Cross-boundary collaboration in waste management research: a network analysis
Jinying Xu*, Weisheng Lu, Fan Xue, Ke Chen, Meng Ye, Jing Wang, and Xi Chen

Department of Real Estate and Construction, Faculty of Architecture, The University of Hong
Kong, Hong Kong
* Corresponding author, email: jinyingxu@connect.hku.hk; Department Real Estate and
Construction, Faculty of Architecture, 7/F, Knowles Building, The University of Hong Kong,
Pokfulam, Hong Kong.

This is the peer-reviewed post-print version of the paper:
Xu, J., Lu, W., Xue, F., Chen, K., Ye, M., Wang, J., & Chen, X. (2018). Cross-boundary
collaboration in waste management research: A network analysis. Environmental
Impact Assessment Review, 73, 128-141. Doi: 10.1016/j.eiar.2018.08.005
The final version of this paper is available at: https://doi.org/10.1016/j.eiar.2018.08.005. The
use of this file must follow the Creative Commons Attribution Non-Commercial No
Derivatives License, as required by Elsevier’s policy.
Abstract
This paper aims to illustrate the cross-boundary research collaboration (CBRC) landscape of
waste management (WM) by various collaboration networks. Through a set of rigorous
procedures, a total of 15,396 research papers were extracted from eight subject-related journals
published between 1981 and 2016. The author utilized CiteSpace, a Java programme that helps
visualize and dissect patterns in scientific literature, to evaluate the content through individual,
institutional, national, and disciplinary perspectives. The evaluations of three former
perspectives revealed a steady rise in CBRC within WM over the last thirty-five years, although
the overall intensities proved fairly low. Inter-individual collaboration groups were limited to
their respective regions and only loosely connected, but as more and more academic institutions
and universities engaged in WM research, the number and quality of the collaborations
increased. Developed countries, chiefly in North America and Western Europe, comprised the
1

bulk of the WM research, whilst the mounting contributions from developing countries, China
in particular, forecasts greater diversity in the future. Analysis also suggested that the intensity
of the interdisciplinary collaboration network declined slightly, however, the intensity proved
low to begin with. Previous WM research focused more on “hard” technologies than “soft”
measures. Future endeavors to encourage CBRC in WM should promote more innovative
research to tackle waste challenges globally in a sustainable way.

Keywords: waste management, cross boundary research collaboration, network analysis,
collaboration network, network structure

1. Introduction
Managing waste is a major global sustainability challenge that demands combined efforts from
a myriad of public and private stakeholders. Increasingly multifaceted, no single discipline, let
alone single researcher, can possess the necessary knowledge to maximize waste management
(WM) efficiency. Given the drift towards internationalization and globalization of knowledge
creation, a growing number of scholars and research institutions seek to conduct their frontier
research outside their immediate surroundings. By sharing workloads, specific expertise and
skills, equipment or resources (Altbach and Knight, 2007), research collaboration helps resolve
personal research limitations. Research collaboration is defined as researchers working
together to produce new scientific knowledge, insights, methodologies, solutions and/or
inventions (Katz and Martin, 1997). Research and researchers collaboration can operate in a
decentralized manner, supported by user-friendly and expedient online platforms, ranging from
email exchange to online manuscript submission systems, e.g., ScholarOne® or
EditorialManager®. Cross-boundary research collaboration (CBRC) can provide a platform
for researchers to communicate research strategies and innovations across the traditional

2

institution, nation, and disciplinary boundaries. To incorporate the multidisciplinary and
multinational nature of WM, research policymakers seem to encourage multi-institutional
collaborations in order to develop complex, intellectually diverse projects (Carley, 2006), e.g.,
the European Waste Management Cluster and the Urban Strategies for Waste Management in
Tourist Cities. Smaller scale, but arguably more active CBRCs materialize more organically,
forming through online communication and idea sharing. This is not to say that such CBRCs
lack big scale funding as many receive backing from major international bodies like EU
agencies, which characteristically insist on cross-boundary collaborations.

Previous studies have tended to understand CBRC by investigating the connections and
structures of social networks formed in knowledge innovation (Tortoriello and Krackhardt,
2010) and information sharing (Pardo et al., 2010). Network structure, in a broad sense,
concerns the pattern of relationships generated by direct and indirect connections between
actors (Hoang and Antoncic, 2003). In CBRC, one of the most significant networks is a coauthorship network. The “explicit product” of a scientific collaboration between two or more
authors (Newman, 2004), co-authorship represent a kind of tangible proof that collaboration
has occurred. Whenever a scholar publishes a co-authored article, he or she has created an
individual co-authorship network (Li et al., 2013). In the meantime, with their institutional,
national and discipline information, inter-institutional, inter-national and inter-disciplinary
networks have also been created. Modeling CBRC networks provides valuable insight into the
patterns of collaborations amongst individuals, institutions, nations and disciplines, the
emergence and the propagation of thoughts in academic society (Cross et al., 2002). WM
research, where different expertise is required, saw intensive CBRC. However, a definitive
analysis of CBRC networks in WM domain has long been overdue. This article seeks to shed
light on the cross-boundary collaborative relationships in WM research on four cross-boundary

3

perspectives using a network analysis. It does so by investigating 15,396 relevant research
papers extracted from eight highly relevant journals published over the past thirty-five years.
This paper is organized as follows, section 2 reviews the prevailing literature of WM research,
CBRC in other relevant areas, and analytical tools; section 3 presents the research methods of
data collection and analysis; section 4 reports the detailed analysis and results from the four
cross-boundary perspectives, namely, individuals, institutions, nations, and disciplines,
together with longitudinal analyses of these four aspects respectively; the last two sections
discuss in depth the problems and solutions facing global and interdisciplinary collaborations
in the WM field and conclude the paper.

2. Literature review
2.1 Why WM demands joint efforts
WM includes all the activities and actions required to manage waste from inception to final
disposal (Division, 1997), such as waste collection, transport, treatment by thermal or
biological processes, disposal, monitoring, and regulation to name a few. WM exemplifies a
global sustainability dilemma that calls for the efforts of governments, private sector, research
institutions, scientists and the general public (Vithanage et al., 2014). In the age of economic
globalization, traditionally local WM activities, e.g., waste collection, can impact another
continent’s environment, e.g., the US-China recycling trade. Meanwhile, the economic
development in emerging countries has triggered an exponential increase of waste generation.
For example, China’s municipal solid waste amounted to 148 million tons in 2006, of which
91.4% became landfill, 6.4% incineration, and 2.2% compost (Zhang et al., 2010). Similarly,
India suffers from the massive pileup of e-waste stemming from its high speed economic and
technological growth (Sinha-Khetriwal et al., 2005), along with that of more typical urban
waste. WM concerns escalate as cities and countries develop, but global joint research in

4

parallel with urbanization can enable knowledge sharing, informed response and innovation
exchange in order to amend WM performance in developed and less developed countries.

