Review Reports - Enhancing the Resilience of the Environment—Economy—Society Composite System in the Upper Yellow River from the Perspective of Configuration Analysis

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Comments to the Authors:

Thank you for the work you have done. Taking the Upper Yellow River (UYR) as an example, this paper constructed a resilience evaluation index system for the environment-economy-society (EES) composite system. The three-dimensional space vector model was built to calculate the resilience development index (RDI) of three subsystems and composite system from 2009 to 2022. Pathways supporting high resilience levels of composite systems were examined using configuration analysis. I have reviewed this paper with interest and would like to propose several revisions to enhance its quality.

(1) The authors have successfully adopted an international perspective and effectively highlighted the importance and relevance of the research topic. This is a notable strength of the manuscript. To further strengthen the introduction, it might be beneficial to incorporate some actual data, such as from international contexts or specific to the ecological environment of the Yellow River Basin, at the beginning section.

(2) “However, most existing research are limited in analyzing the factor dependence issues, and it is difficult to elucidate complex causal relationships such as multiple concurrent and equivalent dependencies among independent variables.” The authors need to support this argument by reviewing the relevant literature.

(3) The innovation of the manuscript needs to be further improved. Clarifying better the novelty of your work than existing literature in the end of section 1.

(4) The authors should explain why the fsQCA methodology was used to conduct the study and what new contributions the fsQCA methodology can bring in section 1.

(5) It is not possible to determine how the seven variables interact with each other. Using the fsQCA methodology to examine the relationship between different (factors and Resilience of EES composite system is too theoretical and not necessarily feasible in practice.

(5) The map in Figure 1 should include its official Map Review Number.

(6) The justification for selecting the three-dimensional space vector model is insufficiently substantiated. The authors should provide a more comprehensive explanation of why this method is more suitable for this study compared to other commonly used approaches (e.g., the coupling coordination degree model).

(7) “In this study, the fsQCA approach was employed to investigate the synergistic interplay among the internal components of the EES composite system in 21 prefecture-level cities in the UYR, and to find the configuration paths that generate high resilience levels.” "The authors appear to have employed a static QCA approach. The configuration pathways for cities exhibit dynamic variations, as demonstrated in Figure 10. For panel data analysis, they should consider using dynamic QCA methods to examine the temporal effects on the results.

(8) In general, some scholars performed robustness tests by adjusting consistency thresholds, the PRI value, and the frequency threshold. The result is robust when changing the parameters, and the original configuration produces a clear subset relationship between the configurations. Did the authors conduct robustness tests?

(9) More generally, this paper is very Upper Yellow River focused. Are there important insights arising from other countries or regions? The latter sections of the paper should be revised to suggest how the findings might apply to other countries or regions.

(10) For a study likely to be published in 2025, the authors should incorporate more up-to-date references. The following recent publications are recommended for the authors to read: 1) Systems-2025-Synergistic Systems of Digitalization and Urbanization in Driving Urban Green Development: A Configurational Analysis of China’s Yellow River Basin. 2) Sustainability-2025-Spatiotemporal Evolution and Influencing Factors of Urban Ecological Resilience: Evidence from the Yellow River Basin, China.

Please use red font to highlight all revised content. Thank you for your cooperation.

Author Response

Response to Reviewer 1 Comments

We really appreciate the time and effort that you dedicated to providing feedback on our manuscript and are grateful for the insightful comments on and valuable improvements to our paper. We have incorporated all the suggestions. Those changes are highlighted within the manuscript. Please see below, for a point-by-point response to your comments and concerns.

Comment 1: The authors have successfully adopted an international perspective and effectively highlighted the importance and relevance of the research topic. This is a notable strength of the manuscript. To further strengthen the introduction, it might be beneficial to incorporate some actual data, such as from international contexts or specific to the ecological environment of the Yellow River Basin, at the beginning section.

Response 1: Thanks for your kind suggestion. We have added some actual data about the ecological environment problems in the UYR, at the beginning section.

Details are as follows:

“In 2023, the soil erosion area in the five provinces (regions) of the UYR was 518900 square kilometers, accounting for 65.3% of the total soil erosion area in the basin. This has had a negative impact on the effective functioning of the regional ecological barrier. Moreover, due to the fragile ecological foundation, the ecological quality index of Gansu, Ningxia, and Qinghai is still lower than the national average level (59.95).” (See page 2)

Comment 2: “However, most existing research are limited in analyzing the factor dependence issues, and it is difficult to elucidate complex causal relationships such as multiple concurrent and equivalent dependencies among independent variables.” The authors need to support this argument by reviewing the relevant literature.

Response 2: Thanks for your kind suggestion. We have reviewed the methods used by previous scholars to measure influencing factors and summarized the limitations of existing research.

Details are as follows:

“Determining the key drivers that influence system resilience is essential for enhancing the resilience level of composite system. As the most widely used methods, correlation analysis, multivariate linear regression, geographically weighted regression model, spatial Durbin model, and other regression-based methods are employed to determine the relationship among variables by building a linear or nonlinear model to describe the relationship between them, to determine the key factors^[20-22]. These methods have the advantage of simplicity and efficiency. In order to further explore potential influencing factors and their interrelationships, complex modeling methods such as the geographic detector model, the obstacle degree model, system dynamics, structural equation model, logarithmic mean divisia index (LMDI model), projection pursuit model, etc. have been applied^[23-27]. These models construct relationship models containing multiple potential influencing factors to reveal the existence and mode of action of factors. In addition, some specific analytical frameworks or models, such as DEMATEL model, ISM model, hierarchical analysis, factor analysis, and fuzzy comprehensive evaluation have also been applied^[28-29]. Recently, with the accelerated evolution of artificial intelligence technology, neural networks, random forests, support vector machines, cluster analysis as well as other machine learning-like methods have been introduced^[30-31]. Such methods systematically analyze historical and real-time data by means of intelligent algorithms to mine key features or patterns related to system resilience, and identify the main factors affecting system resilience.” (See page 3)

Furthermore, there is a lack of analysis on the multiple concurrent equivalent dependencies between factors in existing research, and most of the analysis focuses on the impact of single or multiple factors on the results.

