Modeling the Risks of Green Financing Water-Energy-Food Nexus Projects in BRICS Countries

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This article assesses the investment risk of the BRICS green bond-supported water-energy-Food Link project based on the fuzzy set model. Four countries, namely China, India, Russia and Pakistan, were selected to construct four indicators: economy, green bond market, institutional trust and multi-dimensional poverty. The results show that China has the lowest risk, Russia is below the average, and India and Pakistan have the highest. The author suggests: China should export its experience. Russia's principle of expanding green bonds to other financial instruments; India combines poverty reduction with green finance. Brazil has improved incentives and disclosure to attract private capital. There are the following problems in this article. It is suggested that major revisions be made.

 

1. The contribution of research motivation needs to be more prominent: The abstract and introduction do not clearly define the marginal contribution of this paper compared to existing literature. It is suggested to explicitly supplement "Why the fuzzy set model must be adopted" and "what specific gaps does this study fill".
2. Insufficient explanation of sample selection bias: Only four countries were selected due to the rotating presidency, and the reason for excluding South Africa was merely mentioned as "missing data". A list of missing indicators and sensitivity analysis should be provided, or an explanation of the representativeness of the sample to the BRICS as a whole should be given.
3. The setting of variable weights lacks a basis: The equal weighting of the four indicators directly affects the risk ranking. It is recommended to use expert scoring, analytic hierarchy process or regression weights for robustness tests, and report the changes in results under different weights.
4. Details of reverse relationship handling are not presented: The "reverse" impact of economy, market, trust and poverty on risk is only mentioned in one sentence. It is necessary to supplement formulas, node examples and numerical comparisons before and after the reversal of membership functions to ensure reproducibility.
5. The institutional trust indicators are overly simplified: only the simple average of the three questions in WVS is used, without considering the questionnaire year, sample structure and cultural bias. The data source year and sample size should be reported, and the impact of potential measurement errors on the conclusion should be discussed.
6. Lack of robustness and endogeneity tests: No robustness tests were conducted on model Settings, extreme values, or alternative normalization methods, nor were potential endogenous issues (such as the reverse impact of green bond issuance on GDP) discussed. It is recommended to supplement relevant tests.
7. There is a causal jump between the conclusion and the result: The text repeatedly causalizes "low risk" and "the Chinese model should be promoted", but the model only has a static correlation. It is necessary to clearly define the boundary of "risk" to avoid excessive extrapolation of policy recommendations.
8. The literature review lacks the latest research: The 2023-2025 green bond assessment framework and the update of the BRICS green classification standards were not included. It is suggested to supplement the policy and academic progress of the past two years to enhance the timeliness of the review.
9. Read and quote Smarter is greener: can intelligent manufacturing improve enterprises' ESG performance? Humanities and Social Sciences Communications, 2025; Tax policy and total factor carbon emission efficiency:  evidence from China's VAT reform. International journal of environmental research and public health, 2022; Unlocking carbon reduction potential of digital trade:  Evidence from China's comprehensive cross-border e-commerce pilot Zons.sage Open, 2025; The Urban Renewable Energy Transition:  Impact Assessment and Transmission Mechanisms of Climate Policy Uncertainty. Energies, 2025.
10. Disconnection between policy recommendations and empirical results: Some recommendations (such as Brazil's development of FIDC and Russia's digitalization) have no corresponding variables to support them in the model. These recommendations should be directly linked to significant drivers and their mechanisms of action should be explained.
11. The transparency of charts and data needs to be improved: Key membership functions, the deblurring process and the original data are not provided in the appendix, and Figure 1 has no coordinate axis description. It is recommended to complete the data table, code snippet or at least a detailed algorithm flowchart to ensure repeatability.

 

Author Response

Thank you very much for your comprehensive and detailed review. We truly appreciate the time and effort you devoted to evaluating our manuscript.

