ABSTRACT Museums, including the Victoria & Albert Museum (V&A), are committed to achieving ambitious sustainability goals, focusing on adapting their buildings and operations to adapt to climate change. This paper supports this ambition by developing a method to model internal gallery conditions under future climate projections, using a subset of environmental data from 2015 to 2023 from the V&A South Kensington galleries. The linear regression model, built on this data, predicts scenarios based on Representative Concentration Pathways (RCPs), specifically RCP2.6 and RCP8.5. Preliminary findings indicate little change in gallery closure frequencies in an RCP2.6 scenario compared to the current 0–10 closures per year. Conversely, the RCP8.5 scenario projects an almost tenfold increase in closure days due to high temperatures. This approach, implementable in the R programming language, provides a valuable tool for museums to inform and achieve their sustainability action plans amidst the challenges posed by climate change.
Read full abstract