Research is the action of creating and sharing new knowledge to guide practices (Appleton,
1993). Numerous researchers have entered the field of WM, exploring both hard and soft
approaches. “Hard” approaches denote scientific and technological means of reducing, reusing,
and recycling abandoned resources. For example, researchers have spent countless time and
effort exploring the reuse of solid waste in order to replace natural resources, e.g., reusing waste
iron as a partial substitute for sand in concrete (Ismail and AL-Hashmi, 2008a), plastic waste
as an aggregate replacement to mix concrete (Ismail and Al-Hashmi, 2008b), and converting
fly ash into construction materials, fertilizer and other geotechnical applications (Ferreira et al.,
2003). They have also endeavored to find treatments to remove pollutants or collect biogas
from wastes (Kamala and Rao, 2012). While soft approaches represent economic or managerial
measures, for instance estimating overall waste generation (Lu et al., 2017), designing from
waste (Osmani et al., 2008), public policies (Goorhuis et al., 2012), economic analysis (Lu et
al., 2015), and management strategies (Shen et al., 2004). CBRC plays a crucial role in devising
hard and soft approaches, tackling the global issue, and developing opportunities for mutual
WM learning and idea sharing (Berkes, 2009). However, how and to what extent the global
body of researchers of this field conduct CBRC is still under-researched.

Research collaboration can take various forms. Examples range from online sharing of data
and sources, correspondence by mail, presentations at workshops and conferences, visits to
foreign laboratories, to the exchange of papers. The most obvious and easily measured form of
collaboration is the writing and publication of research findings (Laudel, 2002). One can study
the collaboration of an article publication from different aspects to understand the CBRC

5

patterns between individual researchers, institutions, nations and disciplines. Such activities
automatically generate networks comprised of nodes, e.g., authors, and relationships, e.g., coauthorship (Borgatti et al., 2013). A practical way to demonstrate and visualize the CBRC is to
draw the networks of article collaboration and analyze their outcome variables and structures,
then attempt to reveal some characteristics from the network analyses.

2.2 Cross-boundary research collaboration (CBRC) networks
A boundary signifies the demarcation of activities that marks the limits of an area. This area
can take concrete and non-concrete forms, such as geographical, social, cognitive, relational,
cultural, temporal, spatial, divisional, occupational, organizational, disciplinary, knowledge,
and tasks (Hsiao et al., 2012, Cummings and Kiesler, 2005). A CBRC network constitutes a
reflection of research collaboration, in which valuable resources are shared as information
understanding and knowledge through a social interaction (Newman, 2004). The importance
of networking in research has long been emphasized. By sharing resources and working goals,
collaboration in networks advances knowledge creation, transfer, distribution, and
redistribution (Li et al., 2013). Networks that span multiple communities of practice can convey
complex ideas to diverse audiences (Reagans and McEvily, 2003), so as to exchange and
transfer knowledge from developed countries or regions to the less developed. CBRC networks
are usually examined from individual, institutional, national and disciplinary levels in order to
investigate collaborative patterns and relationships amongst researchers.

The primary concerns in CBRC usually involve the study of co-authorship networks and
citation networks. A co-authorship network is a documented collaboration between two or
more authors working towards a common research aim, such as the publication of a paper or
securing of a patent (Melin and Persson, 1996). A typical co-authorship network can be drawn

6

as nodes connected by lines. Nodes indicate the authors, while lines the relationships among
the authors (Xie et al., 2016). The co-authorship network has been proffered as a critical
indicator of CBRC, with the authors’ affiliation information, inter-institutional and international research collaboration also capable of being investigated. It not only implies the
research trends and popular topics within a domain, but more importantly, the patterns of
research collaboration and the subsequent thoughts and their development, which in turn
support and call for substantial future collaboration (Leydesdorff et al., 2013).

Analysis of interdisciplinary collaboration logically follows analysis of co-authorship networks.
In recent years, as CBRC in academia increased, a wealth of research examining collaboration
between various disciplines did as well. By searching and tallying paper keywords, one could
identify research collaboration between different WM related disciplines (Xie et al., 2016). Li
et al. (2013) suggested this as a promising direction to focus cross-boundary collaboration
features of interdisciplinary research, and different social capitals generated by intra- and interdisciplinary co-authorship networks. However, former inter-disciplinary collaboration research
was done through the survey of researchers in an institution (Van Rijnsoever and Hessels,
2011), or the investigation of the citation index of a database (Leydesdorff, 2007), or at journal
level (Waltman and Eck, 2012). More in-depth understanding of interdisciplinary collaboration
from a micro level (e.g., publications) is highly desired.

Analyzing collaboration networks offers rich information that probes CBRC in a specific
research field. Network analysis concerns the structure and pattern of relationships over time,
as well as the exploration of network relationships within a social system (Parsons, 1951). A
research community or knowledge domain describes large and complex social networks of
individual researchers interacting, developing, and exchanging new knowledge through a

7

collaborative research process. Network analysis provides a new linguistic and mathematical
terminology to understand the development of a specific knowledge domain and the involved
collaborations (Wasserman and Faust, 1994). The past decade has witnessed network analysis
increasingly employed in specific research communities or knowledge domains (Girvan and
Newman, 2002) to portray research collaboration among individuals, institutions, countries,
and disciplines (Newman and Girvan, 2004). To investigate the collaboration development
trajectory in WM research, which requires multifaceted expertise, co-authorship networks can
better describe the collaborations cross individual, institutional, national and disciplinary
boundaries.

3. Methodology
To collect comprehensive, representative and authoritative data for CBRC analysis in WM, a
pilot study was conducted to identify which journals most concern the WM domain. Using
Thomson Reuters’ Web of Science (WoS) Core Collection Database, judged one of the most
comprehensive and reliable sources (Hoekman et al., 2010), and the Elsevier’s Scopus
Database, the pilot gathered a list of papers with titles containing keywords pertaining to waste
management. This helped reveal a fairly accurate and comprehensive picture of the scholarly
WM work available (Meho and Yang, 2007). The deliberately chosen timespan, 1981-2016,
followed the introduction and universalization of the Internet (Leiner et al., 1997), the main
enabler of cross-boundary research collaboration (Wagner and Leydesdorff, 2005). The
selected document types were then narrowed down to research journal articles, the sentiment
being that such work demands more profound collaboration than conference or proceeding
papers, letters, and notes (Zheng et al., 2016). Books were also omitted as search results
identified editors rather than the chapter authors responsible for the research. As a result, the
search found 3,830 and 6,133 research articles and reviews from WoS and Scopus respectively.

8

By comparing the top fifty cited journals in both databases, eight journals (i.e., Environmental
Science &Technology, Journal of Cleaner Production, Journal of Environmental Management,
Journal of Hazardous Materials, Resources, Conservation and Recycling, Waste Management,
Waste Management & Research, and Water Science and Technology) were identified as the
most relevant and representative of the WM field.