Comments 1: The contribution of research motivation needs to be more prominent: The abstract and introduction do not clearly define the marginal contribution of this paper compared to existing literature. It is suggested to explicitly supplement "Why the fuzzy set model must be adopted" and "what specific gaps does this study fill".
Response 1: Thank you for pointing this out. We agree with this comment. We have expanded both the Abstract and the Introduction to clearly explain the research motivation and contribution. The revised sections now highlight that fuzzy-set logic was chosen because it allows for modelling the non-linear interdependence among economic, institutional, and social factors that influence green finance risks, which traditional linear models cannot adequately capture. The revised text also specifies that our study fills a methodological gap by integrating macroeconomic, social, and institutional indicators within a single analytical framework for BRICS countries.
Changes have been made to the text on lines [19-22], [29-33].
Comments 2: Insufficient explanation of sample selection bias: Only four countries were selected due to the rotating presidency, and the reason for excluding South Africa was merely mentioned as "missing data". A list of missing indicators and sensitivity analysis should be provided, or an explanation of the representativeness of the sample to the BRICS as a whole should be given.
Response 2: Thank you for this comment. In response, we have expanded the methodological explanation in the Materials and Methods section to clarify the rationale for the country selection ensure representativeness of the analysis for the BRICS group as a whole.
Changes have been made to the text on lines [53-64]. 
Comments 3: The setting of variable weights lacks a basis: The equal weighting of the four indicators directly affects the risk ranking. It is recommended to use expert scoring, analytic hierarchy process or regression weights for robustness tests, and report the changes in results under different weights.
Response 3: We appreciate this remark. We fully agree that variable weighting is a critical factor that can influence the outcomes of fuzzy-set modelling. In the current version of the paper, we adopted equal weights following the standard practice in sustainability finance research and based on expert consultation; however, we have now clarified and strengthened this justification in the Materials and Methods section.
A new paragraph addressing this comment has been added on lines [258-263].
Comments 4: Details of reverse relationship handling are not presented: The "reverse" impact of economy, market, trust and poverty on risk is only mentioned in one sentence. It is necessary to supplement formulas, node examples and numerical comparisons before and after the reversal of membership functions to ensure reproducibility.
Response 4: Thank you for noting this. We agree that the description of the inverse (reverse) relationships between indicators and the composite risk variable required additional clarification to improve reproducibility. 
The corresponding revisions have been made in the manuscript on lines [270-277].
Comments 5: The institutional trust indicators are overly simplified: only the simple average of the three questions in WVS is used, without considering the questionnaire year, sample structure and cultural bias. The data source year and sample size should be reported, and the impact of potential measurement errors on the conclusion should be discussed.
Response 5: The “Materials and Methods” now provides the year of the World Values Survey used, details about the sample structure, and a note acknowledging potential measurement biases. We clarified that the WVS dataset (Wave 7, 2017–2022) was employed and that the index was constructed as an aggregate measure of institutional trust in government, banks, and large companies. Revisions made on lines [199-204]. 
Comments 6: Lack of robustness and endogeneity tests: No robustness tests were conducted on model Settings, extreme values, or alternative normalization methods, nor were potential endogenous issues (such as the reverse impact of green bond issuance on GDP) discussed. It is recommended to supplement relevant tests.
Response 6: We thank you for this valuable methodological comment. In response, the manuscript has been revised to clarify the basis of variable weighting and to strengthen the methodological justification of the model configuration.
The revised text in the Materials and Methods section (Step 2. Data normalisation) now explicitly explains that all indicators were assigned equal weights following established practice in the green finance risk-modelling literature and supported by expert consultations with analysts of Public Joint-Stock Company “SPB Exchange” (see lines [258-263]).
Comments 7: There is a causal jump between the conclusion and the result: The text repeatedly causalizes "low risk" and "the Chinese model should be promoted", but the model only has a static correlation. It is necessary to clearly define the boundary of "risk" to avoid excessive extrapolation of policy recommendations.
Response 7: Thank you for this important observation. We agree that the interpretation of the fuzzy-set results should remain correlational rather than causal. To address this concern, we have clarified the conceptual definition of “risk” and revised the discussion of the Chinese case to avoid causal phrasing. 
Several related edits have been introduced throughout the section: lines [303-307], [334-339].
Comments 8: The literature review lacks the latest research: The 2023-2025 green bond assessment framework and the update of the BRICS green classification standards were not included. It is suggested to supplement the policy and academic progress of the past two years to enhance the timeliness of the review.
Response 8: The text was updated with the latest academic publications from 2022–2025. These include recent works on ESG performance, tax policy and carbon efficiency, emissions reduction, and climate policy uncertainty, as suggested. 
Comments 9: Read and quote Smarter is greener: can intelligent manufacturing improve enterprises' ESG performance? Humanities and Social Sciences Communications, 2025; Tax policy and total factor carbon emission efficiency:  evidence from China's VAT reform. International journal of environmental research and public health, 2022; Unlocking carbon reduction potential of digital trade:  Evidence from China's comprehensive cross-border e-commerce pilot Zons.sage Open, 2025; The Urban Renewable Energy Transition:  Impact Assessment and Transmission Mechanisms of Climate Policy Uncertainty. Energies, 2025.
Response 9: Thank you for this suggestion. We have studied and incorporated the recommended literature into the revised manuscript (see sources 11-14].
Comments 10: Disconnection between policy recommendations and empirical results: Some recommendations (such as Brazil's development of FIDC and Russia's digitalization) have no corresponding variables to support them in the model. These recommendations should be directly linked to significant drivers and their mechanisms of action should be explained.
Response 10: We agree that the policy recommendations should be more clearly aligned with the empirical variables identified in the fuzzy-set model. To address this concern, we have added a short explanatory paragraph at the beginning of the Conclusions section that establishes these links without altering the main text (lines [499-504]) and restrictions of the conducted research at the end (lines [610-617]).
Comments 11: The transparency of charts and data needs to be improved: Key membership functions, the deblurring process and the original data are not provided in the appendix, and Figure 1 has no coordinate axis description. It is recommended to complete the data table, code snippet or at least a detailed algorithm flowchart to ensure repeatability.
Response 11: Thank you for this valuable comment. Table 4 (“Country Risk Profiles Derived from Fuzzy Logic Model”) has been added to provide complete numerical outputs of the fuzzy-set analysis, including composite risk scores, dominant risk categories, and membership distributions for each BRICS country. A short description of Figure 1 has been added (lines [331-332]). Moreover, the methodological description of the fuzzy-set modelling procedure has been expanded in the Materials and Methods section (see lines [154-158]). 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

