Review on Energy Resilience

REVIEW ARTICLE

Exploring the science of resilience: critical review and bibliometric analysis

Xiaolong Xue1 • Liang Wang1 • Rebecca J. Yang2

Received: 26 November 2016 / Accepted: 1 September 2017 / Published online: 4 October 2017 � Springer Science+Business Media B.V. 2017

Abstract The concept of resilience has experienced extraordinary development since the 1970s. Resilience is now an integral part of human society and has become a hot topic in

different research domains. As an interdisciplinary discipline, resilience science is sup-

ported by multidisciplinary knowledge. Although research and practical work of resilience

have been developed significantly, it is still unclear that how far resilience science has been

progressed as a scientific discipline. In order to reveal the connotation and knowledge

structure of resilience science, we systematically reviewed classic publications on resi-

lience and compared its definitions and related research in different discipline domains.

The evolution trend of resilience science was quantitatively analyzed to identify its

knowledge foundation, geographic distribution, academic community, and collaboration

structure. This analysis revealed the knowledge structure and development path of resi-

lience science for future researchers. The results showed that the publications of resilience

have been explosively increased since the 2000s. The developed countries made sound

contributions to the research of resilience science, and China also presents a significant

growth trend in this area. The collaborative relationship is becoming closer across research

institutions and scholars. The research topics of resilience have been changing in the latest

30 years. The results reveal important highlights and future research directions of resi-

lience science on academic domains including definition of resilience, measurement

methods of network resilience, and mechanisms to forming resilient status. Moreover, this

study will help researchers in resilience science for future collaboration and work.

& Xiaolong Xue [email protected]

Liang Wang [email protected]

Rebecca J. Yang [email protected]

1 School of Management, Harbin Institute of Technology, 92 West Dazhi Street, Nan Gang District, Harbin 150001, China

2 School of Property, Construction and Project Management, RMIT University, Melbourne, Australia

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Nat Hazards (2018) 90:477–510 https://doi.org/10.1007/s11069-017-3040-y

Keywords Resilience science � Definition � Bibliometric analysis � Evolution trend � Social network analysis

1 Introduction

Resilience is derived from a Latin word ‘‘resilio,’’ meaning ‘‘to jump back’’ (Klein et al.

2003). The initial definition of resilience is the ability of a material to absorb energy when

it is deformed elastically, and releases energy upon unloading (Gere and Goodno 2012).

The Oxford English Dictionary defines resilience as the ability of recovery or bounce

(Oxford Dictionaries 2011). However, the concepts of resilience have also been used in a

more metaphorical sense to describe system behavior since the 1970s. The well-accepted

meaning of resilience is the ability of system back to normal state (i.e., original state, or

likely adjusts itself to the new state for new demand or situation) from disadvantaged

environment (Walker and Salt 2012; Strigini 2012). It reflects the adaptability and sur-

vivability of systems (McDaniels et al. 2008). Overall, resilience is still a relatively new

concept, different disciplines have different cognition and understanding, and the concepts

of resilience are constantly evolving in the development of resilience science.

Now, the concepts of resilience have been widely accepted by academic communities

and gradually became an important research theme in different academic domains. Rele-

vant research outcomes have been published in high-quality journals, including Nature and

Science. The concepts of resilience have been applied to different academic domains such

as environmental science (Rockström et al. 2009; Folke 2006; Steinberg 2009; Benson and

Garmestani 2011a, b), social–ecological systems (Adger et al. 2005; Bergstrom 2010;

Walker et al. 2004), engineering and disaster prevention (Bruneau et al. 2003; Chang and

Shinozuka 2004; Xu et al. 2007; Miles and Chang 2006), medical health and neuroscience

(Sood et al. 2011; West et al. 2011), safety science (Costella et al. 2009; Steen and Aven

2011; Tveiten et al. 2012), economic and organizational behavior (Rose and Liao 2005;

Fingleton et al. 2012; Manyena 2006; Haas et al. 2007), and water resources management

(Wang and Blackmore 2009; McNally et al. 2009). Resilience has been progressed as a

scientific discipline supported by multidisciplinary knowledge.

Many resilience studies have been conducted with a few focusing on the theoretical

framework and development trends of resilience research. For example, several studies,

within their domains, have focused on understanding uncertainty and reducing disasters

vulnerability from resilience thinking (Berkes 2007) and analyzed resilience to natural

hazards from a geographic perspective (Zhou et al. 2010). Some others proposed con-

ceptual frameworks to analyze the relationships of vulnerability, resilience, and adaptation

from a disaster risk perspective (Lei et al. 2014) and developed pathways for adaptive and

integrated disaster resilience (Djalante et al. 2013). However, a common problem of these

studies is that their results rely on subjective or empirical judgment within their areas,

which cannot reflect the universal research trends across different resilience domains.

Moreover, it is difficult for new researchers to distinguish the essential academic journals

and core literature from the vast available resilience publications. A bibliometric study of

resilience literature is needed to systematically review and analyze the development trends

of previous resilience research.

In order to fill in this gap, our study reviews previous studies of resilience through

bibliometric analysis and summarizes the development trends of resilience between Jan-

uary 1985 and December 2014. The results could help global researchers to better

understand the research status of resilience and identify the academic frontier of resilience

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research. This paper is structured as follows: First, the authors identify three domains of

resilience (i.e., social and ecology, engineering and disaster, economic and organizational

behavior) and summarize its definitions and related research. Then, this paper undertakes a

critical analysis of 6502 resilience articles published from 1985 to 2014 and interprets

analysis results using bibliometric and network analysis methods. Finally, data analysis

results are summarized and visualized from four aspects to explore the evolution trends of

resilience science.

2 Resilience in different research domains

In this section, we summarized the definitions of resilience and reviewed related research

of resilience in different domains. The definitions of resilience were identified with dis-

ciplinary perspectives and across application domains, including social and ecological,

engineering and disaster, economic and organizational behavior, and others. We reviewed

articles related to resilience studies in high influence journals which included Ecology and

Society, Global Environmental Change, Natural Hazards, Risk Analysis, Ecological

Economics, Regional Studies. These journals were also selected by previous review arti-

cles on resilience research. The research areas of these articles can be classified into: social

and ecological domain, engineering and disaster domain, and economic and organizational

behavior domain. A variety of definitions of resilience have been proposed in these

domains. Social–ecological system studies are usually investigated within human and

natural systems, in which resilience explores the complex relationship between society and

ecology at different spatial and temporal scales (Liu et al. 2007; Guillotreau et al. 2017).

Engineering resilience reflects the ability of human communities to withstand external

disaster and to recover from post-disaster consequences (Bozza et al. 2017; Mojtahedi et al.

2017). There is also close relationship between organization and economic resilience:

Enhancing economic and organizational behavior resilience is an effective way to reduce

system damage in disasters (Youssef and Luthans 2007; Van Der Vegt et al. 2015;

Doughty 2016). Although this classification may vary depending on researcher’s per-

spective, we intend to compromise a variety of definitions on resilience within these

domains through compressive literature review.

