Construction and demolition waste (CDW), the abandoned materials generated during construction, renovation, and demolition [1
], is one of the heaviest and most voluminous waste streams produced globally [2
]. CDW accounts for approximately 35% of all global waste [3
], as well as 70%, 50%, 44%, 36%, and 30% of the total waste in Spain, United Kingdom, Australia, Japan and Italy, respectively [4
]. Over the past few decades, inappropriate treatment and disposal of CDW have given rise to increasing environmental pollution, natural resource consumption, and land price, which places massive pressure and has negative impacts on human living environments [5
]. In this context, the management areas of construction and demolition waste (MA-CDW) are gradually being given increasing global attention.
Numerous scholars have conducted research on the MA-CDW, using various research paradigms and methods, from different perspectives and disciplines. Yuan and colleagues focused on investigating the practices, challenges and strategies of CDW in China, especially in Hong Kong [6
]. Tam and colleagues concentrated on comparing waste management performance in different countries as well as evaluating the environmental and economic benefit of waste measures and policies [9
]. In addition to assessing the environmental sustainability of recycled aggregates [13
], Poon and colleagues also analyzed the waste reduction potential of prefabrication [15
]. However, few studies have attempted to summarize and review the existing research, especially in the MA-CDW.
While scholars such as Gálvez-Martos and Menegaki have reviewed the main research and best practice of CDW management [17
], the former only examined CDW management in Europe, and the latter primarily focused on the factors, barriers and motivations affecting CDW management. In addition, Lu and Yuan developed a framework and identified the three common topics in the MA-CDW (i.e., waste generation, reduction, and recycling), in order to help readers understand CDW management research from 1996 to 2010 [19
]. It should be noticed that the building of new infrastructure and housing construction in many countries has been accelerating since 2010. With the emergence of increasing CDW management challenges, a large number of new studies have been conducted and published in the past eight years. It is imperative to systematically examine the state-of-the-art advancements and emergent trends, in order to encourage future studies and innovative practices. Therefore, the main objectives of this study are: (1) to summarize MA–CDW studies from 2006 to 2018; (2) to understand the holistic research status and evolutionary trend from the perspective of published journal articles, document co-citation, keyword co-occurrence, cluster analysis and burst detection; and (3) to develop a comprehensive framework for MA-CDW, including major knowledge domains, gaps, and future directions. To achieve these research goals, we employ the scientometric analysis method, which is used to map the visualization review of a specific knowledge area. This paper provides valuable guidance and in-depth understanding for researchers, practitioner and policy makers to promote CDW sustainability. Furthermore, this study contributes to the existing body of knowledge of MA-CDW by presenting a new, integrated, and holistic knowledge framework.
4. Comprehensive Framework for the MA-CDW
Systematic scientometric analysis provides the basic components to form an integrated framework for the MA-CDW. The comprehensive framework consists of three major parts: knowledge domains, knowledge evolution and potential future research directions (Figure 7
). Based on the burst detection and a timezone view of keywords, the knowledge evolution in the MA-CDW has been illustrated in Section 3.5
. In this section, we emphasize summarizing the main knowledge domains in the MA-CDW according to the results of keywords co-occurrence and cluster analysis. In addition, knowledge gaps are identified and some possible ideas that need further investigation are identified and discussed.
4.1. Knowledge Evolution
Based on the results of keyword detection and timezone view in Section 3.3
, the evolution of MA-CDW can be detected. According to keyword detection, the major research topics from 2006 to 2009 were some basic concepts, such as “recycling”, “construction”, and “waste management”. These concepts could be regarded as the initial stage of MA-CDW development. Since 2010, themes changed to “concrete” and “aggregate”, implying that the research focus diverted from external challenges to internal component analysis (i.e., material). Publications from 2012 to 2013 focused on waste “reduction”, especially through “design” and “prefabrication” measures (Figure 6
). From 2014 to 2015, “strategy” became the new concerns to prevent illegal dumping and enhance waste recycling. New technologies and methods such as “BIM” and “big data” became hot topics from 2016 to 2018. In a word, the evolutionary trend of MA-CDW from 2006 to 2018 can be summarized as transitioning from basic management concepts to internal and external challenges analysis to organizational strategy and innovative management practices.
