Political or economic attempts to mitigate climate change by increasing fossil fuel prices lead to and an increase in energy poverty, i.e., social effects. The ideal solution would be to combine modernisation activities in terms of energy use in cities with sustainable strategies and redevelopment policies. The article's purpose is to estimate the potential for reducing energy consumption depending on socioeconomic factors (household standard and its location in the city) based on built-in scenarios and searching for the optimal way of conducting development policy at the local level. This assumption enables the implementation of the European Union climate policy. To this aim, modelling based on real and estimated data on the diversity of energy consumption in the structure of a medium-sized city in Europe (Zielona Góra) carried out. While creating scenarios, there used a modelling method based on radial artificial neural networks, which map the input set into the output set by matching many individual approximating functions to setpoints. This approach works well for data whose geolocation is in the city quarters. As a result of the simulations, the minimum and maximum achievable energy saving potential for low-intensity buildings in the quarters was estimated, taking into account the possibilities of investing in renewable energy by individual households. The observations included in the article may be relevant to other regions that are interested in reducing the energy consumption of buildings and pollution emissions from the cities. This is particularly important for the regions of Europe that benefit from the financial support of the European Union (including local development programmes based on financing European priority axes for economic development).
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