A Systematic Review and Conceptual Framework of Urban Infrastructure Cascading Disasters Using Scientometric Methods

Abstract

Urban infrastructure, the lifeline of modern society, consists of inherently multidimensional and interdependent systems that extend beyond various engineered facilities, utilities, and networks. The increasing frequency of extreme events, like floods, typhoons, power outages, and technical failures, has heightened the vulnerability of these infrastructures to cascading disasters. Over the past decade, significant attention has been devoted to understanding urban infrastructure cascading disasters. However, most of them have been limited by one-sided and one-dimensional analyses. A more systematic and scientific methodology is needed to comprehensively profile existing research on urban infrastructure cascading disasters to address this gap. This paper uses scientometric methods to investigate the state-of-the-art research in this area over the past decade. A total of 165 publications from 2014 to 2023 were retrieved from the Web of Science database for in-depth analysis. It has revealed a shift in research focus from single infrastructures to complex, interconnected systems with multidimensional dependencies. In addition, the study of disaster-causing factors has evolved from internal infrastructure failures to a focus on cascading disasters caused by extreme events, highlighting a trend of multi-factor coupling. Furthermore, predicting and modeling cascading disasters, improving infrastructure resilience, and information sharing for collaborative emergency responses have emerged as key strategies in responding to disasters. Overall, the insights gained from this study enhance our understanding of the evolution and current challenges in urban infrastructure cascading disasters. Additionally, this study offers valuable perspectives and directions for policymakers addressing extreme events in this critical area.

1. Introduction

Urban infrastructure refers to the collection of facilities necessary for the operation of a city [1]. As the lifeblood of a city, urban infrastructure is essential for its functioning, exhibiting complexity and systemic interdependencies across functional, physical, and organizational dimensions [2,3,4]. However, the increasing frequency of extreme events and natural disasters has amplified the risk of large-scale functional paralysis in urban infrastructure systems [5,6,7]. For example, extreme events such as the 7/7 terrorist attack in London paralyzed the transportation system and resulted in 775 casualties and 56 deaths among over 4000 passengers; the 2021 Texas winter storm caused widespread electrical blackouts and water outages and became the first billion-dollar disaster of that year; and the 2011 Tohoku earthquake and tsunami caused extensive damage on land and initiated large tsunami waves that devastated many coastal areas of Japan [8,9,10]. These disasters lead to significant adverse impacts that cannot be ignored, including environmental damage, economic loss, casualties, and societal dissatisfaction [11]. Furthermore, due to the interdependence of urban infrastructures, a terrorist attack, a natural disaster, or an internal failure that affects a single infrastructure can lead to the loss of functionality in its associated systems, resulting in cascading phenomena across multiple infrastructures [1,12]. Large-scale power and water outages, for example, can further compound the issue by impeding the proper functioning of urban infrastructure and triggering cascading disasters across various systems in both their functional and service dimensions [13,14].

Cascading phenomena are frequently likened to the “falling domino metaphor” [15]. These disasters escalate and evolve, triggering increasingly severe secondary events [16]. In urban infrastructure, cascading disasters can be defined as the sequence of failures in interdependent infrastructure systems triggered by an initial disruption, such as a natural disaster, a man-made failure, or other emergencies [17,18]. When a single infrastructure fails, it causes a chain reaction that leads to the inability of other interconnected systems, amplifying the impact and escalating the situation into a broader social crisis [19]. Given the increasing frequency of disasters and the growing infrastructure interdependencies, scholars have realized the need to focus more on urban infrastructure cascading disasters. This includes analyzing evolutionary characteristics and developing appropriate strategies to address disaster risks [20]. For example, Gong [21] and Yu et al. [22] employed uncertainty theory to establish a risk model for infrastructure cascading disasters. Similarly, Yu et al. [23] adopted a cascading disasters risk ontology modeling system and a case-driven method to bolster emergency response preparedness. Both studies demonstrate the effectiveness of enhancing organizations’ emergency response capabilities by developing risk models to address cascading disasters in urban infrastructure.

Although research on urban infrastructure cascading disasters has gained a lot of attention, as reflected in

Table 1. List of selected research syntheses.

