Vulnerability of U.S. Cities to Environmental Hazards

Borden, Kevin A.; Schmidtlein, Mathew C.; Emrich, Christopher T.; Piegorsch, Walter W.; Cutter, Susan L.

doi:10.2202/1547-7355.1279

Cited by 145 publications

(149 citation statements)

References 16 publications

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“…Second, some important factors in measuring susceptibility, coping capacity and adaptive capacity and in measuring vulnerability to natural hazards, such as housing conditions, disaster preparedness, early warnings, social networks and institutional dimensions, were not considered in the CDRI. Built-environmental factors amplify or attenuate the adverse effects of natural hazards (Borden et al 2007). Different types of buildings would resist to the impact of disaster events differently, resulting in different damage levels even under the same intensity (Tsao et al 2010;Lo et al 2012).…”

Section: Discussionmentioning

confidence: 99%

Integrated risk assessment of multi-hazards in China

Zhang

Liu

et al. 2015

Nat Hazards

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Maps of population exposure, vulnerability and risk to natural hazards are useful tools for designing and implementing disaster risk mitigation programs in China. The ranking of provinces by relative risk to natural hazards would provide a metric for prioritizing risk management strategies. Using provinces as our study unit, from the perspectives of hazard exposure, susceptibility, coping capacity and adaptive capacity, this study first constructed China's disaster risk index for five types of major natural hazards: earthquakes, floods, droughts, low temperatures/snow and gale/hail. Then, the relative risk level at the provincial scale in China was assessed. Finally, the hotspots with the highest hazard exposure, vulnerability and risk were identified. The results showed that high exposure was a significant risk driver in China, whereas high vulnerability, especially social vulnerability, amplified the risk levels. Similar to the population exposure to disasters, the relative risk levels in the southwestern, central and northeastern regions of China were significantly higher than those in the eastern, northern and western regions. The highrisk regions or hotspots of multi-hazards were concentrated in southern China (less-developed regions), while the low-risk regions were mainly distributed in the eastern coastal areas (well-developed regions). Furthermore, a nonlinear relationship existed between the disaster risk level and poverty incidence as well as per capita GDP, demonstrating that 123Nat Hazards (2015) 78:257-280 DOI 10.1007/s11069-015-1713 disaster losses in middle-income areas are likely to increase if economic policies are not modified to account for the rising disaster risk. These findings further indicated that research on disaster risk should focus not only on hazards and exposure but also on the vulnerability to natural disasters. Thus, reducing vulnerability and population exposure to natural hazards would be an effective measure in mitigating the disaster risk at hotspots in China.

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Section: Discussionmentioning

confidence: 99%

Integrated risk assessment of multi-hazards in China

Zhang

Liu

et al. 2015

Nat Hazards

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“…After reviewing the available literature on EJ indices, we determined that SoVI is best suited for this analysis. Our reasoning is that the index has been applied extensively by others (Borden et al 2007;Schmidtlein et al 2008;Burton and Cutter 2008;Wood et al 2010), and because it is a pure social vulnerability index that does not include any environmental risk factors, including the coastal risks which we model explicitly using the NCPM. For example, an index that includes climate change risks in the vulnerability score (Moss et al 2001) might present issues of effectively double counting climate risk when this information is combined with detailed data on SLR vulnerability.…”

