PCLM-D-25-00185

Bridging the Macro-Micro Divide through a New Paradigm for Climate Resilience Assessment in Data-Scarce Regions

PLOS Climate

Dear Dr. Katende,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Two external reviewers have identified a number of opportunities to improve the manuscript. We ask that you respond carefully to all of their recommendations when making your revisions.

Please submit your revised manuscript by Nov 10 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at climate@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pclm/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Jamie Males

Staff Editor

PLOS Climate

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Additional Editor Comments (if provided):

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Climate’s publication criteria?>

Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?-->?>

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy

Reviewer #1: No

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This paper presents a very interesting and important idea to measure climate resilience. The author proposes to combine a macro-level econometric model for many countries with a micro-level spatial analysis for one country (Uganda). This is very relevant for policy in developing countries. The writing is clear, and the figures are of high quality.

The main strength of the paper is the idea itself: to connect the big picture (macro) with the local reality (micro). The concept of the "Resilience Asymmetry Surface" (RAS) is also a creative contribution.

However, the paper has a major problem: it does not give enough information about the data and the methods. The reader sees the inputs and the final results, but not how the results were actually produced. Because of this, it is impossible to evaluate if the results are correct and reliable. Therefore, I must recommend major revisions before this paper can be considered for publication.

Following are major weaknesses and necessary revisions:

1. Missing Information on Data:

Macro Panel Data: The paper says it uses data for "selected low-income countries". Which countries? And for which years? This is basic information that must be included.

Definition of Variables: The variables in the econometric model are not clearly defined. The author must explain exactly:

What is "sectoral output"? Is it GDP value-added? In which currency?

What is "rainfall anomaly"? How is it calculated? What is the source of the data?

What is "infrastructure access"? Is it roads, electricity, internet? What is the indicator and the source?

Micro Spatial Data: This is very important. Figure 5 shows "field observations" for agricultural productivity. Where does this data come from? Is it from a government survey? A specific research project? How many points of observation are there? What year is the data from? Without knowing the source of this data, the whole spatial analysis is not verifiable.

2. Missing Information on Methods:

Econometric Model (GMM): The results of the dynamic panel model are shown only in a figure (Figure 4). This is not enough. A full regression table is needed, showing the coefficients, standard errors, and p-values for all variables. Most importantly, for System-GMM models, the author must report the results of the diagnostic tests (the Sargan or Hansen test for instrument validity, and the AR(2) test for serial correlation). Without these tests, we cannot know if the GMM model is correctly specified.

Spatial Methods (Kriging): the source of the input data (the "field observations" I mentioned before) must be clear.

Resilience Asymmetry Surface (RAS): The paper presents Equation (5) for the RAS. It has parameters θ1, θ2, θ3. The text says these are "estimated empirically". But how? Was it from the GMM model? From another regression? The paper does not explain this at all. This is a critical missing step. Without this explanation, the RAS is just a theoretical idea, not an empirical result.

3. The Link Between Macro and Micro:

In Figure 12, the author compares "macro-level" scores and "micro-level" scores for different regions inside Uganda. I do not understand how a "macro-level" score for a specific region is calculated from a model that uses data from many different countries. The author must explain the logic of this connection very clearly.

4. Minor Comments

In the text, the author refers to Figure 9 as "Figures 7a and 7b". This should be corrected.

Reviewer #2: 1. How can policymakers effectively navigate these conflicting signals to ensure the accurate allocation of resources, rather than merely identifying the existence of a gap?

2. Given the limitations of the GMM system and the reliability of the estimates, how can we ensure the robustness and generalizability of sectoral resilience estimates (e.g., agriculture, industry, services) across low-income countries?

3. In the context of sparse and uneven field data across many low-income countries, combined with rapidly changing ecological diversity (e.g., in Uganda), how can research effectively test and adjust when these assumptions are not fully met?

4. What risks might arise from overlooking institutional, behavioral, or latent policy-power factors in identifying intervention leverage points, and how might future versions of the RAS be expanded to more fully capture the multidimensional and dynamic nature of resilience, particularly the non-quantifiable aspects of adaptive capacity?