attached

Comments for author File: Comments.pdf

Author Response

Thank you for your thoughtful and constructive review of our work. We greatly appreciate your insightful remarks, which have helped us to refine both the conceptual framework and empirical presentation of the research.

Comments 1: Lack of original data from BRICS countries
Response 1: Thank you for this important comment regarding the data sources. We would like to clarify that the study is based on original country-level data for all BRICS members, collected from internationally recognized primary databases (World Bank, IMF, World Values Survey, World Bank MPI) and that the analysis uses the latest available (2022) data. 
We clarified the empirical basis of the study by adding explicit text in the Materials and Methods section (Step 1: Definition and justification of input variables) to show that the analysis is grounded on country-level data for the BRICS members. New paragraph was added to the revised manuscript on lines [154-158], [198-204].
Comments 2: The simply using of Fuzzy Set model lacks novelty
Response 2: Thank you for this comment. We agree that the fuzzy-set model itself is a well-known analytical method. However, the novelty of our work lies in its conceptual framing and applied integration, rather than in the mathematical procedure.
To clarify this contribution, we have added a new paragraph to the Conclusions section that explicitly summarizes the study’s novelty and its position within the broader research field (see lines [488-495]).
Comments 3: The selection of indicators is un-reasonable, and the research results are uncertain
Response 3: Thank you for this important comment on the selection of indicators and the robustness of the results. The indicators used in this study were chosen based on their theoretical relevance and empirical representation of the main components of green-financing risk at the macroeconomic level. Specifically, the model integrates four dimensions that are consistently cited in sustainability finance and institutional economics literature. 
To address concerns about result uncertainty, we added Table 4 (“Country Risk Profiles Derived from Fuzzy Logic Model”) which presents all membership values and resulting risk scores for each BRICS country. 
Comments 4: Lack of research process, in-depth analysis and academic soundness
Response 4: Thank you for this valuable comment. In response, we have strengthened the clarity and academic depth of the study by expanding the description of the research process and refining the analytical discussion.
First, the research process is now clearly structured and described in steps in the Materials and Methods section. Second, the depth of analysis has been improved by expanding the explanation of how inverse relationships were handled during the fuzzification process. A new paragraph addressing this comment has been added on lines [270-277]. Third, to enhance academic soundness, the revised version includes explicit definitions of the core concepts (risk, institutional trust) and a discussion of methodological limitations (see lines [303-307], [198-204]).
Comments 5: More research result figures and tables are suggested
Response 5: Thank you for this helpful suggestion. In response, we have improved the visual presentation of the research results by adding new and more detailed materials.
Specifically, Table 4 (“Country Risk Profiles Derived from Fuzzy Logic Model”) and Table 2 (“Level Classification”) have been included to present the full numerical outcomes of the fuzzy-set analysis, including country-specific membership values and final composite risk scores.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

