An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach

Abstract

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.

1. Introduction

CDW generally refers to the waste generated from the construction sector. Specifically, it comes from the emergence of construction, renovation, and demolition activities, including excavation, land development, civil construction, building renovation, roadwork, site clearance, and demolition [1]. Currently, over 11 billion tonnes of CDW are produced each year globally. It is expected to be tripled by the end of 2100, accounting for more than 40% of total annual generated waste, of which only 60% can be recycled or recovered during 2018–2019 [2]. Public policies and regulations worldwide emphasise the critical role of sustainable CDW management. For instance, the European Union’s Waste Framework Directive (2008/98/EC) mandates member states to recover specific percentages of waste by weight, depending on material type [3]. Similarly, in Australia, the Commonwealth Government introduced and updated the “National Waste Policy” in 2018, 2019, and 2022, respectively, aiming to reduce waste generation and enhance industry awareness of waste management [4,5,6,7]. Emphasising the significance of waste reduction through legislative procedures, implementing policies implementation at the early design stage, and adopting sustainable design approaches have been prioritised to eliminate unnecessary waste. A reliant quantification is necessary to serve as a foundation and enable data-driven decision-making. Clear metrics on waste volume, composition, and flows allow stakeholders to identify opportunities to reduce, reuse, and recycle materials. This data-based understanding supports forecasting, budgeting, and setting achievable performance targets. These steps ensure that regulations and initiatives remain relevant and feasible. Reliable quantification underpins the choice of demolition methods, as well as precise data collection and reporting. Ultimately, it supports more responsible resource use across the construction industry. Effective CDW management remains a pressing challenge, necessitating accurate quantification and policy alignment.

Numerous studies have proposed different aspects of waste control strategies and quantification methodologies to reduce CDW generation [8,9,10,11,12,13]. However, several gaps persist, listed as follows: (1) current waste estimation methods lack precision and standardisation, making it difficult to trust and make reliable predictions; (2) the effectiveness of the implementation of policies remains unclear; and (3) there is limited integration of digital technologies in the CDW quantification research domain. A comprehensive review to highlight the importance of CDW quantification and its contribution to the reduction in CDW remains unseen in the existing literature.

This study aims to identify effective strategies for quantifying CDW by uncovering key research trends, influential works, and strategic themes from 2014 to 2024. It highlights future challenges, emerging technologies, and potential regulatory frameworks for sustainable practices. Using a cluster research methodology combined with two scientometric tools (CiteSpace and SciMAT), this paper provides a qualitative overview of methods, visualisation of the research landscape, theme evolution, and findings from leading journals. Further analysis identifies emerging topics and clusters for future exploration. This study introduced the integration of advanced digital tools to enhance the accuracy and standardisation of waste estimation, underscored the role of policy mechanisms in promoting sustainable construction practices, and advocated the incorporation of advanced technologies in the support of real-time monitoring, accurate forecasting, and optimisation of efficient resource allocation to reduce overall environmental impact.

This scientometric review offers a comprehensive assessment of CDW quantification practices, identifies critical knowledge gaps, and establishes a foundation for data-driven strategies to support policy development, inform future research, and advance circular economy goals.

2. Results from Data Synthesis

This section represents the data synthesis of CDW-related research articles published over the last decade. The first portion of the analysis is the bibliometric network analysis using the CiteSpace tool, which conducts a co-occurrence analysis of potential terms, leading journal mapping, and prominent authors in this research area (Section 2.1). Leading publications have elaborated on the research trends and focus areas of development. The second portion of this analysis is the evolution of the current landscape using SciMAT, which includes the overlapping, strategic, and relevant clusters of potential themes (Section 2.2). Finally, cluster composition has been analysed from the current CDW management and quantification perspective (Section 2.3).

2.1. Bibliometric Network Analysis

2.1.1. Research Trend Analysis

A growing publication trend was observed over the last decade, as shown in Figure 1. The number of publications regarding CDW reached 143 by the end of the year 2024. Increasing concern about rising waste from the construction sector is the primary issue for the enhanced publication. The trend can be attributed to the rapid global urbanisation since 2000, which significantly increased construction projects and led to high consumption of construction materials, producing more wastage [14].

2.1.2. Co-Occurrence Analysis of the Research Theme

Reading and analysing keywords in traditional ways is not always feasible with many publications. A data-mining-based technique that uses the network methodology, keyword co-occurrence analysis, is then introduced [15]. The methodology facilitates the study of research trends during a predetermined timeframe, provides insight into research structure and knowledge components, enables researchers to comprehend popular topics, and uncovers valuable insights.

The frequency of the key terms has been extensively discussed and presented to reflect the prioritising area within the CDW management and quantification research domain, as shown in Figure 2. In the term frequency network, more frequently discussed terms are presented in larger nodes, indicating higher co-occurrence counts and highlighting the significant impact of critical terms. Terms such as “construction and demolition waste”, “waste management”, “construction”, and “life cycle assessment (lca)” dominate discussions, reflecting their central role in the field. Keywords including “waste generation”, “quantification model”, and “circular economy” expose the growing focus on waste measurement and quantification for effective waste management. In contrast, smaller nodes represent less frequently cited terms, mostly appearing after 2020. These keywords, such as “deconstruction”, “performance quality”, “waste quantification”, and “sustainable development” reflect critical considerations in recent years from this domain, that is, to connect the accurate compression of project waste generation rate and the associated methodologies to evaluate and quantify it across wastes’ different life cycle stages.

Additionally, the emergence of “building information modelling” and “big data analysis” signals a shift towards digitalisation and predictive analytics in CDW management. This evolution clearly demonstrates a transition from traditional waste management approaches to more advanced and data-driven techniques, aiming to eliminate unnecessary waste before construction begins, aligning with broader sustainability and circular economy principles.

The shift improves resource efficiency and waste reduction while fostering a more structured and evidence-based approach to sustainable construction, paving the way for more resilient and environmentally responsible built environments.

