Building renovation is a key initiative to promote energy efficiency, the integration of renewable energy sources (RESs), and a reduction in CO₂ emissions. Supporting these goals, emerging research is dedicated to energy communities and positive energy districts. In this work, an urban neighborhood of six buildings in Trento (Italy) is considered. Firstly, the six buildings are modeled with the Urban Modeling Interface tool to evaluate the energy performances in 2024 and 2050, also accounting for the different climatic conditions for these two time periods. Energy demands for space heating, domestic hot water, space cooling, electricity, and transport are computed. Then, EnergyPLAN coupled with a multi-objective evolutionary algorithm is used to investigate 12 different energy decarbonization scenarios in 2024 and 2050 based on different boundaries for RESs, energy storage, hydrogen, energy system integration, and energy community incentives. Two conflicting objectives are considered: cost and CO₂ emission reductions. The results show, on the one hand, the key role of sector coupling technologies such as heat pumps and electric vehicles in exploiting local renewables and, on the other hand, the higher costs in introducing both electricity storage to approach complete decarbonization and hydrogen as an alternative strategy in the electricity, thermal, and transport sectors. As an example of the quantitative valuable finding of this work, in scenario S1 “all sectors and EC incentive” for the year 2024, a large reduction of 55% of CO₂ emissions with a modest increase of 11% of the total annual cost is identified along the Pareto front.

The design of renewable energy communities requires multi-disciplinary approaches to identify the most promising solutions from technical, economic, and environmental perspectives. Indeed, different simulations tools can be adopted, ranging from urban modelling to energy planning methods. In this framework, this research focused on the Santa Chiara district in Trento, Italy, to assess the performance of different decarbonization strategies, encompassing fossil and renewable energy systems. To achieve this goal, the district energy balance, CO2 emissions, total costs, and impact of incentives for energy communities were analyzed for each scenario. Furthermore, the effects of current and future hot climatic conditions were investigated, coupling urban building energy modelling (umi) and energy planning codes (EnergyPLAN).Results highlighted major modifications to the energy balance of the district due to climate change, with an important increase of space cooling needs. Heat pumps coupled with photovoltaic and solar thermal panels were identified as the most suitable solution, effectively contributing to the transformation of the considered district into an energy community. Finally, the adopted methodology pointed out the relevant role played by different calculation tools when used in an integrated workflow, allowing for a more comprehensive understanding of the available urban decarbonization strategies and more robust design choices.

Data scarcity limits robust assessment of urban overheating and its implications for building energy use, especially in complex-terrain cities such as those in mountain environments. In this context, Land Surface Temperature (LST) from thermal remote sensing can be used to map urban hotspots at high spatial resolution. Nevertheless, it does not provide the full set of hourly atmospheric variables required to run building energy simulations aimed at quantifying their impact and defining mitigation measures. Given these premises, this study proposes a methodology combining satellite-derived LST with ground meteorological measurements to assess Urban Heat Island (UHI) patterns and quantify how measured weather data selection affects urban building energy modeling (UBEM) outcomes. After selecting as a case study Bolzano, an Alpine city in Northern Italy, ECOSTRESS LST (2019–2025, May–August) was first processed and quality-screened to (1) compute ΔLST (urban–rural) and (2) identify diurnal and spatial overheating patterns across the building stock. Second, four measured weather datasets—one rural station and three urban stations located in the city core, in the industrial district, and in the urban edge—were used as boundary conditions in an EnergyPlus-based UBEM parametric campaign for 253 residential buildings, covering multiple envelope insulation levels and window-to-wall ratios. Results show strong diurnal asymmetry in surface overheating, with the largest contrasts in the afternoon and prominent industrial hotspots. Ground measurements confirm persistent intra-urban microclimatic differences, and the choice of measured weather dataset causes systematic shifts in simulated cooling demand and thermal comfort. The study highlights the need for weather data selection strategies based on microclimatic context rather than simple proximity, improving representativeness in UBEM applications for Alpine and other heterogeneous urban environments.

Urban populations are exposed to elevated local temperatures compared to surrounding rural areas due to the urban heat island (UHI) effect, which increases health risks and energy demand. The literature highlights that accurately quantifying UHIs at broader territorial scales remains challenging because of limited microscale climate data availability and, at the same time, the difficulty of increasing the spatial coverage of the outcomes. Within the PRIN2022-PNRR CRiStAll (Climate Resilient Strategies by Archetype-based Urban Energy Modeling) project, this work addresses these limitations by coupling Urban Building Energy Modeling with archetype-based representation of urban form and high-resolution climatic data. Urban archetypes are defined as representative microscale configurations derived from combinations of urban canyon geometries and building typologies, accounting for different climatic zones, use categories, and construction periods. The proposed methodology was applied to the city of Turin (Italy), where representative urban blocks were identified and modeled to evaluate key urban context metrics under short-, medium-, and long-term climate scenarios. The UHI effect was assessed using Urban Weather Generator, while energy simulations were performed with CitySim. The urban archetype approach enables both fine spatial resolution and extensive spatial coverage, supporting urban-scale mapping.