PurposeBuildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings.Design/methodology/approachAn Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed.FindingsThe MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R2 value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages.Research limitations/implicationsThis study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle.Originality/valueBy providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.

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PurposeBuildings are responsible for the consumption of around 40% of energy in the world and account for one-third of greenhouses gas emissions. In Saudi Arabia, residential buildings consume half of total energy among other building sectors. This study aims to explore the impact of sixteen envelope variables on the operational and embodied carbon of a typical Saudi house with over 20 years of operation.Design/methodology/approachA simulation approach has been adopted to examine the effects of envelope variables including external wall type, roof type, glazing type, window to wall ratio (WWR) and shading device. To model the building and define the envelope materials and quantify the annual energy consumption, DesignBuilder software was used. Following modelling, operational carbon was calculated. A “cradle-to-gate” approach was adopted to assess embodied carbon during the production of materials for the envelope variables based on the Inventory of Carbon Energy database.FindingsThe results showed that operational carbon represented 90% of total life cycle carbon, whilst embodied carbon accounted for 10%. The sensitivity analysis revealed that 25% WWR contributes to a significant increase in operational carbon by 47.4%. Additionally, the efficient block wall with marble has a major embodiment of carbon greater than the base case by 10.7%.Research limitations/implicationsThis study is a contribution to the field of calculating the embodied and operational carbon emissions of a residential unit. Besides, it provides an examination of the impact of each envelope variable on both embodied and operational carbon. This study is limited by the impact of sixteen envelope variables on the embodied as well as operational carbon.Originality/valueThis study is the first attempt on investigating the effects of envelop variables on carbon footprint for residential buildings in Saudi Arabia.

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Research has shown that students differ in their preferences of indoor environmental quality (IEQ) and psychosocial aspects of their study places. Since previous studies have mainly focused on identifying these preferences rather than investigating the different profiles of students, this study aimed at profiling students based on their IEQ and psychosocial preferences of their study places. A questionnaire was completed by 451 bachelor students of the faculty of Architecture and the Built Environment. A TwoStep cluster analysis was performed twice separately. First, to cluster the students based on their IEQ preferences, and second based on their psychosocial preferences. This resulted in three clusters under each cluster model. Then, the overlap between these two models was determined and produced nine unique profiles of students, which are: (1) the concerned perfectionist, (2) the concerned extrovert, (3) the concerned non-perfectionist, (4) the visual concerned perfectionist, (5) the visual concerned extrovert, (6) visual concerned non-perfectionist, (7) the unconcerned introvert, (8) the unconcerned extrovert, and (9) the unconcerned non-perfectionist. A number of variables was found to be significantly different among these profiles. This study’s outcome indicates that studying the overlap between IEQ and psychosocial preferences is required to understand the different possible profiles of students.