2.1.3. Mapping of Article Sources for Leading Journals

The journals cover variable aspects of research findings for interdisciplinary topics such as CDW management practices. The top ten (10) journals with the highest number of publications and co-citations have been identified for relevant contributions in this research domain, as shown in

Table 1. Top ten (10) leading journals in CDW management from 2014 to 2024.

SL. No	Journal Name	Number of Publications	Co-Citation Count
1	Construction and Building Materials	44	276
2	Journal of Cleaner Production	44	333
3	Waste Management	39	313
4	Resources Conservation and Recycling	34	305
5	Journal of Building Engineering	28	111
6	Waste Management & Research	19	153
7	Sustainability	16	112
8	Automation in Construction	15	68
9	Journal of Environmental Management	14	86
10	Science of Total Environment	12	87

. Publications from all listed journals focus on waste reduction strategies from different perspectives. Most journals focus on recycling construction waste, reusing it through waste reform, and applying different treatment strategies. Some others concentrate on implementing various techniques to determine the waste streams for effective waste generation control and minimising environmental impacts. Construction and Building Materials, Journal of Cleaner Production, Waste Management, and Resources Conservation and Recycling are the top four (4) journals with the highest co-citation counts, attributed to their published articles’ outstanding originality and quality.

The Construction and Building Materials journal emphasises the repetitive use of construction materials after demolition, focusing on waste reduction techniques and recycling within the construction industry. Meanwhile, the Journal of Cleaner Production broadly covers sustainable construction practices in waste management. The Waste Management journal reviews case studies targeting waste minimisation, recycling, disposal, and policy implementation from construction sites. Recovery of materials, conservation of resources, life cycle assessment (LCA), aspects of sustainability, and waste management in construction practices are covered by journals such as Resources Conservation and Recycling, Journal of Building Engineering, Sustainability, Automation in Construction, and others. Furthermore, journals such as the Journal of Environmental Management and Science of Total Environment have relatively fewer publications but still contain high co-citations because of the specialised focus within their mutual research field. This specialisation ensures that their published papers are closely aligned with the interests of researchers in this domain, resulting in a high frequency of co-citations.

2.1.4. Mapping of Article Sources for Leading Publications

Top ten (10) prominent publications leading the research domain of CDW management have been identified following the number of citations and co-citations, as shown in

Table 2. Top 10 influencing publications with high co-citation impacts for waste quantification strategies involving BIM (building information modelling), WGR (waste generation rate), WMP (waste management policy), WEP (waste estimation and planning), WRR (waste recycling and reuse), CDR (construction, demolition, and renovation), WCA (waste per construction area), RLR (recycling and landfilling rate), REV (recycling economic value), PEI (potential environmental impact), and SE (stakeholders engagement).

Sl. No	Publication Details	Year	Citations	Co- Citation	Quantification Methods	Reference
1	A BIM-based system for demolition and renovation waste estimation and planning, waste management	2013	440	152	BIM, WGR, WMP, WEP, WRR, CDR	[16]
2	Characterising the generation and flows of construction and demolition waste in China, construction and building materials	2017	400	75	WGR, CDR, WCA, WEP, WRR, RLR, REV, PEI	[17]
3	An empirical study of construction and demolition waste generation and the implication of recycling, waste management	2019	272	357	CDR, WGR, REV, WMP, PEI, WEP, SE	[18]
4	Estimation of building-related construction and demolition waste in Shanghai, waste management	2014	245	377	WGR, WMP, WRR, REV, PEI	[19]
5	Quantification of construction waste prevented by BIM-based design validation: case studies of South Korea, waste management	2016	217	144	WGR, BIM, WEP, CDR, WCA, SE	[20]
6	Demolition waste generation and recycling potentials in a rapidly developing flagship megacity of South China: prospective scenarios and implications, construction and building materials	2016	139	423	WGR, PEI, WMP, WRR, REV, RLR	[21]
7	Developing a quantitative construction waste estimation model for building construction projects, resource, conservation, and recycling	2016	128	295	CDR, RLR, WGR, WEP, WRR, PEI	[22]
8	Waste generated in high-rise buildings construction: A quantification model based on statistical multiple regression. Waste management	2015	125	78	WGR, WMP, WEP, WCA, CDR, SE	[23]
9	Material flow analysis of the residential building stock at the city of Rio de Janeiro, Journal of Cleaner Production	2017	95	57	CDR, WGR, WMP, WEP, WCA, PEI	[24]
10	Methods for estimating construction and demolition (C&D) waste, Handbook of Recycled Concrete and Demolition Waste, Woodhead Publishing	2013	28	199	CDR, WGR, WEP, PEI, WMP	[25]

. Necessary details of publications and adopted waste quantification methodology for the best practices of CDW are revealed in this section. Most prominent publications have focused on the waste generation rate (WGR) from the construction and demolition sites. Relevant factors of generated waste are related to the waste management policy (WMP), waste estimation and planning (WEP), waste recycling and reuse (WRR), and others. Recycling CDW materials such as concrete, bricks, and metals offers substantial economic benefits such as resource recovery and reuse. On the contrary, landfill disposal’s financial costs and environmental impacts remain high. Therefore, promoting recycling and reducing reliance on landfills have been prioritised in most of the published articles and have been denoted as the recycling and landfilling rate (RLR), recycling economic value (REV), and potential environmental impact (PEI).

Estimation of waste generation from construction and demolition sites has been a preference for the conducted studies to design deconstruction, implement waste management, recycle waste materials, and minimise environmental impact. Site visits, waste generation rate calculation, and material flow assessment are these articles’ most popular research methods to quantify CDW. They allow for direct observation of waste generation, help researchers assess waste composition, and visually identify waste trends. Some researchers adopt a combination of methods for more in-depth analysis and sometimes draw comparisons between different processes, allowing for cross-validation between data and becoming a more holistic approach to measuring waste. Publications with high co-citations indicate that these papers are frequently cited in conjunction with other influential works, revealing the possibility of being highly cited by authors from different research areas or providing valuable background information to the waste quantification field. Publications with high citations and co-citations reflect their impact and influence in the research domain due to rigour and quality.