2.1 Conceptual definitions of resilience

Despite being proposed for more than 40 years, resilience holds a variety of definitions in

different domains (as summarized in Table 1). The discrepancies in the conceptual defi-

nitions of resilience arise from diverse epistemologies and methodology.

2.1.1 Social and ecological perception

The first definition on resilience in the social and ecology domain dates back to 1973 which

focuses on the capacity to devise systems that can absorb and accommodate future events

in whatever unexpected forms they may take (Holling 1973). This definition is influenced

by the key features of social and ecological systems: (1) diversity, including biodiversity

(Perrings 1997; Folke et al. 2004) and response diversity (Chapin et al. 1997; Elmqvist

et al. 2003), which provides a system with response to various disturbance; and (2) cre-

ativity and dynamic, drew from the adaptive cycles theory (Holling 2001), describes the

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resilience of systems through four phases: rapid growth and exploitation, conservation,

collapse or release, and renewal or reorganization (Gunderson 2001).

2.1.2 Engineering and disaster perception

The concept of resilience spreads to the engineering and disaster domains recently. In the

engineering and disaster filed, resilience is the ability of human communities to withstand

Table 1 Definitions of resilience

Years Scholar Definitions Category

1973 Holling Resilience is defined as the amount of disturbance that can be sustained by a system before a change in system control or structure occurs. It could be measured by the magnitude of disturbance the system can tolerate and still persist

A

1981 Timmerman Resilience is the ability of human communities to withstand external shocks or perturbations to their infrastructure and to recover from such perturbations

B

1995 Holling Resilience is the buffer capacity or the ability of a system to absorb perturbation, or the magnitude of disturbance that can be absorbed before a system changes its structure by changing the variables

A

1999 Comfort The capacity to adapt existing resources and skills to new systems and operating conditions

C

2001 Paton Resilience describes an active process of self-righting, learned resourcefulness, and growth—the ability to function psychologically at a level far greater than expected given the individual’s capabilities and previous experiences

C

2003 Bruneau An analysis of seismic resilience and apply the concept at four levels: (1) technical, physical systems perform when subjected to earthquake forces; (2) organizational, the ability to respond to emergencies and carry out critical functions; (3) social, the capacity to reduce the negative social consequences of loss of critical services; and (4) economic, the capacity to reduce both direct and indirect economic losses

Resilience has four dimensions: (1) robustness, strength to withstand a given level of stress without loss of function; (2) redundancy, the extent to which elements, systems that are substitutable; and (3) resourcefulness, the capacity to identify problems, establish priorities, and mobilize resources; (4) rapidity, the capacity to meet priorities and achieve goals in a timely manner

A resilient system has: (1) reduced probability of failures; (2) reduced consequences from failures; and (3) reduced time to recovery

B

2004 2007

Rose Resilience includes inherent resilience (ability under normal circumstances) and adaptive resilience (ability in crisis situations due to ingenuity or extra effort)

C

2009 Haimes Resilience is the ability of the system to withstand a major disruption within acceptable degradation parameters and to recover within an acceptable time and composite costs and risks

B

2014 Ayyub Resilience definition is provided that meets a set of requirements with clear relationships to the metrics of the relevant abstract notions of reliability and risk

B

Category A—social and ecology; Category B—engineering and disaster; Category C—economic and organizational behavior

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external shocks or perturbations to their infrastructure and to recover from such pertur-

bations (Blaikie et al. 2014). This definition includes two aspects: (1) Resilience refers to

an attribute of communities rather than structures or infrastructure (Haimes 2009); and (2)

resilience is also the ability of prompt, efficient, and effective recovery (Ayyub 2014). For

the case of urban communities and infrastructure, Bruneau et al. provided a comprehensive

description of resilience: the ability of social units to mitigate hazards, the effects of

disasters when they occur, recovery activities in ways that minimize social disruption, and

mitigation of the effects of future disasters (Bruneau et al. 2003).

2.1.3 Economic and organizational behavior perception

Within the organizational behavior domain, the concept of resilience was first introduced

in 1994 (Comfort 1994). It is the restoring process of organizational performance after

disturbance (Comfort et al. 1999). Further, the resilience of organization behavior refers to

the ability of changing resource to a new steady condition (Paton et al. 2001). In the

economic domain, the definition of resilience refers to (1) the adaptive behavior of business

and response behavior of community (Dahlhamer and Tierney 1998); and (2) the com-

prehensive reflection of system performance (Rose et al. 2007). The concept of resilience

can be defined as inherent resilience and adaptive resilience (Rose 2004, 2007).

2.2 Previous resilience studies in different domains

2.2.1 Social and ecology domain

In recent years, the resilience of social–ecological systems has been a major research trend.

Related studies focus on resilience and adaptability of social–ecological systems from

three perspectives including adaptive cycle, adaptive capacity, and adaptive management.

2.2.1.1 Adaptive cycle Resilience Alliance, a research network focusing on social–eco-

logical resilience, advocates using adaptive cycle theory to explain and analyze the resi-

lience of social–ecological systems (Walker et al. 2004; Holling 1973; Pimm 1984; Beisner

et al. 2003). The adaptive cycle theory views the evolution of social–ecological systems

going through four phases: a growth and exploitation phase (r), a conservation phase (K), a

chaotic collapse and release phase (X), and a phase of reorganization (a) (Holling 2001; Folke et al. 2002).

The growth and exploitation phase (r) and conservation phase (K) comprise a slow,

cumulative forward loop of the cycle, during which the dynamics of the system are

reasonably predictable. As the phase (K) continues, resources become increasingly locked

up and the system becomes progressively less flexible and responsive to external shocks. It

is eventually and inevitably, followed by a chaotic collapse and release phase (X) that rapidly gives way to a phase of reorganization (a), which may be rapid or slow, and during which, innovation and new opportunities are opened up. The phases (X) and phases (a) together comprise an unpredictable back loop. The phase (a) leads into a subsequent phase (r), which may resemble the previous r phase or be significantly different. The change in

resilience runs through the entire process of adaptive cycle; resilience manifests different

levels in different stages (Fig. 1; Gunderson 2001).

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2.2.1.2 Adaptive capacity Adaptive capacity is another important theory to analyze the

resilience of social–ecological systems (Walker et al. 2006). Adaptive capacity has rela-

tively independent concepts in social and ecological systems. In ecological systems,

adaptive capacity refers to biological diversity and response diversity (Elmqvist et al.

2003; Carpenter et al. 2001; Smit and Wandel 2006). It is the ability to adjust responses to

changing internal demands and external drivers in ecological systems (Carpenter and

Brock 2008). From the perspective of social systems, adaptive capacity refers to adaptive

behavior of different elements of social systems to learn and store knowledge and expe-

riences. Adaptive capacity plays an important role in enhancing social flexibility and

ability of solving problems (Scheffer et al. 2000; Brown et al. 2010).

