1. Introduction

Climate change is driving an increase in the frequency and severity of natural disasters, amplifying their impacts on human life and ecosystems. Rising global temperatures and shifting weather patterns fuel extreme events such as heatwaves, hurricanes, floods, and wildfires, resulting in widespread destruction and socio-environmental disruptions [1]. Regional disparities in these impacts further underscore the localized nature of climate risks, with some areas experiencing prolonged droughts while others face unprecedented flooding [2]. Children are particularly vulnerable due to their physiological, developmental, and socio-economic susceptibilities. Their developing systems and higher intake of air, water, and food make them more sensitive to extreme weather events, air pollution, and waterborne diseases [3,4]. Additionally, their reliance on caregivers and limited autonomy increases their risks, bearing as much as up to 88% of the global burden of disease from climate change according to some studies [4,5]. In addition, the current national climate adaptation plans – NAPS – do not sufficiently address children’s health [6]. The vulnerability of children under climate change is especially pronounced in Small Island Developing States (SIDS) characterized generally by high climate-sensitivity and vulnerability. These nations face unique challenges due to geographic isolation, limited resources, and dependence on climate-sensitive sectors like tourism and agriculture, which further risk to destabilize children’s health, education, and well-being [7,8].

Understanding and addressing the impacts of climate change on children is urgent and requires robust tools to measure their vulnerability and exposure. However, critical challenges persist in isolating child-specific vulnerabilities due to the limited availability—and, in many cases, the absence—of high-quality, disaggregated data. Existing global models, such as UNICEF’s Children’s Climate Risk Index (CCRI), offer a valuable framework for assessing risk at the national level [9]. The CCRI evaluates children’s climate risk across 163 countries by combining data on exposure to environmental hazards with child-specific vulnerability indicators, such as access to health services, education, nutrition, and water and sanitation. Fig 1 presents the global ranking according to the CCRI. Despite their utility, these models often lack the granularity required to identify subnational disparities, especially in geographically diverse or small island contexts. The index was designed primarily to support global comparisons and national-level advocacy, which limits its capacity to guide local-level interventions. Moreover, the CCRI relies heavily on standardized global datasets that may not capture the unique socio-environmental realities of small or data-constrained countries. Many of these datasets are either outdated, generalized, or available only at the national level, making it difficult to reflect intra-country variations in climate exposure and child vulnerability. The reliance on aggregated or proxy data further limits the ability to capture localized factors that drive child vulnerability, leaving critical gaps in the evidence base needed to inform effective interventions As a result, there is a significant research gap in the availability of child-focused, subnational models that integrate local data and context-specific indicators—particularly in SIDS, where vulnerability profiles may vary substantially within small geographic areas. Addressing these challenges is essential to ensure that policies and programs are responsive to the unique needs of children, particularly in the face of escalating climate risks.

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Fig 1. Global Children’s Climate Risk Index (CCRI), global ranking.

Global map showing relative levels of children’s climate and environmental risk as assessed by UNICEF (2021). The bar plot highlights the ten countries with the highest CCRI scores. Basemap: Natural Earth Admin 0 (1:50m; https://www.naturalearthdata.com), public domain. Visualization created in R.

https://doi.org/10.1371/journal.pclm.0000789.g001

This study aims to identify and map child-specific vulnerabilities to climate and environmental hazards in a small island developing state, Saint Kitts and Nevis (SKN), with the goal of generating actionable insights to inform child-centered adaptation and resilience strategies. To achieve this, we piloted the CCRI-Disaster Risk Model (CCRI-DRM), a subnational extension of the global CCRI. This model is designed to assess climate risk at the local level and to address gaps in spatial and demographic resolution. By integrating localized data with child-centered metrics from SKN, the CCRI-DRM provides a detailed analysis of risks at the parish level, uncovering the factors that contribute to children’s vulnerability to climate and environmental hazards. In doing so, the study contributes to closing critical data and methodological gaps while also demonstrating the value of subnational modeling in contexts with complex vulnerability patterns. By focusing on SIDS, it demonstrates the utility of subnational risk models in understanding and mitigating child-specific vulnerabilities, offering a framework that can be adapted to other vulnerable regions to enhance resilience and safeguard children’s futures.

2. Methods

Model framework and theoretical basis

The development of the subnational CCRI-DRM model started with the adaptation of the theoretical framework of the global CCRI model [10], maintaining its two-pillar structure while introducing changes at the component and indicator levels to reflect the country-specific context (Fig 2).

