the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Jul 2026
County-level disaster resilience to geological hazards in high-altitude mountainous Sichuan, China: an EDE–TA–CCD assessment
Abstract. High-altitude mountainous counties face compound geological hazards and chronic socioeconomic and ecological pressures, yet existing resilience assessments rarely integrate external disturbance, internal adaptive capacity, and subsystem coordination within a dynamic county-level framework. Taking 73 medium- and high-altitude counties in Sichuan Province, China, as the study area, this study develops an EDE–TA–CCD framework that combines Environmental Disturbance Exposure, Total Adaptive Capacity, and Coupling Coordination Degree. Using panel data from 2007 to 2022, an improved coupling coordination model, spatial autocorrelation analysis, and K-means clustering were applied to examine the spatiotemporal evolution, spatial dependence, and typological differentiation of geological disaster resilience. The results show that regional resilience improved overall during the study period, mainly driven by the continuous enhancement of TA and CCD, while EDE remained relatively stable. Spatially, resilience displayed significant clustering and path dependence, with higher resilience concentrated in counties with stronger development foundations and reconstruction support, and lower resilience persisting in remote mountainous areas. Static classification and dynamic trajectory analysis further identified three resilience patterns and five evolutionary pathways, revealing differentiated processes such as low-level lock-in, stable saturation, post-disaster remodeling, rapid catch-up, and high-pressure stress climbing. The proposed framework provides a practical tool for identifying resilience disparities and supporting differentiated disaster risk governance in high-altitude mountainous regions.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-3640', Milad Basirifard, 13 Jul 2026
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RC2: 'Comment on egusphere-2026-3640', Anonymous Referee #2, 13 Oct 2026
This manuscript examines changes in disturbance exposure, adaptive capacity and subsystem coordination across 73 mountainous counties in Sichuan. The county-level perspective and the attempt to consider socioeconomic pressures alongside natural hazards are relevant to NHESS. Based on my expertise, I mainly focused on the disaster-risk assessment framework, the interpretation of socioeconomic resilience indicators, and the temporal and spatial conclusions drawn from them.
I recommend reconsideration after major revisions. The framework could provide a useful comparative assessment, but the present evidence does not yet support several central claims about stable external disturbance, resilience improvement and the mechanisms responsible. In particular, the treatment of EDE over time requires clarification, and there are substantial inconsistencies between the figures and their interpretation. The revisions should establish what the indicators measure, correct these inconsistencies, and bring the conclusions and management recommendations into line with the evidence.
Major comments
- Clarify what EDE measures and how socioeconomic change becomes disturbance.
Sections 2.3 and 2.3.3 explicitly broaden exposure beyond the presence of people and assets in hazardous locations. Including chronic socioeconomic pressures is reasonable, but describing them as an exogenous disturbance burden needs more justification (lines 187-192 and 354-385). Industrial restructuring, fiscal change and migration can arise within the same development and adaptation processes represented by TA. Industrial diversification or population movement may also be beneficial responses rather than adverse disturbances.
Please explain the causal role, direction and operational definition of each EDE indicator. For example, does industrial structure change measure signed change or its absolute magnitude? How are beneficial and adverse changes distinguished? Table 2 labels population ageing as a speed but lists the annual proportion aged 65 and above, which is a level. The definitions of volatility and the periods over which changes are calculated also need to be explicit. Reporting hazard/climate and socioeconomic components separately would help establish whether stable aggregate EDE actually indicates persistent hazard pressure or offsets among different processes. The terminology and conclusions should reflect the broader construct being measured.
- Reconcile the TA indicators with the capacities they are intended to represent.
Table 1 and the description in lines 212-236 do not consistently describe the same implementation. The text discusses warning-system coverage, emergency management personnel and disaster prevention expenditure, whereas the institutional indicators in Table 1 concern insurance participation and urban maintenance/construction tax. Nighttime light is discussed as economic capacity but appears as a negative social indicator in the table. Please distinguish illustrative indicators from those actually used and base the interpretation on the latter.
Several directions also require explanation: water conservation capacity is negative, soil erosion positive, and seismic fortification intensity negative. These may reflect particular variable definitions, but the current labels do not explain them. Please distinguish a hazard-related design requirement from achieved protective capacity and justify the direction of each indicator. For socioeconomic proxies, clarify why total school numbers and fixed-asset investment measure capacity comparably across counties of different sizes, and whether monetary values are expressed in constant prices. Employee insurance coverage also needs a clear denominator and a discussion of its representativeness for rural and informal livelihoods. A targeted sensitivity check for the most consequential ambiguous proxies would be more informative than changing weights alone.