4.2. Knowledge Domains
Based on the results of keywords co-occurrence and cluster analysis, knowledge domains in the MA-CDW are identified and further summarized into four pillars, namely, factor and challenge, composition and quantification, assessment and comparison, and technology and method. Detailed discussion and analysis are as follows.
4.2.1. Factor & Challenge
Cluster #1 (deconstruction), Cluster #4 (disposal), and correlative high-frequency keywords constitute the first pillar (Factor and Challenge), which involves the factors and challenges that impede the management of CDW among various participants. Understanding the benefits, challenges, and processes of deconstruction is critical for successful implementation. Deconstruction and selective demolition are considered to be effective solutions for reducing demolition waste at source [68
] and improving the waste recovery rate [70
]; however, high labor cost, high technology demand, immature salvaged materials market, and other factors restrict its spread [71
]. Construction waste disposal methods can be summarized into four types including on-site reusing, recycling, landfill, and illegal dumping [11
], with landfill being the primary disposal option [73
In addition, considerable research efforts have been devoted to the critical success factors and challenges at the industry level [42
], project level [43
], and individual level [65
]. At the industry level, according to Jiménez-Rivero [74
], more than half of the key factors are concerned with policy, especially regulatory instruments, which must be accompanied by economic incentives and other control strategies to achieve satisfactory results. It is worth noting that the research on an individual level, mainly based on the TPB (theory of planned behavior) framework explores the influence factors shaping waste disposal behavior. This theory pays considerable attention to the relationship between attitude and behavior in waste management [79
]. Bakshan [65
] found that the influence of personal factors such as attitude on behavior in CDW is more significant than that of other corporate factors such as training.
4.2.2. Composition & Quantification
The second pillar Composition and Quantification includes Cluster #2 (waste glass), Cluster #7 (minimization), and correlative high-frequency keywords, which concern estimation and quantification of waste composition, waste generation rate and building stock. Acquiring accurate waste composition and generation data is a critical step for carrying out an effective waste management scheme and enhancing waste minimization. Waste quantification methodology can be divided into six types: site visit method, waste generation rate method, lifetime analysis method, classification accumulation method, variables modeling method, and other particular methods [40
]. Each methodology has its application scope and conditions. It is difficult to say which method is most effective, because the different quantitative targets and different data that are collected determine the need for different methods under different conditions. It is noteworthy that the materials flow analysis method is commonly applied by researchers when estimating waste generation.
Currently, information technologies are commonly applied, and contribute to CDW volume quantification. Banias et al. [81
] developed a web-based CDW quantification system and estimated 21 different waste streams for 4 types of buildings. Based on the research of Banias, Li and Zhang [82
] further developed the web-based system to estimate construction waste, improving system accessibility, the interface, the connection, and information sharing. The accuracy of waste forecasting is highly dependent on the available data. BIM with all the information from the design stage to the demolition stage of the building has potential advantages for predicting the amount of CDW at the project level [83
]. Through extracting material and volume information from BIM, it is possible to automatically estimate the waste generation not only from the construction stage but also from the demolition stage in the early design stage [84
]. In addition, to accurately forecast the waste production based on building stock at the regional level, the geographic information system (GIS) presents as an innovative approach to assessing the amount of demolition waste [85
] and monitoring the demolition activities [86
] in space and time.