Afterwards, an advanced search was conducted in WoS focusing on the eight journals. The
following retrieval codes were used in the WoS Core Collection Database: TS = waste AND
(manag* or reduc* or mitag* or hazadou* or emmis* or develop* or equip* or recyc* or solid
or landfill* or special) and SO = “Name of the identified journal”. TS stands for paper topic;
“waste AND (manag* or reduc* or mitag* or hazadou* or emmis* or develop* or equip* or
recyc* or solid or landfill* or special)” indicates the paper concerns both waste and one of the
keywords, while disregarding word order. These keywords selected based on the authors’
research experience, are mostly used and related to waste management. The * in the condition
means a fuzzy search (e.g., manag* hunts for management, managing, or any terms starting
with manag). SO is publication name. Again, search results had to be published between 1981
and 2016 and primarily in English. Table 1 shows the search results.

9

Table 1. Top eight journals most relevant to WM and their corresponding quantity of papers.

Journal title

Journal Of Environmental
Management
Resources, Conservation And
Recycling
Journal Of Cleaner
Production
Waste Management &
Research
Environmental Science
&Technology
Journal Of Hazardous
Materials
Water Science & Technology
Waste Management

Year of
first
Volume

2016
Impact
Factor

Quantity
of Papers
Collected

hindex

Average
citations
per item

Sum
of
Times
Cited

Without
self
citations

1973

4.01

910

54

17.31

15748

15392

1988

3.313

1200

67

21.59

25910

23974

1993

5.715

1364

58

15.8

21549

19475

1983

1.803

1537

49

10.85

16680

15110

1967

6.198

1839

127

44.77

82327

80355

1976

6.065

2555

111

32.74

83651

81096

1970
1983

1.197
4.03

2918
3073

72
83

14.1
21.2

41152
65140

39720
58201

Data source: “Year of first Volume” and “2016 Impact Factor” are collected from the official website of the
journals; “h-index”, “Average citations per item”, “Sum of Times Cited” and “Without self-citations” were
collected from Web of Science when searching for the papers about WM on Nov, 13 2017, this data is based on
the searched papers.

In total, the journals sourced 15,396 relevant articles, from which this paper’s analysis of
CBRC in WM is based. Figure 1 shows the numbers of research articles published each year.
The data showed the number of waste management papers in the 1980s to be relatively small,
while the 1990s experienced a considerable rise, publishing 36 papers in 1990 to 239 in 1999,
if somewhat of a drag in sustained growth. Not until 2004 did a takeoff in WM research occur,
ultimately plateauing in 2009. Although the following three years witnessed slight decreases,
WM researchers appeared to regain their enthusiasm and recent years show a spike in
collaboration.

10

1600
1400

Count

1200
1000
800
600
400
200
0
Year

Figure 1. The quantity of papers published from 1981 to 2016.

All details of the 15,396 papers were carefully extracted and examined with CiteSpace, a
professional scientific literature software widely used for visualizing patterns, emerging trends
and dynamics in scientific knowledge domains and their corresponding intellective base (Chen
et al., 2010, Zhao, 2017, Chen, 2006). Citespace, having been broadly used to analyze networks
of different aspects in scientometrics (Zhao et al., 2018), is a good choice for CBRC network
analyses. Specifically, CiteSpace 5.1. R1 SE was used to analyze the CBRC in WM from four
perspectives: (a) inter-individual research collaborations; (b) inter-institutional research
collaborations; (c) inter-national research collaborations; and (d) inter-disciplinary research
collaborations.

4. Analysis, results, and findings
4.1 Inter-individual research collaboration network
Co-authorship of paper constitutes an inter-individual collaboration between two or more
authors. Such collaborations form an ‘inter-individual collaboration network’. To understand
the collaboration between individual scholars in the WM domain, an author collaboration
network was drawn in CiteSpace using data of the authors with top fifty levels of publication
11

number from every year of 1981 to 2016. Top fifty levels of most occurred items each year
will cover almost all the authors since each level may include multiple qualified nodes and an
author is incapable of publishing fifty articles in one year. As shown in Figure 2, the nodes
represent the specific authors; the lines linking the nodes, paper co-authorship; and the different
blocks of authors, different collaboration groups. Node colors change from blue to green, then
yellow, orange and finally red as time passes from 1981 to 2016. The bigger the node, the more
productive the author is. The bigger the node label, the more papers the author has published,
like Christensen TH with ninety-three papers and Barlaz MA with thirty-six. The thicker the
line, the more often the authors at either end collaborated with one another. For example,
Christensen TH collaborated a lot with other researchers, particularly Astrup TF early on. The
inter-individual collaboration network consists of 3,427 nodes and 3,074 links, clearly
demonstrating the co-authorship in WM research.

12

Figure 2. Inter-individual research collaboration network from 1981 to 2016.

Note: Some minor clusters and nodes of the network were too small to be highlighted in the figure, only authors
with ten or more articles are labeled in the figure.

The density, the actual number of ties in a network divided by the maximum possible number
of ties, of a network describes the intensity of the interconnection between nodes of the network.
The inter-individual collaboration network, as shown in Figure 2, scored only 0.0005 in
network density as calculated by CiteSpace. This suggests a more modest overall international
collaboration between WM researchers. The modularity, comparing actual with expected tie
values, measures the strength of network divisions into modules, also called groups, clusters
13

or communities. In this case, the modularity, 0.9014, indicates dense connections within
research groups, but sparse connections between groups. Network homogeneity, indicating the
tendencies toward homophile of different categories, measured by the mean silhouette (an
indicator of consistency within clusters of data) of 0.5215, is relatively low, implying the
network is somewhat diverse. Centrality, specifically, betweenness centrality in CiteSpace, is
a very crucial parameter to identify the most important node within a network. It uses the
number of times a node lies on the shortest path between other nodes to measure which nodes
influence the flow around a system. A high centrality could indicate someone holds authority
over collaboration controls between clusters in a network. In the inter-individual research
collaboration network, each author’s the centrality is very low, denoting the lack of any obvious
important central researchers within the network. Generally, the international collaboration in
WM research is relatively decentralized. Many of the collaboration groups in the field appear
to be relatively small and disconnected from each other. There are many small groups,
exploring a certain niche of the field, without any connections with others. This suggests many
scholars are exploring different topics individually.

Regarding collaboration, several research subnets thrive within the network according to
cluster analysis in Figure 2. The largest subnet was comprised of 136 scholars, mainly focused
on waste management problems in China. More than 90% of the 136 authors in the largest
subnet hail from China. Of the remaining 10%, notably Steyer JP, Abanades S and Flamant G,
most are from France. Based on the color of the nodes and lines, this subnet is relatively new.
The second largest subnet, consisting of ninety-three researchers overwhelmingly all from
Europe, such as Christensen TH, Scheutz C, Astrup TF from the Technical University of
Denmark, Rechberger H and Fellner J from Vienna University of Technology and Grosso M
from Politecnico di Torino, mainly worked on greenhouse gas and landfill related topics. Since

14

some of the nodes and lines are green, this subnet proved an older collaboration network. The
third largest subnet, concerned with regional integrated solid WM, includes Zhang H, Wang H,
Yan JH and Li YP, who are also Chinese academics. This third subnet formed at the time
between the first and the second ones.

