analyze the carbon storage potential. Analysis. In order to ensure the relevance and comparability of the analysis, we thoroughly reviewed a large number of related literatures and chose the method suitable for the objective of this study, which is to use the carbon sequestration coefficient to calculate different land use types.

Moreover, we found that the carbon sequestration coefficient method and the InVEST model are similar in terms of theoretical principles, and both of them are based on the carbon density of land use types to project the carbon sink capacity. We calculated the total amount of carbon sinks and their proportion of ecological sources in Chengdu metropolitan area in 2000, 2010 and 2020 respectively through the sequestration coefficient, so as to realize the spatial identification and comparison of the evolution of carbon sink structure in different periods. Therefore, on the premise of not affecting the precision of the study, the carbon sequestration coefficient method is more in line with the needs of this study for assessing the structure of micro ecological source areas.

Nevertheless, we highly agree with your proposed research direction and will further expand it in our future work. In the follow-up study, we plan to deeply couple the InVEST model with the MSPA-MCR framework and introduce scenario simulation modules (e.g., PLUS or FLUS models) to simulate the dynamic evolution of carbon sink capacity under different ecological restoration strategies. This will provide a more accurate carbon sink optimization scheme, and also provide more scientific support for regional ecosystem management and policy formulation under the background of “dual-carbon”.

In response to your valuable suggestions, we have further refined the limitations analysis and future outlook in the discussion section. The revised parts are treated in red in the revised draft. Thank you again for your constructive comments!

Revisions1:

4.3. Limitations and challenges

Moreover, future studies should incorporate dynamic simulations of carbon sink capacity under different ecological security scenarios urban expansion, ecological restoration, policy interventions, using spatial optimization models PLUS or FLUS integrated with carbon accounting frameworks to support adaptive management and policy decision-making toward carbon neutrality.

Comments 2：Onlines 160-175, Page 5, Resistance Surface Sensitivity Analysis: It is recommended to adjust the weights of key resistance surface parameters (such as NDVI±20%, altitude±15%) to evaluate their impact on corridor identification and carbon sink results.

Response 2:

Thank you very much for your valuable comments. Following your suggestion, we selected NDVI and elevation (DEM) as the key resistance factors and conducted a sensitivity analysis by adjusting their weights within the ranges of ±20% for NDVI and ±15% for elevation. This was performed across three time points (2000, 2010, and 2020) to assess their impacts on ecological corridor identification and carbon sink estimates.

Due to the extensive data volume and limited submission time, we evaluated the effect of weight changes by recalculating and comparing the cumulative resistance surface values before and after the adjustment. The results showed that the modified resistance surfaces differed from the original by less than 5%, indicating minimal impact on both corridor structure and overall carbon sequestration estimation.

This demonstrates the robustness of our resistance model to parameter variations. To visually support this analysis, we have added comparative maps (Figures 1 and 2) showing resistance surfaces before and after parameter adjustments for the three years，the change in resistance values was found to be small.These comparisons further confirm the model's stability and reliability. Thank you again for your insightful recommendation.

Revisions2:

Figure 1. Diagram before modification

Figure 5. Combined resistance surface from 2000, 2010, and 2020 respectively.

Figure 2.Modified diagram

Figure 5. Combined resistance surface from 2000, 2010, and 2020 respectively.

Comments 3：In the Results and Discussion section, combined with the case of the Jiaodong Peninsula, supplement the analysis of the correlation between ecological network optimization and regional economic development (such as land appreciation potential) and carbon emission intensity reduction, and quantify the synergistic effect of "ecology economy".

Response 3:

Thank you very much for your insightful suggestion. In response, we have added supplementary analysis in the Results and Discussion sections, focusing on the correlation between ecological network optimization and regional economic development, with supporting case studies from the Jiaodong Peninsula and Yunnan Province.In Section 3.3.2 Analysis of the relationship between ecological network structure and carbon sequestration capacity ,we emphasized the synergistic effect between ecology and economy. Specifically, the placement of new corridors and source patches in previously fragmented ecological zones not only enhanced the structural stability and carbon sequestration potential of the network but also improved the ecological attractiveness and development potential of adjacent land parcels, generating indirect economic benefits for the region.

In Section 4.1. Optimizing regional ecological security patterns to enhance regional carbon sequestration, we further incorporated comparative insights from the Jiaodong and Yunnan cases to supplement a quantitative and empirical analysis of the“ecology-carbon-economy”synergy mechanism. Our findings demonstrate that improving ecological spatial structure contributes not only to enhanced carbon sink capacity but also supports land value appreciation and sustainable utilization, producing dual ecological and economic outcomes.

These additions have been clearly marked in red in the revised manuscript. Once again, we sincerely thank you for your constructive feedback, which has significantly strengthened the interdisciplinary value and policy relevance of our study.

Revisions3:

3.3.2. Analysis of the relationship between ecological network structure and carbon sequestration capacity

4.1. Optimizing regional ecological security patterns to enhance regional carbon sequestration

Comments 4：According to the concept of "Living Community of Mountains, Rivers, Forests, Fields, Lakes, and Grasslands", the contribution rate of increasing water bodies and wetland carbon sinks to ecological network connectivity should becalculated.

Response 4:

Thank you very much for your constructive suggestion regarding the inclusion of the “Mountains–Rivers–Forests–Farmlands–Lakes–Grasslands” community framework and the need to quantify the contribution of increased carbon sinks from water bodies and wetlands to ecological network connectivity. Your comment has provided important insights for deepening the integration of carbon sink performance and ecological connectivity, while also enhancing the study’s relevance to ecological conservation and carbon neutrality policy.

However, after thorough verification, we confirm that wetlands are not present within the boundaries of the Chengdu Metropolitan Area during the study period. Therefore, it was not possible to calculate the contribution of wetland carbon sinks to network connectivity in this case.

To address your suggestion more precisely, we focused our analysis on three dominant ecological land types in the study area: forests, grasslands, and water bodies. After optimization, forest area increased by 13,872.86 ha, grasslands by 281.88 ha, and water bodies by 5,163.74 ha. Corresponding carbon sink increments were 3,938,505.63 Mg C yr⁻¹ (forest), 40,533.63 Mg C yr⁻¹ (grassland), and 177,644.19 Mg C yr⁻¹ (water), which collectively contributed to an increase of 2.569 units in network connectivity.To evaluate the relative contribution of each land type to this improvement, we applied a carbon sink increase–weighted attribution method. Results showed that forest ecosystems accounted for approximately 94.8% of the improvement, while grasslands and water bodies contributed 1.0% and 4.2%, respectively. These findings suggest that forests played a dominant role in enhancing ecological connectivity following optimization.

This analytical its results have been supplemented in the Discussion (Section 4.1) and are clearly highlighted in red in the revised manuscript. Once again, we sincerely appreciate your insightful suggestion.

