Life-cycle assessment is being applied to an increasing number of building projects from one side while the usual dynamic thermal simulations are being conducted from the other side on the same projects. However, there are few observations in the literature linking these two types of calculations: embodied and operating energies are rarely directly compared. This paper compares those energies for some case studies. The challenge is to quantify to what extent chosen structural materials can change their global life-cycle energy balance. This question is raised by the different dynamic thermal behavior of materials. Consequently, the case studies focus on the influence of materials’ thermal mass on the operating energy consumptions.Nonetheless, few software programs can conduct both these calculations (LCA and dynamic thermal modeling). The Swiss regulatory tool LESOSAI has been implemented and offers now these two possibilities. However, its LCA database is arcane, this paper will first assess the LCA results of LESOSAI by comparing it with the French tool ELODIE developed by the CSTB. Measuring the reproducibility of their results provides boundaries to the LCA calculations that LESOSAI can perform. These identified limits enable to set the starting assumptions of the case studies. Two raw materials are compared: wood and concrete structures. Considering thermal mass as a dynamic property, different typologies of building usages and climates have been investigated for the materials comparisons. Finally, the conclusion emphasizes the material that permits the lowest life-cycle impact for each typology and climate.

The challenge of climate change is twofold: to mitigate (prevent) the causes of climate change and to prepare (adapt) to the inevitable effects and consequences. Building and construction are key sectors for decarbonisation (mitigation). The increase in intensity, frequency and duration of heat waves threatens indoor comfort and constitutes a health and comfort risk (adaptation).Therefore, regulations are being changed to take into account related emissions and extreme episodes through new indicators. However, up to now, past climate observations are still used in the calculation of these indicators. This raises the question of how to integrate future climate predictions into regulations. This work aims at characterising the vulnerability of buildings to climate change and aimsat taking into account future climate predictions in building design. It establishes a method for constructing standard weather data based on climate projections and for identifying vulnerable building typologies that are at risk. This project stands out for the use of a large number of building and meteorological data. 77 residential buildings from the Centre Scientifique et Technique du Bâtiment (CSTB) database and 78 years (1981-2058) of weather data for 9 climate models (RCP8.5 scenario) are crossed for Dynamic Thermal Simulations (DTS) on COMETh. The study first highlights the relevance of using reference and extreme years, representative of the climate data, to reduce the number of simulations. The reference year makes it possible to observe the average needs over a period. The extreme year estimates the range of values around this mean.The report then raises the issue of cooling systems as one of the major challenges for energy needs. Under the effect of climate change, heating requirements are decreasing and largely compensate the increase of cooling needs. But few buildings in France are already equipped with cooling systems and the creation of a need exceeding a threshold leads to the purchase of new units. This raises a problem of social equity in access to thermal comfort. Moreover, the environmentalimpact of these systems is more related to refrigerants necessary for the manufacturethan to energy consumption.The research finally proposes a method to classify passive or active buildings (in the sense of cooling needs), that are adapted or not adapted to future extreme weather conditions. This involves applying a clustering algorithm (k-means) to group similar buildings together in terms of energy requirements for different climate models. This method already makes it possible to identify the buildings at risk and to prioritise the measures to be taken (energy renovation). This classification also opens up the possibility of extending this work to newer, larger and more diversified samples. Similar encouraging results were obtained from 2470 offices. They could helpidentify technical and architectural characteristics and assist in the design of efficient passive buildings.