A stochastic approach to LCA of internal insulation solutions for historic buildings

Internal insulation is a typical renovation solution in historic buildings with valuable facades. 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

Muon groups and primary composition at 10 to the 13th power to 10 to the 15th power eV

The data on muon groups observed at Baksan underground scintillation telescope is analyzed. In this analysis we compare the experimental data with calulations, based on a superposition model in order to obtain the effective atomic number of primary cosmic rays in the energy range 10 to the 13th power to 10 to the 15th power eV

Towards Prospective Life Cycle Assessment: How to Identify Key Parameters Inducing Most Uncertainties in the Future? Application to Photovoltaic Systems Installed in Spain

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-09150-1_51International audienceProspective Life Cycle Assessment (LCA) is a relevant approach to assess the environmental performance of future energy pathways. Amongst different types of prospective scenarios, cornerstone scenarios meant for complex systems and long-term approaches, are of interest to assess such performance. They rely on different types of long-term projections, such as projections of technological evolutions and of energy resources. In most studies, scenarios are defined with single values for each parameter, and environmental impacts are assessed in a deterministic way. Inherent uncertainties related to these prospective assumptions are not considered and prospective LCA uncertainties are thus not addressed. In this paper we describe a methodology to account for these uncertainties and to identify the parameters inducing most of the uncertainties in the prospective LCA results. We apply this approach to prospective LCAs of photovoltaic-based electricity generation systems