Figure 3 shows the results of a further investigation into the cited-authors. The links of these
networks are denser and the lines thicker. It is worth noting that many of the authors being
cited are not individuals, but institutions like the American Public Health Association (APHA),
European Commission, United States Environmental Protection Agency (US EPA), European
Commission, Intergovernmental Panel on Climate Change (IPCC), Organisation for Economic
Co-operation and Development (OECD), United Nations Environment Programme (UNEP)
and International Organization for Standardization (IOS). This finding indicates that in WM
governmental and non-governmental organizations have considerable influence through
published report and the policy design. Some scholars, prominnet in the inter-individual
research collaboration network, also pervade the cited author network, for example, Finnveden
G, Christensen TH, Chang NB, Barlaz MA and Poon CS. This phenomenon of certain authors
being both frequently published and frequently cited implies that long-term research ventures
in the WM domain is crucial for developing influence. Given the clustered color distribution
of Figure 3, the change in cited authors as time passes appears to be organized. At the bottom
right part are the latest most cited authors.

15

Figure 3. A network of cited-authors

In order to further examine the longitudinal change of inter-individual research collaboration
networks, the years between 1992 and 2016, when the vast majority of network generation
occurred, were divided into five year intervals and gaged by their number of network nodes,
links, and densities. Figure 4 shows the results. The x-axis represents the timeline inspected
every five years, while the meanings of nodes, links, and densities remain the same as defined
above (i.e., the nodes represent authors; the links, co-authorship; and the densities, the intensity
of the inter-individual collaboration). The y-axis on the left-hand side tallies the number of
nodes and links; while the right one, the network density. Figure 4 displays the number of
16

authors and their collaborations increasing steadily until 2006, while the density curve achieved
the opposite. More and more researchers entered the WM field while collaborations were in a
period of relative slackness, evidenced by the longitudinal collaboration networks as shown in
the upper part of Figure 4. In the past decade, 2006 to 2016, the number of researchers in the
WM field decreased slightly, while also becoming more productive as interpreting Figure 4 in
conjunction with Figure 1 implies. It is also reasonable to conclude that the inter-individual
collaborations over the past decade have intensified, resulting in a greater number of
publications in WM research. Notably, from 2012 to 2016, a larger concentrated research
collaboration subnet formed though there were still many small subnets exploring different
topics.

17

0.012

400

0.008

200

0.004

0

1992-1996

Figure 4. Longitudinal analysis of inter-individual research collaboration networks.
1997-2001

Nodes（author）

2002-2006
Year

Links（co-authorship）

18

2007-2011

Density

2012-2016

0

Density

Count (Nodes/Links)

600

4.2 Inter-institutional research collaboration network
The CBRC as evidenced by the 15,396 papers was further examined using CiteSpace by
shifting focus from individual researchers to institutions. The contributions of institutions and
their impacts are displayed in Figure 5, labeling nodes with seventy or more publication counts.
The density of the network, 0.0035, comprised 546 nodes and 516 links, implying a moderate
overall international collaboration amongst institutions. It is understandable that the indicators
are larger than their counterparts in the inter-individual research collaboration networks, as
researchers from the same institution will be aggregated into a single entity for calculation
purposes. The Chinese Academy of Science (CAS) proved the largest contributor of WM
publications, followed by the Technical University of Denmark. Other major institutions
included China’s Tsinghua University, Tongji University, and Zhejiang University with more
than a hundred articles published each. These Chinese institutions have close research
collaboration with one another. It is also found that the US’s University of Florida showed
connections with multiple Chinese institutions. Although the findings corroborated WM
research transpiring in numerous universities from all over the world, many of the institutions
highlighted were non-university research organizations or environmental protection agencies
(e.g. the CAS, Spanish National Research Council (CSIC), French National Institute for
Agricultural Research (INRA), Council of Scientific & Industrial Research, India (CSIR), and
National Research Council, Italy (CNR)). This emphasizes how WM is not just an academic
issue, but a practical one as well, attracting the close attention of policy and research agencies.
However, as evidenced by their low centralities in the collaboration networks, no principal
institution stood out. This phenomenon indicates that despite WM enjoying pluralistic research
competition and diversification, no single institution plays a leading or unifying role within the
research domain.

19

Figure 5. Cluster view of inter-institutional research collaboration network.

This section further analyzes the longitudinal change of the inter-institutional research
collaboration networks calculated every five years from 1992 to 2016. Results are shown in
Figure 6. The meanings of the x- and y-axis mimic that of Figure 4, while the nodes represent
institutions; the links, the collaborations between institutions; and the density , the intensity of
the inter-institutional collaboration network. Figure 6 signifies that some of the institutions
withdrew from the WM research field. Interpreting this in conjunction with Figure 4, it can be
assumed that the remaining institutions self-organized (Prigogine et al., 1984) and appointed
more researchers. In parallel with this self-organization, inter-institutional collaborations
steadily increased, as substantiated by the links, the densities and the graphical longitudinal
20

networks shown in Figure 6. As for the influence of institutions, which can be extracted by the
centrality of nodes, no definitive leader emerged between 1992 and 2001. However, Nanyang
Technology University and Kyoto University proved relatively more influential than others
while other institutions, such as Technical University of Denmark began to burgeon between
2002 and 2006. CAS gained the most conspicuous presence from 2007 to 2016, exhibiting by
far the highest centrality. Meanwhile, other significant institutions from 2007 to 2011 included
the University of London’s Imperial College of Science, Technology and Medicine, National
Taiwan University, and Hong Kong Polytechnic University, while amongst the leading
institutions from 2012 and 2016, Delft University of Technology, Technical University of
Denmark, Tsinghua University, University of Queensland, and Zhejiang University. It can also
be noticed that from 2007 to 2016, there were some big nodes at the outer ring of the networks,
and they collaborated more as time went by, indicating that some big institutions didn’t enter
the focused main network but kept independent or built their own small networks.

21

0.025

250

0.02

200

0.015

150
100

Figure 6. Longitudinal analysis of inter-institutional research collaboration networks.

50
0

0.01

1992-1996

1997-2001

Nodes(institution)

2002-2006
Year

2007-2011

Links(inter-institutional collaboration)

22

0.005
2012-2016

Density

0

Desnity

Count(Nods/Links)

