the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Mar 2025
Modeling Regional Production Capacity Loss Rates Considering Response Bias: Insights from a Questionnaire Survey on Zhengzhou Flood
Abstract. Flood disasters in specific regions not only cause physical damage but also disrupt the production and operations of enterprises, making economic system more vulnerable. Assessing production capacity loss rate (PCLR) in enterprises is crucial for quickly evaluating disaster losses. However, PCLR in enterprises is difficult to measure through physical damage. On-site investigations offer a compromise method, but inconsistencies between respondents and investigators in understanding production capacity may result in response bias. Therefore, this study employed the vulnerability curve method for categorizing damage states to divide PCLR into different damage states and constructed exceedance probability curves to mitigate response bias. Then, this study utilized distribution function fitting to calculate the expectation of loss rate for each state, and finally integrated the probabilistic information with the expectation of loss rate under each state to construct PCLR curves. The proposed methodology is realized by the questionnaire data from the "7.20" extreme flooding event in Zhengzhou, Henan. We found that when the inundation depth is less than 80 cm, wholesale and retail trade sector suffers the greatest losses; however, when the inundation depth exceeds 80 cm, we should pay more attention to manufacturing sector. Monte Carlo simulation (MCS) established the prediction intervals of PCLR curves, offering an alternative for PCLR. This study effectively accounts for response bias, providing input conditions for assessing ripple losses.
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Status: open (until 08 May 2025)
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RC1: 'Comment on egusphere-2024-3923', Samar Momin, 12 Apr 2025
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General Comments:
This paper presents a robust methodological framework for estimating Production Capacity Loss Rate (PCLR) of enterprises affected by floods, while accounting for response bias in post-disaster survey data. The methodology is validated using empirical data from the 2021 Zhengzhou flood and employs probabilistic modeling, damage state classification, and Monte Carlo Simulation to derive loss estimates and uncertainty bounds.The manuscript is well-structured and offers a clear flow from methodology to results and implications. It contributes meaningfully to the literature on post-disaster economic assessment by bridging micro-level survey data with modeling techniques typically used in structural and hazard analysis. The application to real-world flood data strengthens its practical relevance.
Strengths:
Novelty and Relevance:Introduces a response-bias-tolerant approach to estimating PCLR, addressing a critical and often underexplored issue in survey-based disaster impact assessments.
Solid Methodological Framework:
Combines exceedance probability curves, distribution fitting, and Monte Carlo Simulation to derive robust and sector-specific loss estimates. Incorporates rate of change analysis, which adds depth to understanding vulnerability dynamics across sectors.
Real-World Application:
Empirically applied to the "7.20" Zhengzhou flood using 424 valid enterprise surveys, enhancing both credibility and replicability.
Actionable Sector-Specific Insights:
Finds that wholesale and retail trade is more vulnerable at shallow depths, while manufacturing becomes more vulnerable as inundation depth increases. Provides practical implications for targeted flood preparedness and recovery strategies.
Specific Comments:
Handling of Response Bias:The authors do well to acknowledge and address response bias. The approach of classifying damage states and modeling exceedance probabilities is well-justified. However, a brief comparative note on how this method improves upon traditional regression-based or assumed-PCLR models would help clarify its added value.
Model Generalizability:
The authors acknowledge limitations regarding generalization. Future applicability in diverse geographic and sectoral contexts (e.g., agriculture or public services) could be more explicitly discussed in the conclusion.
Classification of Damage States:
Damage state thresholds (e.g., [0, 1/3), [1/3, 2/3)) are sensible, but the paper could briefly discuss the sensitivity of results to these threshold choices or how alternate classifications might impact robustness.
Economic Modeling Link:
The manuscript suggests that PCLR values can serve as inputs for IO and CGE models, which is important. Including a schematic diagram or example of how these values would be plugged into such models would improve clarity for interdisciplinary audiences.
Policy Implications:
The policy section is informative, especially the recommendation for sector-specific emergency funds and infrastructure investments. This could be enhanced with a brief discussion on data collection protocols for future disasters to enable rapid PCLR estimation.
Minor Suggestions:
Grammar and clarity:Some long sentences (especially in the introduction and methodology) could be split to improve readability.
Figures:
Figures 3–6 are useful and well-labeled, but some could benefit from annotations or key call-outs to highlight major differences across sectors.
Terminology:
The term “response-bias-tolerant framework” is accurate but could be briefly defined when first introduced.
Citation: https://doi.org/10.5194/egusphere-2024-3923-RC1 -
RC2: 'Comment on egusphere-2024-3923', Anonymous Referee #2, 16 Apr 2025
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This study is valuable in the field of natural disaster research. When conducting post-disaster damage assessments, respondents frequently encounter difficulties in providing accurate information. Consequently, addressing response bias effectively becomes paramount for ensuring data reliability and research quality. Some questions and comments are provided for reference to improve this manuscript:
- To what extent can the proposed methodology effectively mitigate respondent biases? Please add more explanations.
- The study should establish clear operational definitions for damage severity categories (“major,” “minor,” and “moderate”), accompanied by a comprehensive discussion of how these classifications influence outcome variables. This should be supported by relevant empirical evidence from existing literature.
- The manuscript introduces responder bias as an input condition for ripple loss calculations. This raises important questions: Why does ripple loss estimation require relatively accurate data? Could responder bias compromise input data quality and consequently lead to inaccurate loss estimates? These relationships warrant more detailed examination.
- While future research directions are proposed, the discussion would benefit from greater depth regarding implementation specifics. For instance, suggestions about increasing sample size and incorporating cross-regional, multi-industry data need to address practical considerations: How will data collection be standardized across different contexts? What integration challenges might arise from heterogeneous data sources?
- A thorough linguistic review is necessary to eliminate grammatical inaccuracies and refine syntactical structures. Particular attention should be paid to ensuring terminological consistency with disciplinary conventions and maintaining an appropriate academic register throughout the manuscript.
- The manuscript would benefit from employing more sophisticated transitional devices (e.g., “This methodological approach demonstrates three principal advantages: Primarily,”). Systematic elimination of lexical repetition through careful editing would enhance the text’s professional tone.
Citation: https://doi.org/10.5194/egusphere-2024-3923-RC2
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