Authors	Title	Year	Typology
Huggins et al. [24]	Infrastructural Aspects of Rain-Related Cascading Disasters: A Systematic Literature Review	2020	Disaster-causing factors are limited to rainfall disasters.
Wang et al. [26]	Literature review on modeling and simulation of energy infrastructures from a resilience perspective	2019	Research is limited to post-disaster emergency response.
Valdez et al. [12]	Cascading failures in complex networks	2020	Research subjects are beginning to focus on complex networks.
AghaKouchak et al. [30]	Climate extremes and compound hazards in a warming world	2020	Cascading disasters resulting mainly from extreme events are analyzed.
Guo et al. [25]	A critical review of cascading failure analysis and modeling of the power system	2017	The object of study is cascading faults in power systems.
Toscano et al. [29]	A domain ontology on cascading effects in critical infrastructures based on a systematic literature review	2022	An overview of theoretical concepts related to cascade effects and the ontology of cascade effects.

, which presents a list of reviews on this topic, a gap remains due to the lack of a comprehensive and systematic analysis from an integrated perspective. Particularly, the existing reviews exhibit several characteristics: First, most early studies focused on a single type of disaster. For example, Huggins et al. [24] focused on rainfall as the main reason for urban infrastructure cascading disasters. Second, some reviews often focus on a single type of infrastructure. For instance, Guo et al. [25] analyzed cascading failures within the power system only. Third, most studies provide the response strategies from a post-disaster recovery perspective. For example, Wang et al. [26] proposed strategies for enhancing urban infrastructure resilience after the disaster. As more scholars have acknowledged the multiplicity of disaster-causing factors and infrastructure interdependencies, some reviews have begun to analyze the convergence of disasters via complex network theory [12,27,28]. For example, Valdez et al. [12] summarized the models for cascading disasters based on complex network science. Nevertheless, the interdependence between urban infrastructures has not been thoroughly underexplored. There is a limited analysis of urban infrastructure cascading disasters triggered by coupled disaster-causing factors. In this regard, Toscano et al. [29] developed a domain ontology of cascading effects using the system approach to build ontology methods, and they then conducted a literature review of cascading effects in infrastructure, but their study still lacked an adequate quantitative analysis of critical research topics and trends.

To address these gaps, this paper uses CiteSpace 6.2 R6, a scientometric methodology. This scientometric analysis tool has powerful visualization features that can intuitively display hot research trends and dynamically present developments and emerging issues in the research field. We provide an in-depth and comprehensive review of current research on urban infrastructure cascading disasters, including their formation and evolution, as well as coping strategies and strategies to improve resilience for recovery in extreme events. The main objectives are as follows:

(1)

Identify the leading research authors, institutions, journals, and countries in the field of urban infrastructure cascading disasters;

(2)

Obtain the research themes, clusters, and emerging trends and map the intellectual structure of the field;

(3)

Develop a conceptual framework based on the analysis of existing studies and propose potential research directions for future research.

The findings of this study contribute to the global knowledge body of urban infrastructure disaster management by employing scientometric methods to review the full profile of cascading disasters. It identifies key research trends and gaps, offering a conceptual framework for exploring the impacts of coupled disaster-causing factors on interdependent infrastructure systems. The findings deepen the theoretical foundations of cascading disasters and highlight underexplored areas, guiding future studies in this domain. Practically, this research equips practitioners and policymakers with a comprehensive view to predict, manage, and recover from cascading disasters in urban environments. Ultimately, this research supports more adaptive, resilient urban infrastructures capable of mitigating the impacts of future extreme events.

2. Research Methodology

This study employed scientometrics methods to examine urban infrastructure cascading disaster research between 2014 and 2023. Scientometrics methods, categorized under science mapping, are an efficient technique for gathering an in-depth and comprehensive understanding of emerging research areas [31]. It helps provide a systematic and data-driven analysis of the research field by evaluating publications, citations, keywords, collaborations, and emerging topics using a set of quantitative techniques [32,33,34,35]. The research flowchart is depicted in Figure 1. It consists of five steps: (i) retrieving datasets through keyword searches and screening for relevant studies that match the research topic; (ii) applying scientometric techniques for quantitative analysis, including a basic analysis of co-authors, co-institutions, countries, and subject areas, as well as co-word analysis, cluster analysis, and keyword citation burst analysis; (iii) categorizing research themes within the field; (iv) proposing a thematic conceptual framework for research on urban infrastructure cascading disasters; and (v) discussing policy implications and future research directions.