- Resolve figure and text inconsistencies before interpreting subsystem contributions.
The discrepancies in Fig. 6 and 11 affect substantive conclusions. Lines 505-510 state that infrastructure and institutional capacity jointly contributed more than 70% of TA in the early period. In Fig. 6, however, the 2007 shares labelled Infrastructure and Rules and Regulations are 17% and 7%; the large shares are labelled Eco-environment and Society. The figure also shows Eco-environment declining from 34% to 24%, whereas the text describes an increasing ecological contribution.
Similarly, lines 587-594 identify industrial disturbance as the largest EDE component, reaching 44% in 2022, and climatic disturbance as approximately 13%. Fig. 11 labels the 44% component as Climate disturbance and the 13% component as Industrial disturbance. Please check the underlying calculations, legends and text against the same data and revise Sections 3 and 4 consistently. Explain how the contribution percentages were calculated, including whether they represent weighted component scores. These are not simply presentation details. They change the interpretation of which capacities and disturbances dominate and therefore the proposed policy priorities.
- Distinguish constructed coordination from demonstrated resilience performance.
The interpretation of CCD as a mechanism or efficiency through which capacity becomes effective resilience is stronger than the analysis establishes (lines 119-120, 552-553 and 764-781). CCD is constructed from the same subsystem scores that form TA. A more balanced or higher set of scores does not by itself demonstrate functional interaction, more efficient resource use or better disaster performance. Please clarify which results concern the coupling term C, the initial D, or the modified D′, and provide a worked county-year example linking the implemented formula to a reported CCD value, including any subsequent normalization.
The outcome interpretation would benefit from a focused check against independent disaster impacts or recovery evidence, accounting for differences in hazard severity and exposed population or assets. Such a check should not reuse an outcome already included in the index. If suitable evidence is unavailable, frame the results as changes in indicator-based capacity and coordination, and treat their implications for realised resilience as hypotheses. The limitations acknowledged in Section 4.5 should also qualify the main claims.
- Align trajectory interpretations with the observation dates and actual changes.
The dynamic features use a 2007 baseline and a trend through 2022 (lines 417-419). Yet lines 704-708 interpret the post-disaster remodelling group in terms of extreme initial shocks from the Wenchuan earthquake and subsequent reconstruction. The earthquake occurred in 2008, after the baseline. Please clarify the time windows behind each snapshot and distinguish pre-existing pressure from the earthquake shock and subsequent reconstruction. A plot of the four observations for representative counties, accompanied by a short event and reconstruction chronology, would help assess this interpretation.
The same caution applies to labels such as stable saturation and low-level lock-in. A relatively small positive slope does not establish a development ceiling, and a below-average trajectory does not establish a trap. Please use descriptive labels unless these mechanisms are supported independently. Section 4.5 already acknowledges that four dates can smooth shocks and recovery; this should change the wording of the results, especially the claim that a declining EDE slope verifies the effect of external reconstruction resources. The data can support exploratory trajectories without proving those causal explanations.
Additional comments
- Spatial interpretation (lines 643-648 and 754-760): persistent Moran’s I or LISA patterns establish spatial association. They do not by themselves distinguish neighbourhood effects from shared terrain, infrastructure or socioeconomic conditions. Please qualify claims of neighbourhood effects and path dependence, or provide evidence that tests those explanations.
- Fig. 5, 8 and 10: define the shaded bands, whiskers and summary statistics in the captions. Fig. 15 should be described as a representation of cluster characteristics unless an actual formation mechanism has been tested.
- Supporting references: please check that each citation directly supports the associated statement. E.g., Acero-Oliete et al. (2022), cited in lines 91-92 concerning specialised resilience indicators, is listed as a study of constructed wetlands and wastewater treatment. The relevance of that citation is unclear.
Citation: https://doi.org/10.5194/egusphere-2026-3640-RC2 - Clarify what EDE measures and how socioeconomic change becomes disturbance.
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- 1
The manuscript presents a potentially useful county-level framework integrating Environmental Disturbance Exposure (EDE), Total Adaptive Capacity (TA), and Coupling Coordination Degree (CCD). The study area and multidimensional dataset are valuable. However, the central methodology currently contains substantial conceptual, mathematical, validation, and reproducibility problems. In particular, the modified CCD formulation and its correction factors are not sufficiently justified and appear to contain internal inconsistencies. The clustering and trajectory analyses also require stronger validation before the proposed resilience types and policy conclusions can be considered reliable.
Minor comments