Abstract

India is projected to have the largest increment of global urban population growth in the coming decades, with its urbanisation trajectory likely to surpass that of China in absolute terms. This expansion is unfolding alongside intensifying climate impacts and an accelerating green transition, positioning Indian cities at the centre of global climate and development debates. Climate emissions and hazards are increasingly shaped by the interplay of broader historical and contemporary processes and local, endogenous dynamics embedded within urban development pathways. India’s ongoing urban transformation represents a critical window during which development choices can move cities away from emission-intensive, risk-amplifying development pathways to low-emission, more resilient trajectories in the climate-resilient development spectrum. To interrogate these dynamics, we develop a multidimensional urban classification framework across 1,876 urban areas in India, consolidating indicators across four key dimensions-urban form, socio-economic development, mitigation and climate hazards, resulting in four distinct cluster assignments per urban area. This classification framework aligns urban areas with different development and climate archetypes, depending on the analytical or policy lens applied, helping preserve heterogeneity while enabling systematic comparison. The resulting classifications support analyses across development, adaptation, and mitigation by examining how different combinations of these dimensions co-occur within and across urban areas, allowing for the identification of co-benefits and trade-offs for urban areas with diverse development and climate characteristics. In doing so, the study advances a scalable methodological approach that anchors the normative goal of climate-resilient development within an evidence-based planning framework across development contexts and scales. This can facilitate all levels of government to identify potential CRD pathways, priority actions to accelerate and deepen implementation at scale and facilitate peer learning across similar contexts.