An Analysis of Social Vulnerability to Natural Hazards in Nepal Using a Modified Social Vulnerability Index

Abstract

Social vulnerability influences the ability to prepare for, respond to, and recover from disasters. The identification of vulnerable populations and factors that contribute to their vulnerability are crucial for effective disaster risk reduction. Nepal exhibits multihazard risk and has experienced socioeconomic and political upheaval in recent decades, further increasing susceptibility to hazards. However, we still know little regarding social vulnerability in Nepal. Here, we investigate social vulnerability in Nepal by adapting Social Vulnerability Index (SoVI) methods to the Nepali context. Variables such as caste, and populations who cannot speak/understand Nepali were added to reflect the essence of the Nepali context. Using principal component analysis, 39 variables were reduced to seven factors that explained 63.02% of variance in the data. Factor scores were summarized to calculate final SoVI scores. The highest levels of social vulnerability are concentrated in the central and western Mountain, western Hill, and central and eastern Tarai regions of Nepal, while the least vulnerable areas are in the central and eastern Hill regions. These findings, supplemented with smaller-scale analyses, have the potential to assist village officers, policymakers, and emergency managers in the development of more effective and geographically targeted disaster management programs.

Benchmarking Community Disaster Resilience in Nepal

“…The transformed variables were then standardized while using z-score standardization in SPSS version 25 (IBM Corp, New York, USA) [57]. The Bartlett sphericity test (with p < 0.05) and the sampling adequacy measure Kaiser-Meyer-Olkim (KMO) (with selection criterion of values between 0.7 and 1) was used to determine whether the chosen variables were adequate for the principal component analysis while following the methodological approach that was used in various index construction methods [6,10,52,58].…”

“…Hence, the variables were carefully chosen, focusing on two things: 1) suitability of the variables in the Nepali context. For instance, we added Dalit Population in the model, as Dalit represents the lowest strata in the caste system of Nepal and it is characterized by a lower level of resilience [52,53]. Additionally, we added Absentee Population to reflect prevalent male outmigration mainly to the Gulf countries (Qatar, Saudi Arabia, United Arab Emirates), Malaysia, and South Korea [54,55], and 2) the availability of the data at the village level.…”

A geospatial analysis of multi-hazard risk in Dharan, Nepal

“…Data were aggregated at the ward level and then normalized and standardized for further processing. Principal component analysis (PCA), using the Dimension Reduction tool in SPSS version 22.0, was used to reduce the 18 variables into a smaller number of more meaningful components (Aksha et al 2019). Varimax rotation and Keiser criterion were employed to identify components with eigenvalues Very strong importance One variable is very strongly favored over another 9…”

A pan-South-America assessment of avoided exposure to dangerous extreme precipitation by limiting to 1.5 °C warming

“…Risk is determined not only by climate and weather events (hazards), but also by the exposure and vulnerability to the hazards (Lavell et al 2012). Identifying vulnerable populations and factors that contribute to their vulnerability are crucial, because social vulnerability influences the ability to respond to, cope with, and recover from a natural disaster (Aksha et al 2019). However, social vulnerability is highly contextspecific, and in contrast to exposure data not meaningfully assessed on a continental scale.…”