2.1. Search Setting

The Web of Science (WOS) core database is the main database for this analysis due to its extensive coverage of studies in engineering, science, management, social sciences, and the humanities, encompassing the vast majority of relevant journals [36]. The topic query was applied to research articles with the following keywords or terms that are in titles, abstracts, and keywords, i.e., T, A, K = ((cascading disasters OR cascading failures) AND (infrastructure)). “Engineering”, “Environmental Science”, “Social Science”, “Management”, and “Multidisciplinary” are set as category labels. The search period was set from 2014 to 2023. Through the initial screening, 352 papers were obtained. Specifically, the screening followed standard criteria, including whether there was the duplication of content and whether the paper was relevant to the research objectives, excluding marginal or weakly relevant studies, research quality prioritizing high-quality study designs, and article accessibility. Following this screening, 165 papers were selected for the scientometric analysis.

2.2. Scientometric Analysis

This paper employs a scientometrics approach for a scientific and intuitive analysis of cascading disaster research in urban infrastructure. CiteSpace, an information-based visualization analysis software developed by Chaomei Chen using the Java 8 language, is employed to analyze co-occurrence networks derived from extensive bibliometric data [37]. It offers clustering diversity, maneuverability, and readability of information [38]. To date, numerous scholars have used CiteSpace to review studies in different areas [39,40]. Notably, scientometric analysis through Citespace also leverages the intrinsic functionalities of the Web of Science, which is considered optimal for literature review, and has been employed in similar studies by Luo and Wang [39], Li and Li [41], and Chen and Fang [42]. After scientometric analysis, an in-depth theme categorization analysis was conducted to propose a conceptual framework and discuss the research agenda in the future.

3. Results

3.1. Annual Publication Trends

Figure 2 illustrates the evolution of publications, which can be divided into three phases. In the first phase (2014–2016), research in this domain began to emerge but remained relatively underexplored, with limited publications. In the second phase (2016–2018), there was a notable surge in research activity, reflecting scholars’ growing interest in exploring this field. In the third stage (2018–2023), research reached a mature stage, exhibiting minor fluctuations but maintaining a generally stable trend.

3.2. Author and Institution Analysis

Author and institution analysis helps to evaluate the contributions and collaborations of individual researchers and their affiliated organizations within a specific domain.

Table 2. Statistics of highly published authors and highly cited authors.

Volume of Publications	Year	Author	Citation Frequency	Year	Author
3	2018	Beyza Jesus	69	2015	Min Ouyang
2	2014	Erdener Burcin Cakir	64	2014	Rinaldi Sa
2	2014	Daqing Li	54	2014	Sergey V. Buldyrev
2	2014	Qing Shuang	35	2014	Reka Albert
2	2014	Yongbo Yuan	35	2014	Motter Adilson E.
2	2014	Mingyuan Zhang	27	2014	Duenas-Osorio Leonardo
2	2015	Michael M. Danziger	26	2014	Dobson Ian
2	2015	Yiping Fang	24	2014	Crucitti Pierfilippo
2	2015	Auroop R. Ganguly	23	2016	Yacov Y. Haimes
2	2015	Gritzalis Dimitris	22	2016	Johansson Jan
2	2015	Kotzanikolaou Panayiotis	20	2014	Newman Mark
2	2015	Sansavini Giovanni	19	2014	Benjamin A. Carreras
2	2015	Theocharidou Marianthi	18	2015	Se Chang

presents the statistics and ranking of these prolific and influential authors. The ranking rule is as follows: When two authors have the same number of articles, their ranking is determined by comparing their earliest publication times. The author with the earlier publication time is given a higher rank. This produces the top 13 authors: Beyza Jesus emerges as the most prolific author. Notably, five authors, Erdener Burcin Cakir, Daqing Li, Qing Shuang, Yongbo Yuan, and Mingyuan Zhang, published two articles in 2014. Among the most cited, Min Ouyang leads with 69 citations, establishing their position as a core author in this domain. Following closely are Rinaldi Sa and Sergey V. Buldyrev, cited 64 times and 54 times, respectively, ranking second and third. These authors have significantly advanced the understanding of cascading disasters in urban infrastructure.

The author co-occurrence map via co-authorship patterns for urban infrastructure cascading disasters is presented in Figure 3, which reflects how closely scholars work together by displaying connections (links) between authors (nodes). It shows 228 nodes and 259 nodes with a network density of 0.01. As indicated in Figure 3, the authors of the published articles initially lacked a well-defined and dense collaborative network, while it should be noted that close cooperation among researchers is beginning to emerge. Notably, Erdener Burcin Cakir, Bony-Landrieu Aurelia, Perez-Jimenez Mario J, Daqing Li, Bhambri, and Rakesh are the center of relatively close connections, forming small-scale collaboration networks in recent years.