Details are as follows:

“Zhang et al.^[⁵⁸^] found that financial investment in environmental protection, the transformation and upgrading of the industrial structure and the efficient resource allocation are the key factors affecting the ecological resilience within the UYR. Yang et al.^[⁵⁹^] found that urbanization rate, education level, and industrial structure upgrading are the key elements affecting the ecological resilience of cities in the UYR. According to Zhang et al.^[⁶⁰^], the primary determinants of ecological resilience in UYR cities are the intensity of land development and the degree of environmental pollution. Li et al.^[6¹^] found that environmental pressure indicators, such as water resources utilization rate and water consumption per 10,000 yuan GDP, are primary factors influencing the resilience of the water resources-social economy-ecological environment composite system in the UYR. All of these factors are identified in this study, which proves the validity and accuracy of the influencing factor identification to a certain extent.” (See pages 16-17)

Comment 3: The innovation of the manuscript needs to be further improved. Clarifying better the novelty of your work than existing literature in the end of section 1.

Response 3: Thanks for your helpful comments. We have improved the innovation of the manuscript in the end of section 1.

Details in the paper are as follows: “There are two main innovations in this paper: (1) The three-dimensional space vector model was used to measure the multidimensional characteristics and dynamic changes of the system resilience. (2) The combined factorial effects on the system resilience were explored using fsQCA method, and the potential paths for improving the system resilience from a configuration perspective. This makes up the unforeseen interaction and combined effects of multiple factors, which provide a scientific support for the resilience enhancement of EES composite system.” (See page 3)

Comment 4: The authors should explain why the fsQCA methodology was used to conduct the study and what new contributions the fsQCA methodology can bring in section 1.

Response 4: Thanks for your helpful comments. Configuration analysis is a perspective used to analyze the collaborative interactions between multiple factors, including QCA, fsQCA, dynamic QCA, and other methods. fsQCA approach was formed by incorporating fuzzy set theory into the QCA method. It can deal with the fuzziness and uncertainty between the factors of system resilience, effectively integrate multi-source information through the flexible setting of set affiliation, reveal the nonlinear relationship and multiple concurrent paths between factors. Besides, it can provide a set of systematic and explanatory resilience improvement path sets for decision makers, and enhance the scientificity and operability of decision-making. Under the configuration perspective, the impact of internal components of the EES on its resilience level is not independent. Therefore, in this study, the fsQCA approach was employed to investigate the synergistic interplay among the internal components of the EES composite system in 21 prefecture-level cities in the UYR, and to find the configuration paths that generate high resilience levels.

Besides, the contributions that fsQCA method can bring have been highlighted in section 1.

Details are as follows:

“(2) The combined factorial effects on the system resilience were explored using fsQCA method, and the potential paths for improving the system resilience from a configuration perspective. This makes up the unforeseen interaction and combined effects of multiple factors, which provide a scientific support for the resilience enhancement of EES composite system.” (See page 3)

Comment 5: It is not possible to determine how the seven variables interact with each other. Using the fsQCA methodology to examine the relationship between different (factors and Resilience of EES composite system is too theoretical and not necessarily feasible in practice.

Response 5: Thanks for your helpful comments. fsQCA approach was formed by incorporating fuzzy set theory into the QCA method. It can deal with the fuzziness and uncertainty between the factors of system resilience, effectively integrate multi-source information through the flexible setting of set affiliation, reveal the nonlinear relationship and multiple concurrent paths between factors. Besides, it can provide a set of systematic and explanatory resilience improvement path sets for decision makers, and enhance the scientificity and operability of decision-making. Theoretically speaking, this method is feasible. Under the configuration perspective, the impact of internal components of the EES on its resilience level is not independent. Therefore, we employed the fsQCA approach to investigate the synergistic interplay among the internal components of the EES composite system in 21 prefecture-level cities in the UYR, and to find the configuration paths that generate high resilience levels.

In practice, this method has been used by many scholars to identify improvement paths for achieving high resilience levels, which prove that the method is reliable. For example, Zhang et al.^[1] applied the fsQCA method to characterize the inherent synergies and complex casual relationships among the influencing factors that affect the platform ecosystem resilience, and ultimately identified three pathways for improving the resilience level of platform ecosystem. Liu et al.^[2] used the fsQCA method to analyze resilience data from 16 national-level new areas, and found that there are differentiated driving paths for enhancing the resilience of innovation ecosystems in the eastern, central, and western regions of China.

References:

[1] Zhang X, Zhang WS, Zhang R. The Improvement Path of Platform Ecosystem Resilience from

the Perspective of Configuration. Soft Science 2024; 38(12): 15-25.

[2] Liu B, Dou ST, Li WH. Strategies for Enhancing the Resilience of National Innovation Ecosystems in New Areas. Journal of Zhengzhou University(Philosophy and Social Sciences) 2023; 56(06): 70-77+140.

Furthermore, this paper analyzes the results obtained by the fsQCA method in the discussion section, which are relatively close to other results and also consistent with the understanding of managers in the Yellow River Basin.

Details are as follows:

“Identifying the influencing factors of resilience levels in composite system is important for optimizing system design, formulating coping strategies, enhancing system stability and reliability, and promoting sustainable development and scientific decision-making. Based on the configuration analysis method, this paper identifies that environmental protection, economic structure, social construction, quality of life and social security are the core elements to achieve high resilience levels. Huang et al.^[5⁷^] found that environmental protection stands as the major factor influencing ecological resilience within the UYR. Zhang et al.^[⁵⁸^] found that financial investment in environmental protection, the transformation and upgrading of the industrial structure and the efficient resource allocation are the key factors affecting the ecological resilience within the UYR. Yang et al.^[⁵⁹^] found that urbanization rate, education level, and industrial structure upgrading are the key elements affecting the ecological resilience of cities in the UYR. According to Zhang et al.^[6⁰^], the primary determinants of ecological resilience in UYR cities are the intensity of land development and the degree of environmental pollution. Li et al.^[6¹^] found that environmental pressure indicators, such as water resources utilization rate and water consumption per 10,000 yuan GDP, are primary factors influencing the resilience of the water resources-social economy-ecological environment composite system in the UYR. All of these factors are identified in this study, which proves the validity and accuracy of the influencing factor identification to a certain extent. At the same time, this illustrates again the multiple concurrency of the multi-dimensional influencing factors of the system resilience. It has been found that the fsQCA method can be used to identify influencing factors and find the optimal development path through the arrangement and combination of factors. Yuan et al.^[62]empirically investigated the driving mechanisms toward high ecological resilience index (ERI) for 280 cities in China with the fsQCA method based on the technology-organization-environment (TOE) framework. Choi^[63] employed the fsQCA method to explore the casual complexities of risk mitigants for the supply chain country risk (SCCR). Cowell and Cousins^[64] analyzed the relationship between the influencing factors of resilience for 22 United States cities using the fsQCA method.” (See pages 16-17)

Overall, we believe that this method is feasible for studying the resilience of the composite system in the UYR.

