Mohajeri, NahidWalch, AlinaSmith, AlisonGudmundsson, AgustAssouline, DanRussell, TomHall, Jim2023-04-102023-04-102023-04-102023-02-2210.1016/j.scs.2023.104451https://infoscience.epfl.ch/handle/20.500.14299/196857WOS:000946721800001Regional-scale urban residential densification provides an opportunity to tackle multiple challenges of sustain-ability in cities. But framework for detailed large-scale analysis of densification potentials and their integration with natural capital to assess the housing capacity is lacking. Using a combination of Machine Learning Random Forests algorithm and exploratory data analysis (EDA), we propose density scenarios and housing-capacity es-timates for the potential residential lands in the Oxford-Cambridge Arc region (whose current population of 3.7 million is expected to increase up to 4.7 million in 2035) in the UK. A detailed analysis was done for Oxfordshire, assuming different densities in urban and rural areas and protecting lands with high-value natural capital from development. For a 30,000 dwellings-per-year scenario, the land allocated in Local Plans could cover housing growth in the four districts but not in Oxford City itself (which accounts for 48% of the demand); only 19% of the need would be covered in low but 59% in high housing density scenarios. Our study suggests a decision-support method for quantifying how the impact of housing growth on natural capital can be significantly reduced using more compact development patterns, protection of land with high-value natural capital, and use of low-biodiversity brownfield sites where available.Construction & Building TechnologyGreen & Sustainable Science & TechnologyEnergy & FuelsConstruction & Building TechnologyScience & Technology - Other TopicsEnergy & Fuelsmachine learningexploratory data analysisoxford-cambridge archousing densitybrownfield landslocal plansregional scalecompact citystrategiescitiestext::journal::journal article::research article