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Fig 2. Localization process for the Children’s Climate Risk Index – Disaster Risk Model (CCRI-DRM).

Schematic overview of the methodological workflow used to adapt the global CCRI to the national context of Saint Kitts and Nevis, including indicator selection, stakeholder engagement, data integration, and model construction.

https://doi.org/10.1371/journal.pclm.0000789.g002

Data sources

The CCRI-DRM draws on both national and global datasets. National datasets included vaccination coverage, pupil-to-teacher ratios, fire station calls, and under-five mortality rates, provided by respective ministries and agencies. Where high-resolution or updated national data were lacking, global datasets were used. These included MODIS NDVI and CHIRPS for drought indicators, WorldPop for population distribution, and WHO/UN databases for environmental exposures such as PM2.5 concentrations and heatwave frequency. Detailed data sources can be found in S2 Table.

The CCRI-DRM retains the two-pillar structure of the global CCRI, adapted to the local context of Saint Kitts and Nevis. Fig 3 presents the full framework, integrating Pillar 1 on exposure to climate and environmental hazards and Pillar 2 on child health, education, and poverty-related vulnerabilities. Each indicator was associated with these pillar components and organized hierarchically into sub-components, indicators, and sub-indicators, as reflected in the framework figures and summarized in S2 Table. This structure enabled a multidimensional understanding of children’s climate risk at the parish level.

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Fig 3. Structure of indicators and data sources in the Children’s Climate Risk Index – Disaster Risk Model (CCRI-DRM) framework.

Diagram illustrating the hierarchical structure of components, indicators, and sub-indicators used to construct the CCRI-DRM for Saint Kitts and Nevis. Exposure indicators (Pillar 1) and child vulnerability indicators (Pillar 2) are shown with their associated data sources. Arrows denote the method of averaging applied during index construction (geometric or arithmetic). Figure created by the authors using draw.io.

https://doi.org/10.1371/journal.pclm.0000789.g003

3. Results

Pillar 1: Exposure analysis

For Pillar 1, which assesses exposure to environmental hazards, we identified extreme heat as the most significant threat to children in SKN, followed by floods, air pollution, and vector-borne diseases (Table 1).

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Table 1. Subnational Pillar 1 and Pillar 2 Scores and Overall Children’s Climate Risk Index – Disaster Risk Model (CCRI-DRM) Scores by Parish in Saint Kitts and Nevis.

https://doi.org/10.1371/journal.pclm.0000789.t001

The parish-level analysis (Fig 4A) revealed that exposure scores were highest in Saint George Basseterre, Saint John Fig Tree, Saint John Capisterre, and Saint Peter Basseterre, with absolute exposure scores of 8,8, 8.6, 8,1, and 8,1, respectively (S3 Table).

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Fig 4. Subnational CCRI-DRM results for Saint Kitts and Nevis.

Maps show parish-level (A) exposure to hazards, (B) child vulnerability, and (C) combined CCRI-DRM risk index. Basemap: GADM v4.1 (Saint Kitts and Nevis, Level 1; https://gadm.org), licensed under CC BY 4.0. Figures visualized in R.

https://doi.org/10.1371/journal.pclm.0000789.g004

4. Discussion

In this study, the CCRI-DRM was piloted for the first time in a SIDS context, showing its effectiveness in integrating global methodologies with local data to assess child-specific, multi-hazard risks. With the localized CCRI-DRM framework, we used the combined index to identify the parishes where children are most vulnerable in SKN to climate and environmental hazards and the factors contributing to these vulnerabilities. The findings revealed significant spatial variability in climate and disaster risk across the 14 parishes of SKN, with Saint Paul Capisterre and Saint George Basseterre emerging as high-risk areas. This contrasts with previous reliance on global climate risk indices, such as INFORM or the original CCRI, which are well-suited for guiding high-level policy and international prioritization but lack the granularity needed for operational decision-making at the local level. Our results emphasized the importance of subnational models in understanding spatially differentiated risks and developing targeted interventions to safeguard the most vulnerable populations.