2.2. Evolution of Key Research Themes

SciMAT has incorporated algorithms, methods, and measuring steps from visualisation mapping process flows to obtain the thematic evolution in CDW studies. Similarity measures, normalisation, and clustering algorithms have been adopted in this visualisation module to represent overlapping maps, strategic diagrams, and cluster networks. SciMAT analyses scientific publications in these research areas to provide valuable insights into thematic evolution, identify emerging trends, and inform future research directions in sustainable policymaking.

2.2.1. Overlapping Map Analysis

The overlapping map presents the results of the longitudinal and temporal analysis conducted using SciMAT, enabling the observation of the evolution of key topics within the research area across four distinct periods, as shown in Figure 3. The depiction illustrates the progression of keywords over the past decade at regular intervals. The numerical values denote the number of key themes enclosed throughout the specified period based on scores of centralities, density, average citations, and the sum of the citations. Outgoing arrows signify themes transitioning out, the incoming arrows indicate newly introduced themes, and the arrows pointing right represent shared themes. For example, between 2014–2016 and 2017–2019, an initial presence of twelve (12) key research themes was widely spotted in this area. Seven (7) themes remained constant across both periods, while five (5) themes shifted out, and twenty-three (23) new themes emerged, collectively forming the thirty (30) critical themes identified in the latter period.

The notable increase in the number of themes over the last decade, from twelve (12) to ninety-five (95), underscores an emerging trend and imperative for exploration within the research domain of CDW management and quantification. The fraction across the phases represents a continuous and stable index for future terms. Furthermore, the prevalence of more themes being introduced than phased out also reflects the expansive nature of the discourse and the ongoing innovation in the thematic areas and technological discussions.

Moreover, strategic diagrams define potential areas for future research, as shown in Section 2.2.2. Potential bridging with other clusters and fostering collaboration across multiple disciplines is elaborated in Section 2.3.

2.2.2. Strategic Diagram Analysis

The two-dimensional strategic diagram characterises CDW research clusters in terms of their centrality (importance impact) and density (specialisation of development), as shown in Figure 4. By employing Callon’s bibliometric measures, elevated figures within each quadrant denote a heightened significance and intensity in the relationships among the keywords under examination. This signifies a more significant level of discussion and connectivity pertaining to the topic within the research domain [15]. This diagram evolves research fronts or clusters over a definite period line to demonstrate motor, isolated, emergence, and developed themes.

Clusters are summarised into a four-quadrant matrix, where each quadrant represents different themes:

• Motor theme (upper right): It demonstrates robust centrality and density metrics attributed to factors such as a large number of publications and citations. Clusters in this theme have been discussed and developed well. Demolition waste, landfill, and management systems exhibit notably high average citation counts of 31.24, 25.17, and 22.8, respectively, representing conceptual solid connections with other themes under consideration.
• Highly developed and isolated theme (upper left): Flexural behaviour (37.5) and waste generation rate (31.6) are characterised by a dense network with low centrality scores, indicating significant internal connectivity but limited external linkages of marginal significance.
• Emerging or declining theme (bottom left): It receives low density and modularity scores, representing weakly developed and connected clusters in this theme. Carbon emission (17.5) and geographic information systems (35) are emerging topics due to their implications in the domain.
• Basic and transversal theme (bottom right): Despite its nascent development, it is considered the most important theme in the research domain. Power (11.44), soil (24.45), and recycled aggregate concrete (30.67) underscore a pressing demand for further investigation and scholarly contributions.

This study aims to serve as a guide for future research directions; therefore, it explores all themes apart from the highly developed and connected theme, the motor theme. Consequently, within the scope of this investigation, the necessity for further research endeavours stand out in the areas of waste generation rate (31.6), with an average citation count of 31.6, and carbon emissions (17.5), selected from highly developed and isolated themes and emerging themes. Power, with an average citation count of 11.44, and its subsidies do not exhibit a robust connection with CDW and, thus, are considered beyond the purview of this study. Furthermore, phase-based waste management (13) stands out as a pivotal amalgamation. It is a critical intersection bridging elements of both general transversal and emerging themes, thereby presenting a strategic avenue for further enhancement and exploration in the research landscape.

2.3. Cluster Network Analysis for CDW Management and Quantification

The generated cluster network using SciMAT analysis represents a visual structure for understanding relationships within the scientific body of CDW literature. This research area’s underlying pattern and dynamics are revealed through the cluster network to gain insights into relevant components within the technical dataset.

Waste quantification is the initial step in implementing sustainable practices in CDW. Effective waste reduction, reuse, recycling, and land disposal strategies are feasible by accurately measuring the amount and types of waste generated from construction sites. The strategic diagram of the analysis shows the potentially developed isolated theme (waste generation rate), emerging theme (carbon emissions), and basic theme (phase-based waste management) for further exploration. Additionally, cluster analysis is a statistical technique to show relative design components associated with principal components. Integrating the attributing factors and specific characterisation from variable themes such as estimation tools, modelling, costs, building information modelling, and transformation techniques contributes to fostering sustainable CDW practices.

2.3.1. Waste Generation Rate as a Highly Developed and Isolated Theme

WGR is highlighted as crucial, necessitating a thorough comprehensive of the building’s lifecycle. It stands as a highly developed but isolated theme in the CDW management and quantification research domain. Figure 5 underscores the importance of relevant estimation tools and modelling techniques as the essential components within the network, demonstrating their critical role in the effective management and understanding of waste generation.

A profound comprehension of the rate at which waste is generated, and the flow of waste generation, is indispensable for formulating efficacious waste management strategies and comprehensive assessment of brought-forward cost and environmental impacts.

Table 3. Waste generation rate in CDW management process and its impact on the waste stream.

