Carolina Ferman Carral , Marija Bockarjova , Marc van den Homberg , Frank Osei , Norman Kerle
{"title":"社会经济脆弱性和洪水影响的空间计量经济模型:马拉维南部的风险分层方法","authors":"Carolina Ferman Carral , Marija Bockarjova , Marc van den Homberg , Frank Osei , Norman Kerle","doi":"10.1016/j.ijdrr.2025.105433","DOIUrl":null,"url":null,"abstract":"<div><div>As climate-related disasters escalate, particularly in vulnerable communities in the Global South effective risk management strategies become necessary. The objective of this work was to examine the spatial dependencies between socioeconomic vulnerability and flood impacts in Southern Malawi, merging geospatial methods with econometric modeling. The analysis revealed significant spatial dependencies and spillover effects from data in the Unified Beneficiary Register, Malawi Census, and Rapid Damage Assessments from the 2020 and 2022 floods. Moran's I analysis emphasized the need to account for those spatial spillover effects. The spatial econometric framework, represented by a spatial lag variable in the Spatial Generalized Linear Model, captured these dependencies effectively. Expected associations emerged between flood impacts and socioeconomic indicators such as wealth, education, and household savings, suggesting that economically secure households are less vulnerable. However, unexpected correlations also appeared: food security was positively associated with flood impact, while disability occurrence showed a negative association. These findings challenge resilience assumptions and raise concerns about data accuracy and aftershock dynamics. Insights highlight the need for DRM strategies that incorporate exposure and socioeconomic indicators, along with improved data collection to ensure vulnerable groups are adequately represented. Despite challenges from data scarcity and spatial granularity, this study demonstrates the potential of Spatial Econometric Models to identify spatially interconnected vulnerabilities. The approach can be transferred to other contexts, but further research is needed to refine spatial resolutions and ensure actionable vulnerability characterizations. Integrating spatial dependencies into risk assessments highlights the need for spatially explicit policy interventions to strengthen resilience and advance risk-layering strategies.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"121 ","pages":"Article 105433"},"PeriodicalIF":4.2000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial econometric modeling of socioeconomic vulnerability and flood impact: Towards a risk-layering approach in southern Malawi\",\"authors\":\"Carolina Ferman Carral , Marija Bockarjova , Marc van den Homberg , Frank Osei , Norman Kerle\",\"doi\":\"10.1016/j.ijdrr.2025.105433\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>As climate-related disasters escalate, particularly in vulnerable communities in the Global South effective risk management strategies become necessary. The objective of this work was to examine the spatial dependencies between socioeconomic vulnerability and flood impacts in Southern Malawi, merging geospatial methods with econometric modeling. The analysis revealed significant spatial dependencies and spillover effects from data in the Unified Beneficiary Register, Malawi Census, and Rapid Damage Assessments from the 2020 and 2022 floods. Moran's I analysis emphasized the need to account for those spatial spillover effects. The spatial econometric framework, represented by a spatial lag variable in the Spatial Generalized Linear Model, captured these dependencies effectively. Expected associations emerged between flood impacts and socioeconomic indicators such as wealth, education, and household savings, suggesting that economically secure households are less vulnerable. However, unexpected correlations also appeared: food security was positively associated with flood impact, while disability occurrence showed a negative association. These findings challenge resilience assumptions and raise concerns about data accuracy and aftershock dynamics. Insights highlight the need for DRM strategies that incorporate exposure and socioeconomic indicators, along with improved data collection to ensure vulnerable groups are adequately represented. Despite challenges from data scarcity and spatial granularity, this study demonstrates the potential of Spatial Econometric Models to identify spatially interconnected vulnerabilities. The approach can be transferred to other contexts, but further research is needed to refine spatial resolutions and ensure actionable vulnerability characterizations. Integrating spatial dependencies into risk assessments highlights the need for spatially explicit policy interventions to strengthen resilience and advance risk-layering strategies.</div></div>\",\"PeriodicalId\":13915,\"journal\":{\"name\":\"International journal of disaster risk reduction\",\"volume\":\"121 \",\"pages\":\"Article 105433\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2025-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of disaster risk reduction\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212420925002572\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of disaster risk reduction","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212420925002572","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Spatial econometric modeling of socioeconomic vulnerability and flood impact: Towards a risk-layering approach in southern Malawi
As climate-related disasters escalate, particularly in vulnerable communities in the Global South effective risk management strategies become necessary. The objective of this work was to examine the spatial dependencies between socioeconomic vulnerability and flood impacts in Southern Malawi, merging geospatial methods with econometric modeling. The analysis revealed significant spatial dependencies and spillover effects from data in the Unified Beneficiary Register, Malawi Census, and Rapid Damage Assessments from the 2020 and 2022 floods. Moran's I analysis emphasized the need to account for those spatial spillover effects. The spatial econometric framework, represented by a spatial lag variable in the Spatial Generalized Linear Model, captured these dependencies effectively. Expected associations emerged between flood impacts and socioeconomic indicators such as wealth, education, and household savings, suggesting that economically secure households are less vulnerable. However, unexpected correlations also appeared: food security was positively associated with flood impact, while disability occurrence showed a negative association. These findings challenge resilience assumptions and raise concerns about data accuracy and aftershock dynamics. Insights highlight the need for DRM strategies that incorporate exposure and socioeconomic indicators, along with improved data collection to ensure vulnerable groups are adequately represented. Despite challenges from data scarcity and spatial granularity, this study demonstrates the potential of Spatial Econometric Models to identify spatially interconnected vulnerabilities. The approach can be transferred to other contexts, but further research is needed to refine spatial resolutions and ensure actionable vulnerability characterizations. Integrating spatial dependencies into risk assessments highlights the need for spatially explicit policy interventions to strengthen resilience and advance risk-layering strategies.
期刊介绍:
The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international.
Key topics:-
-multifaceted disaster and cascading disasters
-the development of disaster risk reduction strategies and techniques
-discussion and development of effective warning and educational systems for risk management at all levels
-disasters associated with climate change
-vulnerability analysis and vulnerability trends
-emerging risks
-resilience against disasters.
The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.