The characterisation and specification of sustainability indicators for the analysis of buildings constitutes one of the key issues in moving towards more sustainable construction and cities. Because of the complexity of interactions between constructions, users and environment, many indicators have been identified in previous works taking into account energy and resources consumption, the “whole” indoor quality (as Predicted Mean Vote-PMV or indoor air quality) and the environmental releases occurring throughout the entire life cycle of the building. Due to the great number of parameters, the decision maker has to face a large amount of complex information and data that are not easy to collect and manage. On the other side, the attempt to summarise these data into a reduced set of synthetic indexes implies the loss of important information and decrease the transparency of decisional processes. This paper focused upon one of the most common tool for assessing the environmental performance of buildings: the GBtool performed by the Green Building Challenge. Two case studies have been investigated regarding two different typologies of constructions. The first (representing a benchmark case study) refers to a common double-familiar house equipped with a medium level of technological plants. The second case study refers to a double-familiar house developed with an energy and environmental approach. The structural system (low thermal conductivity brick walls, floor and roof insulation, etc.) and the thermal plants (solar collectors for warm water, high efficiency illuminating devices, etc.) are designed to reduce the environmental impacts and to enhance the ecological performances. Results from GBtool have been compared to those obtained from Multi Attribute Decision Making approach implemented into a software developed by the Department of Energy and Environmental Researches (DREAM) of Università di Palermo. A comparative study allowed to individuate some problems related to GBtool mainly due to the low transparency and flexibility of the method and to the high number of considered parameters. In particular the work highlighted the great sensitivity of the results obtained with the GBtool on dependence to the input fluctuations (due to data uncertainty) and to the arrangement of the criteria weights. These items represent a weakness of the method and could cause confusion in the decision makers
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