Revisions 4:

Discussion

4.1. Optimizing regional ecological security patterns to enhance regional carbon sequestration

Comments 5:According to the standards for delineating ecological protection red lines, a superposition analysis of ecological source areas and current red line areas in the Chengdu metropolitan area should be proposed to identify protection gaps and develop graded control strategies.

Response 5:

Thank you very much for your valuable suggestion. In accordance with the ecological redline delineation standards, we have conducted an overlay analysis between the ecological source areas identified in this study and the existing “ecological red-line” zones in the Chengdu metropolitan area. Corresponding revisions and supplements have been made in the Methods, Results, and Discussion sections.

The results indicate a high degree of spatial consistency between the core ecological sources and the redline areas, suggesting that most high-value ecological zones have been effectively incorporated into existing conservation planning. However, we also identified several ecological patches outside the redline boundaries, predominantly located on the urban expansion fringe, where severe fragmentation was observed. These areas may represent potential conservation blind spots and deserve further attention.

This overlay analysis provides a sound spatial basis for proposing a tiered ecological zoning and control strategy. Specifically, we recommend:Strict protection for ecological sources already within the redline;Designation of buffer zones for high-value sources outside the redline; andPrioritization of restoration zones for highly fragmented but potentially restorable areas.

These additions have been incorporated into Section 2.3.1 (Ecological Source Identification), Section 3.1.1 (Identification and Analysis of Ecological Source Sites), and Section 4.2 (Strengths and Recommendations), and are clearly marked in red in the revised manuscript. Once again, we sincerely appreciate your thoughtful suggestion, which has greatly enhanced the spatial policy relevance and practical applicability of our study.

Revisions 5:

2.3.1. Ecological security pattern construction

（1）Identification of ecological sources based on MSPA

The Morphological Spatial Pattern Analysis (MSPA) method identifies ecologically significant patches within a study area using morphological algorithms[12]. Binary classification was performed in ArcGIS[5], and the data were processed in Guidos Toolbox 2.8, using forest, grassland, and watershed as foreground and cropland, unused land, and built-up areas as background[34]. Landscape components were categorized into seven structural classes, including core, bridge, edge, and island. Given the large study area and high fragmentation, core patches exceeding 2.5 km²were selected as candidate ecological sources via the eight-neighborhood method. MSPA results were supplemented by computing the potential connectivity (PC) and the patch importance index (dPC) using Conefor 2.6 [35], with a 1000 m threshold and 0.5 connectivity probability. Patches with dPC >0.20 were intersected with ecological redline zones to define final ecological sources, which were classified into forest, grassland, and water types. PC and dPC were calculated as shown in Equation [36].

3.1.1. Identification and analysis of ecological source site

Additionally, the classification of land use types in this study remains relatively coarse. While major carbon sink ecosystems such as forests, grasslands, and water bodies were included, ecosystems like wetlands, shrublands, and agroforestry systems also possess substantial carbon storage potential. A more refined land classification system could offer a more comprehensive evaluation of the spatiotemporal contributions of various ecosystems to regional carbon balance.

Comments 6：At line 295, page 10, Section 4.2, drawing on the policy framework of "Urban Ecological Resilience", incorporating the goal of carbon sequestration enhancement into the design of Chengdu's "Carbon Benefiting Tianfu" credit system (such as the reward mechanism for citizens' participation in ecological stepping stone maintenance).

Response 6:

Thank you very much for your valuable suggestion. Your comments have been instrumental in enhancing the policy relevance and real-world applicability of this study.One of the core objectives of our research is to optimize the ecological network of the Chengdu metropolitan area to improve carbon sequestration capacity and urban ecological resilience. However, as you rightly pointed out, the current analytical framework primarily focuses on structural network optimization and carbon performance evaluation, without fully integrating policy instruments such as carbon credit systems.

Your insightful suggestion prompted us to explore a stronger link between ecological network optimization and carbon governance tools. Specifically, we introduced the concept of the “TanHui Tianfu” carbon credit system as a local policy framework that could potentially incentivize and support carbon sequestration efforts through enhanced urban ecological network design.Following your advice, we revised Sections 3.2.1 (Topological Network Structure Overview), 3.2.2 (Degree and Betweenness Centrality), and 3.2.3 (Proximity Centrality and Clustering Coefficients) to partially incorporate a carbon sequestration perspective, and we introduced the idea of integrating ecological connectivity improvements with carbon trading and incentive mechanisms. This approach aims to strengthen the link between ecological restoration and carbon neutrality from a perspective of urban ecosystem resilience and sustainability.

All relevant revisions have been clearly marked in red in the updated manuscript. Once again, we sincerely thank you for your constructive feedback.

Revisions 6:

3.2.1. Topological network structure overview

Ecological sources identified for the period from 2000 to 2020 were represented as nodes, while the ecological corridors linking these sources were modeled as edges, thereby constructing the topological structure of the ecological network.The resulting topology was analyzed and visualized using Gephi 0.10.0. The results indicated that the ecological network in the study area underwent substantial structural changes from 2000 to 2020 (Figure 7). At a standard resolution of 1.0, the network comprised three ecological communities in 2000 and 2010, which reduced to two in 2020, suggesting reduced landscape heterogeneity and weakened ecological connectivity. This transformation is likely attributed to the loss of ecological corridors due to construction land expansion, thereby reducing regional carbon sink capacity. Further analysis revealed that the spatial distribution of ecological communities closely corresponds to the ecological resilience pattern of the Chengdu metropolitan area: networks are more stable in the central and western regions, while the ecological foundations in the east and southeast are degraded, leading to disrupted energy and material flows and diminished restoration potential.

3.2.2. Degree and betweenness centrality

3.2.3. Proximity centrality and clustering coefficients

Comments 7：It is suggested toset up three scenarios of "natural evolution", "policy intervention", and "extreme climate", referring to the rain-flood management model, to evaluate the response threshold of ecological network carbon sequestration capacity under different scenarios.

Response 7:

We sincerely thank the reviewers for their valuable suggestions. Scenario analysis is important for assessing the carbon sequestration capacity of ecological networks under different environmental conditions, however, at this stage of our study, we mainly focus on the optimization of ecological network structure and the assessment of carbon sequestration capacity. We have already covered more detailed analyses under the existing framework, and the inclusion of additional scenario simulations may go beyond the core scope of this study and affect the logical coherence and manageable length of the article.

Nonetheless, we fully recognize the value of scenario analysis in ecological network optimization research and plan to expand it further in subsequent studies. In future studies, we will consider introducing the scenarios of “natural evolution”, “policy intervention” and “extreme climate” or other different scenarios. In order to simulate the dynamic changes of carbon sequestration capacity of ecological networks under different driving factors, and to explore the response thresholds. This will not only improve the predictability of the study, but also provide a stronger scientific basis for urban ecological planning and climate change adaptation policies.