High adaptive capacity is an important characteristic in social and ecological systems,

which guarantees systems with crucial functions, such as primary productivity, hydro-

logical cycles, social relations, and economic prosperity (Chapin et al. 2010). Adaptive

capacity gives social and ecological systems the ability to reconfigure themselves with

minimum loss of functions. The loss of adaptive capacity means the loss of opportunities,

which reduces systems’ rehabilitation probability during periods of reorganization and

renewal. Enhancing systems’ adaptive capacity is necessary to improve systems’ resi-

lience. There are four critical factors that enhance adaptive capacity of social–ecological

systems: (1) learning to live with change and uncertainty; (2) nurturing diversity for

resilience; (3) combining different types of knowledge for learning; and (4) creating

opportunities for self-organization toward social–ecological sustainability (Berkes et al.

2008).

Adaptive capacity has six characteristics (Lewin 1999; Johnson 2002), which can be

described as: (1) Self-organized personal relationship emerges to complex adaptive

behavior; (2) information from the external environment enters the system and impinges on

these relationships as either positive or negative feedback; (3) the personal relationships

are changed and the complex behavior adapts; (4) system learns to live with change and

uncertainty; (5) system combines different types of knowledge to nurture diversity for

Fig. 1 Change in resilience and adaptive cycle. Adapted from Gunderson (2001) and Carpenter et al. (2001)

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resilience; and (6) system creates opportunity for self-organization toward social–eco-

logical sustainability (Cilliers and Spurrett 1999).

2.2.1.3 Adaptive management Adaptive management (AM), also known as adaptive

resource management (ARM), is an integrated, multidisciplinary approach for confronting

uncertainty in the natural environment (Holling 1978). Adaptive management process

takes an unconventional view on models of renewable resources and function of resource

management, where policies are made based on systems feedback behavior and deliberate

attempts of managers to disturb system environment (Walters 1986). Researchers also view

adaptive management as a systematic approach and process for continual improving

management policies and practices on social–ecological systems (Chapin et al. 2009;

McLain and Lee 1996; Tompkins and Adger 2004). Adaptive management is a learning

process to improve long-term management outcomes. It deals with the dilemma between

gaining knowledge to improve management in the future and achieving the best short-term

outcomes based on current knowledge (Allan and Stankey 2009).

The concept of adaptive management is useful to guide management practices. Adap-

tive management identifies uncertainties and establishes methodologies to test hypotheses

and mitigate the uncertainties. Adaptive management is an action research tool which aims

to solve system challenges and also learn knowledge from this process. Adaptive man-

agement concerns the need to learn and the cost of ignorance, while traditional manage-

ment focuses on the need to preserve and the cost of knowledge (Plieninger et al. 2010). In

the adaptive management process, policy makers should consider not only the present but

also future stakeholders.

2.2.2 Engineering and disaster domain

With the increasing concern on resilience of engineering and disaster, the concept of

resilience is applied to deal with issues raised from urban communities and infrastructure

sustainability from 11 aspects (Fig. 2; Bruneau et al. 2003). Resilience can be conceptu-

alized as encompassing four interrelated dimensions: technical, organizational, social, and

economic (TOSE dimensions model). The concept of resilience demonstrates four prop-

erties: robustness, redundancy, resourcefulness, and rapidity (4R’s), and three results (or

outcomes): (1) more reliable, (2) fast recovery, and (3) low socioeconomic consequences.

Scholars have also applied the concept of resilience in different engineering areas

including the lifelines engineering and distributed infrastructure systems (Chang and

Shinozuka 2004; Xu et al. 2007; Miles and Chang 2006). In existing research, the resi-

lience measurement framework is designed to describe the changing process of system

performance during disasters (Chang and Shinozuka 2004). The research of Chang and

Shinozuka (2004) reflects two characteristics of resilience in disaster scenarios: (1) It

addresses its multifaceted nature; (2) it reframes the measures in a probabilistic context and

demonstrates the concept of disaster resilience through quantitative measures. New

methods should be adopted in the process of building disaster-resilient communities, which

go beyond estimating physical losses and consider the complex, multiple dimensions of

resilience.

Resilience is a criticism of engineering practice and disaster prevention in previous

studies, which includes all aspects of hazard loss reduction during disasters. The concept of

resilience explores natural hazards from a broader perspective and mainly includes two

aspects: (1) the ability of systems holding original state; and (2) the self-organization

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capacity. In recent years, research on resilience of engineering and disaster emergences

some new features. Costs should be considered in optimizing resilience of engineering and

disaster, and the analytical framework of resilience is consisted of system identification,

resilience objective setting, vulnerability analysis, and stakeholder engagement (Francis

and Bekera 2014; Bocchini and Frangopol 2010). With the tendency of climate change

impacting on urban systems, the framework of urban climate resilience has been designed

to integrate theoretical and empirical knowledge (Tyler and Moench 2012). Infrastructure

system presents network feature in large scale, and network resilience has become an

important research theme (Miller-Hooks et al. 2012).

2.2.3 Economic and organizational behavior domain

In the organizational behavior domain, the origin of resilience dates back to the 1990s

(Comfort 1994). Organizational behavior focuses on resilience from a process perspective,

and resilience is a risk management strategy in the process of continuity management.

Resilience shows how communities and organizations cope with dynamic and unpredicted

events and examine the self-organizing processes by which communities act in their own

interests to mitigate risks (Comfort et al. 1999).

Resilience of organizational behavior manifests two main aspects: (1) the ability of

organizations and personals to ‘‘bounce back’’ from adverse states under a contingent

rather than a prescriptive situation (Paton et al. 2001); (2) reducing the consequences of

organization failure, which relates to the concept of dynamic resilience, and focuses on

attaining a target level of functioning (Bruneau et al. 2003). Disaster risk reduction and

vulnerability are also emphasized on organization resilience capacity, which transfers

Technical Organizational

Social Economic

4 Dimensions

Robustness Rapidity

Redundancy Resourcefulness

4 Properties

More reliability Faster recovery

Lower consequences

3 Results

RESILIENCE

Fig. 2 Aspects of resilience in engineering and disaster domian. Adapted from Bruneau et al. (2003)

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emphasis from outcome to process (Manyena 2006; Haas et al. 2007). The organization

resilience is a fundamental dimension of resilience that shows the ability of system

restructuring. The resilience of organizational behavior focuses on competencies and

systems, which is an extension of resilience to organization behavior domain as a set of

adaptive capacities.

There is close relationship between organization and economic resilience. For example,

economic resilience identifies a set of options and assumptions, and organization managers

can optimize their choices through previous options of economic resilience (Rose and Liao

2005). Organizational analysis identifies limitations in managerial abilities and overcomes

them through resilience. In the economic domain, the resilience mode has been investi-

gated to reduce loss and build sustainability after natural hazards, and the roots of resi-

lience are also analyzed, including endogenous driving action and stimulation of private

investors and public policy makers’ actions (Mileti 1999). Economic resilience is the

behavior of business adaptive and community response, which upgrades the economic

performance of systems through resilience assessment methods and analytical framework

(Dahlhamer and Tierney 1998; Rose et al. 2007).

