A multidimensional model for cyclone vulnerability assessment of urban slum dwellers in India: A case study of Bhubaneswar city

Due to climate change, urban city centres face severe cyclonic events over the years in the Indian subcontinent. The local and national government and professionals, including disaster manage-ment scientists, climate change researchers, and meteorologists, are at the forefront of examining and tackling the issues of urban cyclones. Therefore, the study attempts to assess the vulnerability of urban slum households in India's first smart city Bhubaneswar due to cyclonic events. Primary data have been collected by using a structured questionnaire from two slums, namely Phd Sahi and Kedar Palli with a sample size of 200 households. The vulnerability is assessed in the context of five elements: social, economic, physical, institutional, and awareness, and their respective yardsticks have been chosen through previous literature and based on the condition of the slum. The study has developed five individual vulnerability indices considering 38 vulnera-bility indicators mentioned above with a multidimensional vulnerability index. Results show that the slum population is more vulnerable to cyclones based on economic, physical, and awareness vulnerability while less vulnerable in terms of social and institutional. Therefore, the paper ad-vocates some policy suggestions that the state planning should aim at achieving overall economic, physical with infrastructural development to ensure the safety of these marginalized urban communities under participatory slum up-gradation program. Both state and central government should work together to frame policies that will lead to more awareness among the poor slum population, which can, on the other hand, reduce the disaster risks of a cyclone.

Automated summary

This study assesses the vulnerability of slum households in India's first smart city Bhubaneswar to cyclonic events and suggests policy recommendations to improve the safety and awareness of the slum population.