Towards a new neighbourhood-scale climate risk-adaptation approach

A new multimodal analytical approach is proposed to assess, act upon and predict climate risks that materialise differently across neighbourhood contexts. Current methods for assessing neighbourhood responses to climate risks tend to be dominated by either city-level adaptive capacity frameworks or building-level adaptation strategies. These approaches often overlook the empirically grounded and integrative potential of the neighbourhood scale. Neighbourhoods, particularly their capacity for collective action, which is linked to urban form, play a critical role in fostering adaptive behaviour. Despite this recognition, analytical approaches examining adaptation responses to climate risk at this level remain underdeveloped. Using a staged integrative approach, the study involved an initial scoping literature review of 494 documents, from which sources (n = 77) published over the past two decades were selected. This process revealed key environmental, spatial and social dimensions of neighbourhood adaptation in relation to climate risk. This proposed approach enables a richer understanding of climate risk by shifting from simple capabilities at the meso-scale to dynamic adaptive systems. Without this lens, local responses to climate risks remain standardised, lacking the diversity and dynamism required at context-specific levels. PRACTICE RELEVANCE The proposed approach helps planners, policymakers and design practitioners understand how adaptation occurs, why it varies across neighbourhoods, and how climate risks/ responses emerge from urban form, infrastructure, social practices and governance interactions often obscured by city-wide or building-level analyses. Standardised measures thus miss local vulnerabilities/capacities, but this analytical approach assesses neighbourhoods as adaptive systems to enable targeted, context-sensitive interventions. Key actions include integrating neighbourhood-scale analysis into planning, combining spatial/environmental data with community insights, and using iterative monitoring to support adaptive decision-making over one-size-fits-all solutions.