Recycled construction and demolition waste material: a cost–benefit analysis under uncertainty

Abstract

PurposeConstruction and demolition waste (CDW) continuously causes environmental and social problems. These formidable challenges lead to sustainable issues and are an increasingly urgent issue worldwide. Prior studies have neglected to link the triple bottom line (TBL) to a reliable estimation or empirical model for estimating CDW production performance and lack empirical sensitivity analysis in profit maximization. This study proposes an attribute analysis to build a cost–benefit analysis (CBA) to obtain profit maximization.Design/methodology/approachThis study uses fuzzy set theory to develop a cost and benefit analysis (CBA) model to assess the sensitivity analysis in terms of its performance on addressing the environmental, economic and social aspects. The model is used to weigh the sum of benefits such as financial gain and total costs of actions taken to mitigate the negative impacts.FindingsBased on the sensitivity analysis conducted, the environmental, economic and social mean scales were significantly changed, i.e. increased, and profits increased drastically. The results provide an insight into environmental legislation compliance, environmental investment and environmental impact as the cause attributes for the CDW recycling profit increase. The results prove that sensitivity analysis is viable to infer that a sustainable production performance can achieve more revenue and profit through an adequate selection of attributes regarding the TBL aspects to address the firm's uncertainty problem in multiple criteria analysis.Originality/valueThis study builds a CBA model to maximize profits for recycled CDW material by linking of environmental, economic and societal aspects for recycled CDW assessments. It considers a sustainability structure with criteria based on TBL aspects to assess production performance to realize the Sustainable Development Goals and presents fuzzy set theory and sensitivity analysis to solve the uncertainty problem in the construction industry.