Quantifying Climate–Inequality Interactions under Recurrent Hazards: A Geospatial Assessment of Socio-Climate Vulnerability in Bihar, India

Climate hazards systematically intersect with and amplify pre-existing socioeconomic inequalities, producing uneven patterns of exposure, impact, and recovery that undermine progress toward the Sustainable Development Goals (SDGs). This study presents a quantitative, geospatial assessment of climate–inequality interactions in the climate-sensitive districts of Gaya, Arwal, and Aurangabad in Bihar, India, where recurrent droughts, heat extremes, and episodic flooding disproportionately affect marginalized populations.

An integrated analytical framework combines long-term climate records (1981–2022), satellite-derived indicators (MODIS land surface temperature and NDVI, drought and flood exposure metrics), and disaggregated socioeconomic data capturing income source, landholding size, education, gender, infrastructure access, and food security. Climate hazard dynamics are quantified using standardized drought and heat indices and extreme-event frequency analysis, while multidimensional inequality is represented through a GIS-based Socio-Climate Vulnerability Index developed using multi-criteria decision analysis. Results show a statistically significant increase in drought frequency across all districts (Sen’s slope ≈ 0.02–0.03 yr⁻¹, p < 0.05) and a rise in mean growing-season land surface temperature of 0.9–1.3 °C. Spatial hotspot analysis indicates that 35–45% of high-exposure zones overlap with areas characterized by low income, small or landless holdings, and limited infrastructure.

Households in high-vulnerability clusters experience 20–30% lower yield stability, 15–25% higher food insecurity prevalence, and recovery periods that are on average 1.5–2 times longer than district means following major drought or heat events. Repeated exposure to climate extremes is associated with persistent developmental deficits, including reduced livelihood diversification and adverse health outcomes. By integrating remote sensing, spatial statistics, and socio-environmental modelling, this study provides novel, scalable metrics for quantifying climate justice and inequality. The findings underscore the urgency of equity-centered climate adaptation and disaster risk reduction strategies tailored to structurally disadvantaged regions.