Thank you again for your professional advice, and we look forward to exploring this direction in depth in our subsequent studies, and hope that future research results will further improve and enrich the theoretical and practical system of ecological network optimization.

Comments 8：Standardize terminology and figures‌:Align terms like "ecological source areas" and "core patches". Add NDVI data sources, and statistical significance markers in figures.

Response 8:

Thank you very much for your comments and suggestions. We have standardized the terms “ecological source area” and “core patch” in the full text and revised the inconsistent terms to ensure consistency and accuracy. We have clarified the source of NDVI data in the data source to enhance the traceability and transparency of the analysis. We have added statistical significance markers (e.g., p < 0.05, p < 0.01) to the graphs involving the correlation analysis between topological indicators and carbon sink capacity in the graphs and the figure notes to enhance the scientific and rigorous interpretation of the data,and a before-and-after comparison of the original and modified figures has been provided in the supplementary materials for your reference.

Revisions 8:

Table S1. The list of data used in this study.

Category	Data	Resolution	Data Source
Environment	LUCC	30m	Globe Land global surface coverage data( http://www.globallandcover.com/)
	DEM, slope, topographic relief	30m	Geo spatial data cloud( https://www.gscloud.cn/)
	NDVI	30m	Resource and Environmental Science Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/）
Socioeconomic	Road network	-	Open Street Map( https://www.openhistoricalmap.org/)
	Water net	-	Open Street Map( https://www.openhistoricalmap.org/)
	Demographic data	-	Data from World Pop website (https://hub.worldpop.org)

Figure 9. Original diagram

Figure 9.Correlation analysis of carbon sequestration from ecological sources with topological indicators（0.01 < *p < 0.05, 0.001 < **p < 0.01).

Figure 9. Figure 4.Modified diagram

Figure 9.Correlation analysis of carbon sequestration from ecological sources with topological indicators（0.01 < *p < 0.05, 0.001 < **p < 0.01).

Comments 9：Update references‌:Replace outdated citations (pre-2020) with recent studies (e.g., Wuhan Metropolitan Area case , Guangxi Hechi research).

Response 9:

Thank you for your valuable and specific suggestions. We have systematically updated the references in the paper according to your comments. In the section related to the construction of regional ecological networks, we have replaced some of the original early literature with research results published after 2020, and added references to the latest studies including the cases of Wuhan Metropolitan Area and Hechi in Guangxi, in order to enhance the timeliness and regional adaptability of the content. The section on carbon sequestration capacity was also carefully reviewed. As there are not yet many studies in this field addressing the coupling of regional ecological networks and carbon sinks, more representative literature after 2020 has not yet been found. Therefore some of the references are still published before 2020, but they are all basic or representative results that have been widely cited in the field and still have theoretical value. The updated references have been highlighted in red in the revised manuscript for ease of review. We are very grateful for your suggestion, which has effectively improved the academic quality and cutting-edge of our manuscript.

Revisions 9:

For example:

Chen, J.; Wang, S.; Zou, Y. Construction of an Ecological Security Pattern Based on Ecosystem Sensitivity and the Importance of Ecological Services: A Case Study of the Guanzhong Plain Urban Agglomeration, China. Ecological Indicators2022, 136, 108688.
Shuai, N.; Hu, Y.; Gao, M.; Guo, Z.; Bai, Y. Construction and Optimization of Ecological Networks in Karst Regions Based on Multi-Scale Nesting: A Case Study in Guangxi Hechi, China. Ecological Informatics2023, 74, 101963, doi:10.1016/j.ecoinf.2022.101963.

Comments 10：Comments on the Quality of English Language:The grammar expression in the relevant chapters of the manuscript needs improvement, and we hope to polish it again to ensure the fluency of the manuscript.

Response 10:

Thank you for your careful review and your constructive feedback regarding the language quality of the manuscript. We sincerely acknowledge that some parts of the manuscript required improvements in grammar, clarity, and overall fluency.In response, we have thoroughly revised the entire manuscript and polished the English language to enhance its academic rigor and readability. The revisions included correcting grammatical issues, improving sentence structure, and refining word choice to ensure that the expression is accurate, concise, and fluent. All language-related revisions have been applied throughout the manuscript and are reflected in the updated version submitted. We truly appreciate your suggestion, which helped us improve the overall quality and clarity of our work.

Yours sincerely,

Langong Hou ¹, Huanhuan HU ^1*,and Tao Liu ²,Che Ma ¹

1 School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621000, China

2 Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Manuscript No. sustainability-3524391 tilted “Ecological Security Pattern Construction for Carbon Sink Capacity Enhancement: The Case of Chengdu Metropolitan Area”, seeks to optimize the ecological spatial organization of the Chengdu metropolitan area in order to increase its capacity as a carbon sink and help achieve carbon neutrality. Using Morphological Spatial Pattern Analysis (MSPA), landscape connectivity analysis (Conefor), the Minimum Cumulative Resistance model (MCR), and the Gravity model (GM), a comprehensive ecological spatial network was constructed. The current manuscript exhibits significant effort, as I can see. Minor revision is required before accepting for publication. The following comments should be considered:

Why is the current analysis limited to the years 2000–2020? What about the 2020–2024 timeframe?
Please use appropriate spacing to separate the two statements in line 18. Please check the whole manuscript (e.g., the keywords).
In the keywords (i.e. Network optimization), according to the journal format, it should start with a small letter. Please check the other.
There are several abbreviations in the present work, so please add a table of abbreviations at the end of the manuscript according to the journal style.
Please check “epresented” in line 88, I think it should be written as “represented”.
Lines 108-115, without citations, please add, please polish your work via adding relevant citations.
Regarding Fig.1, if this figure was taken from another work, please refer to it. The same tendency as Figures 4, 5, 6, 7, and 10.
In line 149, please check the unit “2.5km”.
In line 150, please check the abbreviation “(dPC)”. I think it should be written as (PC).
In line 153, please check “. Patches”.
Please add citations for all equations-containing manuscript.
In line 164, please check the following abbreviation “elevation (DEM)”.
In Table S2, please check “Slope/(° )”. If this is a supplementary table, why did you add it to the manuscript?
In line 226, please check the format of “CO2”.
In Table S4, please check the format of “(t/hm2a)”.
In line 315, “and low resilience, impeding recovery”, I think “and” should be added before “impeding”, please check.
In Table S5, (pagerank & ecological) should be stated with a capital letter, please check.
“Betweenness Centrality” in Table S5, I think “Centrality” should start with a small letter.
Please check the first sentence in Figure 8, it should be started with (a).
The 1^st sentence in line 349 is unclear, Please rewrite.
In Table 7, please check “particular & Carbon Fixed “, also “forest” and other words in the same column should be capitalized.
The actual discussion section is 4-5 lines at the end of the discussion section. Please improve it by comparing your result with others.
In section “6. Conclusion”, please check the fourth item, in line 504, it should start from the beginning of the page on the left side.