4.2.3. Assessment & Comparison
Cluster #0 (feasibility), Cluster #3 (life cycle assessment) and correlative high-frequency keywords constitute the third pillar (assessment and comparison), which can be divided into two dimensions, economic feasibility analysis, and environmental impact assessment in different waste disposal scenarios (e.g., on-site reusing, recycling, landfill). Within the dimension of economic feasibility analysis, multiple scholars have examined the economic viability of CDW recycling plants [87
], as well as economic viability of recycling programs [92
]. According to studies about recycling plants, the results vary from high economically feasible [87
], to feasible in certain circumstances such as charging gate fees [88
], or extra revenue from location advantage [89
], or installation of second-hand equipment [91
] to not feasible [90
]. Moreover, emerging studies are conducted from the perspective of a regional waste recycling network. For example, Fu proposed a reverse logistics network model based on the trade-off between cost and recycling rate, considering the location of facilities and best transport route [94
]. Hiete presented a model that integrates CDW supply and recycling demand for minimum costs, concluding that disposal taxes are a cost-effective lever to enhance recycling [95
]. In summary, economic viability is closely related to specific region and is influenced by physical, economic, and social factors [87
In terms of environmental impact assessment, life cycle assessment (LCA) is a commonly used decision support tool in evaluating environmental impacts associated with the life cycle of products (goods or services) [96
]. In the LCA approach, energy consumption and CO2
emission are the two most evaluated impact categories [98
]. Application of the LCA method can be classified into six aspects: environmental impacts of a building [44
], environmental impacts of construction waste in the construction phase [101
], environmental impacts of demolition waste in the end-of-life phase [103
], environmental impacts of demolition waste in the refurbishment phase [105
], environmental impacts of recycled aggregates [106
], and environmental impacts under different CDW management strategies [14
]. Results show that, compared with landfill, most of waste recycling and reuse methods bring net environmental benefits [14
]. On-site recycling environmental benefits are higher than off-site recycling [14
], and the environmental benefits of off-site recycling are affected by transport distance [113
4.2.4. Technology & Method
The fourth pillar, technology and method, includes Cluster #5 (big data), Cluster #6 (BIM), and correlative high-frequency keywords, and centers on the adoption of information technology (i.e., big data, BIM, GIS, and RFID) and methodology (i.e., prefabrication) in transforming traditional CDW management. The existing CDW management tools, such as waste management plan templates and guides, waste data collection and audit tools, waste quantification tools, and environmental impact assessment tools have the following problems, including insufficient data quality for waste management, inability to integrate with the design process and lack of interoperability with other software [61
]. In view of the potential deviation caused by insufficient quality data, big data provides the possibility of achieving a more comprehensive scene [114
]. For example, Lu compared the construction waste management performance between public and private sectors through big data [115
]. Furthermore, Chen recognized factors influencing demolition waste generation by connecting several databases [116
GIS has advantages in data acquisition, storage, correlation, processing, and analysis. In addition to estimating generation of demolition waste, GIS can build a bottom-up material stock model which integrates with the LCA to assess the environmental impact at the urban scale [103
] and integrates with GPS technology to provide real-time location of the material and its arrival time to the construction site [117
]. More recently, Seror and Portnov employed GIS to identify areas at potential risk of illegal CDW dumping [118
]. RFID tags are another data collection technology that can be employed to track CDW movement. Zhang proposed a framework that combined Rule-based Reasoning technology and RFID technology to track, schedule, and intelligently handle incidents of waste movement [119
]. BIM is one of the space technology and data communication technologies commonly used in the architecture, engineering, and construction (AEC) industries, and can be effectively integrated with identification and data acquisition technologies (i.e., GIS, RFID, GPS). Integrating these technologies into BIM facilitates location-based management, tracking of building materials, and remote data collection.
4.3. Knowledge Gaps and Future Research Directions
According to the review of CDW management research from 2006 to 2018, this paper aims to determine gaps and some potential ideas that need further investigation. The gaps and future research directions are listed as follows.
The first knowledge gap is to seek stakeholder participation and collaborative governance in the life cycle of construction project. Collaborative governance is regarded as the best management model for various practices in public administration, which means that the public sector, the private sector, and other stakeholders participate in collective forums for consensus-oriented decision making [120
]. The governance of CDW recycling is extremely complex, as it involves a large number of stakeholders from different sectors throughout the construction process. While existing research has largely investigated the recycling of CDW from a specific participant’s perspective, more research is needed to explore how to promote multi-sectoral participation and collaborative governance.
The second knowledge gap is to explore the relationships between individual recycling behavior and organization (i.e., construction company, design company). Since individual behavior is influenced by the interaction between the organization and the environment, studying the psychology and behavioral rules of individuals in a particular organization may improve the ability of managers to predict and guide the behavior of workers, thus achieving the goals of the organization more effectively. While some studies have focused on surveying behavior and attitudes that contribute to waste reduction, most studies operate from an individual behavioral perspective [67
]. As the body of construction waste generation and recycling, how the organization interferes with the individual’s recycling behavior, and how the individual is affected (e.g., through performance assessing) are significant issues that require further exploration.
The third knowledge gap is the inadequate attention to social sustainability in assessing CDW treatment strategies. It is widely acknowledged that sustainability analysis should include the assessment of the environmental, social, and economic impacts. However, existing studies have only focused on the economic and environmental aspects of recycling CDW, while overlooking the social sustainability. Therefore, future studies are suggested to develop a social sustainability assessment method, including framework, categories, indicators, system boundaries, and impact assessment index. In addition, more research is desired to consider the economic, environmental, and social indicators simultaneously to draw more comprehensive conclusions.
