In urban contexts, the use of energy in buildings is one of the main causes of greenhouse gas emissions. The reduction of energy-use in buildings could be one of the main drivers to improve the sustainability, livability and quality of urban environment, together with the production of energy from the available renewable sources. To achieve energy sustainability in the most critical high-density urban contexts, it is necessary to optimize: the energy consumptions compatibility of different users; the distribution of heat, for example through the district heating network; the use of all urban spaces, such as building envelopes and urban surfaces, to produce energy from the available renewable sources. The energy models at urban scale are a complex issue as they should be simplified to be applied on a vast territory. Indeed, detailed information are not given at territorial scale and short time of simulation are preferable; however, information should be detailed enough to describe properly the energy consumption of the whole urban environment from building to urban scale. Therefore, energy models should take into account also the urban morphology, people's behavior, social and economic conditions, local and national regulation, and the use of outdoor public spaces. The challenge of this work is to present three different energy-use models, to compare their characteristics and to find the best features of an "optimum" model to analyze and represent energy resources, future scenarios, energy efficiency solutions and best energy policies. The aim is to drive a smarter use of energy, matching it with the available and more efficient energy sources to help also public administrations in defining policies adapted to the real buildings heritage. The urban energy models can also be applied in future climatic scenarios, to evaluate the impact of climate change in the energy demand/supply of buildings, as well as in the potential of retrofit scenarios.

As the population of people migrating to cities keeps increasing, concerns have been raised about air quality in cities and how it impacts everyday life. Thus, it is important to demonstrate ways of avoiding polluted areas. The approach described in this paper is intended to draw attention to polluted areas and help pedestrians and cyclists to achieve the lowest possible level of air pollution when planning daily routes. We utilise real-time air quality data which is obtained from monitoring stations across the world. The data consist of the geolocation of monitoring stations as well as index numbers to scale the air quality level in every corresponding monitoring stations. When the air quality level is considered having a moderate health concern for people with respiratory disease, such as asthma, an alternative route that avoid air pollution will be calculated so that pedestrians and cyclists can be informed. The implementation can visualize air quality level in several areas in 3D map as well as informs health-aware route for pedestrian and cyclist. It automatically adjusts the observed air quality areas based on the availability of monitoring stations. The proposed approach results in a prototype of a health-aware 3D navigation system for pedestrian and cyclist.