The article presents a technically competent and methodologically rigorous study of green financing risk assessment across BRICS countries using a fuzzy-set modeling approach. The integration of economic, institutional, and social indicators (GDP PPP, green bond volume, institutional trust, and multidimensional poverty) is conceptually strong and aligns well with current sustainability finance discourse. The manuscript is well structured, with clear sections for introduction, literature review, methods, results, discussion, and conclusions.

However, despite these strengths, the paper would benefit from substantial polishing in structure, academic tone, and clarity. The abstract, while informative, is overly dense and descriptive; it should more clearly highlight the research gap, objectives, and the main findings in quantitative terms. The introduction should end with a concise statement of the research question, methodological approach, and contribution to the literature. The literature review, though rich, tends to be narrative and country-focused; synthesizing it around key analytical variables would enhance coherence.

The methodology section is detailed but occasionally inconsistent in mathematical notation and explanation. Equations (1) and (2) would benefit from clearer definitions, and the steps of aggregation and defuzzification should be explained more explicitly. The results section could be strengthened with a comparative summary table showing the main indicators for each country, as well as a brief comment on the robustness of the model.

In the discussion and conclusion, the analysis is insightful but at times repetitive. These sections could be reorganized to separate comparative interpretation, sectoral discussion, and policy implications. The conclusion should also include a short reflection on the study’s limitations and avenues for future research.

Author Response

Thank you very much for taking the time to review our manuscript. We sincerely appreciate your thoughtful feedback and constructive suggestions. 

Comments 1: The abstract, while informative, is overly dense and descriptive; it should more clearly highlight the research gap, objectives, and the main findings in quantitative terms. The introduction should end with a concise statement of the research question, methodological approach, and contribution to the literature. The literature review, though rich, tends to be narrative and country-focused; synthesizing it around key analytical variables would enhance coherence.
The methodology section is detailed but occasionally inconsistent in mathematical notation and explanation. Equations (1) and (2) would benefit from clearer definitions, and the steps of aggregation and defuzzification should be explained more explicitly. The results section could be strengthened with a comparative summary table showing the main indicators for each country, as well as a brief comment on the robustness of the model.
In the discussion and conclusion, the analysis is insightful but at times repetitive. These sections could be reorganized to separate comparative interpretation, sectoral discussion, and policy implications. The conclusion should also include a short reflection on the study’s limitations and avenues for future research.
Response 1: Thank you very much for your detailed and thoughtful feedback on the structure and clarity of the manuscript. We carefully reviewed each section and made targeted improvements throughout the paper.
The abstract was revised to better highlight the research gap, objectives, methodological approach, and the main quantitative findings. The corresponding revisions have been made in the manuscript on lines 25-33.
In the Introduction several related edits have been introduced: [This study addresses this gap by developing a fuzzy-set model that quantifies and compares the country-level risks of using green bonds to finance WEF nexus projects across BRICS economies. The model integrates four key dimensions (macroeconomic strength (GDP PPP), development of the green securities market, institutional trust, and multidimensional poverty) into a single composite risk index. By employing fuzzy logic, the analysis captures the gradual, non-binary nature of risk, allowing for more nuanced cross-country comparisons].
The Literature Review section has been restructured to improve coherence and to align more closely with the analytical framework of the study. The end of the section now includes a new integrative paragraph that synthesizes prior studies around the analytical dimensions of the model: [Prior studies rarely address how macroeconomic, institutional, and social dimensions jointly determine the risk of green financing. Moreover, empirical models often assume linear relationships, overlooking the fuzzy and uncertain nature of sustainability investment environments. To bridge this gap, the present study applies a fuzzy-set modelling approach that integrates economic development, green market maturity, institutional trust, and multidimensional poverty into a unified risk-assessment framework].
The Materials and Methods section now explicitly presents the research process step by step to specify data sources and variable definitions. We added an explanation of how the cultural (institutional trust) indicators were processed and incorporated into the model. Specifically, the revised text explains the derivation of the institutional trust variable and its role in the fuzzy-set equations (see lines [198-204]).