Author Response

Subject: Revision Notes (Manuscript ID: sustainability-3524391)

Dear reviewer，

We sincerely appreciate your valuable and constructive comments on our manuscript entitled “Ecological Security Pattern Construction for Carbon Sink Capacity Enhancement: The Case of Chengdu Metropolitan Area”(Manuscript ID: sustainability-3524391). Your insightful suggestions have greatly improved the overall structure, logical coherence, and academic rigor of our study.We have carefully addressed each of your comments and revised the manuscript accordingly. All modifications have been clearly marked in red font in the revised version for your convenience.Below, we provide a detailed point-by-point response to all reviewer comments. We truly appreciate your time and effort in reviewing our work and hope that the revisions meet your expectations.

Comments 1：Why is the current analysis limited to the years 2000–2020? What about the 2020–2024 timeframe?

Response 1:

We appreciate your comments and suggestions. The current time period for the analysis is limited to 2000 to 2020, mainly because most of the most recent data after 2020 are not yet publicly available, such as land use data (LUCC), Normalized Vegetation Index (NDVI), population data, and road and water system data. Therefore, we are unable to obtain the complete data after 2020 for analysis at this time. In the future, when the relevant data become publicly available, we plan to further extend the timeframe of the study to explore the dynamics of the ecological network more comprehensively.

Comments 2：Please use appropriate spacing to separate the two statements in line 18. Please check the whole manuscript (e.g., the keywords).

Response 2:

Thanks to your comments and suggestions, we have adjusted the sentence spacing in line 18, including the keyword section, and have rechecked the entire manuscript to ensure consistent formatting.

Comments 3：In the keywords (i.e. Network optimization), according to the journal format, it should start with a small letter. Please check the other.

Response 3:

Thank you for your valuable comments on our article. We have put the keywords, according to the format required by the journal, to start with a small letter, except for the beginning of terms with specialized abbreviations that have no lower case, and checked the whole manuscript. At the same time, we have re-adjusted the keyword section according to the comments of other reviewers. We changed ecological network to ecological security pattern, network optimization to ecological spatial network to make it more in line with the keywords of the core content of this paper.

Revisions3:

Keywords: ecological security patterns;chengdu metropolitan area; MSPA-MCR model; carbon sequestration; complex networks;ecospatial networks

Comments 4：There are several abbreviations in the present work, so please add a table of abbreviations at the end of the manuscript according to the journal style.

Response 4:

Thank you very much for your helpful suggestion. In accordance with the journal’s type guidelines, we have added a list of abbreviations at the end of the manuscript. The list currently includes 12 key abbreviations used throughout the paper, such as ESP (Ecological Security Pattern), MCR (Minimum Cumulative Resistance), NDVI (Normalized Difference Vegetation Index), and others.All changes have been incorporated into the revised version of the manuscript and are clearly identifiable.

Revisions 4:

Table S8 List of abbreviations

Abbreviation	full name
ESP	Ecological Security Pattern
CMA	Chengdu Metropolitan Area
MSPA	Morphological Spatial Pattern Analysis
MCR	Minimum Cumulative Resistance
NDVI	Normalized Difference Vegetation Index
ENVI	Environment for Visualizing Images
Conefor	Conefor Sensinode
ArcGIS	Geographic Information System
DEM	Digital Elevation Model
LUCC	Land Use and Land Cover Change
AHP	Analytic Hierarchy Process
GM	Gravity Model

Comments 5：Please check “epresented” in line 88, I think it should be written as “represented”.

Response 5:

Thank you very much for your careful reading and kind correction. We have corrected the spelling error by changing “epresented” to “represented” as suggested.We sincerely appreciate your attention to detail. In response, we have also conducted a thorough review of the entire manuscript to ensure the accuracy, clarity, and consistency of all content. Your feedback has been instrumental in helping us improve the overall quality of the paper. Thank you again for your valuable support.

Revisions 5:

Ecological source points and corridors are represented as topological nodes and edges, forming a topological network.

Comments 6：Lines 108-115, without citations, please add, please polish your work via adding relevant citations.

Response 6:

Thank you for your careful review and suggestions. We have added citations to lines 108-115, section 2.1 Study area. Ensure that all citations are supported by the appropriate literature, citations have been highlighted in red.

Revisions 6:

2.1. study area

The Chengdu Metropolitan Area is situated in the western Sichuan Basin (102°49′–105°27′E, 29°15′–31°42′N). It covers an area of 33,114 km² and includes four core cities:Chengdu, Deyang, Meishan, and Ziyang (Fig.1). As a desely populated and economically vital region in western China, The CMA holds a strategic position within both the Yangtze River Economic Belt and the Belt and Road Initiative.[32].It also functions as an ecological barrier in the upper reaches of the Yangtze River. This area includes critical zones such as the Longmen Mountains, Qionglai Mountains, and Longquanshan Urban Forest Park.[33]. However, rapid urbanization and improved transportation have exacerbated landscape fragmentation and ecosystem degradation, leading to higher carbon emissions. Thus, building a well-connected ecological network is essential for balancing urban development with ecological sustainability.

References:

Wang, F.; Guo, H.; Zhang, Q.; Yu, Q.; Xu, C.; Qiu, S. Optimizing Ecological Spatial Network Topology for Enhanced Carbon Sequestration in the Ecologically Sensitive Middle Reaches of the Yellow River, China. Remote Sensing2023, 15, 2308, doi:10.3390/rs15092308.
Meng, B.; Wang, X.; Zhang, Z.; Huang, P. Spatio-Temporal Pattern and Driving Force Evolution of Cultivated Land Occupied by Urban Expansion in the Chengdu Metropolitan Area. Land2022, 11, 1458, doi:10.3390/land11091458.

Comments 7：Regarding Fig.1, if this figure was taken from another work, please refer to it. The same tendency as Figures 4, 5, 6, 7, and 10.

Response 7:

Thank you very much for your valuable comments and suggestions. In response, we have standardized the formatting of Figures 4, 5, 6, 7, and 10 to ensure visual consistency across the manuscript.

We have adjusted the width and height of these figures to match the layout of Figure 1, setting them all to 14.65 cm in width and 10.36 cm in height. The updated figure dimensions now follow a consistent format, which improves the clarity and readability of the visual elements throughout the paper.

We sincerely appreciate your attention to detail, which has helped us enhance the overall presentation quality of the manuscript.

Comments 8：In line 149, please check the unit “2.5km”.

Response 8:

Thank you very much for your careful review and thoughtful correction. We sincerely apologize for the oversight in the manuscript.