By combining locally sourced data with child-specific indicators, the CCRI-DRM moves beyond broad national risk rankings to generate actionable, sub-parish-level insights that can directly inform the planning and delivery of child-centered climate adaptation strategies. Through this case study, the CCRI-DRM provided a scalable framework for addressing child vulnerabilities to climate risks in SIDS and beyond. Furthermore, our case study highlighted clear gaps in how disaster preparedness and resilience efforts account for children’s specific needs. These shortcomings point to an urgent need for better planning, targeted support, and stronger policies that focus on protecting children during and after climate-related disasters.

Children’s vulnerability to climate risks is not homogenous; it varies substantially across regions due to differences in environmental exposure, socioeconomic status, and the strength of public service systems. Elevated risk scores in various parishes were influenced by distinct factors. For instance, severe vulnerabilities stemming from poverty, education, and inadequate health infrastructure contribute to the risk in Saint Paul Capisterre, while Saint George Basseterre experiences high exposure to multiple environmental hazards, including extreme heat, air pollution, and flooding. Notably, these two parishes reflect different dimensions of risk—one primarily vulnerability-driven, the other exposure-driven—demonstrating that spatial patterns of hazard and vulnerability often diverge.

This distinction is clearly visualized in the CCRI-DRM mapping outputs, where disentangling the two pillars reveals distinct hotspots for exposure versus vulnerability. However, the composite CCRI-DRM score, which integrates both, is essential for pinpointing where these dimensions intersect most acutely—thus identifying where interventions will be most impactful. This layered understanding reinforces the need to view exposure and vulnerability in tandem rather than in isolation when prioritizing adaptation measures.

Our findings from Saint Kitts and Nevis islands, which highlight parish-level disparities in both exposure and vulnerability, align with previous studies emphasizing the intersectionality of climate risk factors. For example, research from sub-Saharan Africa and Southeast Asia has shown that children’s vulnerability is heightened in regions with poor access to healthcare, limited educational infrastructure, and high poverty rates [9,10]. Similarly, studies from Pacific Island nations have underscored how remoteness and uneven service distribution create pockets of extreme vulnerability despite national-level resilience [7]. The CCRI-DRM findings echo these global patterns, demonstrating how climate hazards like heatwaves and floods interact with systemic weaknesses—such as poor health coverage or inadequate school facilities—to drive localized child risk. Unlike global indices, which often mask these subnational disparities, our pilot demonstrates the value of fine-scale, localized assessments in identifying and targeting the hazard pathways and child-specific vulnerabilities that matter most in a given community.

This reinforces the need for subnational assessments to reveal context-specific vulnerabilities that would otherwise be obscured by national averages. Furthermore, the observed variation between high-exposure urban areas and high-vulnerability rural areas in SKN aligns with other studies that point to urban-rural divides in risk distribution [4,5]. Such differences must be accounted for when designing child-centered adaptation policies, as one-size-fits-all solutions risk leaving the most at-risk groups behind.

The CCRI-DRM’s findings on child health vulnerabilities emphasize children’s heightened susceptibility to climate risks. The model’s identification of heat exposure, air pollution, and vector-borne diseases as primary threats reflects trends documented by the Lancet Countdown, which highlights physiological and developmental factors that increase children’s vulnerability [4,11]. However, the CCRI-DRM could benefit from incorporating greater intersectionality in its assessments. For instance, while it accounts for poverty and access to education, it does not fully capture the interplay of age, gender, and socio-economic disparities in shaping vulnerability. Research shows that girls in SIDS are disproportionately affected due to gendered barriers in education and healthcare during crises [12]. Integrating these nuances would improve the model’s capacity to inform targeted and equitable interventions.

This study highlights the importance of adapting global frameworks such as the CCRI-DRM to reflect local contexts and realities. To ensure that the model effectively informs policy and implementation, future applications should include a structured process for local calibration and participatory engagement. This involves working closely with national agencies, NGOs, and community representatives to identify locally relevant indicators, refine weighting schemes, and address data limitations. Participatory diagnostics, capturing local perceptions of risk, community priorities, and lived experiences, are essential for ensuring that the model reflects real development dynamics and social conditions. Incorporating multidimensional indicators such as poverty indices, the GINI coefficient, and access-to-services measures can further enhance its ability to capture structural inequalities that shape vulnerability. Likewise, integrating gender, generational, and life-cycle perspectives, including factors related to early childhood development and caregiver well-being, would strengthen the model’s capacity to represent diverse pathways of risk. Through these refinements, the CCRI-DRM can evolve from a diagnostic framework into a co-created, actionable tool that bridges evidence with equitable, community-informed climate and disaster risk policies.

