Assessing the Social Vulnerability to Floods in India: An Application of Superefficiency Data Envelopment Analysis and Spatial Autocorrelation to Analyze Bihar Floods

Jha, Rupak Kumar; Gundimeda, Haripriya; Andugula, Prakash

doi:10.1016/b978-0-12-817465-4.00033-9

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…First, this study finds strong spatial autocorrelation across regions for natural disasters, consistent with prior studies (Jha et al, 2021; Wang et al, 2010, 2019). For example, Wang et al (2010) highlight that natural disasters show significant spatial autocorrelation because the Moran's I value of the disasters is greater than 2.54 at the 0.01 confidence level.…”

Section: Discussionsupporting

confidence: 90%

Spatial autocorrelation between human responses and Winter storm Grayson

Yum

2022

Geographical Journal

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Section: Discussionsupporting

confidence: 90%

Spatial autocorrelation between human responses and Winter storm Grayson

Yum

2022

Geographical Journal

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“…In terms of the study of input and output efficiency between different regions, some scholars used conventional DEA, which likely lead to the situation that the calculated value of many units is 1, and the efficiency value of some units has little difference, so it is not better to compare the efficiency values of different regions. This result is the same as study of some scholars [32,[54][55][56][57][58][59][60][61]. In contrast, the super efficiency SBM used in this paper can solve this problem well, which is also an advantage of efficiency comparison in this paper.…”

Section: Discussionsupporting

confidence: 82%

Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces

Chen

Yao

2021

Forests

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Forests play a very important role in carbon dioxide emissions and climate change, and the development of China’s forestry is of great significance to our citizens. However, it is an arduous task for us to improve forestry output at a high and good level while taking environmental factors into account. In this paper, the non-expected super-efficiency SBM (slacks-based measure) model was used to measure the forestry ecological efficiency (FEE) of 31 provinces in China from 2004 to 2018, and the spatial and temporal evolution of FEE in different regions of China was analysed by using spatial econometric method. Tobit regression and random forest algorithm were selected to analyze the influence on FEE. The results showed that, firstly, the average annual increase of the national total factor productivity change of China’s forestry was 1.2%, and that the average annual increase of the national total factor productivity change in the eastern region was lower than that in the central and western regions. Secondly, the distribution of China’s FEE of the northeast and the south was higher, and FEE of China’s middle regions was relatively lower in 2004, but then the FEE in Northeast China has decreased, and the FEE has increased gradually from north to south in 2018. The agglomeration of high-tech industries in most regions of China had obvious positive spatial correlation characteristics in 2018. Thirdly, there was a negative correlation between forestry fixed assets investment and FEE, environmental regulation was an important factor affecting the ecological efficiency of forestry in China, and the level of economic development and industrial structure also had a certain impact on FEE.

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“…Several methods are used in the previous studies, such as the analytic hierarchy process (AHP) and the assignment of weightage to different indicators based on expert opinions, and these methods may also have some errors leading to incorrect results. In the studies on the Indian subcontinent, the vulnerability studies are mostly limited to climate change vulnerability and flood hazard vulnerability [43][44][45][46][47][48]; for the seismic hazard vulnerability, most of the studies are focused on the built or physical environment [49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Socioeconomic Vulnerability to Seismic Hazards in the National Capital Region of India Using Factor Analysis

2021

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The seismicity of the National Capital Region (NCR) of India increased significantly over the last decade. Communities in the NCR face significant exposure to damaging seismic events, and the seismic risk arises not only from the region’s proximity to the Himalayan mountains, but also from the socioeconomic vulnerabilities in its communities and the current capacities of different localities to respond to and recover from any unforeseen large seismic event. GIS-based spatial distribution of exposure to seismic hazards (SH) can help decision-makers and authorities identify locations with populations at high seismic risk, and to prepare risk-mitigation plans. Socioeconomic vulnerability (SeV) studies serve as a basis for quantifying qualitative measures. For this purpose, in the present study, the hazard of place (HoP) model is used to assess SeV to seismic hazards in the NCR. Social indicators like age, gender, literacy, family size, built environment, etc., comprising a total of 36 variables, are used to assess a socioeconomic vulnerability index (SeVI) based on factor and principal component (PCA) analyses. Based on PCA, 20 variables were retained and grouped into four factors: socioeconomic status, employment status, building typology, and family size. Ground-motion parameters, estimated from probabilistic seismic hazard assessment, are integrated with the socioeconomic vulnerability index to quantify exposure to seismic hazards. The spatial distributions in the produced socioeconomic-vulnerability index and seismic–hazard–exposure maps highlight the critical areas. The results reveal that areas of low literacy, high unemployment, and poor housing condition show moderate-to-high vulnerability. The south-eastern region of the study area is assessed as a high-risk zone by an integrated SeV–SH risk matrix. The results of this study emphasize the importance of the socioeconomic vulnerability component of disaster risk–reduction programs, from a holistic perspective, for the areas with high seismicity.

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Assessing the Social Vulnerability to Floods in India: An Application of Superefficiency Data Envelopment Analysis and Spatial Autocorrelation to Analyze Bihar Floods

Cited by 5 publications

References 37 publications

Spatial autocorrelation between human responses and Winter storm Grayson

Spatial autocorrelation between human responses and Winter storm Grayson

Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces

Assessment of the Socioeconomic Vulnerability to Seismic Hazards in the National Capital Region of India Using Factor Analysis

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