The most recent report on climate change from the IPCC (Intergovernmental Panel on Climate Change), in 2023, states that urgent action is needed to tackle global warming. The IPCC points out that by 2040, there is a greater than 50% risk that the temperature worldwide will approach or exceed 1.5 degrees Celsius (2.7 degrees Fahrenheit). On top of that, under high-emissions scenarios, the global temperature could increase to that borderline even earlier, before 2037. Since building stock accounts for 40% of total global energy usage and 33% of greenhouse gas emissions each year, their continuous high demand for energy leads to the rapid growth of CO2 emissions. Accounting for that, the energy performance of buildings in urban scale under the future climate scenarios is a significant factor in immediately assisting with climate change mitigation. The purpose of this project was to estimate the influence of the climate change on the energy demand of two neighbourhoods in Nottingham, in United Kingdom, by comparing their current energy performance to the future. The methodology consists of the use of geospatial data for the building geometric parameters, in combination with energy-related data from the EPC (Energy Performance Certificate) dataset. The datasets were processed with Python programming language and the QGIS software, and the final dataset was imported to an energy model that was constructed with the use of Rhino and Grasshopper, with EnergyPlus simulations on the background. The model was run under 9 different climate scenarios, namely under the present, under 2050s and 2080s for 4 different future scenarios each year. The results have shown that the absence of building stock renovation will lead to an accountable decrease in the heating demand of buildings, while the risk of overheating will be critically escalating.