Since 1990s, economic resilience has been a systematic research area, in which con-

struct theory was been proposed and applied in practice. Economic resilience mainly

covers two aspects: (1) Incorporation of static and dynamic concepts: static economic

resilience refers to efficient allocation of existing resources, and dynamic economic resi-

lience means speeding recovery through repair and reconstruction of the capital stock

(Rose 2004, 2007). (2) Study hierarchy: the microeconomic level on individual firms,

households, or organizations; the mesoeconomic level on economic sector, individual

market, or cooperative group; and the macroeconomic level on all individual units and

markets combined, including interactive effects (Dahlhamer and Tierney 1998; Rose 2007;

Rose and Lim 2002; Wein and Rose 2011; Godschalk 2003).

Regional economic has become a mainstream of economics research. Regional eco-

nomic resilience has been one important research theme in recent years. Some scholars

have done studies on regional economic resilience from different aspects, such as

designing modeling, analyzing evolutionary approach, and reaction to major recessionary

shocks (Rose and Liao 2005; Simmie and Martin 2010; Martin 2012; Navarro-Espigares

et al. 2012).

3 Data collection and analysis

3.1 Data collection

Web of Science (WoS) is the most frequently used database for bibliometric analysis in

academic domains, such as civil engineering and medicine (Rojas-Sola and de San-An-

tonio-Gomez 2010; López-Illescas et al. 2008). Thirty-year data (i.e., 1985–2014) of

resilience publication were collected from the Web of Science online interface (http://

www.isiknowledge.com) on July 20, 2015. The database range included the Social Science

Citation Index and the Science Citation Index provided by WoS.

For each article, all information relevant to the bibliometric analysis was downloaded as

text format. The information included Author(s), Editor(s), Title, Source, Addresses,

Publication Time, Times Cited, Cited References, Keywords, Language, and Web of

Science Category. We retrieved articles whose titles included ‘‘resilience’’ in the Social

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Science Citation Index and the Science Citation Index. In total, 6502 articles were retrieved

between 1985 and 2014 and used for bibliometric analysis.

3.2 Analysis method

In order to quantitatively analyze the evolution trends of resilience science, we divided the

analysis process into two steps. First, we created citation reports and analyzed retrieval

results in the Web of Science. Through this process, we obtained the citation statistical

indicators and number distribution of articles. Then, we used the CiteSpace software to

further analyze the retrieval results and interpreted analysis results by using bibliometric

and network analysis methods.

CiteSpace was selected as it has different parameters settings and selections which can

be used to undertake bibliometric analysis (Song et al. 2016; Chen 2017): The counting of

publication is the most commonly used method for country-level evaluation of scientific

research and its impact (Zheng et al. 2014; Merigó et al. 2016). Research institutes are the

basic units of scientific research and technology development. The output level of institute

reflects its influences on knowledge diffusion (Han et al. 2014; Cassi et al. 2014). Highly

productive journals and authors can help junior scholars to understand a new academic

field (Huang 2015; Yu et al. 2017). The evolution of research topics reflects the devel-

opment trends of science (Song et al. 2014). Based on the above analysis, four aspects of

the research evaluation process were explored: (1) dynamic evolution trend of publication

production; (2) evolution trend by geographic distribution; (3) evolution of academic

communities, including main research institutions, journals, and authors; and (4) knowl-

edge diffusion and collaboration networks structure of the most important research topics.

Bibliometric analysis method has been used to analyze the evolution trends in different

science research areas (Li and Ye 2016; Diez-Vial and Montoro-Sanchez 2017), such as

information science (Levitt and Thelwall 2016; Qu et al. 2017), computer science (Fer-

nandes and Monteiro 2017), communication science (Khan et al. 2016), agricultural sci-

ence (Winarko et al. 2016), environmental science (Rabiei et al. 2017), and management

science (Nair and Gibbert 2016). This bibliometric analysis method is also used to analyze

the trends in resilience science from a socio-ecological perspective (Xu and Marinova

2013). CiteSpace is an effective set of effective visualization software for bibliometric

analysis (Chen 2017) and can be used to analyze the evolution trends of science from the

perspectives of geographic distribution, academic communities, and research topics (Fang

2015; Song et al. 2016; Zhu and Hua 2017). Thus, the bibliometric analysis methodology

used in this study can support the analysis results and conclusions to explore the science of

resilience.

4 Data analysis results and visualization

In this section, the evolution trend of resilience science was quantitatively analyzed by

identifying its knowledge foundation, geographic distribution, academic community, and

collaboration structure. The analysis results explained how far resilience has been pro-

gressed as a scientific discipline. These analyses provided insights regarding key scholars

and research institutions, the state of the research field, core topics of focus, and primary

development trends in resilience science for future researchers.

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4.1 Evolution of publications production

The final database including 6502 articles was retrieved in the Social Science Citation

Index and the Science Citation Index of WoS between 1985 and 2014. Figure 3 shows the

evolution trend of publication production between 1985 and 2014. The number of articles

published per year shows a nonlinear rising trend as shown in Fig. 3. The number of

publications shows an upward trend in recent 30 years. Basically three stages of the

evolution trend can be identified: initial stage (1985–1994), developing stage (1995–2004),

and prosperous stage (2005–2014).

There were a stable number of publication productions at the initial stage, after which

the number of publications increases rapidly. During 1985–1994, the number of papers

published each year is less than 20 except that in 1993. At the developing stage, the number

of publications shows a rapid increasing trend and previous studies shows a slow

increasing trend (Janssen et al. 2006). Comparing to studies in 1995, the number of

publications increased six times in 2004. During 1995–2004, various academic commu-

nities began systematic research on resilience science, such as Resilience Alliance. After

2005, the number of publications shows a sharp increase at the prosperous stage. This sharp

increase coincides with the increased attentions on environmental change, climatic change,

and extreme disasters (Rose 2004; Cardona et al. 2008; Dong and Frangopol 2016; Brown

2014; Hawkes and Keitt 2015).

The proportion of publications presents huge differences at different stages (Fig. 4). The

numbers of articles are 126 and 697, respectively, at the initial and developing stages,

1986 1988 199 0 19 92 1 994 1996 1998 2000 20 02 20 04 2006 2008 2010 201 2 2 014

0

200

400

600

800

1000

1200

N um

be r o

f p ub

lic at

io n

Year of publication

Fig. 3 Articles published per year during 1985 and 2014

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which is only 12.7% of total number across 30-year data. At the prosperous stage, the

number of articles is 5679, which constitutes 87.3% of total number in the latest 30 years.

4.2 Evolution of countries

Different countries show different evolution trends in the resilience science. In order to

analyze evolution trend of different countries, this paper analyzed the number of articles

published in different countries and periods. This study also analyzed the evolution trend of

productive countries in the latest 30 years.

