Quantifying overheating risk in English schools: A spatially coherent climate risk assessment

Abstract

Climate adaptation decision making can be informed by a quantification of current and future climate risk. This is important for understanding which populations and/or infrastructures are most at risk in order to prioritise adaptation action. When assessing the risk of overheating in buildings, many studies use advanced building models to comprehensively represent the vulnerability of the building to overheating, but often use a limited representation of the meteorological (hazard) information which does not vary realistically in space. An alternative approach for quantifying risk is to use a spatial risk assessment framework which combines information about hazard, exposure and vulnerability to estimate risk in a spatially consistent way, allowing for risk to be compared across different locations. Here we present a novel application of an open-source CLIMADA-based spatial risk assessment framework to an ensemble of climate projections to assess overheating risk in ∼20,000 schools in England. In doing so, we demonstrate an approach for bringing together the advantages of open-source spatial risk assessment frameworks, data science techniques, and physics-based building models to assess climate risk in a spatially consistent way, allowing for the prioritisation of adaptation action in this vulnerable young population. Specifically, we assess the expected number of days each school overheats (internal operative temperature exceeds a high threshold) in a school-year based on three global warming levels (recent past, 2 °C and 4 °C warmer than pre-industrial). Our results indicate an increase in this risk in future warmer climates, with the relative frequency of overheating at internal temperatures in excess of 35 °C increasing more than at 26 °C. Indeed, this novel demonstration of the approach indicates that the most at-risk schools could experience up to 15 school days of internal temperature in excess of 35 °C in an average year if the climate warms to 2 °C above pre-industrial. Finally, we demonstrate how the spatial consistency in the output risk could enable the prioritisation of high risk schools for adaptation action.