An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach
Construction and demolition waste (CDW) management remains a pressing challenge in the construction industry, contributing significantly to environmental degradation and resource depletion. Accurate waste measurement is essential for improving resource recovery and circular economy adoption. However...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Recycling |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-4321/10/3/115 |
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| Summary: | Construction and demolition waste (CDW) management remains a pressing challenge in the construction industry, contributing significantly to environmental degradation and resource depletion. Accurate waste measurement is essential for improving resource recovery and circular economy adoption. However, existing research lacks standardised estimation methods, the integration of digital technologies, and comprehensive lifecycle analysis approaches, limiting the effectiveness of waste prediction and management strategies. This study addresses the gap by conducting a scientometric analysis using CiteSpace and SciMAT, examining research trends, thematic clusters, and knowledge evolution in CDW quantification and management from 2014 to 2024. It establishes a conceptual framework for integrating digital systems and sustainable practices in CDW, focusing on waste generation rate, carbon emission, and phase-based waste management analysis. Network cluster analysis reveals the integral role of estimation tools and modelling techniques in refining waste generation quantification for building constructions. It also examines the interplay of digital tools, their influence on environmental cost reduction, and factors affecting waste production and environmental protection across project phases. This conjugate approach highlights the importance of the successful implementation of waste quantification and the imperative of machine learning for further investigation. This review offers an evidence-based framework to identify key stakeholders, guide future research, and implement sustainable waste management policies. |
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| ISSN: | 2313-4321 |