Comment 6: The map in Figure 1 should include its official Map Review Number.

Response 6: Thanks for your helpful comments. We have added the official Map Review Number of Figure 1.

Details are as follows:

Figure 1. Study area. This map was based on the standard map with the approval number GS (2022) 4309 by the Ministry of Natural Resources, China. (See page 4)

Comment 7: The justification for selecting the three-dimensional space vector model is insufficiently substantiated. The authors should provide a more comprehensive explanation of why this method is more suitable for this study compared to other commonly used approaches (e.g., the coupling coordination degree model).

Response 7: Thanks for your kind suggestion. We have added the coupling coordination degree of UYR in Figure 8. Through quantitative comparative analysis with the coupling coordination degree model, it has been proven that the results of the three-dimensional space model are accurate and reasonable.

Details in the paper are as follows:

Figure 8. RDI of composite system in the UYR. (See page 12)

“In addition, it can be seen from Figure 8 that the system resilience evaluated by the coupling coordination degree model is similar to the three-dimensional space vector model. It is demonstrated that this method is reliable. However, the development trend presented by the three-dimensional space vector model is clearer, especially for the change-points (such as 2014).” (See page 16)

Comment 8: “In this study, the fsQCA approach was employed to investigate the synergistic interplay among the internal components of the EES composite system in 21 prefecture-level cities in the UYR, and to find the configuration paths that generate high resilience levels.” "The authors appear to have employed a static QCA approach. The configuration pathways for cities exhibit dynamic variations, as demonstrated in Figure 10. For panel data analysis, they should consider using dynamic QCA methods to examine the temporal effects on the results.

Response 8: Thanks for your kind suggestion. As you stated, the fsQCA approach is a static QCA method. By dividing panel data into multiple cross-sectional datasets according to time period, we have applied the fsQCA method to analyze the data for each time period separately. This ensure the insights of configuration changes of conditional variables at different time points. Furthermore, we analyzes the results obtained by the fsQCA method in the discussion section, which are relatively close to other results and also consistent with the understanding of managers in the Yellow River Basin. Overall, we believe that this method is feasible for studying the resilience of the EES composite system in the UYR.

Details are as follows:

“Identifying the influencing factors of resilience levels in composite system is important for optimizing system design, formulating coping strategies, enhancing system stability and reliability, and promoting sustainable development and scientific decision-making. Based on the configuration analysis method, this paper identifies that environmental protection, economic structure, social construction, quality of life and social security are the core elements to achieve high resilience levels. Huang et al.^[5⁷^] found that environmental protection stands as the major factor influencing ecological resilience within the UYR. Zhang et al.^[⁵⁸^] found that financial investment in environmental protection, the transformation and upgrading of the industrial structure and the efficient resource allocation are the key factors affecting the ecological resilience within the UYR. Yang et al.^[⁵⁹^] found that urbanization rate, education level, and industrial structure upgrading are the key elements affecting the ecological resilience of cities in the UYR. According to Zhang et al.^[6⁰^], the primary determinants of ecological resilience in UYR cities are the intensity of land development and the degree of environmental pollution. Li et al.^[6¹^] found that environmental pressure indicators, such as water resources utilization rate and water consumption per 10,000 yuan GDP, are primary factors influencing the resilience of the water resources-social economy-ecological environment composite system in the UYR. All of these factors are identified in this study, which proves the validity and accuracy of the influencing factor identification to a certain extent. At the same time, this illustrates again the multiple concurrency of the multi-dimensional influencing factors of the system resilience. It has been found that the fsQCA method can be used to identify influencing factors and find the optimal development path through the arrangement and combination of factors. Yuan et al.^[6²^]empirically investigated the driving mechanisms toward high ecological resilience index (ERI) for 280 cities in China with the fsQCA method based on the technology-organization-environment (TOE) framework. Choi^[6³^] employed the fsQCA method to explore the casual complexities of risk mitigants for the supply chain country risk (SCCR). Cowell and Cousins^[6⁴^] analyzed the relationship between the influencing factors of resilience for 22 United States cities using the fsQCA method” (See pages 16-17)

Comment 9: In general, some scholars performed robustness tests by adjusting consistency thresholds, the PRI value, and the frequency threshold. The result is robust when changing the parameters, and the original configuration produces a clear subset relationship between the configurations. Did the authors conduct robustness tests?

Response 9: Thanks for your kind suggestion. We have performed robustness tests by adjusting PRI value and the threshold of case frequency. The test results are as follows, which can be seen to be robust.

Details are as follows:

“We adjusted the PRI consistency from 0.70 to 0.75, and the threshold of case frequency from 1 to 2. It is demonstrated that the results have high robustness with a very small change from unadjusted state.” (See page 15)

The results are as follows, it can be seen that the values of consistency, raw consistency, unique coverage, overall consistency and overall coverage have not changed. Therefore, the results of the configuration analysis in the paper are robust.