300

4.3 Inter-national research collaboration network
The CBRC in waste management research is further examined by focusing on national level.
Figure 7 scrutinizes the networks of collaborations between nations and regions, along with the
geographical dispersal of WM research. This research collaboration network comprises 86
nodes and 477 links, with a density of 0.1305, indicating that cross-boundary research
collaboration, if examined at the national level, is more intensive than co-author or institutional
networks. All countries and regions are labeled with the same size of texts but differs in the
size of nodes which indicates the number of publications. Countries with more than 200
publications consist of the US, China, Spain, UK, Italy, Japan, Germany, India, France, Canada,
Australia, Taiwan, Sweden, Brazil, Netherlands, South Korea, Turkey, Denmark, Greece,
Belgium, Switzerland, Portugal, Austria, and Malaysia. American scholars published 2,187
articles in total, followed by Chinese with 2,106, while the UK, Spain, Italy, Japan, Germany,
India, France, Canada, and Australia each published between 500 to 1000 papers. The principal
cluster of collaboration, however, transpired between researchers from the UK, India, Turkey,
Greece, Malaysia, Iran, Egypt, Ireland, Israel, Pakistan, Nigeria, Saudi Arabia, Hungary,
Jordan, United Arab Emirates, Iraq, Cyprus, Lebanon, and North Ireland. While the other very
productive countries, such as the US, China, Spain, Italy, Japan, and Germany, fell outside this
cluster. The UK has the largest centrality (0.24), followed by Belgium (0.14), the US (0.13),
Japan (0.13), Netherlands (0.12), and France (0.11), and as such have central (i.e., influential,
with higher centrality in the network than others) positions within the network. It is worth
noticing that US has the strongest citation burst among all the countries, evidenced by the
purple ring circled the node. Other countries with very strong citation bursts are the UK, France,
Belgium, and the Netherlands. The citation bursts indicate the sharp increases of interest in the
published articles (Chen, 2006). However, countries with very high publication counts such

23

as China, Spain, Italy, and India, experienced relatively lower influence, likely owing to factors
of languages, research capacities, quality, previous impact in WM, and their respective state’s
level of international influence. A greater number and depth of efforts are necessary to further
internationalize research collaboration across different country boundaries.

Figure 7. Cluster view of inter-national research collaboration network.

This section further analyzes the longitudinal change of the inter-national research
collaboration networks calculated every five years from 1992 to 2016 as shown in Figure 8.
The meanings of the x- and y-axis are the same as in Figure 4 and 6, while the nodes represent
countries and regions; the links, the collaborations between countries and regions; and the
24

density, the intensity of the inter-national collaboration network. Figure 8 reveals that the
number of countries present in the networks stopped increasing in 2012, while density kept
rising throughout the entire period. From 1992 to 2011, the US and UK enjoyed the highest
numbers of papers published and the highest centralities. The UK kept its leader’s position
until 2016. However, after 2006, China edged into the group with the highest number of
published papers despite an overall low centrality score. Nevertheless, its extraordinary number
of publications made make China conspicuous. France came third from 2002 to 2016. Germany
consistently made one of the top four most influential countries from 1992 to 2011, while
several other developed European countries, particularly Belgium and Sweden, along with
Canada and Japan rounded out the leader’s board.

25

400

0.18
0.14

300

0.12
0.1

200

Figure 8. Longitudinal analysis of inter-national research collaboration network.

0.08
0.06

100

0.04
0.02

0

1992-1996

1997-2001
Nodes (country)

2002-2006

2007-2011

Links (inter-national collaboration)

26

2012-2016
Density

0

Density

Count(Nodes/Links)

0.16

4.4 Inter-disciplinary research collaboration network
Given that hardly anyone will have all the disciplinary knowledge necessary to manage every
aspect of WM, inter-disciplinary research collaboration would be extremely advantageous for
the development of the field. This section investigates inter-disciplinary research collaboration
in WM. Since the papers extracted from WoS cannot be appropriately categorized into
disciplines based on their data structure, the authors of this paper adopt an alternative method.
By combining keywords from WoS and disciplines from the university ranking agency, QS,
keywords and subjects were manually mapped one by one. There are forty-seven subjects in
QS, among which only a few closely relate to WM research. The first step, to confirm the most
relevant subjects with the adoption of Delphi, was accomplished by inviting ten experts in WM
research and practice to conduct three rounds of Delphi until they reached an agreement. The
six subjects/disciplines deemed closely relating to WM were Chemical Engineering, Civil and
Structural Engineering, Biological Sciences, Environmental Sciences, Materials Science, and
Business and Management. Meanwhile, the authors shepherded a pilot study using all the
keywords of the 15,396 papers from CiteSpace and found out 1,252 keywords appearing more
than ten times in the dataset. The total occurrence of these keywords, 75,924, characterizes 88%
of all the keywords. The authors then studied the 1,252 representative keywords thoroughly
and sorted them into different disciplines as identified in the first step. Depending on its nature,
a keyword may fit several disciplines. Finally, the authors replaced all the keywords with the
disciplines and employed CiteSpace to analyze the network of the disciplines in order to
understand their inter-disciplinary collaboration. If one paper contains keywords from different
disciplines, then there is a very high chance that the paper is collaborated in an interdisciplinary way. Otherwise, if keywords of a paper belong to a single discipline, then likely
that the paper has no inter-disciplinary collaboration.

27

Figure 9. Inter-disciplinary research collaboration network.

Figure 9 exhibits the network of inter-disciplinary collaboration and the inter-disciplinary
characteristics of WM research. In the figure, chemical engineering and environmental science
are the apparent pillars of WM research. The nodes of environmental science and chemical
engineering are highlighted by purple circles because they have high centrality and thus
significance within the network. The thicker the circle, the bigger the centrality. However,
though more research studies relate to chemical engineering, it is environmental science that
boasts a higher centrality. Material science, civil and structural engineering, and biological
science have similar counts, while the former two are more significant than the latter. Business
and management proves comparatively less present in WM research, although basically these
papers explore the management of waste. The strength of the links between disciplines offers
a brief insight into the closeness of their combinations. Material science and civil and structural
engineering, chemical engineering and environmental science, together with environmental
science and material science are the most closely collaborated. Chemical engineering and
material science, biological science and environmental science, biological science and
chemical engineering, material science and business and management also closely collaborate.
Interestingly, chemical engineering and biological engineering do not collaborate at all with
business and management. Overall, the share of “soft” approaches (i.e., business and
28

management, half of environment science) is just 15.5%, and the rest goes to hard approaches.
This phenomenon implies that WM research has paid more attention to the “hard” approaches
characteristic of fields like chemical engineering and biological engineering than the “soft”
methods of fields like business and management.

Longitudinal changes of the inter-disciplinary research collaborations were also studied and
shown in Figure 10. In this analysis, nodes, links, and densities of the networks were calculated
every five years from 1992 to 2016. The meanings of the x- and y-axis are the same as those
in Figures 4, 6, and 8, while the nodes represent disciplines; the links, the collaborations
between disciplines; and the density of the network, the closeness of the inter-disciplinary
collaborations. The analysis intriguingly revealed that the overall links and density of the
networks declined slightly from 1992 to 2016. The count and closeness of the inter-disciplinary
collaborations also changed together with the centrality of the disciplines in WM research. In
1992-1996, the biological science closely collaborated with chemical engineering and material
science. Material science (centrality=1.2) was the most influential discipline. From 1997 to
2002, the era when most inter-disciplinary collaboration materialized, chemical engineering
(centrality=1.2) became the leading discipline and collaborated mostly with biological
engineering and environmental science. During 2002 through 2006, the collaborations between
different disciplines declined. Chemical engineering ceased collaboration with civil and
structural engineering (Park & Tucker, 2017), as did material science and environmental
science. In 2007 to 2011, the linkage between disciplines remained the same as 2002 to 2006,
while the leading discipline was environmental science (centrality=1.1). The collaboration
patterns fluctuated from 2012 to 2016, during which there were only six connections between
the six disciplines in total. Chemical engineering and material science (both centrality=1.2)
became the most significant disciplines of WM research.