5. Beyond challenges related to computational resources and technical capacity, what specific contextual factors (e.g., governance systems, land-use policies, market structures, social safety nets) in other low-income countries might hinder the successful application and scaling of this framework without substantial adjustments, and how can the framework be made flexible enough to adapt to such diverse contexts?

6. What specific mechanisms or practical steps are proposed for policymakers at different levels (national and local) to directly translate vulnerability hotspot maps or RAS-derived information into concrete, effectively funded adaptation programs and projects, especially within decentralized governance systems such as Uganda?

7. Beyond the obvious solutions of investing in irrigation or infrastructure, how can this framework detect, measure, or provide insights into these underlying institutional, social, or market factors, and how can policymakers effectively address these deep-seated 'inertia' issues to transform agricultural resilience?

8. Although useful, satellite data can have limitations in resolution, accuracy, or in fully capturing micro-local conditions—especially when combined with sparse field observations. How can research validate or adjust the veracity and potential errors of these satellite-derived indices to ensure they accurately reflect agricultural and climatic realities at the micro level, particularly when interpolation methods (e.g., Kriging) also have their own limitations in predictive accuracy?

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Reviewer #1: No

Reviewer #2: No

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PCLM-D-25-00185R1

Bridging the Macro–Micro Divide through a New Paradigm for Climate Resilience Assessment in Data-Scarce Regions

PLOS Climate

Dear Dr. Katende,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Kindly make the revisions suggested by the reviewers. 

Please submit your revised manuscript by Jan 08 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at climate@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pclm/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Anamika Barua

Academic Editor

PLOS Climate

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. 

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Kindly make the revisions suggested by the reviewers

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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publication criteria?>

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?-->?>

Reviewer #2: Yes

Reviewer #3: (No Response)

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4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: As a reviewer of the article "Bridging the Macro–Micro Divide through a New Paradigm for Climate Resilience Assessment in Data-Scarce Regions" (Manuscript No.: PCLM-D-25-00185R1), I acknowledge that the authors have addressed substantial feedback from previous rounds, particularly enhancing transparency, statistical robustness, and reproducibility. However, there remain several minor points (minor revisions) that require clarification or additional detail to optimize the manuscript's precision and readability.

1. The conclusion emphasizes that governance quality significantly enhances resilience. However, the dynamic panel regression results for the Agriculture sector (Table 3 and A3) show the Governance index coefficient is negative (-0.472, p < 0.001). The author must clarify this directional contradiction in the Discussion section, explaining whether this negative sign aligns with theoretical expectations for agriculture (e.g., poor governance hinders adaptation) or if the general positive statement applies only to the Industry and Services sectors (Tables A4 and A5, where the coefficient is positive).

2. The Methodology section identifies Ordinary Kriging as the primary interpolation technique. Yet, the Validation section reports results using Gaussian Process (GP) interpolation with a Matérn kernel. While GP often encompasses Kriging, the author needs to add a brief sentence in the Methods or Validation section to explicitly state the relationship or justification for using the GP Matérn kernel for the final reported predictive performance.

3. The System-GMM Methodology section promises to report standard diagnostic tests, specifically Hansen J, AR(1), and AR(2). While tables report coefficients (Table 3, A3–A5) and residual autocorrelation is discussed (Tables A6 and A7), the primary regression tables lack the reported p-values for the Hansen J test and the AR(2) test (which verifies no second-order autocorrelation, crucial for GMM validity). These values must be included in or immediately below Tables 3, A4, and A5.

4. Table 6, which details the Spatial-block cross-validation and Moran’s I diagnostics, notes that the "Combined residuals" test was skipped due to a technical error: "Data merge failed (no residuals)". Given the manuscript's emphasis on reproducibility and integration, this technical gap in the diagnostic pipeline must be corrected and the complete results reported to ensure full transparency of the macro–micro synthesis validation.