Waste Generation Rate	Specific Tools or Methods	Waste Stream Characterisation
Estimation Tools	Mathematical model development	Weight per area method, weight per capita method, and material flow analysis are used to estimate the WGR and categorise waste types in the proportion of total waste stream based on project specifications, gross floor area, structure type, etc. [17,21,22,23,24,25]. Requires a reasonable level of data parameters and is very high in model scalability. Mostly used for logistics optimisation and policy simulation and offers strong control over inputs, but are complex and less responsive to real-time changes.
Modelling	Prediction model	Multiple linear regression, gene expression programming, and machine learning models, including artificial neural networks and support vector machines, are used to identify trends in waste generation and their relationship with influential factors in waste prediction [26,27,28,29]. Requires a very high level of requirement on data input and offers a medium level of model scalability. Provides high-accuracy waste estimation with clean, large datasets, but is prone to overfitting and requires significant data input.
	Forecasting model	S-curve, ARIMA, and Grey Model have been used to forecast future waste generation rates and compare them with actual observed values based on historical data of construction projects; they are then used to convert collected data into a suitable format and perform analysis [30,31,32]. Requires a medium level of data parameters and is high in model scalability. Supports long-term national or regional planning of CDW trends, although it is heavily reliant on social and economic assumptions.
	Sensitivity analysis	Conducts a sensitivity analysis of model input parameters to assess the effect of model output using the mathematical model: mean absolute error, mean squared error, root mean squared error, etc. [33,34]. Requires a medium level of data but is also relatively low in model scalability. Identifies key variables for risk mitigation in uncertain conditions but is computationally demanding and may overlook nonlinearities.
	Salvage modelling	BIM tools (Autodesk Revit, ArchiCAD, and Bentley Systems’ AECOsim), project tools (Procore, PlanGrid, and Autodesk BIM 360), management tools (Wastebits, Waste Logics, or Re-TRAC), Environmental Management Systems (Enablon or Intelex), and Enterprise Resource Planning (SAP HANA) incorporate features to salvage planning and consider the feasibility of salvaging materials, the extent of recovering materials, and the impact on generating waste [35,36,37,38,39,40]. Requires a high level of data input and a medium-to-low level of model scalability. Facilitates planning for material reuse and deconstruction, requiring detailed building data to be effective.
Buildings	Life cycle assessment	LCA software SimaPro version 8.2.3.0, GaBi version 6.5, OpenLCA version 1.5.0, and other databases provide holistic and statistical data on the environmental impacts of materials, facilitate the selection of more durable material, and result in longer service life [41,42]. Requires a very high level of data input but a medium level of scalability. Delivers comprehensive environmental impact analysis across the project lifecycle, but is highly data-intensive and sensitive to system boundaries.
	Onsite waste monitoring and reporting system	Smart bins and sensors measure the amount of generated waste, recognise types of waste for reuse, recycling, and landfill, and eliminate hazardous waste [43].
		Radio-frequent identification (RFID) is used to track waste movements and precisely monitor waste types and quantities [44,45].
		Re-TRAC Connect, the cloud-based waste tracking and reporting platform, reports on disposal methods, diversion, and waste metrics [46,47]. Requires a low-to-medium level of data parameters, but is high in scalability to perform the system on different sites. Enables real-time compliance and corrective action on construction sites, although it involves high implementation costs and site-specific management.

provides a thorough exploration of methodologies and tools dedicated to characterising, estimating, and managing waste streams in the construction industry. The table also includes a comparison the prediction tools and methods, and each tool is evaluated based on its data requirement, model scalability, application context, strength, and limitations to enable a detailed evaluation of applicability across diverse scenarios. It highlights the diverse use of estimation tools, predictive and forecasting models, and sensitivity analyses for accurate waste generation rate calculation. Additionally, it details waste monitoring and assessment methods aimed at waste minimisation and environmental sustainability enhancement.

To evaluate the performance of the prediction tools listed in Table 3,

Table 4. Performance comparison of prediction tools on waste generation.

Tool/Method	Key Metrics and Findings	References
Multiple Linear Regression	Achieved R2 > 0.8 in estimating concrete waste based on floor area and project parameters.	[23]
Artificial Neural Networks	RMSE = 9.72, MSE = 6.13, R2 = 0.91	[28]
Support Vector Machines	RMSE = 0.149, MSE = 0.0222, R2 = 0.8687	[28]
ARIMA	RMSE = 3.15, MAE = 2.42, R2 = 0.79	[29]
Grey Model	RMSE = 1.68, MAE = 2.699, R2 = 0.9978	[30]
BIM (Revit)	Accuracy in waste volume prediction (R2 = 0.92); deviation < 5% using Dynamo + Revit waste quant model.	[16]
BIM (ArchiCAD)	Supports waste quantification per material type in early-stage design to enable real-time and accurate prediction of waste types and quantities. Results show a 56% reduction in waste generation and 49% improvement in material recycling.	[20]
BIM (Bentley Systems’ AECOsim)	Used in compliance testing and lifecycle waste assessment; quantitative comparison with Revit showed <7% variation.	[35]
Procore	Improved resource allocation accuracy by 18%; reduced project waste disposal delays by 25% in monitored case studies.	[48]
PlanGrid	Reduced paper-based error logs by 22%; improved waste-related issue resolution time by 16% on average.	[36]
SAP HANA (ERP System)	Used for predictive analytics in resource/waste flow; enables >90% data processing efficiency; supports waste forecasting via integration with IoT sensors.	[37]
SimaPro	Widely used for detailed waste scenario analysis; integrated databases (Ecoinvent, ELCD). In case studies, error margins for C&D materials were <10% when datasets were properly localised.	[39,41]
GaBi	Supports mass flow and material-specific LCA modelling; reportedly offers deviation <8% in embodied carbon and waste estimates across construction assemblies.	[39]
OpenLCA	Open-source tool validated in over 50 academic publications; when paired with appropriate databases (e.g., AGRIBALYSE), reports MAE in waste prediction ~5–12%.	[39,41]

presents a comparative summary of key performance metrics, including R² (coefficient of determination), RMSE (root mean square error), and MSE (mean squared error) or MAE (mean absolute error). These metrics serve to validate the effectiveness and accuracy of predictive models and the effectiveness of those tools in practical applications.

