{"title":"Sustainable waste management of construction materials: Mathematical modelling and analysis","authors":"Mayowa Emmanuel Bamisaye , Babatunde Oluwaseun Ajayi , Issara Sereewatthanawut","doi":"10.1016/j.rcradv.2025.200274","DOIUrl":null,"url":null,"abstract":"<div><div>The construction industry remains one of the most significant contributors to global energy consumption and CO<sub>2</sub> emission, with construction and demolition waste management emerging as a critical leverage point for environmental improvement. This study employs a hybrid approach that integrates System Dynamics (SD) modelling with Random Forest (RF) algorithm to optimize concrete waste management systems. The analysis encompasses the entire waste processing lifecycle—including demolition, sorting, transportation, recycling, and landfilling—with specific focus on material recovery, landfill use, energy consumption, and CO<sub>2eq</sub> emissions. Findings revealed that transportation and demolition account for the majority of energy use and emissions. However, strategic interventions such as expanding recycling infrastructure, transitioning to natural gas and electric trucks, and optimizing truck load capacity can reduce energy consumption and emissions by 20–30 %. Additionally, the adoption of demolition robots further decreases energy use by 18 % and emissions by 47 %. By enhancing material processing efficiency and increasing the use of recycled concrete in new construction, this study reinforces circular economy principles. This study provides a quantitative basis for policy measures aimed at promoting upcycling, improving energy efficiency, and supporting net-zero emission goals in construction sector.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200274"},"PeriodicalIF":6.4000,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Resources, conservation & recycling advances","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266737892500032X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The construction industry remains one of the most significant contributors to global energy consumption and CO2 emission, with construction and demolition waste management emerging as a critical leverage point for environmental improvement. This study employs a hybrid approach that integrates System Dynamics (SD) modelling with Random Forest (RF) algorithm to optimize concrete waste management systems. The analysis encompasses the entire waste processing lifecycle—including demolition, sorting, transportation, recycling, and landfilling—with specific focus on material recovery, landfill use, energy consumption, and CO2eq emissions. Findings revealed that transportation and demolition account for the majority of energy use and emissions. However, strategic interventions such as expanding recycling infrastructure, transitioning to natural gas and electric trucks, and optimizing truck load capacity can reduce energy consumption and emissions by 20–30 %. Additionally, the adoption of demolition robots further decreases energy use by 18 % and emissions by 47 %. By enhancing material processing efficiency and increasing the use of recycled concrete in new construction, this study reinforces circular economy principles. This study provides a quantitative basis for policy measures aimed at promoting upcycling, improving energy efficiency, and supporting net-zero emission goals in construction sector.