Exploring how waste management is being approached in green building rating systems: A case study.

Green building rating systems (GBRS) propose sets of indicators to measure the level of sustainability of buildings, and include waste as one of the categories to be assessed. Yet, both the number of indicators and their aim, as well as the waste fraction they refer to, vary greatly from one system to another. This study identifies the waste-related indicators included in 10 global GBRS. They are classified on the basis of different criteria (waste fraction assessed, stages of the life cycle of the building, waste hierarchy and stages that make up the waste management system) so as to make it possible to subsequently analyse the importance given to each indicator through the specific weightings of each GBRS. Finally, the indicators are implemented in the case study of a building located in Colombia, in order to quantify the current level of sustainability achieved in the waste category of each system and to propose improvement actions that allow this score to be improved. In this way, it is shown that proper waste management can increase the level of sustainability of a building.

Bayesian and network models with covariate effects for predicting heating energy demand.

The spatial effect is an element presented in many geostatistical works and it should be incorporated into studies regarding the heating energy demand of residential building stocks. The most common approaches have been made by simple descriptive statistics or using analyses by Markov random fields. In this work, we propose two different methods. First, the Stochastic Partial Differential Equation with the Integrated Nested Laplace Approximation to model the variable heating energy demand in Castellón de la Plana, Spain also considering covariates and the spatial effect. Second, simulated street networks for analysing data. We describe and take advantage of the Bayesian methodology in the modelling process in all the scenarios, including covariates and the possibility of creating a simulated street network with the data for the modelling issue. Our results show that the spatial location of the building is a crucial element to study the heating energy demand using both methodologies.