Multicriteria Decision-Making Methodology for Credit Selection in Building Sustainability Rating Systems
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Uncertainty is an inherent characteristic of decision making related to sustainable construction projects. The main reason for this uncertainty is the incomplete information available to stakeholders on the relative impact of their decision criteria and the full consequences of the sustainability decisions on the projects' financial and environmental performance. One of the critical decisions made to achieve the objectives of sustainable construction projects is the choice of credits that qualify projects for certification by sustainability rating systems like Leadership in Energy and Environmental Design (LEED). The objective of this paper is to create an objective methodology for the sustainability credit selection along with a modeling tool that incorporates the proposed methodology and then compare the performance of this tool with that of the commonly used cognitive approaches to decision making. Therefore, structured interviews were held with industry professionals, the results of which are used to develop an ordered-probit statistical model that identifies the significant independent parameters affecting the subject credit-selection decisions. Subsequently, the proposed credit-selection methodology is developed based on the ELECTRE III technique and compared with the intuition-based approach. The validation of the model results is performed by comparing the model output with credits actually selected in a number of actual projects targeting certification by LEED and the Qatar Sustainability Rating System (QSAS). The results of this comparison indicate that the developed methodology is capable of providing valuable guidance in the credit-selection process in the early stage of project design. The methodology also provides a systematic and consistent way for credit selection based on in-depth analyses of the evaluation criteria and perceived project priorities.
- Center for Sustainable Development Research [99 items ]