2.3.2. Carbon Emissions as an Emerging Theme

Carbon emissions have emerged as a critical and distinct theme, demanding immediate and focused attention. As shown in Figure 6, costs and BIM are integral to the carbon emissions network. The network furnishes compelling evidence of how diverse strategies and applications of BIM significantly contribute to carbon emission reduction and effective management. Including these components underscores their indispensable role in addressing the complex challenges associated with carbon emissions within the construction industry, highlighting the necessity for a concrete and strategic approach to mitigate environmental impact.

Carbon emissions prioritise CDW management from diverse cost perspectives, alongside the integration of BIM in construction projects.

Table 5. Carbon emissions in the CDW management process and their impact on waste stream.

Carbon Emissions	Specific Tools or Methods	Waste Stream Characterisation
Costs	Energy efficiency policies and environmental benefits	Policies promoting energy efficiency and subsidised PV panels and battery storage aim to reduce energy and carbon emissions, offering significant economic benefits [49]. It is essential in reducing material waste generation and promoting environmentally friendly disposal practices [50].
	Government regulation and incentives	The carbon tax mechanism is inspired by the government imposing a fixed price on carbon emissions, encouraging emission reduction to avoid high taxes, and promoting the shift to cleaner energy and technology for construction activities [49].
	Carbon offsetting and sustainable material substitution	Carbon compensation is performed through funding equivalent carbon-negative projects elsewhere to neutralise project emissions [51].
		Waste management initiatives are supported by promoting more sustainable low-carbon materials and availability in the market as raw material substitutes [52,53].
Building Information Modelling (BIM)	Design optimisation	The optimisation of architectural space and project layout reduces energy and material consumption, consequently minimising overall waste generation and the ecological footprint [53,54,55,56].
	Project simulation and planning	Integrated BIM, combined with construction scheduling (4D BIM) and cost estimation (5D BIM), significantly enhances construction efficiency and reduces construction and demolition waste (CDW). Accurate material quantification reduces the likelihood of overordering and surplus generation. Enhanced scheduling minimises delays, rework, and miscommunication between trades. These measures can collectively reduce up to 20% of total waste generation and 50% of carbon emissions, supporting both material efficiency and environmental sustainability [57,58,59].
		BIM enhances material traceability throughout the project, allows for advanced planning and material tracking, and reduces waste generation from delivery and stockpiling [60,61,62,63].
	Life cycle assessment	BIM allows for evaluation in energy performance and material durability analysis through simulation. This supports material optimisation and reduces both operational carbon emissions and future retrofit-related waste [64,65,66,67,68].
		BIM simulates the enhanced performance of building systems to compare carbon emissions between different construction methods, allowing for the prediction of future retrofitting scenarios and planning [69]. BIM as the centralised platform flags the designer in the selection of more durable materials and/or modular materials to avoid frequent replacement and allows for deconstruction at its end-of-service life [70]. BIM facilitates coordination and collaboration among team members by providing a centralised platform to resolve clashes or conflicts between different building systems. This leads to potential waste reduction from rework and a lower carbon footprint throughout construction [71,72,73].

provides a comprehensive overview of an integrative approach to reducing carbon emissions, emphasising the pivotal role of social, environmental, and economic costs, detailing their respective influences on waste streams. The implementation of BIM within construction projects and waste management practices is also prescribed as a strategic measure to mitigate carbon emissions from design, project planning, and operational perspectives. Integrating these prescribed studies enhances the adoption of sustainable technologies, practical environmental measurement frameworks, and innovative construction practices, collectively contributing to the sustainability outcomes within the construction industry.

2.3.3. Phase-Based Waste Management as the Emerging and Transversal Theme

Phase-based waste management is the cross-disciplinary theme within the cluster network because of its unique position at the intersection of emerging, basic, and transversal themes as shown in Figure 7. The theme emphasises the need for a holistic and continuous approach to sustainable building management across that spans all stages, from design to operational management oversight, and until the consideration of deconstruction or other demolition methods. The articulation of ‘Phase-based waste management’ as a critical theme reinforces the necessity for a holistic and forward-thinking approach in the construction and environmental management sectors, ensuring that sustainability is embedded at every level of decision-making and operational execution.

The critical importance of waste management across different project phases is significantly underscored due to the influence it can bring to waste generation flow. Therefore, crucial facets of sustainable waste management throughout the construction project, spanning waste characterisation, technological advancements, tools, and techniques for deconstruction, and emission reduction strategies, are further emphasised in

Table 6. Phase in CDW management process and its impact on waste stream.

