Internal insulation is a typical renovation solution in historic buildings with valuable façades. However, it entails moisture-related risks, which affect the durability and life-cycle environmental performance. In this context, the EU project RIBuild developed a risk assessment method for both hygrothermal and life-cycle performance of internal insulation, to support decision-making. This paper presents the stochastic Life Cycle Assessment method developed, which couples the LCA model to a Monte-Carlo simulation, providing results expressed by probability distributions. It is applied to five insulation solutions, considering different uncertain input parameters and building heating scenarios. In addition, the influence of data variability and quality on the result is analyzed, by using input data from two sources: distributions derived from a generic Life Cycle Inventory database and “deterministic” data from Environmental Product Declarations. The outcomes highlight remarkable differences between the two datasets that lead to substantial variations on the systems performance ranking at the production stage. Looking at the life-cycle impact, the general trend of the output distributions is quite similar among simulation groups and insulation systems. Hence, while a ranking of the solutions based on a “deterministic” approach provides misleading information, the stochastic approach provides more realistic results in the context of decision-making.

This article presents the descriptive statistics of service life data of building elements, gathered through an international, European and Swiss literature review of LCA, LCC and other sources called “Real-Estate Management sources” that include building owners, banks, insurances, associations of tenants and owners, etc. Furthermore, the properties of the fitted lognormal distribution are given. The data are structured, using a hybrid decomposition (functional decomposition, according to the eBKP-H – SN506511 and material decomposition, as well). These data and the derived statistical distributions were used in a research study, in order to quantify the uncertainty and sensitivity of the LCA and LCC output, due to the variability of the building elements’ service lives.

This paper presents a life cycle assessment (LCA) of the solar heat injected in a District Heating Network (DHN) by a new solar thermal power plant in Geneva. An environmental Life Cycle Assessment (LCA) is conducted according to ISO 14040, ISO 14044 and SN EN 15804+A2:2019. The scope of the study includes the complete life cycle of the solar power plant heat generation system. The baseline GHG emissions results show that the solar heat injected is responsible of about 15 to 18 gCO_2-eq per kWh depending on whether we account for the environmental impacts of the steel gratings or not. This value is mostly determined by the manufacturing and use stages. Different sensitivity analyses are then performed in order to test the robustness of the main influential parameters (e.g., lifetime, efficiency, electricity mix) and better quantify the variability of the GHG emissions. Finally, this LCA study of a solar thermal plant connected to a Swiss DHN provides a first value of the solar heat in comparison with other heat production routes for DHN already provided in LCA databases such as the Swiss KBOB 2009/1:2022 platform for LCA in the construction sector.

The performance gap, defined as the difference between the measured and the calculated performance of energy-efficient buildings, has long been identified as a major issue in the building domain. The present study aims to better understand the performance gap in high-energy performance buildings in Switzerland, in an ex-post evaluation. For an energy-efficient building, the measured heating demand, collected through a four-year measurement campaign was compared to the calculated one and the results showed that the latter underestimates the real heating demand by a factor of two. As a way to reduce the performance gap, a probabilistic framework was proposed so that the different uncertainties of the model could be considered. By comparing the mean of the probabilistic heating demand to the measured one, it was shown that the performance gap was between 20–30% for the examined period. Through a sensitivity analysis, the active air flow and the shading factor were identified as the most influential parameters on the uncertainty of the heating demand, meaning that their wrong adjustment, in reality, or in the simulations, would increase the performance gap.

The renovation of historic buildings is essential to meet the Swiss objectives for energy consumption in 2050. These buildings offer a great saving potential, however, the heritage preservation has to be considered in the renovation scenarios. While essential for the historic conservation, this consideration restricts the renovation possibilities to achieve the heating demand requirements according to the SIA 380/1 standard. This study introduces a framework for identifying the suitable historic buildings’ renovation schemes considering life cycle costs, energy and life cycle environmental impacts. With a case study, the feasibility of achieving the energy performance SIA 380/1 standard is then discussed.