When analyzing publication production of different countries form a quantitative per-

spective, the result shows that only a few countries contributed a large proportion of total

publication production (Fig. 5). The top 10 countries produced 88.2% of all publications

during the latest 30 years. The USA has the highest production level by providing more

than 43.6% of the total publications, followed by three countries that surpass 5%: UK

(13.3%), Australia (9.2%), and Canada (6.2%) (Table 2).

Figure 5 shows the global evolution processes. The key observations include: (1) The

USA, Canada, Australia, and European developed countries have an absolute contribution

on resilience science research, especially USA being always in the first place; (2) the

number of publications in the top 10 countries shows a nonlinear increasing trend; (3) some

Asian countries show a relative decline trend on research of resilience science, such as

India and Japan; (4) China presents a significant growth trend on research of resilience

science since 1995; and (5) the relative ranking of the top 10 countries has been constantly

changing in the latest 30 years (Fig. 6).

4.3 Evolution of research institutions

This analysis is used to assess the ranking of research institutions leading in resilience

science. The indicator of publication production (i.e., number of papers) is analyzed; the

Initial stage Development stage Prosperous stage 0

20

40

60

80

100

pr op

or tio

n of

p ap

er s

(% )

Fig. 4 Percentage of publications production at different stages

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Fig. 5 Publications production of top 10 countries in different periods

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result is used to identify the ranking of research institutions in the latest 30 years. The

collaboration network of research institutions is also considered using bibliometric method.

There are more than 4000 research institutions that have studied on resilience science

during the latest 30 years. The Arizona State University has the highest production with 93

articles (1.43% of the total 6502 articles), and in total 10 research institutions surpass 1%

of the total articles. The top 20 research institutions contribute nearly 20% of the total

production during the latest 30 years, and most production belongs to the research insti-

tutions which have higher production amount (Table 3). Among the 100 most productive

research institutions, the USA represents the highest number (55), followed by the Aus-

tralia (12), the UK (10), Canada (6), South Africa (4), Sweden (3), Norway (3), and Israel

(3) (Fig. 7).

Table 3 shows that research institutions have different academic performance in dif-

ferent periods. There are nine main research institutions always in the top 20 on resilience

research. The ranking of above nine research institutions has been changed during the latest

30 years (Fig. 8).

The collaboration between different research institutions is frequent in the latest

30 years. The collaboration frequencies of six research institutions are higher than 50.

They are Arizona State University (78), University of Minnesota (60), University of

Michigan (59), Columbia University (57), Yale University (54), and University of

Table 2 Evolution of number of articles in the most productive countries

Country 1985–1994 1995–2004 2005–2014 1985–2014

Np Rank Np Rank Np Rank Np Rank

USA 68 1 358 1 2410 1 2836 1

UK 13 2 75 2 777 2 865 2

Australia 5 4 45 3 545 3 595 3

Canada 8 3 39 4 357 4 404 4

Germany 3 5 17 7 264 5 284 5

China 20 5 228 6 248 6

Sweden 20 6 167 7 187 7

South Africa 3 6 12 9 163 9 178 8

France 2 10 10 11 166 8 178 9

Netherlands 3 7 14 8 155 10 172 10

Italy 5 19 137 11 142 11

Spain 6 18 134 12 140 12

Brazil 5 20 130 13 135 13

Israel 3 8 8 14 111 14 122 14

Switzerland 11 10 92 16 103 15

Norway 8 15 94 15 102 16

Japan 3 9 8 16 82 17 93 17

New Zealand 1 11 9 12 63 19 73 18

South Korea 8 17 64 18 72 19

Belgium 9 13 51 20 60 20

Np number of articles published, Rank position in different periods

490 Nat Hazards (2018) 90:477–510

123

Washington (52). The Arizona State University (83.9%) represents the highest percentage

of collaboration in the top 10 research institutions, while that of other nine research

institutions also exceeds 50% (Fig. 9).

The collaboration of research institutions presents different characteristics. More than

4000 research institutions participate in resilience research. Only 502 research institutions

have collaborative history, which means that most research institutions are isolated. The

top 20% research institutions contribute more than 60% collaboration frequencies. The

networks characteristic of scientific collaboration becomes more and more obvious. Sci-

entific collaboration networks have a structured characteristic and can be divided into

different communities (Newman 2001). There are two active communities in the collab-

oration network of research institutions, which have higher collaboration frequency and

proportion (Fig. 10).

USA

UK

CANADA

AUSTRALIA

SOUTH AFRICA

NETHERLANDS

JAPAN

ISRAEL

INDIA

GERMANY

USA

UK

AUSTRALIA

CANADA

SWEDEN

GERMANY

NETHERLANDS

CHINA

SOUTH AFRICA

SWITZERLAND

USA

UK

AUSTRALIA

CANADA

GERMANY

CHINA

SWEDEN

FRANCE

SOUTH AFRICA

NETHERLANDS

4102-50024002-59914991-5891

Fig. 6 Evolution trends of top 10 countries in different periods

Nat Hazards (2018) 90:477–510 491

123

Figure 10 shows two active communities in collaboration network of research institu-

tions. There are differences on topology characteristic of these two active communities.

The first community (the right of Fig. 10) has more nodes and link frequencies, and the

second community (the left of Fig. 10) has relative few nodes and link frequencies. In the

first community, there are eight key nodes including Arizona State University, University

of Michigan, Columbia University, Yale University, University of Wisconsin, Stockholm

University, Stanford University, and University of California San Diego. These eight

research institutions are all within the top 20 research institutions, and they are all US

research institutions except the Stockholm University. As a leading research institution of

resilience, the Stockholm University established Stockholm Resilience Centre in 2007 and

has some well-known scholars on research of resilience: For example, Folke is the No. 2

author on number of publications and the No. 1 author with the highest citation frequency.

In the second community, four key nodes are found including University of Minnesota,

Dalhousie University, Harvard University, and University of British Columbia. These four

research institutions also are part of the top 20 research institutions except the University of

British Columbia which was the birthplace of resilience.