The number of articles published by countries and institutions is a key criterion for research authority in the field [43].

Table 3. Top 10 international publishing institutions and countries.

Frequency	Centrality	Year	Country	Frequency	Centrality	Year	Institution
64	0.53	2014	USA	6	0.07	2014	Universite Paris Saclay
57	0.11	2014	China	6	0.01	2018	ETH Zurich
17	0.2	2014	England	6	0.08	2014	Beihang University
15	0.11	2014	Italy	5	0	2018	Swiss Federal Institutes of Technology Domain
10	0.13	2018	Canada	4	0.02	2016	Harbin Institute of Technology
10	0.03	2014	France	4	0.01	2019	McMaster University
10	0.02	2018	Germany	4	0.03	2015	Rice University
10	0.07	2017	Spain	4	0.03	2016	Columbia University
8	0.18	2018	Switzerland	3	0.05	2021	Centre National de la Recherche Scientifique (CNRS)
7	0.11	2014	Netherlands	3	0.01	2018	City University of Hong Kong

lists the top 10 countries and organizations in terms of the number of articles. Figure 4 illustrates the collaborative network of countries and institutions. Among countries, the USA leads with 64 articles, accounting for 39% of the total, followed by China with 57 articles (35%). Other contributors include the UK (17 articles), Italy (15 articles), Canada, France, Germany, and Spain, each with 10 articles. Switzerland and the Netherlands follow with eight and seven articles. In terms of institutions, Universite Paris Saclay, ETH Zurich, and Beihang University tied for the lead with six articles each.

Through a detailed analysis of articles published by specific countries, we found that the United States has adopted a three-tiered response mechanism that emphasizes cross-sectoral collaboration and the division of authority and responsibility but suffers from an uneven distribution of resources [44]; China stresses grass-roots mobilization and cross-sectoral cooperation and integrates emergency management into grass-roots governance assessments [45]. Italy, Canada, France, Germany, and Spain, on the other hand, emphasize the specialization of civil defense, building community resilience, and strengthening engineering standards [46,47,48,49,50,51,52]. Several countries generally focus on rationalizing governance structures, applying early warning technologies and data-sharing platforms, and emphasizing public participation. As national cascading disaster management models have their focus, there is a need to strengthen cross-regional sharing of experiences, promote “localization” of policies, and strengthen international cooperation on disaster prevention in the future.

3.3. Literature Sources and Subject Categories

Figure 5 ranks the leading journals in urban infrastructure cascading disaster research. Among these journals, Reliability Engineering and System Safety ranks first with 23 publications, accounting for 14% of the total. This journal mainly publishes research focused on analyzing the reliability of loaded systems and related content. For example, Xian et al. [53] analyzed the damage inflicted by critical infrastructure systems during natural disasters, including cascading events, and modeled post-disaster recovery by considering the interdependencies of infrastructures. The rest of the top ten journals include International Journal of Disaster Risk Reduction (17 articles), International Journal of Critical Infrastructure Protection (13 articles), Journal of Infrastructure Systems (9 articles), Risk Analysis (6 articles), IEEE Access (5 articles), Scientific Reports (5 articles), Disaster Prevention and Management (4 articles), Computer-aided Civil and Infrastructural Engineering (4 articles), and IEEE Transactions on Industrial Informatics (4 articles). This reflects the interdisciplinary nature of the field, with publications emphasizing methods to protect, manage, maintain, enhance, and ensure the sustainability of critical infrastructure.

Subject categories are analyzed to present a temporal view of disciplinary focus in urban infrastructure cascading disaster research, as shown in Figure 6. The evolution of this research can be divided into three phases: Phase I, 2014–2016, is dominated by studies in engineering, electrical, computer, automation, and management field. For Phase II, 2017–2019, research expanded to environmental science, sustainable science and technology, transportation, and materials. For Phase III, 2020–2023, the focus shifted toward meteorology, earth sciences, economics, and interdisciplinary academic programs. The analysis underlines that the increasing multidisciplinary nature of urban infrastructure cascading disaster-related research. This field has evolved from addressing cascading failures in networked infrastructure systems to investigating various types of urban infrastructure failures and service disruptions triggered by natural disasters.

