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• W2904705369 abstract "The green building rating systems endeavor to achieve the extent of sustainability in building scale; however, they are lacking in assessing the urban scale. Few green building rating systems have developed the neighborhood sustainability assessment (NSA) tools to this extent. Qatar has adopted some of these tools; while the local tool is needed that fits government sustainability policies and international agendas. Hence, this research developed the QSAS (Qatar Sustainability Assessment System) Neighborhood Development (ND) assessment model. The main goal of QSAS-ND is to promote, enhance, and create a sustainable neighborhood in urban areas. To develop QSAS-ND, the researchers applied the Waterfall Process method, and accordingly, has conducted three phases; the requirement study, model design, and model validation. Phase one identified eight criteria for sustainable neighborhood assessment using Compression Matrix Development (CMD) method. Phase two designed the model and applied the Analytical Network Process (ANP) method to obtain the weights of criteria. The ANP analysis determined that compactness has received the highest limited value (LVC2 = 0.30730) among the criteria which is followed by urban connectivity (LVC1 = 0.29143). The model was implemented at Lusail city for validation. The implementation resulted that Lusail city achieved grade A (Superior), means that it has high performances in sustainable neighborhood design, but needs minor improvements regarding diversity and transportation. Indeed, using the QSAS-ND model helps architects and urban planners to assess sustainability accreditation of urban and neighborhood areas in Qatar." @default.
• W2904705369 created "2018-12-22" @default.
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• W2904705369 date "2019-03-01" @default.
• W2904705369 modified "2023-10-11" @default.
• W2904705369 title "Qatar Sustainability Assessment System (QSAS)-Neighborhood Development (ND) Assessment Model: Coupling green urban planning and green building design" @default.
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• W2904705369 doi "https://doi.org/10.1016/j.jobe.2018.12.006" @default.
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