Table 3 Number and proportion of articles in top 20 research institutions

Research institution 1985–1994 1995–2004 2005–2014 1985–2014

Np Np (%) Np Np (%) Np Np (%) Np Np (%)

Arizona State University 1 0.79 13 1.87 79 1.39 93 1.43

Yale University 1 0.79 6 0.86 74 1.30 81 1.25

University of Michigan 1 0.79 10 1.44 70 1.23 81 1.25

Columbia University 12 1.72 66 1.16 78 1.20

University of Minnesota 5 3.97 14 2.01 57 1.00 76 1.17

Harvard University 3 2.38 7 1.00 63 1.11 73 1.12

University of Wisconsin 7 5.56 15 2.15 49 0.86 71 1.09

Stockholm University 12 1.72 59 1.04 71 1.09

University of Washington 1 0.79 10 1.44 57 1.00 68 1.05

University of Queensland 3 0.43 62 1.09 65 1.00

University of California, San Diego 6 0.86 55 0.97 61 0.94

Stanford University 4 0.57 52 0.92 56 0.86

Duke University 2 0.29 51 0.90 53 0.82

University of Maryland 1 0.79 5 0.72 46 0.81 52 0.80

Dalhousie University 3 0.43 49 0.86 52 0.80

James Cook University 1 0.14 50 0.88 51 0.78

University of California, Los Angeles 3 2.38 1 0.14 46 0.81 50 0.77

University of Melbourne 1 0.14 46 0.81 47 0.72

Kings College London 2 0.29 45 0.79 47 0.72

University of Oxford 3 0.43 42 0.74 45 0.69

Np number of articles published

492 Nat Hazards (2018) 90:477–510

123

4.4 Evolution of journals and authors

This paper analyzed the evolution trend of journal from two aspects: (1) Which journals

published more articles in various knowledge categories? (2) Which journals got more

citations in the process of knowledge diffusion? In total, 6502 articles were retrieved

between 1985 and 2014, which have been published in about 2189 different journals. This

shows a disperse trend of the research topics covered in this paper.

A total of 6502 articles were analyzed by using the CiteSpace software. Through above

process, the top 10 journals in which most papers have been published were summarized,

and the top 10 journals which had most citations are also summarized in Table 4.

Table 4 (left) lists the top 10 journals in which most papers have been published.

Gerontologist published the most articles (265), followed by Ecology and Society (159)

and International Journal of Psychology (94). All of top 3 journals published more than 100

articles. Top 10 journals can be divided into four categories: multidisciplinary (3), psy-

chology (3), medicine (2), and ecology and environment (2).

Table 4 (right) shows the top 10 journals which get the most citations. As the origin of

resilience, five ecological journals contribute 44.7% cited frequencies in the top 10 jour-

nals. Then, four psychology and medicine journals also contribute 39.1% citation fre-

quencies. Science also contributes 10.3% citation frequencies, which shows that resilience

has become an important and hot research theme in academic domains.

The 6502 articles were published by nearly 16,000 authors. This shows that the col-

laboration of authors is frequent. Who have published the most articles in various

knowledge domains of resilience and who got the most citations? How authors mutually

USA Australia UK Canada South Africa Sweden Norway Israel China Netherlands Singapore 0

10

20

30

40

50

60 N

um be

r o f t

op 1

00 re

se ar

ch in

st itu

tio ns

Fig. 7 Number of top 100 research institutions in main countries

Nat Hazards (2018) 90:477–510 493

123

collaborated on research of resilience science? In order to answer these questions, this

paper identified the most productive and most collaborative authors (Table 5).

Table 5 (left) shows the top 11 authors who have the highest number of publications,

and Table 5 (right) shows the top 11 authors who have the highest number of citations.

Ungar (Professor of Resilience Research Centre at the Dalhousie University) has the

highest number of publications. Folke (Professor of Resilience Research Centre at the

Stockholm University) is the most cited author.

A total of 1117 authors have 561 times of collaborations in the total 16,000 authors,

which means that most authors are isolated. The authorship collaboration network presents

different characteristics in different periods as shown in Figs. 11 and 12.

Figure 11 (top) shows network structures of authorship collaborations during

1985–1994. The network trend is not obvious and shows discrete isolate on the whole.

Collaboration between authors is not active, and none active community could be found in

the coauthor network. Figure 11 (bottom) shows network structures of authorship col-

laborations during 1995–2004. The network trend becomes obvious with comparison to the

last 10 years (1985–1994). One active community is identified in the coauthor network.

The community has five key nodes including Folke C, Walker B, Elmqvist T, Nystrom M,

UNIV WISCONSIN

UNIV MINNESOTA

HARVARD UNIV

UNIV CALIF LOS ANGELES

ARIZONA STATE UNIV

UNIV MICHIGAN

UNIV WASHINGTON

YALE UNIV

UNIV MARYLAND

UNIV WISCONSIN

UNIV MINNESOTA

ARIZONA STATE UNIV

UNIV MICHIGAN

HARVARD UNIV

YALE UNIV

UNIV MARYLAND

UNIV WASHINGTON

UNIV CALIF LOS ANGELES

ARIZONA STATE UNIV

YALE UNIV

UNIV MICHIGAN

HARVARD UNIV

UNIV MINNESOTA

UNIV WASHINGTON

UNIV WISCONSIN

UNIV MARYLAND

UNIV CALIF LOS ANGELES

4102-50024002-59914991-5891

Fig. 8 Evolution trends of top 9 research institutions in different periods

494 Nat Hazards (2018) 90:477–510

123

Ar izo

na St

ate U

niv

Un iv

M inn

eso ta

Un iv

W ash

ing ton

Co lum

bia U

niv

Un iv

M ich

iga n

Un iv

Qu ee

ns lan

d

Ya le

Un iv

Sto ck

ho lm

U niv

Un iv

W isc

on sin

Ha rva

rd Un

iv

40

50

60

70

80

90

100 Fr

eq ue

nc y

of c

ol la

bo ra

tio n

Frequency of collaboration

40

50

60

70

80

90

100

Percentage of collaboration (%)

Pe rc

en ta

ge o

f c ol

la bo

ra tio

n (%

)

Fig. 9 Frequency and percentage of collaboration in top 10 research institutions

Fig. 10 Two active communities in collaboration network of research institutions

Nat Hazards (2018) 90:477–510 495

123

and CS Holling. They are all top cited authors except CS Holling who is known as the

founder of resilience science.

Figure 12 shows the network structures of authorship collaborations during 2005–2014.

The network trend becomes more obvious comparing to the last 10 years (1995–2004).

There are four active communities in the coauthor networks which are independent to each

Table 4 Top 10 journals during 1985–2014

Rank Papers published and cited 1985–2014

Journal No. of papers

Journal No. of citations (no. of papers)

1 Gerontologist 265 Ecology and Society 3677 (159)

2 Ecology and Society 159 American Psychologist 2826 (15)

3 International Journal of Psychology

94 Ecosystems 2605 (23)

4 Psychology and Health 82 Science 2256 (17)

5 PloS One 66 Global Environmental Change— Human and Policy Dimensions

2250 (29)

6 Biological Psychiatry 41 American Journal of Orthopsychiatry

2100 (20)

7 Development and Psychopathology

37 Child Development 1845 (13)

8 Natural Hazards 37 Development and Psychopathology

1830 (37)

9 Global Environmental Change— Human and Policy Dimensions

29 Journal of Personality and Social Psychology

1306 (8)

10 Annals of the New York Academy of Sciences

27 Trends in Ecology and Evolution 1288 (8)

Table 5 Top authors during 1985–2014

Rank Papers published and cited 1985–2014

Authors No. of papers Authors No. of citations

1 Ungar M 39 Folke C 7150

2 Folke C 31 Walker B 4744

3 Bonanno GA 27 Hughes TP 3211

4 Masten AS 21 Rutter M 3068

5 Walker B 21 Bonanno GA 2823

6 Allen CR 20 Nystrom M 2773

7 Liebenberg L 20 Bellwood DR 2586

8 Greeff AP 19 Carpenter S 2493

9 Berkes F 18 Elmqvist T 2344

10 Charney DS 18 Luthar SS 2245

496 Nat Hazards (2018) 90:477–510

123

other. These four communities have more nodes and links. Figure 12 (top left) shows that

Ungar M and Liebenberg L have the closest collaborative relationship among the entire

collaboration networks. Figure 12 (top right) shows more network diversity comparing

with above-mentioned network (Ungar M and Liebenberg L). Figure 12 (bottom) shows

the other two active communities in coauthor network. Charney DS and Folke C are the

key nodes in each community.