3.4. High-Frequency Co-Keyword Analysis

The co-occurrence of keywords is one of the most important analyses of science mapping that reveals the hot keywords and highlights the main research areas in the subject domain through time [54]. As presented in Figure 7, the author keyword was considered, and a network of 256 nodes and 1053 links was generated. The number of nodes indicates the number of keywords, while the links between keywords suggest that they have appeared in at least one article. The size of the keyword node reflects the frequency of its occurrence [55]. These high-frequency keywords represent the core content and research interests of authors in this field [56].

As presented in

Table 4. Top 10 high-frequency keywords in urban infrastructure cascading disasters research.

Ranking	Count	Centrality	Keywords
1	64	0.39	cascading failures
2	38	0.15	vulnerability
3	34	0.25	critical infrastructure
4	31	0.19	model
5	25	0.2	resilience
6	22	0.09	systems
7	21	0.19	framework
8	16	0.06	simulation
9	14	0.12	complex networks
10	12	0.15	dynamics

, the keyword “cascading failures” was identified 64 times across 165 papers. Its earliest occurrence dates back to 2014, peaking at eight instances in 2018. It represents the focus of this study. The second keyword is “vulnerability”, first appearing in 2014 and peaking in 2019. “Vulnerability” describes the susceptibility of urban infrastructure to damage during disasters [57]. Korkali et al. [58] have analyzed coupled power communication network systems and concluded that higher coupling levels can reduce system vulnerability. Ranked third is “critical infrastructure”, which emerged in 2014 and gained attention in 2018. For example, Wu et al. [59] modeled and analyzed cascading failures in interdependent infrastructures during a terrorist attack. The high-frequency keyword “modeling” has been an essential element in this research area since its first appearance in 2014. Although less prominent in 2015 and 2016, it has remained an integral part of research on urban infrastructure cascading disasters over the last decade. Furthermore, the keyword “resilience” describes the capacity of infrastructure to withstand, adapt, and recover from disasters [60]. “Systems” gained attention in 2018, particularly with the integration of systems thinking into research. “Frameworks” and “simulations” are vital for modeling urban infrastructure dependencies, simulating cascading disasters, and analyzing path propagation. These terms frequently co-occur with other high-frequency keywords. For instance, Goldbeck et al. [61] utilized the words “simulation”, “framework”, and “vulnerability” to assess resilience in interconnected infrastructure via dynamic network flow modeling. Lastly, the keywords “complex network” and “dynamic” are widely employed to analyze how dynamic propagation mechanisms can result in behaviors like infrastructure collapse [12]. These keywords could help researchers understand the interdependencies and cascading failures in urban infrastructure systems.

3.5. Keyword Cluster Analysis

Keyword clustering analysis involves the grouping and clustering of keywords to pinpoint crucial research areas [32]. In this analysis, clusters were generated by using keywords from the documents, selecting “All in One” and “K” for “Source of Labels”, checking the similarity of clustered labels, and merging similar labels. For each cluster, the most encountered term is labeled as the name of the cluster. Figure 8 shows eight clusters labeled as #critical infrastructure, #flood risk, #modeling, #power system risk, #cascading failures, #complex networks, #cascading effects, and #grid, with each silhouette value exceeding 0.7, ensuring robust clustering. These eight clustering labels were then divided into four categories according to their nature, see Figure 9.

• Category 1: formation of critical infrastructure networks: #critical infrastructure, # power grids, and #complex networks.

The power grid emerged as the primary research focus at the inception of studies on cascading failures in infrastructure [62,63]. Initial research centered on analyzing cascading failures within power grids. However, with the development of large-scale modern infrastructure integrating power, transportation, water supply, and other sectors, researchers have paid more attention to complex infrastructure systems characterized by various interdependencies, including geographic, logical, and network dimensions [64]. Complex networks have become a prevalent focus for such kinds of interdependent systems [65], with multilayer networks frequently used to represent these systems [66]. The construction of complex networks of urban infrastructures and the simulation of cascading disasters through modeling have consistently been research priorities [28,67,68,69,70]. As disaster scenarios have become diverse, the research emphasis has progressively moved towards resilience and disaster management in urban infrastructure [71].

2.

Category 2: disaster risk: #power system risk and #flood risk.