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The quality of the article has significantly improved. I think this article can be accepted.

Author Response

We sincerely thank you for this positive assessment and are grateful for the careful evaluation of our revisions. We appreciate the recognition of the improvements made and are pleased that the manuscript is now considered suitable for acceptance.

Reviewer 2 Report

Comments and Suggestions for Authors

Attached

Comments for author File: Comments.pdf

Author Response

We are sincerely grateful for the thorough and insightful assessment of our manuscript. The valuable feedback and suggestions provided have been pivotal in strengthening the theoretical foundations and clarifying the empirical analysis of our research. We have addressed each point raised with careful consideration, and our responses below detail the specific revisions made to the manuscript to reflect this constructive input.

Comments 1: In the revision, the author mainly added some data and the data processing steps, there are still significant deficiencies in the academic level of the manuscript.
Response 1: Thank you for this comment. In response to this concern, we strengthened the academic foundations of the study by expanding the theoretical justification for the fuzzy-set methodology [see lines 196-201], clarifying the conceptual links between each indicator and green-financing risk, and situating our approach within the broader scholarly discourse on WEF nexus governance and sustainable finance. We also refined the methodological framework by detailing the rationale for indicator selection, incorporating a sensitivity analysis of weighting schemes, and validating the results against an independent global investment-risk index. These additions enhance the conceptual rigor and methodological transparency of the manuscript. Detailed descriptions of each specific revision are provided in responses to the subsequent comments.
Comments 2: The selected indicators cannot fully reflect the risks of the project, nor can they reflect the properties of green finance and water energy food.
Response 2: We appreciate this valuable comment. To address the concern, we clarified the conceptual links between each selected indicator and green-financing risks, explaining how macro-level factors affect WEF-related investment risk [see lines 229-233, 263-265, 321-325].
We also clarified the distinction between project-level financial risk and macro-institutional risk, explaining that our approach focuses on broader economic, regulatory, and social factors, which is particularly relevant for WEF nexus projects requiring stable, long-term financial commitments and strong governance capacity. This conceptualization supports the rationale for our indicator selection [lines 416-420].
Comments 3: The weight allocation of indicators is unclear and lacks scientific methods.
Response 3: We thank you for this important observation. In response, we performed a sensitivity analysis using multiple alternative sets of weights for the indicators and demonstrated that the overall risk rankings of countries remained stable across scenarios, which confirms the robustness and interpretability of our weighting approach [lines 339-343]. The full set of sensitivity-analysis outputs is not included in the manuscript because it consists of several large scenario matrices that would considerably increase the length of the paper without adding additional conceptual insights. However, we are fully prepared to provide these detailed results upon request. The selection of equal weights was also further confirmed by expert assessment.
Comments 4: The indicators and original data are too limited to support a special project, let alone reflect the BRICS countries' projects. Therefore, the accuracy of the research results is also questionable.
Response 4: Thank you for raising this issue. To strengthen the robustness of our results, we validated the fuzzy-set model outcomes against an independent global investment-risk index, showing consistency in the relative risk profiles of BRICS countries [see lines 582-601]. We also clarified the limitations of the current indicator set and outlined plans to expand it in future work with WEF-specific metrics to better capture sectoral vulnerabilities. These points are addressed in the manuscript [lines 825-832].

Author Response File: Author Response.docx