The CCRI-DRM exhibits several limitations that warrant critical evaluation, particularly concerning data challenges. A major issue is the reliance on outdated or national-level datasets, which often fail to provide the high-resolution, sub-national detail covering the entire nation required for a granular analysis of a small country like SKN. This limitation hampers the model’s ability to accurately capture localized variations in child vulnerability and exposure, critical for a nuanced understanding of risks within smaller geographic contexts. Additionally, global datasets, while valuable for comparative analysis, often lack the granularity needed to reflect the socio-economic and geographic complexities of sub-national areas. Notably, there was no child-specific population layer available, meaning that while the selected indicators are suitable for addressing child-specific risks, the model ultimately assesses population risk rather than accurately isolating child-specific vulnerabilities.

Like many risk models, the CCRI-DRM faces challenges related to data quality and accessibility, particularly in under-resourced contexts like SKN. Reliance on global datasets and proxies, while practical, limits the model’s granularity and accuracy for sub-national analysis. Similar limitations are evident in models such as INFORM and WRI, where static datasets fail to capture rapidly evolving vulnerabilities [8,13]. Addressing these issues requires targeted efforts to enhance local data collection and standardization. Initiatives such as capacity-building programs and cross-sector collaboration can mitigate data gaps, while emerging technologies like remote sensing and AI offer opportunities for more precise, high-resolution analyses [8,13] By strengthening data accessibility and integrating advanced methodologies, the CCRI-DRM can improve its reliability and applicability, enabling it to better support evidence-based decision-making for vulnerable populations in SIDS and beyond.

Beyond data granularity, the CCRI-DRM also relies on static datasets that do not capture rapidly evolving socio-economic conditions or emerging climate hazards. This static nature, coupled with the use of proxies for certain indicators where direct data is unavailable, reduces the model’s ability to provide a fully accurate assessment of child-specific risks. Furthermore, the current use of uniform weighting across indicators within each pillar, while methodologically consistent, may obscure the differential importance of certain risk factors. In the context of SKN, for example, poverty or lack of healthcare infrastructure may have stronger predictive power than other indicators in shaping child vulnerability. Further calibration of the risk index—potentially through empirical techniques such as regression modeling or machine learning—could help optimize indicator weights to better reflect their contribution to observed risk patterns. These challenges are compounded by the lack of future climate projections—such as rising sea levels and increasing storm intensity—which are vital for anticipating evolving risks.

Validation and generalizability further complicate the model’s applicability. The absence of empirical validation, such as ground-truthing, limits the reliability of its outputs, even as stakeholder and expert feedback adds contextual relevance.

In addition to refining indicator weights, future iterations of the CCRI-DRM would benefit from further differentiation of exposure and vulnerability pathways, particularly for risks where interaction effects are known. For example, infectious disease hazards such as vector-borne illness or diarrheal disease may be magnified when vulnerability indicators like low immunization coverage or inadequate sanitation coincide. Conversely, high exposure to certain hazards—such as heatwaves—may be misinterpreted as high risk even when vulnerability is low due to effective public health interventions. Without accounting for such interdependencies, composite scores could inadvertently mislead policy responses. A more modular approach that highlights interaction effects between specific hazards and vulnerabilities may therefore enhance the CCRI-DRM’s operational accuracy.

Parish-level aggregation, while practical for data analysis, overshadows localized disparities within SKN and generalizes child-specific vulnerabilities without accounting for heterogeneity in age, gender, or special needs. Finally, reliance on secondary global datasets introduces potential biases, as these are often inconsistently collected and may not align with the specific needs of sub-national analyses. Addressing these data challenges, including the absence of child-specific population data, is critical to enhancing the accuracy, utility, and adaptability of the CCRI-DRM, particularly for localized applications in SIDS and similar contexts.

While the CCRI-DRM exhibits certain limitations, these weaknesses do not fundamentally undermine the validity of the study’s conclusions. Although the lack of a child-specific population layer means the model captures population risk rather than isolating child-specific vulnerabilities, the selected indicators remain highly relevant for assessing risks faced by children within their communities. The reliance on proxies and static datasets, while imperfect, leverages the best available data and aligns with widely accepted practices in risk modeling. Moreover, the inclusion of stakeholder and expert input adds substantial contextual relevance, mitigating the lack of empirical validation through ground-truthing. While global datasets may lack granularity, their integration ensures a consistent framework that is adaptable for localized use. Similarly, parish-level aggregation, though not fully reflective of intra-parish variations, provides actionable insights at a manageable scale for policy and planning in SKN. Taken together, these considerations affirm that the CCRI-DRM effectively identifies critical vulnerabilities and highlights opportunities for strengthening child-centered resilience strategies, even as ongoing refinements to data quality and model design are needed.