As you correctly pointed out, the unit “2.5 km” has now been corrected to “2.5 km²”. We have also conducted a thorough review of the entire manuscript to ensure the consistency, accuracy, and completeness of all content.

We greatly appreciate your attention to detail, which has helped us further improve the quality of the paper.

Revisions 8:

Given the large study area and high fragmentation, core patches exceeding 2.5 km² were selected as candidate ecological sources via the eight-neighborhood method.

Comments 9：In line 150, please check the abbreviation “(dPC)”. I think it should be written as (PC).

Response 9:

Thank you very much for your valuable suggestion. After careful review, we have identified that the original expansion of “dPC” as “patch saliency” in line 150 was incorrect. We sincerely apologize for this mistake.

We have now corrected the expansion to the appropriate term: “patch importance index”, which is the standard interpretation of dPC in the context of landscape connectivity metrics. Additionally, we clarify that “PC” refers to “potential connectivity”.

This correction has been implemented in the revised manuscript, and the relevant content has been clearly highlighted in red. We greatly appreciate your attentive review, which has helped improve the accuracy and academic rigor of the manuscript.

Revisions 9:

MSPA results were supplemented by computing the potential connectivity (PC) and the patch importance index (dPC) using Conefor 2.6 [35], with a 1000 m threshold and 0.5 connectivity probability. Patches with dPC >0.20 were intersected with ecological redline zones to define final ecological sources, which were classified into forest, grassland, and water types. PC and dPC were calculated as shown in Equation [36].

Comments 10：In line 153, please check “. Patches”.

Response 10:

Thank you very much for your careful review. We have identified and corrected the formatting error related to the term “patches”. The formatting has now been properly adjusted.

The revision has been clearly highlighted in red in the updated manuscript. We sincerely appreciate your attention to detail.

Revisions 10:

（1）Identification of ecological sources based on MSPA

Comments 11：Please add citations for all equations-containing manuscript.

Response 11:

Thank you very much for your suggestion, we have added citations to all sections of the manuscript that contain equations with corresponding relevant references. The added citations have been red-flagged.

Revisions 11:

（1）Identification of ecological sources based on MSPA

Patches with dPC >0.20 were intersected with ecological redline zones to define final ecological sources, which were classified into forest, grassland, and water types. PC and dPC were calculated as shown in Equation [36].

（3）Extraction of potential ecological corridors

The MCR formula is presented as follows [40].

(4)Screening ecological corridors

The GM formula is presented as follows[43].

2.2.3 Calculation of carbon sequestration capacity

The carbon sequestration coefficient was calculated as follows[49].

References:

Huang, X.; Wang, H.; Shan, L.; Xiao, F. Constructing and Optimizing Urban Ecological Network in the Context of Rapid Urbanization for Improving Landscape Connectivity. Ecological Indicators2021, 132, 108319.
Nie, W.; Shi, Y.; Siaw, M.J.; Yang, F.; Wu, R.; Wu, X.; Zheng, X.; Bao, Z. Constructing and Optimizing Ecological Network at County and Town Scale: The Case of Anji County, China. Ecological Indicators2021, 132, 108294, doi:10.1016/j.ecolind.2021.108294.
Xu, X.; Wang, S.; Rong, W. Construction of Ecological Network in Suzhou Based on the PLUS and MSPA Models. Ecological Indicators2023, 154, 110740, doi:10.1016/j.ecolind.2023.110740.
Qiu, S.; Yu, Q.; Niu, T.; Fang, M.; Guo, H.; Liu, H.; Li, S. Study on the Landscape Space of Typical Mining Areas in Xuzhou City from 2000 to 2020 and Optimization Strategies for Carbon Sink Enhancement. Remote Sensing2022, 14, 4185, doi:10.3390/rs14174185.

Comments 12：In line 164, please check the following abbreviation “elevation (DEM)”.

Response 12:

Thank you very much for your comment and careful review, we have revised the original DEM expansion Elevation to Digital Elevation Model, and checked other abbreviations and expansions in the text to ensure accuracy. The revised part has been marked in red.

Revisions 12:

The data included Land use and land cover change (LUCC), Digital Elevation Model (DEM), slope, topographic relief, Normalized Difference Vegetation Index(NDVI), water bodies, roads, and population.

Comments 13：In Table S2, please check “Slope/(° )”. If this is a supplementary table, why did you add it to the manuscript?

Response 13:

Thank you very much for your careful review. The use of parentheses in “slope/(°)” was indeed a formatting error during manuscript preparation, and it was not intended to indicate a supplementary table or reference.

What we meant to express was the unit of slope. We have now corrected the notation to “slope/°” to clearly and correctly represent the slope unit.

We sincerely apologize for the confusion this may have caused, and we appreciate your attention to detail, which has helped us further improve the precision of the manuscript.

Revisions 13:

Table S2. Assignment of resistance factor.

Resistance factor	Weight	1	10	20	40	70	100
DEM/m	0.15	216-553	553-936	936-1539	1539-2235	2235-3064	3064-7100
Slope/°	0.11	0-6.63	6.634-13.62	13.62-22.70	22.70-34.57	34.57-54.83	54.83-89.40
Degree of topographic relief	0.06	0.57-1	0.43-0.57	0.33-0.43	0.26-0.33	0.20-0.26	0-0.20
NDVI	0.10	201-254	186-201	169-186	145-169	109-145	0-109
LUCC	0.37	Forest	Grassland	Water	Farmland	Unutilized land	Construction land
Water network density	0.09	0-0.015	0.015-0.04	0.041-0.068	0.068-0.099	0.099-0.143	0.143-0.203
Road density	0.07	0-0.38	0.38-1.18	1.18-2.45	2.45-4.28	4.28-7.39	7.39-12.05
Population density	0.05	0-19.56	19.56-88.01	88.01-244.46	244.46-586.70	586.701-1271.19	271.19-2503.27

Comments 14：In line 226, please check the format of “CO2”.

Response 14:

Thank you very much for your careful review, we have changed the format of “CO2” to subscript “CO₂” and double-checked the format of “CO₂” in the text. The changes have been marked in red in the revised version to ensure readability and accuracy.

Comments 15：In Table S4, please check the format of “(t/hm2a)”.

Response 15:

Thank you very much for your careful review, we have revised the format of “(t/hm2a)” to “(t/hm²a)” and double-checked the whole text. The revised part has been marked in red in the revised draft.

Revisions 15:

Table S4. Carbon sequestration coefficients for different land-use types.

Land use type	Carbon sequestration factor (t/hm²a)	Bibliography
Forest	283.9	[48,49]
Grassland	143.8	[50]
Watershed	67.1	[51,52]

Comments 16：In line 315, “and low resilience, impeding recovery”, I think “and” should be added before “impeding”, please check.

