Measuring Capacity for Resilience among Coastal Counties of the U. S. Northern Gulf of Mexico Region

Abstract

Many have voiced concern about the long-term survival of coastal communities in the face of increasingly intense storms and sea level rise. In this study we select indicators of key theoretical concepts from the social-ecological resilience literature, aggregate those indicators into a resilience-capacity index, and calculate an index score for each of the 52 coastal counties of Louisiana, Texas, Mississippi, Alabama and Florida. Building upon Cutter’s Social Vulnerability Index work [1], we use Factor Analysis to combine 43 variables measuring demographics, social capital, economic resources, local government actions, and environmental conditions within the counties. Then, we map the counties’ scores to show the spatial distribution of resilience capacities. The counties identified as having the highest resilience capacities include the suburban areas near New Orleans, Louisiana and Tampa, Florida, and the growing beach-tourist communities of Alabama and central Florida. Also, we examine whether those counties more active in oil and gas development and production, part of the region’s “energy coast”, have greater capacity for resilience than other counties in the region. Correlation analyses between the resilience-capacity index scores and two measures of oil and gas industry activity (total employment and number of business establishments within five industry categories) yielded no statistically significant associations. By aggregating a range of important contextual variables into a single index, the study demonstrates a useful approach for the more systematic examination and comparison of exposure, vulnerability and capacity for resilience among coastal communities.

Measuring Recovery to Build up Metrics of Flood Resilience Based on Pollutant Discharge Data: A Case Study in East China

“…The results show that health service, sex ratio, ratio of urban to rural, access to open space, road density and slope condition are statistically significant in characterizing disaster resilience. The six variables validated in this study are consistent with the set of variables mentioned as proxy for community resilience to other natural hazards [8,13,16,61]. This implies that, in general, investment in promoting adaptation activities and policies for one kind of natural hazard should have a synergistically positive effect on community resilience to other natural hazards; and to flash flood hazards, especially in regions within China, the six variables play more dominant roles in determining the recovery capability that was represented by the return speed of people's living and industrial productivity.…”

“…Previous literature had found that a larger amount of urban green areas can improve the ecological condition [15], while lower river density and a flatter surface reduce the risk of storm surge inundation and secondary landslides [12]. Lower elevation and slope also provide an improvement of the accessibility and ease of rescue work [13,58]. Note: * Statistic unit: per sub-district (or town); The demographic data was derived from the published 2010 Population Census of China (PCC); The GDP data was obtained from a 1 km grid GDP data of China (2010) (Global Change Research Data Publishing and Repository, 2014) (GCRD); Information Points were downloaded from the Open Street Map (OSM); Build-up Area was derived from the artificial land cover in GLOBELAND30 dataset [50]; DEM and slope data were derived from the ASTER GDEM dataset published in 2009.…”

“…Unfortunately, as there is no agreed definition of disaster resilience, directly measuring resilience is challenging [9,10]. Most attempts to quantitatively measure disaster resilience define a set of characteristics or attributes previously identified through practical experience or research [7,[11][12][13][14][15]. The characteristics of resilience usually include a set of attributes representing various dimensions, such as social, economic, infrastructural, and so on.…”

Case Study: Reviewing Methods of Assessing Community Adaptive Capacity for Jefferson County, Texas

“…Previous assessments of the County's adaptive capacity are: Cutter et al [17], Reams et al [18], NOAA's Human Dimensions [19], and SHELDUS™ [20]. Cutter et al [17] collected six resilience parameters: ecological, social, economic, infrastructure, institutional capacity, and community competence.…”

“…A later version of the method showed Jefferson County having a medium SoVI, where principal component analysis was used to point to the main components/indicators that account for the greatest variance within the dataset [21]. Reams et al [18] followed the methodology of Cutter et al [17] but with applied the method to fifty two coastal counties of the US northern Gulf of Mexico region using forty three indicators to measure community resilience represented by demographics, social capital, economic resources, local government actions, and environmental aspects. Principal component analysis was performed to derive eleven components that accounted for 76.4% of variance of all the indicators.…”