The CCRI-DRM represents an advancement in risk modeling through its emphasis on child-specific metrics and uniform weighting, providing a multidimensional lens to assess vulnerabilities. Unlike general-purpose models such as INFORM and the World Risk Index (WRI), which often use aggregated indicators for broader applicability [14,15], the CCRI-DRM integrates localized and child-specific data to offer a nuanced understanding of risks. However, its reliance on proxies and global datasets mirrors a common challenge among risk models, particularly in contexts where granular sub-national data is scarce [9]. This reliance can limit precision and comparability, as exemplified by the exclusion of 34% of SIDS from INFORM due to insufficient data [16].

Validation techniques further differentiate risk models. While the CCRI-DRM relies on expert-based validation to contextualize its findings, empirical validation and ground-truthing, as employed by frameworks like CLIMADA, offer a more robust alignment of model outputs with real-world conditions [17]. Incorporating such empirical approaches into the CCRI-DRM could enhance its reliability and applicability, particularly in regions with limited validation datasets. Additionally, models like CLIMADA and HAZUS integrate future climate projections, such as increasing storm intensity or sea-level rise, to forecast long-term risks [18]. Embedding such dynamic climate trends into the CCRI-DRM could increase its utility for forward-looking policy and planning, particularly as climate variability intensifies.

Applying sub-national models like the CCRI-DRM in SIDS presents unique challenges due to the limited geographic and demographic variability in such settings. In small island nations like SKN, the granularity offered by sub-national models may fail to yield distinct insights, contrasting with their effectiveness in larger countries where regional disparities are more pronounced [13,19].

For SIDS, sub-regional models that aggregate data across neighboring islands offer a promising alternative. These models could address shared vulnerabilities, such as dependence on tourism, coastal hazards, and resource limitations, while fostering regional collaboration and policy alignment. Aligning the CCRI-DRM with global frameworks such as the IPCC and INFORM could further facilitate its adaptation to regional contexts, enabling it to provide insights that are both locally actionable and globally relevant.

The CCRI-DRM holds significant potential as a policy tool for informing disaster preparedness and child-centered public health strategies. By identifying high-risk parishes and specific vulnerabilities, such as exposure to extreme heat and deficiencies in educational infrastructure, the model enables policymakers to target interventions effectively. Similar risk models, such as WRI and CLIMADA, have demonstrated success in translating results into actionable policies, highlighting the CCRI-DRM’s potential to drive resilience-building efforts [17].

However, the proliferation of risk models poses challenges for decision-makers, as discrepancies in methodologies, indicator selection, and data sources often result in conflicting outputs. For example, global models like INFORM and the WRI prioritize generalizable metrics, potentially overlooking localized nuances captured by the CCRI-DRM [8,15]. Conversely, the CCRI-DRM’s reliance on proxies and limited datasets introduces uncertainties in its outputs. This lack of standardization can create decision-making paralysis, where policymakers struggle to determine which model provides the most reliable guidance. Nevertheless, despite methodological differences, risk models often converge in identifying the most at-risk regions or populations. For instance, global indices like INFORM and WRI consistently highlight SIDS as vulnerability hotspots due to shared characteristics such as geographic isolation, limited resources, and exposure to extreme weather events [13,19]. Similarly, the CCRI-DRM identifies high-risk areas within SIDS, reflecting broader trends in vulnerability highlighted by other frameworks. This convergence suggests that while models may vary in their specific outputs, they collectively reinforce the need for prioritizing interventions in regions facing systemic and multidimensional risks.

To address this, harmonization efforts among risk models are essential. Aligning methodologies or transparently documenting assumptions and limitations can reduce discrepancies and enhance model comparability. Multi-model approaches that triangulate findings from several tools could also provide a more comprehensive basis for policy decisions. Ultimately, while the global CCRI serves as a valuable policy and advocacy tool for guiding strategic priorities, the CCRI-DRM demonstrates its utility as an actionable, subnational tool for implementing targeted interventions. Broader efforts to harmonize risk assessment frameworks and enhance their transparency remain essential for maximizing their collective impact.