29

With a deeper investigation of the keywords citation bursts as shown in Figure 11, the topics
and approaches that received the most and the least attention over the past few years were
identified. Food waste was the most researched topic and life-cycle assessment (LCA) proved
the most adopted method in WM. Besides, “China”, waste impact, waste-to energy, and
sustainability received a great deal of attention with very strong citation bursts in recent years.
While topics like nitrification, landfill, as well as methods including mathematical modelling
and simulation were losing concentration from WM academics recently. Other keywords, such
as “risk assessment”, “biological treatment”, “waste water” and “hazardous waste” became
old-fashioned in the twenty-first century. These changes reflect the evolution of research
hotspots in WM, i.e. from biological or chemical waste treatment to the utilization of waste as
energy and resource for sustainability, from hazardous waste and waste water to food waste,
from developed countries to developing countries. It is safe to assume that next research
frontier of WM is how to convert daily produced waste to energy and other resources in a
sustainable way.

30

1

8

0.8

6

0.6

4

0.4

2

1992-1996

1997-2001

Nodes（disciplines）

2002-2006
Year

2007-2011

Links（interdisciplinary collaboration）

2012-2016

Density

Figure 10. Longitudinal analysis of inter-disciplinary research collaboration network.

31

0.2

Density

Count(Nodes/Links)

10

Figure 11. Top 30 keywords with the strongest citation bursts

5. Discussion
Based on a series of network analyses covering 15,396 relevant papers sourced from the eight
most relevant journals, a clear and vivid picture of cross-boundary research collaborations in
WM research emerged. Overall, the cross-boundary research collaboration between WM
researchers lacks intensity. Several scholars, such as Christensen TH from the Technical
University of Denmark, published a substantial amount of WM research, but no core scholars
32

stood out in the framework of cross-boundary research collaboration (CBRC) networks.
Examining the CBRC from an institution level, no institution occupied a critical position as
evidenced by their low centralities in the collaboration networks. Interestingly, many
governments and non-government institutions, such as APHA, the European Commission, and
the US EPA, played positive roles in fostering CBRC in the WM field. Countries known for
their high research capability like the US and the UK enjoyed greater significance in the field
than their developing counterparts, even though few influential scholars dominated the
networks. Emerging countries like China made rapid progress in research publication numbers,
but need to endeavor more into the aspects of CBRC and pioneering achievements in order to
enhance their connectivity with international peers and gain greater influence. Surprisingly, the
extension around inter-disciplinary research collaboration in WM has yet to be seen. It is found
that “hard” approaches (e.g., WM-related technologies from chemical engineering, biological
science, material science, and civil and structural engineering), are widely researched, while
“soft” economic, managerial, or policy measures are relatively sparse in CBRC. It appears that
the “soft” and “hard” researchers are working on different spheres. The authors of this paper
encourage them to step out from their comfort zones and collaborate with other disciplinary
researchers in order to stimulate more innovative methods for handling waste.

Longitudinal evaluations of WM CBRC are exceptionally informative. Although CBRC from
the individual, institutional, and national perspectives remains quite limited, evidence suggests
a steady if modest increase in collaborations over time. As the need for sensible and sustainable
methods for handling waste becomes more urgent, in both developing and developed countries
alike, CBRC will likely rise. A de facto world waste market subsists, trafficking solid wastes
like paper, plastics, waste electrical and electronic equipment (WEEE) to developing
economies as cheap material resources (Manomaivibool, 2009). Conflicting involvement on

33

the part of multi-national companies with local initiative groups, cities and national
governments, etc. will both directly and indirectly affect the waste sector (Saarikoski, 2000).
Indeed disposal is not the only outcome for waste. Rejected items can enjoy second lives as
cheap resource material (Bergeron, 2017), energy via combustion (Brunner and Rechberger,
2015), fertilizer (Sower, 2002), scrounged food (Goula and Lazarides, 2015) and wealth
(Mumtaz et al., 2010) when reprocessed using appropriate technologies and creative ideas. In
addition to the ethical issues around international waste trading (Lu, 2013), lack of knowledge
in managing exported waste has exasperated ecological problems in developing countries
(Marshall and Farahbakhsh, 2013). A crucial need exists for collaboration in the management
of waste from a global perspective. Diverse and cutting-edge methods, borrowed from
computer science, engineering, management and data science, should be developed and applied
to WM research. Calling for free, transparent and accessible data, the “Open Data” movement
(Auer et al., 2007) may solve the CBRC in WM shortage of data hurdle, and by crossing
traditional institutional and national boundaries to achieve big data levels, CBRC can accelerate
to deal with the grand challenges of waste management.

The findings supplement previous research, which taken as a whole principally addressed
statistical analyses of WM research. For example, Chen et al. (2015) examined WM research
also extracted from WoS, but between 1997 and 2014, covering 9401 papers including
proceeding papers, reviews and others. Although Chen et al.’s findings, e.g., publication output
trend, predominant countries, institutes and journals, are very similar with ours, they focused
solely on the top keywords, while our paper included citation burst detection, which illustrates
more specific keywords trends. For example, this paper’s most cited emerging keyword,
“China”, indicates the recent and growing popularity of WM research in the region. The second
major difference between Chen et al. and this paper concerns this paper’s emphasis on the

34

collaboration networks. Through a deeper investigation into the CBCR networks at individual,
institutional, national and disciplinary perspectives, this paper identified level of closeness in
WM research collaboration. This reveals the demand for further and enhanced collaboration to
meet the ever-increasing need to address sustainability in WM, especially in developing
countries.

However, a number of limitations remain. From the research perspective, this research
investigated cross-boundary research collaboration based on published articles, a major form
of research collaboration, but not the only one. Also, the research started with a default
supporting of research collaboration, which sometimes is not needed. Theoretically, research
collaboration has ill-defined borders, and the “boundary” may vary across institutions, fields,
countries, as well as over time (Katz and Martin, 1997), this research only examined some of
it. From the aspect of research methods, one limitation of this research is that only data in .txt
format can be inputted into CiteSpace for analysis and only WoS provides dataset download
in .txt. Thus, only data retrieved from WoS can be used in CiteSpace network analysis,
preventing analysis of articles available only in other databases like Scopus and GoogleScholar.
Fortunately, both the data source and analysis software are representable and reliable. Another
limitation concerns CiteSpace’s inability to differentiate authors with the same name and its
abbreviation, such work must be done manually which is time-consuming and arduous. A third
limitation concerns how disciplinary information is poorly sorted and tagged in big databases.
WoS and Scopus only provide a paper’s category information based on the sorting of the
journals. Analyzing such information requires an indirect approach, which can be imperfect
and hard to accomplish. Future research can explore other databases or other software to
analyze CBRC in both WM and of course other research fields. Though there might be
inadequacies due to the availability of data and functions of the analytical tool, ultimately this

35

investigation can help seasoned researchers and newcomers in the WM sector find and connect
with other researchers, institutions, regions, and disciplines to build collaborations.