Phase-Based Waste Management	Specific Tools or Methods	Waste Stream Characterisation
Transformations	Technological advancement	Automated sorting facilities, digital tracking, radio frequency identification (RFID), barcodes, and sensor technologies are to be used for the traceable, transparent, and accountable blockchain of material and waste transactions from procurement to deconstruction [74,75].
	Training, simulation, and hotspots	Augmented reality (AR) and virtual reality (VR) improves worker training, enabling project simulations and identifying safety or design risks early. They reduce the possibility of rework, improve safety, and contribute to smarter, faster, and more efficient project delivery [76,77,78].
Deconstruction	Tools and equipment	Heavy machinery (excavators, cranes, and dumpers), handling equipment (forklifts), hand tools (nail remover, hammer, chainsaw, screwdrivers, and wrenches), power tools (demolition hammers, grinders, saws, and others), and personal protective equipment (PPE) are essential to prioritise the safety and precise dismantle of materials. Less waste will be generated, and more salvaged materials can be preserved for further use [79,80].
	Economic appraisal	The economic viability of deconstruction depends on market demands for salvaged materials, labour costs, and the sale value of recovered materials. Collaborations among local government, contractors, recycling facilities, and non-profit organisations can further optimise the process by sharing resources, lowering logistics, and expanding material recovery markets [81,82].
Emissions	Emission reduction	Emission assessment can be conducted for actions at each project phase to identify the lowest emission strategies. These strategies—for example, the selection of durable materials, optimised logistics, and improved system efficiency—can also reduce material loss and increase recoverability [83,84].
Transition	Direct material transition	Deconstructable architectural components such as doors, windows, prefabricated roofs, slabs, etc., can be directly reused in new construction [85].
	Refined material transition	Sorted materials undergo recycling in facilities for further refinement and turn into recycled products through specific treatment processes [86,87].
Sustainable Buildings	Material selection	Knowledge of compounds and performances of different types of materials helps to plan for direct and indirect recycling of resources and aids in redesigning material debris as a resource [72,73,74,75,82,85,86].
	Design strategies	Sustainable design strategies, such as design for deconstruction and BIM-integrated design decisions, embed circularity and material forecast into early stages of projects, enabling better tracking, forecasting, and reduction in CDW by aligning design intent with construction practices, operational durability, and end-of-life recoverability [88,89,90,91].
	Green building rating system	LEED (USA), BREEAM (UK), Green Star (Australia), Green Star (South Africa), CASBEE (Japan), Green Mark (Singapore), and Estidama Pearl Rating System (UAE) are leading rating systems worldwide for sustainability assessment and uniquely feature promotions, projects, and buildings that have higher green building rating and tend to use more recycled materials to reduce the carbon footprint associated with material production, transportation, and disposal [92,93,94,95,96,97,98].
		Minimises waste production as more sustainable materials such as prefabricated materials are adopted since the design stage, which helps reduce raw materials and promotes reused and recycled materials during construction [99,100,101,102,103].

. It also highlights the transition to sustainable building practices through the reuse of salvaged materials, refinement of recycled materials, and adherence to different green building rating systems, advocating for a comprehensive approach to minimising construction waste and promoting environmental sustainability.

3. Discussion

Estimation tools play a fundamental role in establishing precise baselines for waste streams, enhancing resource allocation and project planning. Predictive and forecasting models refine this data, enabling proactive waste management and real-time adjustments [21,22,23,24,25]. Salvage modelling, aided by models such as BIM and project management software, facilitates the early identification of reusable materials, thus minimising waste generation at its source. LCA software further integrates environmental impacts across the building’s lifespan, encouraging durability and reduced consumption of virgin materials [41]. Finally, onsite monitoring provides immediate feedback on waste volumes and diversion. Collectively, these methods ensure precise waste quantification, promote efficient resource recovery, and accelerate the transition to a circular economy by keeping materials in circulation longer and minimising overall environmental impacts [42,43,44,45]. This finding underscores the importance of developing integrated conceptual frameworks that combine accurate waste estimations, predictive modelling, and onsite monitoring to enable real-time, data-driven decision-making. The result enhances the circular economy by providing clear projections of future material flows, enabling targeted reuse and recycling fractures and creating stable supply channels for secondary resources, reducing reliance on virgin materials, and securing long-term sustainability benefits. It suggests the exploration of synergy between salvage planning and LCA for enhanced resource recovery, the application of advanced analytics for more accurate forecasting, and the influence of policy incentives and stakeholders’ collaboration on circular economy adaption.

Carbon emissions have emerged as a central challenge in the construction industry, requiring cost-effective mitigation strategies and the integration of innovative digital solutions. BIM supports carbon reduction by enabling accurate waste quantification and performance optimisation. Government policies, including subsidies, carbon taxes, and financial incentives, promote the use of low-carbon and recyclable materials. These measures reduce CDW generation and strengthen secondary material markets [49,50,51,52,53]. BIM also facilitates design optimisation, construction scheduling (4D BIM), and cost estimation (5D BIM). These tools minimise material waste through improved planning, reduced rework, and early waste detection [53,54,55,56,57,58,59,60,61,62,63]. Life cycle assessment, embedded within BIM, enables the evaluation of energy use and material durability. This reduces operational and retrofit-related emissions. The integration of these methods extends the material lifespan and reduces reliance on virgin resources. It is closely aligned with the Ellen MacArthur Foundation’s circular economy principles by designing out waste, keeping materials in continuous use, and minimising negative externalities. By embedding low-carbon, recyclable materials into the design process and utilising real-time BIM tracking, the dependence on virgin resources can be further reduced. Policy mechanisms such as carbon taxes also mirror the Polluter Pays Principle and Extended Producer Responsibility, further supporting circular economy transitions in the construction sector [98].

“Phase” emerges as a critical and cross-disciplinary theme in sustainable construction, bridging initial design considerations, operational oversight, and end-of-life deconstruction processes. A phase-based approach facilitates the implementation of targeted strategies at each stage [74,75,76,77,78]. Technological advancements, including RFID tracking, automated sorting, and blockchain-based material traceability, enable transparent and accountable waste management from procurement to deconstruction. Tools such as augmented and virtual reality enhance worker training, reduce rework, and improve project delivery efficiency. During deconstruction, specialised equipment and personal protective gear support the safe recovery of reusable materials, while economic appraisal evaluates the market viability of salvaged components [81,82]. Emission reduction can be addressed through assessments at each phase to guide material selection, optimise logistics, and reduce loss. Direct and refined material transition strategies support the reuse and recycling of building components and materials [85,86,87]. Sustainable building practices, including informed material selection and BIM-integrated design strategies, embed circularity and forecasting into early decision-making processes. Green building rating systems further reinforce sustainability by encouraging reuse, reducing emissions, and aligning project outcomes with internationally recognised standards [92,93,94,95,103]. The results-oriented methodology mirrors the principles of a circular economy by retaining salvage material in circulation, decreasing environmental impact, and optimising resource efficiency. It underscores the importance of developing integrated conceptual frameworks that combine proactive waste management, robust LCA insights, and continual monitoring to enable data-driven and real-time decision-making across projects’ lifespans. The result highlights the synergy between salvage planning and advanced analytics for enhanced resource recovery, while also underscoring the influence of policy incentives and stakeholder collaboration for widespread circular economy adoption.