The application of the CCRI-DRM in SKN underscores its utility as a practical tool for advancing child-centered climate resilience and disaster preparedness. By identifying high-risk areas and pinpointing specific vulnerabilities, such as extreme heat exposure and deficiencies in education and healthcare infrastructure, the model equips policymakers with actionable insights to prioritize resource allocation and intervention strategies. For instance, the findings can inform the implementation of targeted measures, such as heatwave early-warning systems, the strengthening of school infrastructure, and enhanced access to healthcare services in high-risk parishes. Beyond SKN, the CCRI-DRM offers a scalable framework for integrating child-specific metrics into national and regional disaster risk management strategies, particularly in SIDS facing similar challenges. However, the study also highlights the importance of harmonizing risk models to guide decision-making effectively, as the proliferation of tools with varying methodologies can create confusion for policymakers. By fostering cross-model alignment and leveraging localized data, the CCRI-DRM has the potential to bridge global frameworks with actionable local insights, driving more equitable and effective climate resilience strategies for vulnerable populations worldwide. Ultimately, continued refinement through empirical validation, indicator weighting, and interaction-based modeling will be essential to ensure that the CCRI-DRM not only identifies high-risk areas but also illuminates the causal pathways driving child vulnerability in climate-vulnerable settings.

5. Conclusion

This study developed the Children’s Climate Risk Index - Disaster Risk Model (CCRI-DRM) to assess child-specific vulnerabilities to climate and environmental hazards in Saint Kitts and Nevis (SKN), providing a subnational analysis of risk across the country’s 14 parishes. The findings highlight significant disparities in exposure and vulnerability, emphasizing the need for targeted interventions to protect children from climate-related risks.

The analysis identified Saint Paul Capisterre (risk score: 8.1) and Saint George Basseterre (7.6) as the highest-risk parishes, driven by severe socio-economic vulnerabilities (poverty and education deficits) and high exposure to multiple environmental hazards such as extreme heat, air pollution, and flooding. In contrast, lower-risk parishes, such as Trinity Palmetto Point and Saint Thomas Lowland, exhibited fewer systemic vulnerabilities and reduced exposure levels. The results underscore the importance of localized risk assessments in understanding spatial inequalities in climate vulnerability, particularly for children in SIDS, who face compounding challenges from economic constraints, governance limitations, and geographic isolation.

To further advance the CCRI-DRM and strengthen its applicability, future research should prioritize the development of child-specific population datasets to better capture the unique vulnerabilities faced by children. Efforts should also be directed toward reducing reliance on global hazard datasets by incorporating more context-sensitive, high-resolution subnational data that reflects local conditions. Additionally, enhancing the model’s capacity to account for dynamic, evolving socio-economic and environmental factors is essential for improving its predictive power. This could be achieved by integrating longitudinal data sources and incorporating future climate projections to simulate risk trajectories under various climate scenarios.

Further calibration of the risk index is also needed to investigate whether the current equal weighting of exposure and vulnerability components adequately reflects their relative importance. Empirical approaches—such as regression analysis or machine learning—could help refine these weights based on their predictive validity, potentially guiding future iterations of the model.

Moreover, expanding validation efforts through empirical techniques, including ground-truthing and statistical benchmarking, will be critical in improving the model’s robustness and credibility. Another area for refinement involves separating and analyzing the interaction between specific hazard exposures and corresponding vulnerabilities. For instance, infectious disease hazards and low immunization coverage often amplify each other’s effects, while high exposure to heat might not pose a serious risk in areas with robust health systems. If not disentangled, composite scores may obscure these interaction effects and mislead intervention strategies. A modular approach that allows for compound risk pathways could improve the interpretability and actionability of the model’s outputs. Together, these advancements would significantly strengthen the CCRI-DRM’s potential as a forward-looking, evidence-based tool for child-centered climate adaptation and disaster risk planning.

Ultimately, this study demonstrates the potential utility of the CCRI-DRM as a decision-support tool for child-centered climate resilience and disaster risk management. By providing granular, parish-level insights, the model can support targeted policy interventions, disaster preparedness strategies, and resource allocation to reduce climate risks for children in SKN and other vulnerable SIDS. Moving forward, strengthening data collection efforts, fostering regional collaborations, and aligning with global risk frameworks will be critical in ensuring more effective, child-focused climate adaptation strategies.

Supporting information