Precedent conditions	High resilience configuration
Precedent conditions	Configuration 1	Configuration 2	Configuration 3	Configuration 4	Configuration 5
Environmental pressure	●	⨂	⨂	⨂	●
Environmental endowment	⨂	●	●	⨂	●
Environmental protection	●	●	●	●	●
Economic strength	⨂	●	⨂	●	●
Economic structure	●	●	●	●	●
Economic efficiency	⨂	●	⨂	●	●
Social construction	●	●	●	●	●
Quality of life	⨂	●	●	●	●
Social security	●	⨂	●	●	●
Consistency	0.969	0.987	0.995	0.995	0.995
Raw consistency	0.191	0.236	0.212	0.186	0.207
Unique coverage	0.047	0.108	0.053	0.033	0.047
Case	Yinchuan (2011), Lanzhou (2011,2017,2021)，Tianshui (2010,2012)，Ordos (2011,2019,2021)	Xining (2012,2015,2020), Yinchuan (2009), Shizuishan (2009,2011), Hohhot (2012,2020), Baotou (2012), Ordos (2009,2012,2022)	Xining (2017,2018,2019), Haidong (2014,2017), Shizuishan (2016), Wuhai (2018,2019)	Xining (2020,2021), Shizuishan (2010), Lanzhou (2020,2021), Ordos (2012),	Yinchuan (2010), Wuwei (2016), Ordos (2017,2019,2020)
Overall consistency	0.983
Overall coverage	0.476

Comment 10: More generally, this paper is very Upper Yellow River focused. Are there important insights arising from other countries or regions? The latter sections of the paper should be revised to suggest how the findings might apply to other countries or regions.

Response 10: Thanks for your kind suggestion.As a typical ecologically fragile area, the study on the resilience of the EES composite system in the UYR can provide reference for ecologically fragile areas in other countries or regions. Specifically, this paper adopts the three-dimensional space vector model and configuration analysis to study the resilience of the EES composite system in an ecologically fragile area. The results have been proven to be accurate and reliable, displaying a great potential for the resilience research of other ecologically fragile areas in the world.

Details are as follows:

“At present, relevant studies have been carried out in the Qinling-Daba Mountains in southern Shaanxi^[67], the Sanjiangyuan region^[68], the Tarim Basin^[69], the Loess Plateau region^[70], the Hinh River Basin^[71], the Indian Himalayan region^[72], the Aravalli range^[73], etc., which provide support for the formulation of sustainable development strategies for the basin or region. However, the results are often limited due to the lack of consideration for the multiple combinatorial effects of the influencing factors. This paper adopts the three-dimensional space vector model and configuration analysis to study the resilience of the EES composite system in an ecologically fragile area. The results have been proven to be accurate and reliable, displaying a great potential for the resilience research of other ecologically fragile areas in the world.” (See page 18)

Comment 11: For a study likely to be published in 2025, the authors should incorporate more up-to-date references. The following recent publications are recommended for the authors to read: 1) Systems-2025-Synergistic Systems of Digitalization and Urbanization in Driving Urban Green Development: A Configurational Analysis of China’s Yellow River Basin. 2) Sustainability-2025-Spatiotemporal Evolution and Influencing Factors of Urban Ecological Resilience: Evidence from the Yellow River Basin, China.

Response 11: Thanks for your helpful comments. We have revised the references and incorporated more up-to-date references. Especially, we have carefully read the above two papers and cited them in the paper.

Details are as follows, for example:

“[11] Zhang ZJ, Wu Y. Spatiotemporal Evolution and Influencing Factors of Urban Ecological Resilience: Evidence from the Yellow River Basin, China. Sustainability 2025; 17: 20.”( See page 19)

“[33] Tan SZ, Li W, Liu XG, Li PF, Yan L, Liang C. Synergistic Systems of Digitalization and Urbanization in Driving Urban Green Development: A Configurational Analysis of China's Yellow River Basin. Systems 2025; 13: 23.” (See page 20)

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

The manuscript investigates how to enhance the resilience of the environment-economy-society (EES) composite system in the Upper Yellow River (UYR), applying a resilience evaluation index system, a three-dimensional space vector model, and fuzzy-set Qualitative Comparative Analysis (fsQCA). The study covers the period 2009-2022 and identifies multiple interacting pathways, rather than single determinants, leading to higher resilience levels across regions. Key findings suggest that environmental protection, economic structure, social construction, quality of life, and social security jointly shape resilience outcomes, with region-specific variations.

The topic is highly relevant to Sustainability and to the Special Issue on hydrology, water resources, and ecosystems. The UYR is an ecologically fragile region of national strategic importance, and the paper’s integration of spatial-temporal analysis with configuration analysis adds value. The combination of quantitative RDI assessment with fsQCA provides a multidimensional perspective that moves beyond conventional single-factor or regression-based approaches.

The methodological framework is clearly described, combining entropy weighting, the three-dimensional vector model, and fsQCA. The inclusion of 32 indicators across environmental, economic, and social dimensions provides a comprehensive basis for resilience measurement. The step-by-step explanation of data normalization, weighting, and calibration is transparent and replicable.

That said, there are some limitations that could be better acknowledged or addressed:

Originality and positioning: While the integration of vector modeling and configuration analysis is interesting, much of the empirical analysis confirms trends already noted in previous UYR studies (as you also cite). The manuscript could sharpen its claim of novelty by showing more clearly what the configuration approach reveals that traditional econometric or regression methods cannot.
Data transparency: The use of linear interpolation for missing data is mentioned, but the extent of missingness and its potential effect on the results are not fully discussed. A sensitivity check would improve credibility.
Interpretation of results: The RDI values are reported with precision, yet the substantive meaning of very low ranges (e.g., 0.01-0.06 for environment and economy) is not fully elaborated. Readers may struggle to connect these abstract values with policy implications.
Temporal scope: The study covers 2009-2022, which is a relatively short period for structural resilience trends. The authors briefly mention this in limitations, but the conclusions sometimes read more definitive than the dataset allows.
Conclusions: The policy implications are sensible (prioritizing environmental protection and balanced development), but they remain somewhat general. More explicit recommendations (e.g., how local governments in low-resilience cities can operationalize the identified “paths”) would strengthen the applied value.