6. Conclusion
With data from WoS between 1981 and 2016 and scientific citation network analysis software
CiteSpace, this paper investigated cross-boundary research collaboration (CBRC) in the field
of waste management (WM) from four perspectives, namely, inter-individual, interinstitutional, inter-national, and inter-disciplinary. Surprisingly, CBRC in the WM field proved
rather limited. Real boundaries, be they physical or ideological, prevent CBRC from fully
realizing universally. However, according to the longitudinal analyses, the research
collaborations at the four cross-boundary perspectives became more and more concentrated in
a self-organized way. Researchers are encouraged to abandon silo mentalities and collaborate
more inter-disciplinarily.

Specifically, the CBRC in WM between individuals, institutions, and nations have been
respectively increasing gradually if slowly over the examined time period, 1996 to 2016.
Modern information and communication technologies (ICTs) afforded the infrastructure
necessary to catalyze CBRC, although it has never been easy. Evidenced by the inter-individual
and inter-institutional networks, the CBRC in WM research is relatively decentralized. No
researcher or institution enjoys an obvious leading role. Developed countries like the US, the
UK, and others in Western European comprise the epicenter of WM research, likely owing to
their research capabilities, quality standards, and respective countries’ international influence,
while developing countries, China especially, are catching up in both the amount of research
published and its value and importance. Two discipline “islands” in particular, one mainly
formed by science and technology, and the other by business and management, exemplify the

36

disconnections between boundaries. Finally, the keywords burst detection analysis revealed
frontiers for future research.

This research filled the gap of a missing quantitative investigation into CBRC in WM, which
is significant for new researcher, further research and education of WM. It also confirmed the
feasibility of co-authorship as a method to study research collaboration (Melin and Persson,
1996). It contributed to the measurement of research collaboration by promoting a convenient
and effective method, i.e. social network analysis (SNA), to measure research collaboration
from four cross-boundary perspectives. Other researchers who want to investigate the CBRC
in other fields can also conduct SNA. The findings provide some implications to the WM
research group by: highlighting the CBRC gaps between the developed and developing poles,
and among different disciplines, shedding light on the increasing popularity of energy and
resource regeneration from daily waste. This research can also provide guidance to WM
practice by pointing out directions, i.e., calling for collaboration among academia, industry and
government from different economic entities to boost sustainable WM, such as applying
advanced technologies from developed countries to handle the growing waste from developing
countries.

Acknowledgement
This research was jointly supported by the National Nature Science Foundation of China
(NSFC) [Grant No.: 71273219]; and the Hong Kong Research Grants Council (RGC) General
Research Fund (GRF) [Grant No.: 17201917]. Special thanks to Anna Zetkulic for her
proofreading.

37

References
ALTBACH, P. G. & KNIGHT, J. 2007. The internationalization of higher education:
Motivations and realities. Journal of studies in international education, 11, 290-305.
APPLETON, K. 1993. Using theory to guide practice: Teaching science from a constructivist
perspective. School Science and Mathematics, 93, 269-274.
AUER, S., BIZER, C., KOBILAROV, G., LEHMANN, J., CYGANIAK, R. & IVES, Z.
2007. Dbpedia: A nucleus for a web of open data. The semantic web, 722-735.
BERGERON, F. C. 2017. Analytical method of waste allocation in waste management
systems: Concept, method and case study. Environmental Impact Assessment Review,
62, 35-48.
BERKES, F. 2009. Evolution of co-management: role of knowledge generation, bridging
organizations and social learning. Journal of environmental management, 90, 16921702.
BORGATTI, S. P., EVERETT, M. G. & JOHNSON, J. C. 2013. Analyzing social networks,
SAGE Publications Limited.
BRUNNER, P. H. & RECHBERGER, H. 2015. Waste to energy–key element for sustainable
waste management. Waste management, 37, 3-12.
CARLEY, K. M. 2006. Destabilization of covert networks. Computational & Mathematical
Organization Theory, 12, 51-66.
CHEN, C. 2006. CiteSpace II: Detecting and visualizing emerging trends and transient
patterns in scientific literature. Journal of the Association for Information Science and
Technology, 57, 359-377.
CHEN, C., IBEKWE‐SANJUAN, F. & HOU, J. 2010. The structure and dynamics of
cocitation clusters: A multiple‐perspective cocitation analysis. Journal of the
Association for Information Science and Technology, 61, 1386-1409.
CHEN, H., JIANG, W., YANG, Y., MAN, X., & TANG, M. (2015). A bibliometric analysis
of waste management research during the period 1997–2014. Scientometrics, 105(2),
1005-1018.
CROSS, R., BORGATTI, S. P. & PARKER, A. 2002. Making invisible work visible: Using
social network analysis to support strategic collaboration. California management
review, 44, 25-46.
CUMMINGS, J. N. & KIESLER, S. 2005. Collaborative research across disciplinary and
organizational boundaries. Social studies of science, 35, 703-722.
DIVISION, U. U. N. S. 1997. Glossary of Environment Statistics. F. USA: United Nations
Statistics Division.
FERREIRA, C., RIBEIRO, A. & OTTOSEN, L. 2003. Possible applications for municipal
solid waste fly ash. Journal of hazardous materials, 96, 201-216.
GIRVAN, M. & NEWMAN, M. E. 2002. Community structure in social and biological
networks. Proceedings of the national academy of sciences, 99, 7821-7826.
GOORHUIS, M., REUS, P., NIEUWENHUIS, E., SPANBROEK, N., SOL, M. & VAN
RIJN, J. 2012. New developments in waste management in the Netherlands. Waste
Management & Research, 30, 67-77.
GOULA, A. M. & LAZARIDES, H. N. 2015. Integrated processes can turn industrial food
waste into valuable food by-products and/or ingredients: The cases of olive mill and
pomegranate wastes. Journal of Food Engineering, 167, 45-50.HOANG, H. &
ANTONCIC, B. 2003. Network-based research in entrepreneurship: A critical review.
Journal of business venturing, 18, 165-187.