5. The manuscript reports two different spatial validation metrics: Leave-One-Out Cross-Validation (LOOCV) RMSE is 0.502 (Table 2), while the Mean RMSE for 5-fold Spatial-block Cross-Validation is approximately 0.66 (Table 6). The author should add a concise explanation in the Discussion of Validation Results clarifying why these two methods yield different RMSE values (e.g., block validation is a stricter test for generalization) and what practical metric (0.502 or 0.66) policymakers should rely on when assessing the model's accuracy.

6. The study uses Uganda as a "core test case" but relies on synthetic data for both macro and micro components to demonstrate the framework's robustness and reproducibility. While the data section clarifies the data is synthetic, the Abstract and Introduction should explicitly state that the Uganda analysis utilizes a synthetic, realistic testbed to avoid the implication that empirical, ground-collected data from Uganda was used directly.

7. Detail Required for Governance Index Definition: Table A11 (Variable definitions) describes gov idx merely as "Governance quality index (unitless)". To meet the high standard of full reproducibility promised for the synthetic data generation, the author should add a brief note detailing the conceptual components (e.g., corruption, political stability, rule of law, etc.) used to construct this synthetic index, even if it is not sourced from a public repository.

8. The Resilience Asymmetry Surface (RAS) regression (Table 4, A8) found that the interaction term Stress × Infrastructure was statistically negligible (p=0.900), which the text notes implies an approximate additive relationship. The Discussion section should include a brief commentary on this finding, discussing whether this additive result aligns with the existing literature or theoretical expectations regarding the non-linear mitigating effect of capacity on climate stress.

9. The text introducing the revised figures and tables contains an unresolved internal reference: "Figures 3–5 and Table ?? are all newly introduced...". This placeholder must be corrected to the accurate table number (which appears to be Table 2, documenting the Gaussian Process LOOCV performance).

10. The Reproducibility section promises that all files are available in the outputs/ directory. While it generally lists outputs/*.png, for maximum transparency, the author should list the specific filenames for the subfigures introduced in the Appendix, particularly Figure A4 (a) and (b), in the Appendix's file inventory section (similar to how CSV files are listed explicitly).

Reviewer #3: • While the manuscript incorporates some field-based observations (e.g., Figure 5), the density and spatial distribution of these points are very limited. Several key components of the analysis including the cluster perturbations (Fig. 9b), the resilience surfaces (Figures 7 and 11), and portions of the productivity interpolation, are generated using simulated or artificially perturbed datasets. These synthetic elements substantially influence the resulting spatial patterns, cluster boundaries, and resilience gradients. As a result, the findings are difficult to interpret as representative of actual conditions in Uganda. The study, in its current form, reads more like a methodological demonstration than a fully empirical case study. The manuscript would benefit from either clearer framing as a methodological proof-of-concept or the inclusion of stronger empirical grounding.

• Additionally, several sentences in the Introduction and Discussion are overly long and would benefit from more concise phrasing for clarity.

• Figures (for example, Figures 5, 6, 8, and 9) are not adequately explained in the main text and require captions that clearly state what the reader should focus on. Additionally, the placement of figures and tables should be aligned with their corresponding discussion in the manuscript.

• Finally, the Resilience Asymmetry Surface (RAS), which is presented as a central methodological contribution, is insufficiently defined. It requires a clearer explanation, justification for the functional form, and some form of validation or sensitivity analysis to support its interpretability and usefulness.

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Reviewer #2: No

Reviewer #3: No

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Bridging the Macro–Micro Divide through a New Paradigm for Climate Resilience Assessment in Data-Scarce Regions

PCLM-D-25-00185R2

Dear Mr Katende,

We are pleased to inform you that your manuscript 'Bridging the Macro–Micro Divide through a New Paradigm for Climate Resilience Assessment in Data-Scarce Regions' has been provisionally accepted for publication in PLOS Climate.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team. 

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Climate.

Best regards,

Anamika Barua

Academic Editor

PLOS Climate

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Additional Editor Comments (if provided):

All suggested review comments have been incorporated into the paper. The authors have made substantial revisions, and the manuscript reads much better now

Reviewer Comments (if any, and for reference):