Constructive feedback for improvement

Clarify contribution: Emphasize what is new in your approach compared with other resilience assessments in the Yellow River Basin. Highlight how fsQCA enriches the understanding of multi-factor interactions.
Deepen discussion of regional differences: The spatial maps and city-level findings are rich. Linking these more explicitly to socio-economic or ecological characteristics (beyond resilience scores) would help readers grasp the drivers of divergence.
Address data issues: Provide more detail on the extent of missing data, and, if possible, test whether alternative imputation approaches affect key results.
Strengthen interpretation of RDI: Explain in more intuitive terms what an RDI of, say, 0.05 versus 0.4 means for resilience capacity in practice.
Tighten language: Some sections (e.g., literature review) are lengthy and could be streamlined to avoid repetition. Careful English editing would also improve readability, particularly in the abstract and conclusion.
Policy recommendations: Expand the implications with more specific strategies differentiated by city type (resource-dependent, agricultural, service-oriented). This would make the work more actionable for policymakers.

These improvements would not only enhance the robustness of the manuscript, but also make its contribution more directly relevant to the Special Issue on hydrology, water resources and ecosystems, by strengthening the policy applicability of resilience assessment in ecologically fragile basins.

I recommend major revision. The manuscript addresses an important problem with a robust methodological framework, but it needs clearer positioning of its novelty, stronger discussion of data limitations, and more concrete interpretation of results and implications. With these revisions, it has the potential to make a valuable contribution to the literature on resilience and sustainable development in fragile regions.

Author Response

Response to Reviewer 2 Comments

Comment 1: Clarify contribution: Emphasize what is new in your approach compared with other resilience assessments in the Yellow River Basin. Highlight how fsQCA enriches the understanding of multi-factor interactions.

Response 1: Thanks for your helpful comments. fsQCA approach was formed by incorporating fuzzy set theory into the QCA method. It can deal with the fuzziness and uncertainty between the factors of system resilience, effectively integrate multi-source information through the flexible setting of set affiliation, reveal the nonlinear relationship and multiple concurrent paths between factors. Besides, it can provide a set of systematic and explanatory resilience improvement path sets for decision makers, and enhance the scientificity and operability of decision-making. Theoretically speaking, this method is feasible. Under the configuration perspective, the impact of internal components of the EES on its resilience level is not independent. Therefore, we employed the fsQCA approach to investigate the synergistic interplay among the internal components of the EES composite system in 21 prefecture-level cities in the UYR, and to find the configuration paths that generate high resilience levels. Furthermore, it has been found that the fsQCA method can be used to identify influencing factors and find the optimal development path through the arrangement and combination of factors in the discussion section.

Details are as follows:

“Identifying the influencing factors of resilience levels in composite system is important for optimizing system design, formulating coping strategies, enhancing system stability and reliability, and promoting sustainable development and scientific decision-making. Based on the configuration analysis method, this paper identifies that environmental protection, economic structure, social construction, quality of life and social security are the core elements to achieve high resilience levels. Huang et al.^[⁵⁷^] found that environmental protection stands as the major factor influencing ecological resilience within the UYR. Zhang et al.^[⁵⁸^] found that financial investment in environmental protection, the transformation and upgrading of the industrial structure and the efficient resource allocation are the key factors affecting the ecological resilience within the UYR. Yang et al.^[⁵⁹^]found that urbanization rate, education level, and industrial structure upgrading are the key elements affecting the ecological resilience of cities in the UYR. According to Zhang et al.^[6⁰^], the primary determinants of ecological resilience in UYR cities are the intensity of land development and the degree of environmental pollution. Li et al.^[6¹^] found that environmental pressure indicators, such as water resources utilization rate and water consumption per 10,000 yuan GDP, are primary factors influencing the resilience of the water resources-social economy-ecological environment composite system in the UYR. All of these factors are identified in this study, which proves the validity and accuracy of the influencing factor identification to a certain extent. At the same time, this illustrates again the multiple concurrency of the multi-dimensional influencing factors of the system resilience. It has been found that the fsQCA method can be used to identify influencing factors and find the optimal development path through the arrangement and combination of factors. Yuan et al.^[62] empirically investigated the driving mechanisms toward high ecological resilience index (ERI) for 280 cities in China with the fsQCA method based on the technology-organization-environment (TOE) framework. Choi^[63]employed the fsQCA method to explore the casual complexities of risk mitigants for the supply chain country risk (SCCR). Cowell and Cousins^[64]analyzed the relationship between the influencing factors of resilience for 22 United States cities using the fsQCA method.” (See pages 16-17)

Comment 2: Deepen discussion of regional differences: The spatial maps and city-level findings are rich. Linking these more explicitly to socio-economic or ecological characteristics (beyond resilience scores) would help readers grasp the drivers of divergence.

Response 2: Thanks for your helpful comments. We have revised the discussion section carefully. We added the explanations about findings linking to socio-economic or ecological characteristics.

Details are as follows:

“Spatially, the resilience of composite system in the UYR shows a trend of high in the west and low in the east, which is consistent with the study of Zhao et al.^[54]. The high resilience value is distributed in the cities with good ecological background (with high greening coverage in built-up areas, high green space per capita in parks, and high per capita water resources) and low demand for ecological resources, such as Xining, Baiyin, and Ordos. Economically undeveloped areas and resource-oriented cities tend to have lower resilience, such as Yinchuan and Wuzhong. Besides, the RDI of the composite system of several cities in Ningxia is relatively low, and the gap between them is small. This finding aligns with the outcomes derived from the investigations conducted by Yang et al.^[55] and Zhang et al.^[56]. However, most of these cities are underdeveloped regions with low levels of GDP and per capita GDP. Wuzhong, Guyuan, Zhongwei, Shizuishan and Yinchuan are facing environmental problems such as outstanding conflict between supply and demand of water resources and low utilization efficiency of water resources, which constrain economic development and social progress. Therefore, it is imperative to prioritize the development of these regions and elevate the resilience level.” (See page 16)

Comment 3: Address data issues: Provide more detail on the extent of missing data, and, if possible, test whether alternative imputation approaches affect key results.

Response 3: Thanks for your kind suggestion. In this study, only a very small part of data was missing, including the comprehensive utilization rate of general industrial solid waste [A33] of Xining in 2015 and 2018, [A33] of Ordos in 2019-2020, and urban sewage treatment rate [A31] of Xining in 2016, [A31] of Pingliang in 2010, [A31] of Longnan in 2018. According to the relevant studies^[1-2], it has been found that linear interpolation is an effective interpolation method for small data samples and few missing data.