Fig. 11 Network structures of authorship collaborations during 1985–2004

Nat Hazards (2018) 90:477–510 497

123

4.5 Evolution of research topics

The development process of resilience science can be reflected by the evolution of research

topics. This analysis describes the diffusion process of resilience science in different

academic research areas and lists the most important topics in each period. In order to do

so, the main categories and important keywords have been considered and used in ana-

lyzing the development process of resilience science in the latest 30 years.

The development process of resilience science shows different characteristics in dif-

ferent categories. A total of 6502 articles refer to more than 200 categories. Psychiatry

leads categories with 700 articles (10.8%). The top 10 categories represent 63.9% of the

production during the entire time period. Different categories have different evolution

trends during the entire time period (Fig. 13).

Figure 13 shows the evolution trends of multi-category in the latest 30 years. At the

initial stage (1985–1994), important categories construct co-categories network which had

a few key nodes. Psychiatry is an important node of an independent sub-network. At the

developing stage (1995–2004), co-categories networks were expanded to different cate-

gories and become complicated comparing to the initial stage. There is no independent

sub-network, and all of the main categories generate a whole co-categories network. At the

prosperous stage (2005–2014), the co-categories network became more complex and

showed obvious network characteristics. Co-categories network has more category nodes

and link frequencies. Top categories are identified by two indicators of occurrence fre-

quency and network centrality which continually change in different periods (Table 6).

Table 6 shows the evolution of top categories in different periods. Generally the ranges

of top categories become more and more wide. At the initial stage (1985–1994), top

categories only include environmental sciences, ecology, and engineering. Ecology is the

origin of resilience science, and environmental science is a category which closely relates

Fig. 12 Network structures of authorship collaborations during 2005–2014

498 Nat Hazards (2018) 90:477–510

123

Fig. 13 Evolution trends of multi-category during 1985–2014

Nat Hazards (2018) 90:477–510 499

123

to ecology. Engineering is also an important origin of resilience. At developing stage

(1995–2004), psychology and psychiatric also become top categories. At the prosperous

stage (2005–2014), the top categories diffuse to some non-traditional areas, such as eco-

nomics and behavioral science.

Resilience science is a new discipline supported by multidisciplinary knowledge.

Although different categories show some unique features, these categories have the same

roots and core connotations. The definitions of resilience in different categories are all

derived from the ecology domain (Holling 1973; Hosseini et al. 2016). The core conno-

tation is that resilience reflects the ability of system to maintain or rapidly return to desired

functional status in the face of a disturbance, to adapt to change, and to quickly transform

systems that limit current or future adaptive capacity (Lozupone et al. 2012; Meerow et al.

2016). Although the TOSE dimensions model is proposed in the engineering domain, this

model provides a common framework for the resilience research in different categories

(Bruneau et al. 2003; Aldrich and Meyer 2015).

TOSE model designs a resilient system which has four properties: robustness, redun-

dancy, resourcefulness, and rapidity. The functional status of system can be described from

four interrelated dimensions: technical, organizational, social, and economic (Cimellaro

et al. 2010; Pagano et al. 2017). These measures ensure that the system is resilient and

achieves three goals in the face of a disturbance: more reliable, fast recovery, and low

socioeconomic consequences (Tyler and Moench 2012; Francis and Bekera 2014). Resi-

lience research in different domains focuses on certain dimensions, properties, and goals.

For example, community resilience is built from social and economic dimensions and

encompasses contemporary understandings of stress, adaptation, wellness, and resource

dynamics (Norris et al. 2008). From the perspective of behavioral science, social capital

plays an important role in enhancing community resilience (Aldrich and Meyer 2015). In

the domains of engineering and disaster, resilience studies emphasize the robustness of

technical dimension (Mattsson and Jenelius 2015; Frangopol and Soliman 2016). Resi-

lience research in different domains constitutes a unified resilience science.

Thus, the resilience research has the common core connotation in different domains. We

can use clusters of ‘‘keywords’’ and correlations among those keywords to match groups of

researchers from different domains, different countries, and different communities. The

analysis of keywords can provide the basic information of an article’s core content and

help researchers to track the development trends of research topics in the different phases

of resilience research. The change in keywords represents the most important research

Table 6 Top categories in different periods

Period 1985–1994 1995–2004 2005–2014

Categories Environmental science Ecology Engineering

Psychology Environmental science Ecology Engineering Environmental studies Psychiatric

Psychology Environmental science Ecology Engineering Environmental studies Psychiatric Neuroscience Business and economics Social science Behavioral science

500 Nat Hazards (2018) 90:477–510

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topics on resilience science during the 30 years. Through the analysis of keywords, 500

keywords were detected, appearing 16,215 times. 69.2% keywords appear more than once,

while 41.6% more than 10 times and 7.4% more than 100 times. Among these keywords,

some are widely used far more than the average frequency. Resilience is used 1744 times

and represents 10.8% of the total number of times that keywords were used. The ten most

widely used keywords represent 27.7% of the total number of times. The percentage

increases to 47.1% for the top 30 keywords and to 77.9% for the top 100 keywords.

Through analyzing occurrence frequency of 500 keywords, 30 keywords can be found,

which are widely used by researchers and occupy the top ranking in the latest 30 years

(Table 7). The top 30 keywords have only ten keywords which always are presented during

the entire period. Most top 30 keywords had emerged at development stage (1995–2004).

Table 7 Evolution of top 30 keywords in different periods

Keywords Occurrence frequency

1985–1994 1995–2004 2005–2014

Resilience 8 112 1624

Stress 34 329

Vulnerability 31 321

Children 3 56 282

Risk 28 296

Management 20 285

Health 23 260

Depression 16 249

Adaptation 9 248

Climate change 4 373

Systems 12 225

Posttraumatic stress disorder 8 203

Social–ecological systems 205

Mental-health 15 181

Adolescents 1 17 177

Trauma 179

Biodiversity 22 149

Social support 20 133

Scale 19 127

Diversity 1 14 129

Adjustment 17 124

Dynamics 4 18 112

Sustainability 13 119

Model 1 17 123

Ecosystems 1 16 115

Framework 4 126

Stability 8 31 83

Recovery 5 11 106

Perspective 1 11 105

Protective factors 22 90

Nat Hazards (2018) 90:477–510 501

123

Social–ecological systems and trauma were relatively new concepts on resilience science,

which became important research topics after 2005.