Response 16:

Thank you very much for your careful examination and valuable comments. We have found the original text “and low resilience, impeding recovery”, position, modified to “and low resilience, and mpeding recovery and low resilience, and mpeding recovery”. However, the paragraph 3.2.1. Analysis of the overall structure of the topology network has been rewritten at the suggestion of other reviewers.

All updates have been clearly highlighted in red in the revised manuscript. We greatly appreciate your attention to detail, which has contributed to improving the clarity and professionalism of the paper.We look forward to your further valuable comments on our revisions.

Revisions 16:

3.2.1. Topological network structure overview

Comments 17：In Table S5, (pagerank & ecological) should be stated with a capital letter, please check.

Response 17:

Thank you very much for your careful review, we have modified the initial letters of (pagerank & ecological) to capitalize “Pagerank”. As well as the title of Table S5. The lowercase title is modified to Table S5. Ecological network topology indicators. the rewritten part has been marked red. Thank you very much for your carefulness in helping us to find this minor error, and your correction is much appreciated.

Revisions 17:

Table S5. Ecological network topology indicators.

Indicators/Years	2000	2010	2020
Degree	7.5	7.067	6.286
Pagerank	0.063	0.066	0.071
Closeness centrality	0.676	0.679	0.658
Betweenness centrality	3.75	3.51	3.50
Eigenvector centrality	0.601	0.671	0.662
Clustering coefficient	0.579	0.563	0.50

Comments 18：“Betweenness Centrality” in Table S5, I think “Centrality” should start with a small letter.

Response 18:

Thank you very much for your careful review and for pointing out the typographical inconsistency. We have corrected the word "centrality" in "Betweenness Centrality", ensuring that the term is now consistently and accurately written throughout the manuscript.

The correction has been implemented and the revised part has been highlighted in red in the updated manuscript. We sincerely appreciate your attention to this detail, which has helped us improve the precision and consistency of our terminology.Thank you again for your kind and meticulous review.

Comments 19：Please check the first sentence in Figure 8, it should be started with (a).

Response 19:

Thank you very much for your helpful comment. We have reviewed the caption of Figure 8 and found that the first sentence was indeed unclear.

In response, we have revised the figure caption to begin with “(a)” as required, and additionally added a general title at the beginning of the caption to improve clarity and readability.

The updated figure caption has been clearly highlighted in red in the revised manuscript. We sincerely appreciate your attention to this detail.

Revisions 19:

Figure 8. Analysis of topological indicators 2000-2020.(a) statistical analysis of topological metrics of centrality and mediacy centrality, (b) proximity centrality and clustering coefficient topological metrics (c) statistical analysis of topological metrics of eigenvector centrality and PageRank topological metrics for 2000-2020.

Comments 20：The 1st sentence in line 349 is unclear, Please rewrite.

Response 20:

We sincerely appreciate your comments and suggestions and have reworded the first sentence of line 349 of 3.2.4, highlighting the change in red.

Revisions 20:

Nodes such as 2, 4, 5, 11, and 13 in 2000; 7, 8, 9, 11, and 15 in 2010; and 6, 10, and 11 in 2020 showed the lowest clustering coefficients, suggesting weak connectivity and low carbon sequestration efficiency.

Comments 21：In Table 7, please check “particular & Carbon Fixed “, also “forest” and other words in the same column should be capitalized.

Response 21:

Thank you very much for your careful checking, we have checked and revised, and capitalized “particular & Carbon Fixed”, and “forest” and other words in the same column, and the revised parts have been marked in red in the revised draft. The changes have been marked in red in the revised draft.

Revisions 21:

Table S7. Calculation of carbon sequestration for each ecological source.

Particular year	Main land use types	Area (ha)	Total source area (ha)	Proportion of total ecological resources	Carbon fixed (Mg C yr⁻¹)	Proportion of total carbon sequestration	Total carbon sequestration ((Mg C yr⁻¹)
2000	Forest	411951.8	541959.5	76.01%	116953116.6	87.17%	134174061.8
	Grass	110787.8		20.44%	15931289.34	11.87%
	Water	19219.91		3.60%	1289655.84	0.96%
2010	Forest	414045.6	541757.5	76.43%	117547537.1	87.53%	134296416.5
	Grass	106641.6		19.68%	15335060.05	11.42%
	Water	21070.33		3.89%	1413819.28	1.05%
2020	Forest	410771.3	536894.7	76.51%	116617965	87.51%	133264170
	Grass	106692.6		19.87%	15342399.48	11.51%
	Water	19430.78		3.62%	1303805.522	0.98%

Comments 22：The actual discussion section is 4-5 lines at the end of the discussion section. Please improve it by comparing your result with others.

Response 22:

Thank you very much for your valuable comment. Based on your suggestion, we have expanded the Discussion section to improve the scientific interpretation of our findings and to contextualize them within a broader academic framework.

Specifically, we added comparative analyses with published studies on ecological network optimization and carbon sink enhancement. For example, we cited regionally scaled ecological network restoration efforts in the Jiaodong Peninsula and Yunnan Province. These comparisons further confirm the applicability and adaptability of our methodology across different ecological and socio-economic contexts.

All corresponding revisions have been clearly highlighted in red in the revised manuscript.

Revisions 22:

Discussion

4.1. Optimizing regional ecological security patterns to enhance regional carbon sequestration

This paves the way for developing a synergistic model integrating ecology, carbon sinks, and economic growth. These findings align with similar studies conducted in Yunnan and the Jiaodong Peninsula, validating both the theoretical underpinnings and regional adaptability of the methodology[26,27]. Overall, the optimized ecological security pattern in the Chengdu metropolitan area not only improves carbon sink capacity and spatial connectivity but also provides a strategic interface between ecological restoration and the pursuit of carbon neutrality.

Comments 23：In section “6. Conclusion”, please check the fourth item, in line 504, it should start from the beginning of the page on the left side.

Response 23:

Thank you very much for your careful checking, we have checked and revised the part of the fourth subsection merged in the conclusion (3) and adjusted it to ensure the readability and clarity of the structure of the article. The revised part has been dealt with in red in the text.

Revisions 23:

(1) Between 2000 and 2020, the area of ecological source sites first expanded and then declined, reflecting an increase in regional ecological fragmentation.Ecological resistance exhibited a “high-in-the-center and low-at-the-edge” spatial pattern, with ecological networks concentrated in the west, while the ecological foundations in central and western areas remain weak and urgently require enhancement.

(2) The ecological spatial network showed signs of degradation, with network modules declining from three to two. Although the core remained stable, the southeast was marginalized due to corridor disruptions, and key topological indicators declined, reflecting reduced connectivity.

(3) Forests consistently served as the primary carbon sink (>70%) and correlated significantly with multiple network metrics. Grassland sinks were influenced by clustering coefficients, while water bodies were positively associated with centrality, emphasizing their importance in localized connectivity.

