A Comprehensive Socioeconomic Vulnerability Analysis Using Robust DEA Technique at the Finest Resolution of Sub-District Scale in Entire Maharashtra State of India: Identifying Significant Vulnerability Drivers

Maharashtra is India’s second-largest state in population and third-largest in area. It faces escalating environmental challenges from diverse hydroclimatic extremes, including droughts, floods, and cyclones. IPCC reports underscore the need for a comprehensive understanding of socioeconomic vulnerability (SEV) to address the inequality and differential impacts of these hazards within a robust risk assessment framework. Several national and regional vulnerability assessments have been conducted in India and Maharashtra. These studies lack a finer-resolution assessment of socioeconomic vulnerability (SEV), limiting the understanding of localised variations. They also fall short of incorporating a broad range of SEV indicators, which hinders comprehensive vulnerability analysis. The major drivers contributing to vulnerability need to be identified.

The current study advances local adaptation planning by thoroughly evaluating socioeconomic vulnerability (SEV) at Maharashtra's finest resolution of sub-district (talukas/tehsils) level based on the availability of the demographic data. The study utilised composite indicators, which were procured and derived from the latest available Census of India (CoI, 2011) data. This method offers a thorough grasp of susceptibility patterns by concentrating on the finest possible spatial resolution based on the limited availability of the resource for socioeconomic indicator information. The subjectivity constraints of weighing these socioeconomic indicators have been addressed using the non-parametric Data Envelopment Analysis (DEA) optimisation technique. The study also utilised variance-based factor analysis to identify the major contributing drivers of the SEV for Maharashtra. Additionally, a localised cluster-level SEV analysis is also performed based on multiple administrative divisions to identify the local-level significant indicators. Applying this methodology to 357 sub-districts of Maharashtra reveals a concentration of highly vulnerable sub-districts in the Central and Eastern Vidarbha Zone, moderately vulnerable districts in the Central Maharashtra Plateau Zone, and less vulnerable districts in the North Konkan Coastal. The factor analysis results also highlight agricultural labourers, marginal working populations, and marginal female working populations as the most critical drivers influencing vulnerability for the entire Maharashtra State.

This proposed framework is generic and comprehensive and can be applied to any other state or spatial scale. The results of this study can assist policymakers and stakeholders in identifying vulnerable hotspots and developing proper social and economic policies to better understand and improve the socioeconomic situations of Maharashtra at the sub-district scale.

Keywords: Data envelopment analysis, Principal component analysis, Socioeconomic indicators, Sub-district level, Vulnerability analysis.