Finest-resolution socioeconomic vulnerability assessment and key driver identification for hydro-climatic extremes in Maharashtra, India

Hydro-climatic extremes are intensifying across India, underscoring the need for fine-resolution socioeconomic vulnerability (SEV) assessments that can guide adaptation and disaster risk reduction (DRR). Most existing SEV studies in the Global South rely on coarse spatial units, subjective weighting, and inconsistent indicator frameworks, limiting their utility for local planning. This study presents the first statewide, sub-district (taluka)-scale SEV assessment in India, covering all 357 talukas of Maharashtra. A dual-scenario framework—sensitive and adaptive—is introduced to capture complementary dimensions of vulnerability. Composite indicators derived from principal component analysis are used to reduce multicollinearity and subjective bias, while the Banker–Charnes–Cooper variant of data envelopment analysis provides objective, non-parametric vulnerability scoring. Results show that the largest share of the population (22%) falls in the low vulnerability category, followed by 18% in medium and 16% in very low, while 8% lies in the very high vulnerability category. A pronounced rural–urban disparity is observed, with nearly 62% of the urban population concentrated in the very low vulnerability class, whereas rural populations are more evenly distributed and disproportionately represented in higher vulnerability categories. Indicator contribution analysis identifies main agricultural workers (4.176%), marginal workers (4.161%), and marginal female workers (4.094%) as the highest-contributing indicators to SEV. Spatial clustering and local hotspot diagnostics reveal clear regional contrasts, with Central and Eastern Vidarbha exhibiting the highest SEV, while North Konkan shows comparatively lower vulnerability. The proposed framework provides a scalable and transferable blueprint for sub-national vulnerability assessment and supports evidence-based, place-specific climate adaptation and DRR planning.