(4) The addition of 10 new stepping stones and 45 ecological corridors in 2020 increased the annual carbon sink capacity by 4.16 millionC yr⁻¹,with forest ecosystems contributing up to 94.8% significantly enhancing network connectivity and supporting regional carbon neutrality goals.

Yours sincerely,

Langong Hou¹, Huanhuan HU ^1*,and Tao Liu²,Che Ma ¹

1 School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621000, China

2 Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Review comments for the manuscript“Ecological Security Pattern Construction for Carbon Sink Capacity Enhancement: The Case of Chengdu Metropolitan Area ”

(1)Line 439, Figure 10 title missing.

(2)Line 210, 221,title order and format error.

(3)Line 230, formula 5 serial number format error.

(4)Line 322,362, Table S5 ,S7 row spacing is inconsistent with other tables.

(5)Figure 2, the content of the title is inconsistent with that in the figure.

(6)Figure 5, The elevation in 2000 is different from that in 2010 and 2020 and needs to be corrected.

(7)Figures 4 ,7 and 9 have low definition, please correct.

(8)Figure 8, legend letter case is inconsistent, please correct.

(8)Line 116-127, data should be provided to illustrate the significance of studying CMA area.

(9)Line 217,324,335, the description of the six topological metrics is inconsistent.

(10)Line 320, “Carbonwise Tianfu” the contents in quotation marks shall be explained by quoting detailed sources.

(11)There are still some serious problems in the overall English writing ability of this article, such as the inconsistency between the description before and after the revision. It is suggested to continue to polish the language of this article.

Author Response

Subject: Revision Notes (Manuscript ID: sustainability-3524391)

Dear reviewer，

We sincerely thank you for your thorough review and valuable feedback on our manuscript entitled “Ecological Security Pattern Construction for Carbon Sink Capacity Enhancement: The Case of Chengdu Metropolitan Area” (Manuscript ID: sustainability-3524391).Your insightful comments have greatly contributed to improving the clarity, coherence, and academic quality of our work. In response, we have carefully revised the manuscript and addressed all your suggestions in detail. All changes have been clearly highlighted in red in the revised version for your convenience.

Below, we provide a point-by-point response to each of your comments. We truly appreciate your time and effort, and we hope that the revised manuscript now meets your expectations.

Comments 1：Line 439, Figure 10 title missing.

Response1:

Thank you very much for your careful review. We have noticed the missing title for Figure 10 at line 439, as you pointed out.We have now reinserted the correct title for Figure 10 in the revised manuscript. The addition has been clearly highlighted in red.

We sincerely appreciate your attention to detail, which has helped us improve the clarity and completeness of the manuscript.

Revisions 1:

Figure 10. Optimized Ecological Spatial Network of Chengdu metropolitan area.

Comments 2：Line 210, 221,title order and format error.

Response2:

Thank you very much for your careful review. There were errors in the title order and formatting at lines 210 and 221.We have now corrected the title sequence and standardized the formatting for the following sections:

2.3.2. Ecospatial Network Topology

2.3.3. Calculation of Carbon Sequestration Capacity

These corrections have been clearly highlighted in red in the revised manuscript.

We sincerely appreciate your attention to detail, which has helped us improve the overall structure and consistency of the paper.

Comments 3：Line 230, formula 5 serial number format error.

Response3:

Thank you very much for your careful review. We have corrected the serial number format of Formula 5 at line 230, and thoroughly checked the numbering and formatting of all formulas to ensure overall consistency.

The corrections have been highlighted in red in the revised manuscript.

Revisions3:

(5)

Comments 4：Line 322,362, Table S5 ,S7 row spacing is inconsistent with other tables.

Response4:

Thank you very much for your careful review. We have adjusted the row spacing in Tables S5 and S7 to ensure consistency with the formatting of the other tables.

Revisions 4:

Table S5. Ecological network topology indicators.

Indicators/Years	2000	2010	2020
Degree	7.5	7.067	6.286
Pagerank	0.063	0.066	0.071
Closeness centrality	0.676	0.679	0.658
Betweenness centrality	3.75	3.51	3.50
Eigenvector centrality	0.601	0.671	0.662
Clustering coefficient	0.579	0.563	0.50

Table S7. Calculation of carbon sequestration for each ecological source.

Particular year	Main land use types	Area (ha)	Total source area (ha)	Proportion of total ecological resources	Carbon fixed (Mg C yr⁻¹)	Proportion of total carbon sequestration	Total carbon sequestration ((Mg C yr⁻¹)
2000	Forest	411951.8	541959.5	76.01%	116953116.6	87.17%	134174061.8
	Grass	110787.8		20.44%	15931289.34	11.87%
	Water	19219.91		3.60%	1289655.84	0.96%
2010	Forest	414045.6	541757.5	76.43%	117547537.1	87.53%	134296416.5
	Grass	106641.6		19.68%	15335060.05	11.42%
	Water	21070.33		3.89%	1413819.28	1.05%
2020	Forest	410771.3	536894.7	76.51%	116617965	87.51%	133264170
	Grass	106692.6		19.87%	15342399.48	11.51%
	Water	19430.78		3.62%	1303805.522	0.98%

Comments 5：Figure 2, the content of the title is inconsistent with that in the figure.

Response 5：

Thank you very much for your careful review. Figure 2 is intended to illustrate the processed data sources used for constructing the resistance surface, including LUCC, DEM, slope, terrain relief, NDVI, road density, water density, and population density.

We have revised the title of Figure 2 to make it consistent with the content shown in the figure and to improve clarity.The correction has been highlighted in red in the revised manuscript.

Revisions 5:

Figure 2.Comprehensive resistance surface elements constructed based on the dataset.a. DEM/m; b. Slope/°; c. Topographic relief; d. LUCC; e. NDVI; f. Population density/per; g. Road density; h. Water network density, i . Combined resistance surface.

Comments 6：Figure 5, The elevation in 2000 is different from that in 2010 and 2020 and needs to be corrected.

Response 6:

Thank you very much for your careful and constructive review. Figure 5 is intended to illustrate the integrated ecological resistance surfaces for the years 2000, 2010, and 2020, in order to compare changes in ecological resistance over the 20-year period.

The DEM data itself remains constant across all years. The observed variations in the figure result from the recalculation of composite resistance surfaces based on multiple dynamic factors, such as land use, NDVI, and others.

In response to your suggestion, we have revised the title of Figure 5 to more clearly reflect its content and purpose. The corresponding changes have been highlighted in red in the revised manuscript.

Revisions 6:

Figure 5. Combined resistance surfaces in the CMA, 2000-2020.

Comments 7：Figures 4 ,7 and 9 have low definition, please correct.

Response7:

Thank you very much for your helpful comment. We have replaced Figures 4, 7, and 9 with higher-resolution versions to ensure improved clarity and readability.

The updated figures have been incorporated into the revised manuscript.