Table 7 shows the evolution trend of the top 30 keywords in the latest 30 years. The top

30 keywords were identified through occurrence frequency. Three groups of top keywords

are identified at different periods, which reflect the evolution of research topics at different

periods (Fig. 14).

Figure 14 shows the evolution trends of the top 10 keywords in different periods. The

top 10 keywords of each period have been changed in the latest 30 years, which reflects the

transformation of important research topics on resilience science. The top 10 keywords in

early years fell out the top 10 ranking at later stages. There are only few top 10 keywords

with relatively stable ranking.

resilience

stability

recovery

disturbance

behavior

dynamics

desert stream

adolescence

children

growth

resilience

children

stress

stability

risk

competence

health

vulnerability

biodiversity

protective factors

resilience

climate change

stress

vulnerability

children

risk

management

health

depression

adaptation

4102-50024002-59914991-5891

Fig. 14 Evolution trends of research topics during 1985–2014

502 Nat Hazards (2018) 90:477–510

123

5 Future research directions

Through analyzing the evolution of research topics, we find that the core topics and

research frontier of resilience science are constantly changing between 1985 and 2014. In

order to guide future resilience research, we identify the following important research

problems in resilience science from bibliometric records.

5.1 Definition of resilience

The definition of resilience is still an important research area. A definition applicable to

different domains needs to be given in future research (Hosseini et al. 2016; Meerow et al.

2016). Although the definitions of resilience have been proposed by researchers in the

different sub-categories of resilience science, these definitions have significant limitations

(Fletcher and Sarkar 2013; Hohenstein et al. 2015; Meerow et al. 2016). These definitions

apply to a single different domain, such as social and ecological domain (Holling

1973, 2001; Cuppens et al. 2012), engineering and disaster domain (Timmerman 1981;

Bruneau et al. 2003; Haimes 2009; Ayyub 2014), economic and organizational behavior

domain (Comfort et al. 1999; Paton et al. 2001; Rose 2004; Upton et al. 2016), and

psychology domain (Theron et al. 2013; Soenke et al. 2015). A common definition of

resilience should be proposed in the future research.

5.2 Measurement methods of network resilience

The measurement of resilience is an important step for resilience research. Hosseini et al.

(2016) reviewed the measuring methods of system resilience and divided previous mea-

suring methods into qualitative assessment approaches and quantitative assessment

approaches. Qualitative assessment approaches measure system resilience conceptually or

design the evaluation index which can be used to measure the level of system resilience

(Speranza et al. 2014; Labaka et al. 2015). Quantitative assessment approaches measure

system resilience by deterministic approaches and probabilistic approaches (Henry and

Ramirez-Marquez 2012; Franchin and Cavalieri 2015). The complexity of system is get-

ting stronger and presents a networked external representation. Network resilience should

be emphasized in the future research (Baroud et al. 2014; Zhang and Miller-Hooks 2014).

Network resilience can be optimized by changing network topology, which is important to

plan resilience (Ash and Newth 2007; Sterbenz et al. 2010; Baroud et al. 2014).

5.3 Mechanisms forming resilient status

Systems can achieve resilient status through three mechanisms: persistence, transition, and

transformation (Matyas and Pelling 2015; Meerow et al. 2016). Persistence is focused

largely on resilience research, which reflects the system resilience from the social and

engineering perspective (Fleischman et al. 2010). Persistence reflects that systems can

resist disaster and try to maintain the origin status. Transition and transformation reflect

system’s adaptive capacity to external disturbances or disasters (Seeliger and Turok 2013;

Bousquet et al. 2016). Future research should focus on the path to above three mechanisms.

Transition and transformation need to be more emphasized in the realization process of

resilient system. The priority of three mechanisms should also be considered in the future

research.

Nat Hazards (2018) 90:477–510 503

123

6 Conclusions

This paper systematically summarizes the definition and application of resilience in dif-

ferent domains. This study contributes to a global vision of evolution in the resilience

science during the latest 30 years. The results of this study can be of great utility for future

research on resilience science. This paper can be highlighted from the following aspects:

1. Summarizing the definition of resilience in social and ecological domain, engineering

and disaster domain, and economic and organizational behavior domain.

2. Demonstrating the great expansion of research in the domain of resilience in the latest

30 years by quantifying the evolution trend of publication numbers. This study divided

the latest 30 years into three stages: initial stage (1985–1994), development stage

(1995–2004), and prosperous stage.

3. Determining the evolution trend of research on resilience science by geographic

distribution. The result shows that the research of resilience science is extremely

concentrated in a few countries (e.g., top 10 countries are responsible for 88.2% of

scientific production in the latest 30 years). The USA has an absolute advantage on

research of resilience, and China presents a significant growth trend. The top 10

countries have maintained excellent levels of impact in resilience science.

4. Discussing the evolution of main research institutions leading in resilience science as

its research objective by the indicator of productivity. The result shows that research

institutions have different academic performance in different periods. Research

institutions construct a collaboration network which has two active communities with

higher collaboration frequency and proportion.

5. Establishing the publication preferences in the journals and authors with productive

and citation indicators. The results contribute to the identification of the leading

journals and authors on research of resilience science. The collaboration of authors

presents networks trend, and the networks trend of coauthor collaboration presents

different characteristics in different periods.

6. Identifying the evolution trend of categories and keywords on research of resilience

science in the latest 30 years. The results show that the ranges of top categories are

becoming wider, and the networks characteristic of co-categories has become obvious

in the latest 30 years. Top 10 keywords have taken place great change in the latest

30 years, which reflects the transformation of important research topics on resilience

science. Most early top 10 keywords fell out the top 10 ranking at later stages. There

are only few top 10 keywords (such as resilience and child) with relatively

stable ranking.

7. Identifying the important and difficult research problems in resilience science by

bibliometric records, the future research directions of resilience science should focus

on the common definition of resilience, the measurement methods of network

resilience, and the mechanisms of system to resilient status.

This study is valuable to guide the future studies in resilience science. The analysis

results provide information regarding influential scholars and research institutions, core

journals, primary countries, core topics of focus, and development trends in research on

resilience science for researchers in this field. This study analyzes the evolution trends of

resilience science by using the WOS core collection database, which includes the

important journals in which the research results of resilience are typically published.

Therefore, the bibliometric records analyzed in this study represent a sufficiently large and

504 Nat Hazards (2018) 90:477–510

123

high-quality body of research that accurately reflects the global picture of resilience

research, which constitute the knowledge base of resilience science. The results reveal

important highlights and future research directions of resilience science, which will help

researchers in resilience science for future collaboration and work.

Acknowledgements This research was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 71671053, 71390522, 71271065). The work described in this paper was also funded by the National Science and Technology Program, China (No. 2014BAL05B06), and the National Key Research and Development Program, China (No. 2016YFC0701808).

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