This study contributes significantly to CDW quantification and management through a scientometric review, highlighting the paramount importance of an accurate understanding of waste production throughout a project’s life cycle and its influencing factors. This study highlights several key contributions: (1) it enhanced waste quantification accuracy by integrating cost estimation models, digital modelling, and digital technologies to improve waste prediction, salvage planning, and material reuse; (2) it bridges waste quantification with circular economy principles; (3) it strengthens carbon emission reduction strategies by incorporating BIM-based design optimisation; and (4) it introduces a phase-based management approach by leveraging digital technologies to enable better material planning and tracking.

This paper indicates the path to integrating digital systems and sustainable practices in construction waste management, combining estimation tools, BIM technology, carbon strategies, and phase-based planning to support circular economy transitions. Future research can be pursued in (1) the integration of AI and predictive analytics for real-time waste optimisation, (2) the development of comprehensive lifecycle assessment frameworks combining emissions, recovery, and economic factors, and (3) the enhancement of phase-based planning using emerging technologies.

4. Methodology

This review paper follows the scientometric review method to investigate the literature on CDW management and quantification between 2014 and 2024. The scientometric review method is one of the most recognised review methods and uses computational approaches to visually analyse information, including keywords in the field of research, timeliness, etc. [104]. This paper uses CiteSpace and SciMAT as scientific tools for further analysis. CiteSpace is chosen for keyword co-occurrence analysis, leading journal analysis, and leading publication analysis [105]. In contrast, SciMAT is used to identify overlapping maps of key research themes, strategic diagrams, and cluster analysis [106]. Figure 8 shows the following steps taken to achieve these purposes as research workflow: (a). data collection and cleaning using Web of Science (WoS), (b). visualisation of the scientific landscape by using CiteSpace, (c). evolution analysis of key research themes by using SciMAT, and (d). CDW management and quantification analysis.

4.1. Data Collection

Web of Science (WOS) is the world’s leading literature search platform. The core collection of WoS was chosen as the central database because of its scientific citation search feature and the number of influential papers published on the platform [13]. In this search, (((“construction and demolition” OR “C&D” OR (“construct” OR “build”) AND “demolish”) AND “waste”) OR “CDW”) AND (“management” OR “manage” OR “estimation” OR “estimate” OR “measurement” OR “measure” OR “prediction” OR “predict” OR “quantification” OR “quantify”) were used as search keywords. To ensure focus and maintain a manageable dataset, the timespan of sample filtering was confined to publications dated during the last decade, from the start of 2014 (1 January 2014) to the end of 2024 (31 December 2024). Although the earliest paper on CDW management and quantification appeared since 1999, less than 5% of the total publications fall outside of the selected time span from the search criteria. Those papers are also mostly focused on material characteristics and their recycling potentials; therefore, their inclusion does not alter the core thematic clusters. To further enhance relevance, we conducted a systematic screening of titles and abstracts to exclude studies not directly concerned within the research topic. Following this temporal and topical filtering, nine hundred fifty-four (954) results were retrieved from the initial return from the Web of Science as of 10 January 2025, composed of different document types, including research articles, conference proceeding papers, review articles, and book chapters. The influencing factors of CDW generation, effective management, research trends, and frequently appearing keyword composition are perceived from the existing literature. To achieve this purpose, CiteSpace and SciMAT are employed to generate graphic scientometric analysis based on data extracted from WoS, encompassing the following:

(a)

Co-occurrence analysis: The analysis aids in discerning connections or disparities among various topics, facilitating the identification of their interrelationships or isolation.

(b)

Leading journals and publications: The publications provide insight into the scholarly contributions within the specific research domain, enabling a deeper understanding of the academic landscape.

(c)

Overlapping maps: The maps shed light on the dynamic nature of prominent research themes and allow for tracking of their evolution over the periods, enhancing the comprehension of theme development.

(d)

Strategic diagram analysis: The analysis aids in delineating future research directions by mapping out highly developed, mortar, emerging or declining, and basic or transversal themes.

(e)

Cluster network analysis: The analysis identifies potential opportunities and delineates research domain interactions.

4.2. Data Analysis from CiteSpace Visualisation

The CiteSpace visualisation tool efficiently extracts and comprehensively analyses key terms from imported WoS databases, which also provides a geographical mapping of essential nodes and labels. Moreover, CiteSpace facilitates the generation of diverse network mapping, encompassing citation, co-citation, co-authorship, and co-occurrence analysis. The tool offers flexibility in analysis by allowing users to select different node types from the database, including author, institution, country, keyword, references, cited author, and cited journals. In this study, CiteSpace is employed to identify key research themes through term frequency network analysis. Furthermore, CiteSpace is utilised to lead journal and publication analyses on CDW management and quantification strategies. A default value of k = 25 was set for the g-index scale factor to preserve the inherent number of nodes, maintain consistency across all analyses, and effectively reveal research topics within the research domain.

In this network, all detected keywords were first reviewed and cleaned before visualisation analysis proceeded. The list contained frequently appeared keywords, including “construction and demolition waste management”, “construction waste”, “waste management”, “circular economy”, “building information modelling”, “demolition waste”, etc. It included all the articles in the initial analysis trial, but the appearance of frequent keywords in different formats made the network too complex and challenging to read. Hence, data cleaning was performed in the project-aligned folder; for example, the same indicator, such as “life cycle assessment”, was put under the term “life cycle assessment (lca)” to eliminate recurring and reduce bias.