References:

[1] Liu ZH, Yuan S, Zhang JL, et al. Comprehensive Measurement, Spatial Differentiation, and Impact Effects of China’s Ecological Environment Resilience. Environmental Science 2025; 1-22.

[2] Shi M, Zhang DY. Resilience measurement and spatiotemporal dynamic evolution analysis of China’s energy system. Statistics & Decision 2025; 17: 53-57.

Comment 4: Strengthen interpretation of RDI: Explain in more intuitive terms what an RDI of, say, 0.05 versus 0.4 means for resilience capacity in practice.

Response 4: Thanks for your kind suggestion. The RDI truly reflects the level of system resilience in the UYR. The resilience level of the composite system was classified into five distinct categories (lower, low, medium, high, and higher resilience) through the application of the ArcGIS natural breakpoint approach. As for the resilience level of EES composite system, 0.4 means that the region is at higher resilience level.

Comment 5: Tighten language: Some sections (e.g., literature review) are lengthy and could be streamlined to avoid repetition. Careful English editing would also improve readability, particularly in the abstract and conclusion.

Response 5: Thanks for your helpful comments. We have carefully streamlined the Introduction and Discussion sections. In addition, we revised the writing of the paper, particularly in the Abstract and Conclusion sections.

Details are as follows, for example:

Abstract section:

“Pathways supporting high resilience levels of composite system were examined using fuzzy-set qualitative comparative analysis (fsQCA) method from a configuration perspective.”

“To achieve high resilience levels, all the cities must prioritize both environmental protection and economic structure as core strategic pillars.” (See page 1)

Comment 6: Policy recommendations: Expand the implications with more specific strategies differentiated by city type (resource-dependent, agricultural, service-oriented). This would make the work more actionable for policymakers.

Response 6: Thanks for your helpful comments. We have expanded the policy recommendations with more specific strategies differentiated by city type (resource-dependent, agricultural, service-oriented).

Details are as follows:

“Resource-dependent cities in the eastern region, such as Ordos, Hohhot, and Baotou should prioritize environmental protection, economic structure, social construction, and quality of life as the core conditions. Service-oriented cities in the western region, such as Xining, Haidong, and Wuwei should prioritize environmental protection, economic structure, social development, quality of life, and social security as core conditions. Agricultural cities in Ningxia and Gansu, such as Yinchuan, Shizuishan, and Lanzhou have multiple paths that can generate high resilience levels. These regions have more flexibility in formulating strategies and can choose the appropriate path according to their actual situation.” (See page 18)

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The article is well written. However, the following changes are necessary.

The theoretical foundations of the three-dimensional model. The use of the three-dimensional space vector model to appraise resilience is not adequately justified, in that there is not a systematic confrontation of it with alternative, proven procedures such as Structural Equation Models (SEM), Data Envelope Analysis (DEA), or coupling coordination models, that would render feasible assessing its fitness and potential superiority. The solution may be to impose a comparative assessment upon a subset of data, in order to test its coherence, robustness, and value added with respect to rival schemes. In case it is not possible to resort toadditional data, measures, or algorithms, concern could be eliminated by means of a critical literature review, openly divulging the rationale of a methodological choice and describing in the methodology section the relative limitations, and then returning upon the issue in the limitations, discussion, or conclusions sections, in order to justify about scope and conditions of validity of results attained.
Validity of indicator selection (total 32). The construction of resilience index with thirty-two indicators does pose a few issues, in that there is no explicit justification of scientific criteria under which such choices of indicators were made, beyond citing earlier studies. What's more, no regard of possible redundancy in, or collinearity of, variables, which might impact robustness in results, is taken. In order to compensate for this limitation, it may be worthwhile to make use of statistical techniques such as multicollinearity tests, factor analysis, or dimensionality reduction such as PCA (Principal Component Analysis) in order to validate non-redundancy and validity of indicators. Such index construction will render it more robust and transparent. In case it is out of reach to do so due to availability of data, metric, and tools, such limitation could be compensated by critical review of literature, in favor of choosing indicators and candid discussion of limitations of methods in sections dedicated to methodology, limitations, discussion, or conclusions, in that it makes explicit about scientific coherence of approach taken.
Handling missing data. The linear interpolation of missing data, as in the paper, contains a few key flaws, in that in such instances, this approach creates significant distortions when time series are non-linear or under extreme circumstances. Furthermore, sensitivity tests or robustness tests are not demonstrated in order to verify what happens under alterations of this methodological choice. A more robust solution would be to cross-validate linear interpolation by means of alternative strategies, such as multiple regression, multiple imputations, or machine learning models such as k-NN or random forest, and compare results in order to verify stability. In parallel, sensitivity tests would enable us to measure the influence of imputation choices in terms of final estimates of resilience index. When it is not possible to deploy more complex methods due to constraints in data, metrics, or resources, then such concerns should be addressed by means of critical literature review, openly acknowledging this work's limitations of methodology in this section and referencing them back in limitations, discussion, or conclusions sections, therefore opening clear avenues to potential bias introduced.
Calibrated fsQCA variable. Variable calibration, required in order to use the fsQCA approach, is flawed, in that anchor points (0.05, 0.50, and 0.95 quantiles) drawn from reference literature are not justified in terms of particular case study features. The proximation of calibrated measures also hinders model discriminative ability and loses informative power. Another solution would be to test various calibration thresholds, both over quantiles and empirically estimated measures, and compare outputs in order to test model sensitivity. Alternative hybrid solutions, that combine statistical criteria along with expert judgment, could also be employed in order to adjust anchor points more in agreement with data distribution. In case it should not be possible to include new metrics/agorithms, it should be addressed by means of critical literature review, openly discussing implications of such approach choices as a means of emphasizing limitations in the methodology section as well as in limitations, discussion, and conclusions sections, in order to ensure transparency and robustness of interpretation.
The 2009-2022 period does not seem long enough to cover all socio-ecological resilience processes. In most instances, such processes evolve over longer periods. The period also does not necessarily cover events like COVID-19 or new policy, both of which might dramatically influence results. In response, consider longer time-series extension or historical proxies to cover longer periods. Use scenario planning or models to predict such event influences. If time-series extension of databases, or tools is not feasible, then include critical literature review and transparently document this constraint of methodology. Mention it in limitations, discussion, or conclusions, describing in clear terms its influence upon results and interpretation.
The model's generalizability is a direct concern. The model utilizes a combination of data, indicators, and weights that originated in Chinese provinces and municipalities. Due to this, transferability and comparative validity in other places are suspect. In reaction, test the model using external cases containing similar data or adjust indexes for different regions. Consider simpler versions that base themselves upon more transferable, universal indexes in countries worldwide. In instances of infeasibility of empirical application, use a critical literature review to demonstrate limitations in generalizability. Raise in open discussion in methodology, limitations, discussion, or conclusions to elucidate conditions of use and results. The paper's comparisons of critical work in contemporary literature are shallow. The paper enumerates related works, yet there is no quantitative comparison of alternative resilience indices or models used in the same basin, or in equivalent contexts. Qualitative convergences alone are highlighted, without confrontation of divergences, advantages of new models over mainstream models, like SEM, DEA, or coordination models. Enhance by introducing quantitative comparative analysis in order to reveal agreement, differences, and value added in approach. If this is impossible, then critical bibliographic review in terms of strengths and limitations of alternative methods should be included in discussion of strengths and limitations of alternative methods. Place such discussion in methodology, and revisit in limitations, discussion, or conclusions in favor of improving clarity as well as credibility.

