The city-level analysis used the Shannon Entropy method to assign objective weights to indicators and the TOPSIS technique to rank cities. Chennai received the highest score for safety, Hyderabad ranked highest for inclusivity and resilience, and Surat led on sustainability. The combined index produced a separate overall ranking, with Hyderabad and Mumbai in the top two positions and Surat and Jaipur in the bottom two. These findings describe relative performance within the ten-city sample and the study’s chosen framework; they do not show that particular policies caused one city to score above or below another.

The authors developed their dedicated SDG-11 index because existing Indian indices cover domains such as SDG performance, liveability, municipal efficiency and mobility but do not fully capture city-level progress specifically related to SDG-11. The framework assesses four pillars intended to capture whether urban development is safe, inclusive, sustainable and resilient. The results also show why a single ranking can hide variation within a city: the leaders in individual pillars were not the same as the leaders in the composite measure.

At the micro level, the study examined primary data collected from 611 slum households in six cities. Its logistic regression identified inadequate living conditions, poor ventilation, inadequate drainage and insecure land tenure as factors associated with a higher likelihood of poverty among slum dwellers. These are reported statistical associations, not proof that any one condition directly causes economic hardship. The analysis nevertheless provides a household-scale counterpart to the metropolitan rankings by showing how differences in housing and basic services relate to economic vulnerability.

Taken together, the two analytical scales suggest that urban sustainability cannot be understood solely through aggregate city scores. A metropolitan index can reveal broad differences across safety, inclusion, sustainability and resilience, while the household model highlights structural inequalities involving housing, drainage, ventilation and land security. The authors present this combination as a contribution to inclusive and resilient urban governance. Its reach is limited, however: the city assessment covers ten metropolitan cities, and the household analysis covers six cities, so the findings should not be treated as representative of every Indian city or settlement. The article was published as an early-access version subject to further editorial changes, and it does not establish that the framework itself will improve governance outcomes.

Subject of Research: City-scale SDG-11 performance and household poverty vulnerability in Indian cities

Article Title: Assessing urban sustainability and slum vulnerability in Indian cities through a dual-scale SDG-11 framework

Article References: Mishra, A. K., Thimmavajjala, P. K., Vyas, I., Mallick, A. K., & Singh, P. K. (2026). Assessing urban sustainability and slum vulnerability in Indian cities through a dual-scale SDG-11 framework. Discover Sustainability. https://doi.org/10.1007/s43621-026-04409-w

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04409-w

Keywords: SDG 11, Indian cities, urban sustainability, slum households, urban poverty, housing conditions, ventilation, drainage, land tenure, TOPSIS, logistic regression, Assessing