4.3. Data Analysis from SciMAT Visualisation

SciMAT is a novel science mapping analysis tool that can provide information for research concepts’ thematical and conceptual evolution. Complete records and cited references of all 954 articles were imported to SciMAT, and various research themes were identified using the longitudinal framework within the specified periods. In this review, periods were sliced into three years (2014–2016, 2017–2019, 2020–2022), where the last slice is two years (2023–2024). Words were selected as the type of unit analysis, whereas the author’s and source’s words were chosen as the specific component. Following that, the keywords were to be refined and filtered according to a predetermined minimum frequency threshold. The analysis employed a co-occurrence matrix, with the equivalence index as the normalisation metric. The Simple centre algorithm clustering algorithm was then utilised to designate the “core mapper” document mapper. This research domain’s quality assessment used matrices including h-index, average citations, and sum citations. Jacquard’s index was chosen to measure the longitudinal overlapping map.

5. Conclusions

This study delved into sustainable methodologies for addressing the escalating challenges of CDW generation on a global scale. Utilising a synergistic method that integrated CiteSpace and SciMAT, this review aimed to comprehensively understand the research landscape, including potential themes, strategic overview, research trends, and cluster network analysis over the last decade (2014–2024). Relevant literature data were retrieved from the Web of Science (WoS) with full records. CiteSpace created visualisation maps identifying influential research themes, leading journals, and leading authors using co-occurrence, co-citation, and bibliographic coupling. Co-occurrence analysis highlighted the growing interest in research themes such as waste generation, quantification models, performance, and circular economy. The identified network of potential themes showed that prediction, machine learning, and the deconstruction of generated waste are expanding over time. SciMAT incorporated normalisation, clustering algorithms, and a document mapper to represent strategic diagrams, cluster networks, and the thematic overlapping of CDW. The conceptual explanation of the strategic diagram identified waste generation rate as a specialised theme, carbon emissions as an emerging theme, and the waste generation phase as the potential and emerging theme. Therefore, this study considered these three (3) influential themes and their relevant clusters for further investigation.

5.1. Waste Generation Rate as a Specialised Theme

The cluster analysis of the waste generation rate interpreted methodologies for estimating and managing waste generation from construction and demolition activities, emphasising and highlighting the effectiveness of mathematical models and BIM in material estimation and waste reduction. Predictive modelling techniques, including regression and machine learning for forecasting waste generation, were explored with an assessment of different sensitivity analysis methods, and salvage modelling was also discussed for its potential to facilitate waste recovery. LCA and onsite waste monitoring and reporting systems were presented to maximise material reuse and recycling and minimise landfill disposal. This trajectory aligns with circular economy principles, guiding policymakers and stakeholders toward collaborative strategies that further reduce environmental footprints, encourage robust secondary markets for recycled materials, and sustain long-term economic viability in the sector.

5.2. Carbon Emissions as an Emerging Theme

The analysis of carbon emissions highlighted methods for reducing emissions in construction, focusing on renewable energy use, energy efficiency measures, government incentives, technological innovations, and carbon offsetting. It emphasised the importance of life cycle assessments to optimise resource reuse and reduce emissions throughout the construction lifecycle. BIM was noted for its role in design optimisation, project simulation, and material performance analysis, aiming to minimise waste and carbon emissions. Through 4D and 5D BIM applications, it also supported improved construction scheduling, cost estimation, and early-stage waste detection. Material tracking and collaborative workflows were also discussed to enhance project efficiency and sustainability. This synergy between emission control and digital construction closely aligns with circular economy principles by keeping materials in circulation longer, stimulating secondary markets for salvaged components, and minimising reliance on virgin resources. Policy mechanisms like the Polluter Pays Principle and Extended Producer Responsibility solidify these gains, incentivising innovation and collaboration among stakeholders. Collectively, these integrated approaches fortify the construction industry’s capacity to mitigate carbon impacts while enhancing overall sustainability, fostering a resilient and resource-efficient future.

5.3. Phase-Based Waste Management as an Emerging and Transversal Theme

Phase-based waste management addresses advancements in waste tracking and safety training simulations alongside a detailed economic analysis of building deconstruction. It examines construction-related emissions and presents strategies for their reduction, highlights the significance of design in temperature regulation, and advocates for sustainable materials, including waste-based geopolymers and electronic waste management. Technological interventions such as RFID tracking, automated sorting, and blockchain-based material traceability enable transparent waste management across the project lifecycle—from procurement to deconstruction. Tools like augmented and virtual reality support worker training, reduce rework, and improve delivery efficiency. During deconstruction, the use of specialised equipment and personal protective gear ensures the safe recovery of reusable materials, while economic appraisal supports the evaluation of salvaged material market viability. Additionally, the cluster reviews the impact of green building certifications such as LEED, Green Star, and BREEAM in waste reduction, underscoring their important role in promoting sustainability in construction projects. Phase stands as a cross-cutting framework in construction and demolition waste (CDW) management, integrating advanced technological interventions, economic viability, and sustainable building practices across every lifecycle stage to foster a resource-efficient and environmentally responsible future. It mirrors circular economy principles by keeping salvage materials in circulation, optimising resource efficiency, and minimising environmental impact. The approach underscores the importance of integrated frameworks that combine proactive waste management, robust LCA insights, and real-time monitoring, while highlighting the influence of policy incentives and stakeholder collaboration in advancing circularity in construction.

6. Limitations and Future Research Directions

While concentrating on CDW quantification and management, this review paper acknowledges certain limitations due to its exclusive reliance on publications from the last decade sourced solely from WoS. Incorporating studies from additional databases such as Scopus could enrich the analytical depth of this research. Furthermore, the recyclability of CDW is fundamental to reducing the environmental impact of construction and demolition activities. It holds high strategic importance within the broader CDW management research domain and aligns closely with the core principles of the circular economy. In particular, identifying the quantities and compositions of green construction waste and assessing their recyclability is an emerging area of significance and warrants further in-depth study. This will support early material separation, improve treatment planning, and maximise recovery outcomes, contributing directly to sustainable construction practices.

Including these research areas will support the development of more comprehensive future review studies. This, in turn, will help inform policy, drive innovation, and promote best practices in sustainable construction. Also, current waste quantification methods are evident in limitations in delivering precise and comprehensive measurements. The development of waste quantification and prediction strategies, including adopting artificial intelligence such as machine learning, is a positive sign and can be suggested for further development.