Section: Methods -A Framework For Assessing Environmental Justice Impmentioning

confidence: 99%

Risks of sea level rise to disadvantaged communities in the United States

Martinich

Neumann²,

Ludwig³

et al. 2012

Mitig Adapt Strateg Glob Change

107

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Climate change and sea level rise (SLR) pose risks to coastal communities around the world, but societal understanding of the distributional and equity implications of SLR impacts and adaptation actions remains limited. Here, we apply a new analytic tool to identify geographic areas in the contiguous United States that may be more likely to experience disproportionate impacts of SLR, and to determine if and where socially vulnerable populations would bear disproportionate costs of adaptation. We use the Social Vulnerability Index (SoVI) to identify socially vulnerable coastal communities, and combine this with output from a SLR coastal property model that evaluates threats of inundation and the economic efficiency of adaptation approaches to respond to those threats. Results show that under the mid-SLR scenario (66.9 cm by 2100), approximately 1,630,000 people are potentially affected by SLR. Of these, 332,000 (∼20%) are among the most socially vulnerable. The analysis also finds that areas of higher social vulnerability are much more likely to be abandoned than protected in response to SLR. This finding is particularly true in the Gulf region of the United States, where over 99% of the most socially vulnerable people live in areas unlikely to be protected from inundation, in stark contrast to the least socially vulnerable group, where only 8% live in areas unlikely to be protected. Our results demonstrate the importance of considering the equity and environmental justice implications of SLR in climate change policy analysis and coastal adaptation planning.

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“…As the model gained support from other researchers, it allowed for improvements, such as depicting temporal changes of vulnerability (Cutter and Finch, 2008;Schmidtlein et al, 2008). Moreover, many studies scrutinized the model in the US and nearby areas, at various scales (Boruff et al, 2005;Cutter and Emrich, 2006;Borden et al, 2007;Boruff and Cutter, 2007;Cutter and Finch, 2008;Schmidtlein, 2008;Schmidtlein et al, 2008;Yeletaysi et al, 2009;Morath, 2010), and more recently in Europe (de Oliveira Mendes, 2009;Holand et al, 2011;Holand and Lujala, 2013), to show different constructs in algorithm (Chakraborty et al, 2005;Rygel et al, 2006;Yeletaysi et al, 2009;Lopes et al, 2010) and changes in the way in which variables should be interpreted to the local conditions. In one respect, the model was de-constructed to a more complex index -Disaster Preparedness Index (Simpson and Katirai, 2006).…”

Section: Aim and Motivationmentioning

confidence: 99%

Social vulnerability assessment using spatial multi-criteria analysis (SEVI model) and the Social Vulnerability Index (SoVI model) – a case study for Bucharest, Romania

Armaş

Gavriş

2013

Nat. Hazards Earth Syst. Sci.

140

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Abstract. In recent decades, the development of vulnerability frameworks has enlarged the research in the natural hazards field. Despite progress in developing the vulnerability studies, there is more to investigate regarding the quantitative approach and clarification of the conceptual explanation of the social component. At the same time, some disaster-prone areas register limited attention. Among these, Romania's capital city, Bucharest, is the most earthquake-prone capital in Europe and the tenth in the world. The location is used to assess two multi-criteria methods for aggregating complex indicators: the social vulnerability index (SoVI model) and the spatial multi-criteria social vulnerability index (SEVI model). Using the data of the 2002 census we reduce the indicators through a factor analytical approach to create the indices and examine if they bear any resemblance to the known vulnerability of Bucharest city through an exploratory spatial data analysis (ESDA). This is a critical issue that may provide better understanding of the social vulnerability in the city and appropriate information for authorities and stakeholders to consider in their decision making. The study emphasizes that social vulnerability is an urban process that increased in a post-communist Bucharest, raising the concern that the population at risk lacks the capacity to cope with disasters. The assessment of the indices indicates a significant and similar clustering pattern of the census administrative units, with an overlap between the clustering areas affected by high social vulnerability. Our proposed SEVI model suggests adjustment sensitivity, useful in the expert-opinion accuracy.

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Vulnerability of U.S. Cities to Environmental Hazards

Cited by 145 publications

References 16 publications

Integrated risk assessment of multi-hazards in China

Integrated risk assessment of multi-hazards in China

Risks of sea level rise to disadvantaged communities in the United States

Social vulnerability assessment using spatial multi-criteria analysis (SEVI model) and the Social Vulnerability Index (SoVI model) – a case study for Bucharest, Romania

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