By applying the following changes, the article can be readied for publication.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 3 Comments

Comment 1: The theoretical foundations of the three-dimensional model. The use of the three-dimensional space vector model to appraise resilience is not adequately justified, in that there is not a systematic confrontation of it with alternative, proven procedures such as Structural Equation Models (SEM), Data Envelope Analysis (DEA), or coupling coordination models, that would render feasible assessing its fitness and potential superiority. The solution may be to impose a comparative assessment upon a subset of data, in order to test its coherence, robustness, and value added with respect to rival schemes. In case it is not possible to resort to additional data, measures, or algorithms, concern could be eliminated by means of a critical literature review, openly divulging the rationale of a methodological choice and describing in the methodology section the relative limitations, and then returning upon the issue in the limitations, discussion, or conclusions sections, in order to justify about scope and conditions of validity of results attained.

Response 1: Thank you for your kind suggestion. We have added some explanations in sections 2 and 4.1, which theoretically compare the relationship and differences between the three-dimensional space vector model and the coupling coordination model, demonstrating the applicability and potential advantages of the three-dimensional space vector model.

Details are as follows:

“Mathematically, the value of OP' is essentially a weighted sum of the RDI of three subsystems. It has been proven to be highly correlated with the results of coupling coordination degree model^[41]”. (See page 8)

Furthermore, We have added the coupling coordination degree of UYR in Figure 8. Through quantitative comparative analysis with the coupling coordination degree model, it has been proven that the results of the three-dimensional space model are accurate and reasonable.

Details are as follows:

Figure 8. RDI of composite system in the UYR. (See page 8)

Comment 2: Validity of indicator selection (total 32). The construction of resilience index with thirty-two indicators does pose a few issues, in that there is no explicit justification of scientific criteria under which such choices of indicators were made, beyond citing earlier studies. What's more, no regard of possible redundancy in, or collinearity of, variables, which might impact robustness in results, is taken. In order to compensate for this limitation, it may be worthwhile to make use of statistical techniques such as multicollinearity tests, factor analysis, or dimensionality reduction such as PCA (Principal Component Analysis) in order to validate non-redundancy and validity of indicators. Such index construction will render it more robust and transparent. In case it is out of reach to do so due to availability of data, metric, and tools, such limitation could be compensated by critical review of literature, in favor of choosing indicators and candid discussion of limitations of methods in sections dedicated to methodology, limitations, discussion, or conclusions, in that it makes explicit about scientific coherence of approach taken.

Response 2: Thank you for your kind suggestion. Avoiding indicator redundancy was a core principle guiding the construction of our indicator system. In the initial stage of indicator selection, we strictly followed the principle of independence. Each indicator was guaranteed to have a clear and distinct meaning without conceptual overlap. Furthermore, The CRITIC method used in this study inherently prevents information overlap among indicators, as it accounts for not only the contrast intensity but also the correlation of indicators. Specifically, it usually assigned small weights to the indicators with high correlation coefficients. Therefore, our indicator system is reasonable and effective.

Comment 3: Handling missing data. The linear interpolation of missing data, as in the paper, contains a few key flaws, in that in such instances, this approach creates significant distortions when time series are non-linear or under extreme circumstances. Furthermore, sensitivity tests or robustness tests are not demonstrated in order to verify what happens under alterations of this methodological choice. A more robust solution would be to cross-validate linear interpolation by means of alternative strategies, such as multiple regression, multiple imputations, or machine learning models such as k-NN or random forest, and compare results in order to verify stability. In parallel, sensitivity tests would enable us to measure the influence of imputation choices in terms of final estimates of resilience index. When it is not possible to deploy more complex methods due to constraints in data, metrics, or resources, then such concerns should be addressed by means of critical literature review, openly acknowledging this work's limitations of methodology in this section and referencing them back in limitations, discussion, or conclusions sections, therefore opening clear avenues to potential bias introduced.

Response 3: Thank you for your kind suggestion. We have selected a total of 14 years of data from 2009 to 2022 in this research. Due to the small range of data, the complex methods such as machine learning might be not applicable. In addition, only a very small part of data was missing, including the comprehensive utilization rate of general industrial solid waste [A33] of Xining in 2015 and 2018, [A33] of Ordos in 2019-2020, and urban sewage treatment rate [A31] of Xining in 2016, [A31] of Pingliang in 2010, [A31] of Longnan in 2018. According to the relevant studies^[1-2], it has been found that linear interpolation is an effective interpolation method for small data samples and few missing data.

References：

[1] Liu ZH, Yuan S, Zhang JL, et al. Comprehensive Measurement, Spatial Differentiation, and Impact Effects of China’s Ecological Environment Resilience. Environmental Science 2025; 1-22.