The fourth knowledge gap is the integration of new technologies and methods. With the rapid development of information technology, more and more new technologies and methods are applied to construction projects, such as BIM, RFID, GIS, GPS, and big data. For instance, Kim proposed a BIM-based method that calculates demolition waste in the design phase [84
]. Akinade developed a BIM-based model to determine the deconstruct ability in the design stage [121
]. As such, future studies can extend the application of BIM from the design stage to construction, maintenance, and end-of-life stage, and simultaneously extend the BIM functions from waste generation estimation to waste management cooperation among stakeholders and waste analysis throughout a building’s life cycle. Moreover, integrating other data collection and data processing technologies (i.e., big data, GIS and RFID) into BIM is also necessary.
In addition to the gaps described above, close-loop material recycling of CDW need to be further studied in the context of a circular economy. In other words, the materials and components at the end of their life should be reused or recycled as resource in the future life cycles other than disposed as waste to landfill [122
]. With respect to regional construction and demolition waste management, the reverse logistics network design with uncertainties from multiple objectives (i.e., economic, environmental, and social benefits) or parameters (i.e., supply, demand, cost, distance, waste quality, and recycling rate) will also be a significant direction.
This study systematically reviewed MA-CDW publications from 2006 to 2018 by using the scientometric analysis method. A total of 261 papers were selected for co-citation analysis, keyword co-occurrence, cluster analysis, and burst detection, in order to provide a holistic knowledge summary of the MA-CDW.
Resources Conservation and Recycling, Waste Management, Journal of Cleaner Production, and Waste Management & Research were identified as the four major journals associated with research on the MA-CDW. Yuan and Shen (2011), Solís-Guzmán et al. (2009), Kofoworola et al. (2009), Lu and Yuan (2010), and Llatas (2011) were recognized as the five most critical articles. By measuring the high-frequent co-occurrence keywords, the major research topics in this area include “waste recycling”, “estimation of waste generation”, “waste management system”, “life cycle assessment”, “waste reduction”, “waste reuse”, and “impediment for waste management”.
Based on scientometric analysis, this paper has further proposed a comprehensive framework for the MA-CDW, including knowledge evolution, knowledge domains, knowledge gaps, and potential research directions. The overall trends of MA-CDW from 2006 to 2018 were summarized as from basic management concepts to internal and external challenges analysis, organizational strategy, and innovative management practices. The main knowledge domains of MA-CDW were identified and further classified into four pillars, namely: (1) factor and challenge; (2) composition and quantification; (3) assessment and comparison; and (4) technology and method.
Based on the analyses of knowledge evolution and domains, knowledge gaps and future research directions were ultimately discussed as well. (1) Considering that most of the existing research investigated the recycling of CDW from a single participant’s perspective, more research is needed to explore multi-sectoral participation and collaborative governance. (2) The second knowledge gap is to examine the relationships between individual recycling behavior and organization (i.e., construction company, design company). (3) Current CDW life cycle assessment research mainly focuses on economic and environmental impacts, while neglecting the social impact. Future studies are recommended to develop a social sustainability assessment method for the CDW, which include framework, categories, indicators, system boundaries, and impact assessment. (4) The fourth knowledge gap is the integration of new technologies and methods. In the future, more work is needed to extend the application of BIM from the design stage to construction, maintenance, and end-of-life stage. Furthermore, it is essential to extend the BIM functions from waste generation estimation to waste management cooperation among stakeholders and waste analysis. In addition, further research is needed to explore how to integrate other data collection and data processing technologies into CDW management process. (5) With the widespread of the concept of circular economy, more research is desired to devise effective CDW management frameworks and strategies. (6) Reverse logistics network design with uncertainties from multiple objectives or parameters is also a significant direction.
This study contributes to the existing MA-CDW body of knowledge by constructing a comprehensive knowledge framework and providing current status, evolutionary trend, and future directions. These findings can help researchers and practitioners quickly understand MA-CDW research. In particular, knowledge domains and evolutionary trend can offer clear and in-deep cognition of MA-CDW research. The knowledge gaps point out some specific and urgent issues, as well as the research directions.