38

HOEKMAN, J., FRENKEN, K. & TIJSSEN, R. J. W. 2010. Research collaboration at a
distance: Changing spatial patterns of scientific collaboration within Europe.
Research Policy, 39, 662-673.
HSIAO, R. L., TSAI, D. H. & LEE, C. F. 2012. Collaborative knowing: The adaptive nature
of cross‐boundary spanning. Journal of management studies, 49, 463-491.
ISMAIL, Z. Z. & AL-HASHMI, E. A. 2008a. Reuse of waste iron as a partial replacement of
sand in concrete. Waste Management, 28, 2048-2053.
ISMAIL, Z. Z. & AL-HASHMI, E. A. 2008b. Use of waste plastic in concrete mixture as
aggregate replacement. Waste Management, 28, 2041-2047.
KAMALA, R. & RAO, B. K. 2012. Reuse of solid waste from building demolition for the
replacement of natural aggregates. International Journal of Engineering and
Advanced Technology (IJEAT) ISSN, 2249-8958.
KATZ, J. S. & MARTIN, B. R. 1997. What is research collaboration? Research policy, 26, 118.
LAUDEL, G. 2002. What do we measure by co-authorships? Research Evaluation, 11, 3-15.
LEINER, B. M., CERF, V. G., CLARK, D. D., KAHN, R. E., KLEINROCK, L., LYNCH,
D. C., POSTEL, J., ROBERTS, L. G. & WOLFF, S. S. 1997. The past and future
history of the Internet. Communications of the ACM, 40, 102-108.
LEYDESDORFF, L. 2007. Betweenness centrality as an indicator of the interdisciplinarity of
scientific journals. Journal of the Association for Information Science and
Technology, 58, 1303-1319.
LEYDESDORFF, L., WAGNER, C., PARK, H. W. & ADAMS, J. 2013. International
collaboration in science: The global map and the network. arXiv preprint
arXiv:1301.0801.
LI, E. Y., LIAO, C. H. & YEN, H. R. 2013. Co-authorship networks and research impact: A
social capital perspective. Research Policy, 42, 1515-1530.
LU, W. 2013. Construction waste management in Hong Kong: 10 years, no change? Building
Journal.
LU, W., CHEN, X., PENG, Y., & LIU, X.J. (2018). The effects of green building on
construction waste minimization: triangulating ‘big data’ with ‘thick data’. Waste
Management. Forthcoming.
LU, W., PENG, Y., WEBSTER, C. & ZUO, J. 2015. Stakeholders’ willingness to pay for
enhanced construction waste management: a Hong Kong study. Renewable and
Sustainable Energy Reviews, 47, 233-240.
LU, W., WEBSTER, C., PENG, Y., CHEN, X. & ZHANG, X. 2017. Estimating and
calibrating the amount of building-related construction and demolition waste in urban
China. International Journal of Construction Management, 17, 13-24.
MANOMAIVIBOOL, P. 2009. Extended producer responsibility in a non-OECD context:
The management of waste electrical and electronic equipment in India. Resources,
Conservation and Recycling, 53, 136-144.
MARSHALL, R. E. & FARAHBAKHSH, K. 2013. Systems approaches to integrated solid
waste management in developing countries. Waste Management, 33, 988-1003.
MEHO, L. I. & YANG, K. 2007. Impact of data sources on citation counts and rankings of
LIS faculty: Web of science versus scopus and google scholar. Journal of the
American Society for Information Science and Technology, 58, 2105-2125.
MELIN, G. & PERSSON, O. 1996. Studying research collaboration using co-authorships.
Scientometrics, 36, 363-377.
MUMTAZ, T., YAHAYA, N. A., ABD-AZIZ, S., YEE, P. L., SHIRAI, Y. & HASSAN, M.
A. 2010. Turning waste to wealth-biodegradable plastics polyhydroxyalkanoates from
39

palm oil mill effluent–a Malaysian perspective. Journal of Cleaner Production, 18,
1393-1402.
NEWMAN, M. E. 2004. Coauthorship networks and patterns of scientific collaboration.
Proceedings of the national academy of sciences, 101, 5200-5205.
NEWMAN, M. E. & GIRVAN, M. 2004. Finding and evaluating community structure in
networks. Physical review E, 69, 026113.
OSMANI, M., GLASS, J. & PRICE, A. D. 2008. Architects’ perspectives on construction
waste reduction by design. Waste Management, 28, 1147-1158.
PARDO, T. A., GIL-GARCIA, J. R. & LUNA-REYES, L. F. 2010. Collaborative
governance and cross-boundary information sharing: envisioning a networked and ITenabled public administration. The future of public administration around the world:
The Minnowbrook perspective, 129-39.
PARK, J. & TUCKER, R. (2017). Overcoming barriers to the reuse of construction waste
material in Australia: a review of the literature. International Journal of Construction
Management, 17(3), 228-237.
PARSONS, T. 1951. The social system, Glencoe. IL: Free Press of Glencoe.
PRIGOGINE, I., STENGERS, I. & PRIGOGINE, I. 1984. Order out of chaos: Man's new
dialogue with nature, Bantam books New York.
REAGANS, R. & MCEVILY, B. 2003. Network structure and knowledge transfer: The
effects of cohesion and range. Administrative science quarterly, 48, 240-267.
SAARIKOSKI, H. 2000. Environmental impact assessment (EIA) as collaborative learning
process. Environmental impact assessment review, 20, 681-700.
SHEN, L., TAM, V. W., TAM, C. & DREW, D. 2004. Mapping approach for examining
waste management on construction sites. Journal of construction engineering and
management, 130, 472-481.
SINHA-KHETRIWAL, D., KRAEUCHI, P. & SCHWANINGER, M. 2005. A comparison of
electronic waste recycling in Switzerland and in India. Environmental Impact
Assessment Review, 25, 492-504.
SOWER, L. P. 2002. Methods for producing fertilizers and feed supplements from
agricultural and industrial wastes. Google Patents.
TORTORIELLO, M. & KRACKHARDT, D. 2010. Activating cross-boundary knowledge:
The role of Simmelian ties in the generation of innovations. Academy of Management
Journal, 53, 167-181.
VAN RIJNSOEVER, F. J. & HESSELS, L. K. 2011. Factors associated with disciplinary and
interdisciplinary research collaboration. Research policy, 40, 463-472.
VITHANAGE, M., WIJESEKARA, S., SIRIWARDANA, A., MAYAKADUWA, S. S. &
OK, Y. S. 2014. Management of municipal solid waste landfill leachate: a global
environmental issue. Environmental Deterioration and Human Health. Springer.
WAGNER, C. S. & LEYDESDORFF, L. 2005. Network structure, self-organization, and the
growth of international collaboration in science. Research policy, 34, 1608-1618.
WALTMAN, L. & ECK, N. J. 2012. A new methodology for constructing a publication‐
level classification system of science. Journal of the Association for Information
Science and Technology, 63, 2378-2392.
WASSERMAN, S. & FAUST, K. 1994. Social network analysis: Methods and applications,
Cambridge university press.
XIE, Z., OUYANG, Z. & LI, J. 2016. A geometric graph model for coauthorship networks.
Journal of Informetrics, 10, 299-311.

40

ZHANG, D. Q., TAN, S. K. & GERSBERG, R. M. 2010. Municipal solid waste management
in China: status, problems and challenges. Journal of environmental management, 91,
1623-1633.
ZHAO, X. 2017. A scientometric review of global BIM research: Analysis and visualization.
Automation in Construction, 80, 37-47.
Zhao, X., Zuo, J., Wu, G., & Huang, C. (2018). A bibliometric review of green building
research 2000–2016. Architectural Science Review, 1-15.
ZHENG, X., LE, Y., CHAN, A. P., HU, Y. & LI, Y. 2016. Review of the application of
social network analysis (SNA) in construction project management research.
International Journal of Project Management, 34, 1214-1225.

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