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Keywords: GIS Urban planning Energy demand Geothermal energy iGuess Smart city energy a b s t r a c t Due to the rapidly increasing percentage of the population living in urban centres, there is a need to focus on the energy demand of these cities and the use of renewable energies instead of fossil fuels. In this paper, we develop a spatial model to determine the potential per parcel for using shallow geothermal energy, for space heating and hot water. The method is based on the space heating and hot water energy demand of each building and the specific heat extraction potential of the subsurface per parcel. With this information, along with the available space per parcel for boreholes, the percentage of the energy demand that could be supplied by geothermal energy is calculated. The potential reduction in CO 2 emissions should all possible geothermal energy be utilised, is also calculated. The method is applied to Ludwigsburg, Germany. It was found that CO 2 emissions could potentially be reduced by 29.7% if all space heating and hot water requirements were provided by geothermal energy, which would contribute to the sustainability of a city. The method is simple in execution and could be applied to other cities as the data used should be readily available. Another advantage is the implementation into the web based Smart City Energy platform which allows interactive exploration of solutions across the city.

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Reducing the energy consumption of buildings is a priority for carbon emissions mitigation in urban areas. Building stock energy models have been developed to support decisions of public authorities in renovation strategies. However, the burdens of renovation interventions and their temporal distribution are mostly overlooked, leading to potential overestimation of environmental benefits. Life Cycle Assessment (LCA) provides a holistic estimation of environmental impacts, but further developments are needed to correctly consider spatio-temporal aspects.We propose a spatio-temporal LCA framework to assess renovation scenarios of urban housing stocks, integrating: 1) a geospatial building-by-building stock model, 2) energy demand modelling, 3) product-based LCA, and 4) a scenario generator.Temporal aspects are considered both in the lifecycle inventory and the lifecycle impact assessment phases, by accounting for the evolution of the existing housing stock and applying time-adjusted carbon footprint calculation.We apply the framework for the carbon footprint assessment of housing renovation in Esch-sur-Alzette (Luxembourg). Results show that the renovation stage represents 4% to 16% of the carbon footprint in the residual service life of existing buildings, respectively after conventional or advanced renovations. Under current renovation rates, the carbon footprint reduction would be limited to 3-4% by 2030. Pushing renovation rates to 3%, enables carbon reductions up to 28% by 2030 when combined with advanced renovations. Carbon reductions in the operational stage of buildings are offset by 8-9% due to the impacts of renovation. Using time-adjusted emissions, results in higher weight for the renovation stage and slightly lower benefits for renovation.

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