[2] Shi M, Zhang DY. Resilience measurement and spatiotemporal dynamic evolution analysis of China’s energy system. Statistics & Decision 2025; 17: 53-57.

Comment 4: Calibrated fsQCA variable. Variable calibration, required in order to use the fsQCA approach, is flawed, in that anchor points (0.05, 0.50, and 0.95 quantiles) drawn from reference literature are not justified in terms of particular case study features. The proximation of calibrated measures also hinders model discriminative ability and loses informative power. Another solution would be to test various calibration thresholds, both over quantiles and empirically estimated measures, and compare outputs in order to test model sensitivity. Alternative hybrid solutions, that combine statistical criteria along with expert judgment, could also be employed in order to adjust anchor points more in agreement with data distribution. In case it should not be possible to include new metrics/agorithms, it should be addressed by means of critical literature review, openly discussing implications of such approach choices as a means of emphasizing limitations in the methodology section as well as in limitations, discussion, and conclusions sections, in order to ensure transparency and robustness of interpretation.

Response 4: Thank you for your kind suggestion. We have added explanations about the method in the 2.2.3 section. We tried various methods of setting anchor points and found that 0.05, 0.50, and 0.95 quantiles were the most suitable for our study.

Details are as follows:

“The most common settings for the anchor points are 0.05, 0.50, and 0.95 quantiles, with a 90% confidence interval^[46]. For this study, it was found that there is a high clustering of the data. The difference would be very small using a small interval (such as the 0.25, 0.50, 0.75 quantiles), leading to the the unavailability of the calibrated results. Therefore, we selected 0.05, 0.50 and 0.95 quantiles, respectively representing “completely not affiliated”, “intersection” and “fully affiliated”.” (See page 9)

Comment 5: The 2009-2022 period does not seem long enough to cover all socio-ecological resilience processes. In most instances, such processes evolve over longer periods. The period also does not necessarily cover events like COVID-19 or new policy, both of which might dramatically influence results. In response, consider longer time-series extension or historical proxies to cover longer periods. Use scenario planning or models to predict such event influences. If time-series extension of databases, or tools is not feasible, then include critical literature review and transparently document this constraint of methodology. Mention it in limitations, discussion, or conclusions, describing in clear terms its influence upon results and interpretation.

Response 5: Thank you for your kind suggestion. The provincial-level data in the UYR is relatively complete, but we took the prefecture-level cities as the research object. There were notable data gaps in the periods before 2009 and after 2022, especially for the environmental data. In order to improve the credibility of the results, we selected the research period from 2009 to 2022.

We agree that events like COVID-19 or new policy might affect the system resilience. We will continue this research and further analyze the impact of these events on system resilience in the future. The limitations have been added in section 4.3.

Details are as follows:

“In addition, a short time period of 2009-2022 was considered limited by data, which did not cover some important events like COVID-19. It is necessary to further analyze the impact of these events on system resilience in the future.” (See page 17)

Comment 6: The model's generalizability is a direct concern. The model utilizes a combination of data, indicators, and weights that originated in Chinese provinces and municipalities. Due to this, transferability and comparative validity in other places are suspect. In reaction, test the model using external cases containing similar data or adjust indexes for different regions. Consider simpler versions that base themselves upon more transferable, universal indexes in countries worldwide. In instances of infeasibility of empirical application, use a critical literature review to demonstrate limitations in generalizability. Raise in open discussion in methodology, limitations, discussion, or conclusions to elucidate conditions of use and results. The paper's comparisons of critical work in contemporary literature are shallow. The paper enumerates related works, yet there is no quantitative comparison of alternative resilience indices or models used in the same basin, or in equivalent contexts. Qualitative convergences alone are highlighted, without confrontation of divergences, advantages of new models over mainstream models, like SEM, DEA, or coordination models. Enhance by introducing quantitative comparative analysis in order to reveal agreement, differences, and value added in approach. If this is impossible, then critical bibliographic review in terms of strengths and limitations of alternative methods should be included in discussion of strengths and limitations of alternative methods. Place such discussion in methodology, and revisit in limitations, discussion, or conclusions in favor of improving clarity as well as credibility.

Response 6: Thank you for your kind suggestion. We have added some references that using fsQCA method to analyze the development path of resilience levels in other countries or regions in the discussion section, which proved the generality of the fsQCA method.

Details are as follows:

“It has been found that the fsQCA method can be used to identify influencing factors and find the optimal development path through the arrangement and combination of factors. Yuan et al.^[62] empirically investigated the driving mechanisms toward high ecological resilience index (ERI) for 280 cities in China with the fsQCA method based on the technology-organization-environment (TOE) framework. Choi^[63] employed the fsQCA method to explore the casual complexities of risk mitigants for the supply chain country risk (SCCR). Cowell and Cousins^[64]analyzed the relationship between the influencing factors of resilience for 22 United States cities using the fsQCA method.” (See page 17)

“[62] Yuan XL, Liu R, Huang T. Analyzing Spatial-Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force-Pressure-State-Influence-Response and Environment-Economy-Society Model: A Case Study of 280 Cities in China. Systems 2024; 12: 24.

[63] Choi K. A complexity configurations of risk mitigants for supply chain country risk. Annals of Operations Research 2024: 25.

[64] Cowell M, Cousins T. Equity and the Chief Resilience Officer in the era of 100 Resilient Cities: A qualitative comparative analysis of US resilience strategies. Cities 2022; 131: 8.” (See page 21 of references section)

Moreover, in this study, we added a quantitative comparative analysis with the coupling coordination degree model. It can be seen from Figure 8 that the system resilience evaluated by the coupling coordination degree model is similar to the three-dimensional space vector model. It is demonstrated that this method is reliable. However, the development trend presented by the three-dimensional space vector model is clearer, especially for the change-points (such as 2014).

Detail are as follows:

Figure 8. RDI of composite system in the UYR. (See page 12)

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have made significant revisions and it is recommended for acceptance.

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

The latest version of the manuscript incorporates all the suggestions I provided, so I believe it is suitable for publication.