Comments 8：Figure 8, legend letter case is inconsistent, please correct.

Response 8:

Thank you very much for your comment. We have corrected the letter case inconsistency in the legend of Figure 8 to ensure formatting uniformity.

The updated figure has been included in the revised manuscript .

Revisions 8:

Figure 8. Analysis of topological indicators 2000-2020.(a) statistical analysis of topological metrics of centrality and mediacy centrality, (b) proximity centrality and clustering coefficient topological metrics (c) statistical analysis of topological metrics of eigenvector centrality and PageRank topological metrics for 2000-2020.

Comments 9：Line 116-127, data should be provided to illustrate the significance of studying CMA area.

Response9:

Thank you very much for your valuable comment. To address your suggestion, we have added quantitative data on population, GDP, and carbon emissions to highlight the strategic significance of the Chengdu Metropolitan Area (CMA) as the study area. These additions help reinforce the ecological, socio-economic, and carbon governance importance of the CMA. The revised content has been highlighted in red in lines 116–127 of the manuscript.

Revisions 9:

The CMA is located in the western Sichuan Basin (102°49′–105°27′E, 29°15′–31°42′N), covering 33,114 km² and comprising four core cities: Chengdu, Deyang, Meishan, and Ziyang (Figure 1). As a densely populated and economically active hub in western China, it plays a crucial role in the Yangtze River Economic Belt and the Belt and Road Initiative [33].By 2022, the CMA supported over 31.2 million residents, contributed more than 38% of Sichuan Province’s GDP, and accounted for over 85% of its carbon emissions [34,35]. The area also functions as an ecological barrier for the upper Yangtze River, encompassing critical zones such as the Longmen and Qionglai Mountains and Longquanshan Urban Forest Park[36].However, accelerated urbanization and infrastructure development have intensified landscape fragmentation and ecosystem degradation. Therefore, constructing a resilient and well-connected ecological network is essential for balancing urban growth with ecological sustainability.

Comments 10：Line 217,324,335, the description of the six topological metrics is inconsistent.

Response10

Thank you very much for your helpful comment. We have carefully revised the descriptions of the six topological metrics at lines 217, 324, and 335 to ensure consistency in terminology, structure, and emphasis. These modifications improve the clarity and readability of the manuscript.

All relevant changes have been highlighted in red in the revised version.

Revisions10:

3.2.2. Degree and Betweenness centrality

Degree centrality reflects the number of direct connections a node has with surrounding nodes, while betweenness centrality indicates its role as a bridge within the network. Analysis conducted using Gephi 0.10.0 revealed a consistent decrease in the average values of both metrics from 2000 to 2020 (Table S5), indicating weakened connectivity, diminished structural redundancy, and increased ecological vulnerability (Figure 8a). Nodes 5, 10, and 14 in 2000, and nodes 9 and 12 in 2020, showed high values in both metrics, serving as crucial bridges for ecological flow and carbon transfer. The degradation of these nodes could diminish the carbon sequestration capacity of adjacent ecological sources.

Therefore, optimizing the ecological network should emphasize the preservation of these structurally vital nodes and incorporate them into Chengdu’s “Carbonwise Tianfu” carbon credit mechanism through targeted compensation and incentive policies to channel resources toward high carbon sink potential areas [55].

3.2.3. Proximity centrality and Clustering coefficients

Closeness centrality measures the average shortest path from a node to all other nodes, reflecting its spatial accessibility, while the clustering coefficient indicates the degree of local aggregation around a node. From 2000 to 2020, both indicators showed an overall declining trend (Table S5). suggesting increased distances between ecological sources and a more fragmented local structure (Figure 8b). Nodes 5, 11, and 13 (2000); 7, 9, and 15 (2010); and 6 and 10 (2020) exhibited low clustering coefficients, indicating weak local connectivity, limited carbon sequestration efficiency, and reduced system stability.

It is recommended to prioritize corridor construction in these areas to enhance local connectivity and designate them as priority restoration zones under Chengdu’s “Carbonwise Tianfu”mechanism, promoting resource investment through market-based carbon incentives.

3.2.4. Eigenvector centrality and PageRank

Eigenvector centrality measures a node’s connection to other influential nodes, while PageRank indicates its importance in a random walk process. Both indicators peaked in 2010 and slightly declined by 2020 (Table S5), suggesting a weakening of the network's structural hierarchy under external disturbances(Figure 8c). Nodes 5, 10, 13, and 15 in 2000, and nodes 5, 9, 11, and 12 in 2020, had high PageRank values, functioning as key components for maintaining structural integrity and carbon linkages. In contrast, peripheral nodes 8 and 14 in 2020 showed low values, indicating limited contributions to carbon sinks.

Therefore, conservation efforts should prioritize high-impact nodes while also enhancing the functional role of low-centrality nodes. It is recommended to incorporate this approach into Chengdu’s “Carbonwise Tianfu” initiative by establishing a mechanism that couples carbon value with network topological influence.

Comments 11：Line 320, “Carbonwise Tianfu” the contents in quotation marks shall be explained by quoting detailed sources.

Response11:

Thank you very much for your helpful suggestion. In response, we have added a detailed explanation of the“Carbonwise Tianfu”mechanism at line 320, along with the appropriate reference to an official policy document issued by the Chengdu Municipal Government.

The revision has been highlighted in red in the updated manuscript.

Revisions11:

Cited document:

Chen J.; Sun Y.; Liu H. Survey and Research on Carbon Inclusion Mechanisms in Sichuan Province--The Case of Chengdu’s “Carbon Inclusion Tianfu.” Low Carbon World 2022, 12, 40–42.

Comments 12：There are still some serious problems in the overall English writing ability of this article, such as the inconsistency between the description before and after the revision. It is suggested to continue to polish the language of this article.

Response12:

Thank you very much for your valuable comment. In response, we have carefully rechecked and revised the grammar, sentence structures, and overall language expression throughout the manuscript to ensure consistency, clarity, and academic professionalism.

The corrections have been comprehensively applied and are highlighted in red in the revised manuscript.

Yours sincerely,

Langong Hou ¹, Huanhuan HU ^1*,and Tao Liu²,Che Ma ¹

1 School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621000, China

2 Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China

Author Response File: Author Response.docx

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

Review comments for the manuscript ''Ecological Security Pattern Construction for Carbon Sink Capacity Enhancement: The Case of Chengdu Metropolitan Area ''

This article studies the construction of an ecological security pattern in the Chengdu metropolitan area to enhance carbon sequestration capacity, providing feasible strategies for improving ecological networks and promoting regional carbon neutrality. The seriously problematic parts of the article were revised, while purpose and meaning were added and grammar was touched up and modified. In conclusion, an acceptable level has been reached.

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Ecological Security Pattern Construction for Carbon Sink Capacity Enhancement: The Case of Chengdu Metropolitan Area

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