The types of disaster risks involved in the related research underwent two stages. The first phase emphasizes cascading disasters within a single infrastructure system, particularly energy infrastructure. Early studies focused on major outages, such as grid overloads and service interruptions, caused by events like terrorism. Examples include large-scale blackouts in Canada and the United States in August 2003 due to overloaded and aging power lines and short-term terrorist attacks on electrical infrastructure in Colombia (1998–2003) [72,73,74]. As incidents of urban infrastructure failures caused by terrorist attacks and natural disasters increased, research emphasis gradually shifted from internal physical failures to cascading disasters triggered by different disasters [12]. Thus, the second phase has emerged, characterized by the growing impact of disasters like floods, hurricanes, and other extreme events. Floods, in particular, often result in infrastructure failures, which are further exacerbated by secondary hazards transmitted between interconnected infrastructures, leading to cascading disasters [75]. This progression highlights a shift in research focus from internal to external triggers of cascading disasters. Scholars now explore various factors that initiate disaster risks, providing strategies and insights for more effective disaster prevention and mitigation.

3.

Category 3: modeling and analytical methods: #model.

Analytical methods in this area mainly focus on dynamic predictive simulations of infrastructure cascading disasters, urban infrastructure resilience enhancement models, vulnerability assessment tools, and related emergency response approaches. Gong et al. [21] applied uncertainty theory to model cascading disaster risks, offering new insights into the complexities of these events. Similarly, Wang et al. [76] developed models for cascading disasters of interdependent infrastructure systems, providing a new solution to simulate the dynamic impact of cascading disasters on critical infrastructure. These models play a key role in mitigating, preventing, and recovering from systemic cascading disasters [32].

4.

Category 4: cascading mechanisms: # cascading failures and # cascading effects.

These clustering results highlight the dynamic evolution of concepts within this field of research. Initially, studies focused on individual infrastructure systems, where disasters and potentially destructive processes are considered cascading failures [77]. Over time, the focus shifted toward cascading failures of networked infrastructures such as water, gas, and distribution networks [78]. With increasing complexity in infrastructure systems, the interdependencies and vulnerabilities between these systems have driven the emergence of research into cascading effects and cascading disasters [15]. Although some ambiguity remains in understanding these two concepts [79], some scholars have attempted to clarify their differences. Cascading effects characterize the dependencies and propagation of failures within and between systems. For instance, Codetta-Raiteri et al. [80] examined cascading effects in power grids and described them as phenomena arising from systemic dependencies. In contrast, cascading disasters emphasize the evolution and destructive consequences of disaster events [24]. It often involves a series of domino effects resulting from natural disasters, human-made disasters, or internal system failures [13]. An example is the February 2021 freeze in Texas, which caused water shortages, frozen pipes, and power outages [81]. These evolving conceptual definitions reflect the ongoing maturation and development of research in cascading disasters.

3.6. Keywords with the Strongest Citation Bursts Analysis

Keywords with the strongest citation bursts are analyzed to reflect key research trends [82]. In CiteSpace 6.2 R6, import the data and find “Analysis”; then, choose “Burst Detection” to identify the “Keywords with the Strongest Citation Burst”. The part marked in red in

Table 5. Top 15 keywords with the strongest citation bursts.

Top 15 Keywords with the Strongest Citation Bursts
Keywords	Year	Strength	Begin	End	2014–2023
failure	2014	1.99	2014	2017	▃▃▃▃▂▂▂▂▂▂
inoperability	2014	1.7	2014	2018	▃▃▃▃▃▂▂▂▂▂
critical infrastructures	2014	1.59	2014	2016	▃▃▃▂▂▂▂▂▂▂
numerical simulation	2014	1.12	2014	2016	▃▃▃▂▂▂▂▂▂▂
network	2014	1.12	2014	2016	▃▃▃▂▂▂▂▂▂▂
dependency risk graphs	2015	1.2	2015	2016	▂▃▃▂▂▂▂▂▂▂
reliability	2016	1.28	2016	2018	▂▂▃▃▃▂▂▂▂▂
simulation	2017	2.72	2017	2019	▂▂▂▃▃▃▂▂▂▂
systems	2018	1.09	2018	2020	▂▂▂▂▃▃▃▂▂▂
framework	2014	2.77	2019	2020	▂▂▂▂▂▃▃▂▂▂
damage	2019	1.38	2019	2020	▂▂▂▂▂▃▃▂▂▂
Bayesian networks	2020	1.02	2020	2021	▂▂▂▂▂▂▃▃▂▂
climate change	2018	0.98	2020	2021	▂▂▂▂▂▂▃▃▂▂
complex network theory	2021	1.35	2021	2023	▂▂▂▂▂▂▂▃▃▃
cascade failure	2021	1.15	2021	2023	▂▂▂▂▂▂▂▃▃▃

shows keywords that were frequently used during a specific period, indicating their significance as trending research topics at that time [83]. Keywords with the strongest citation burst are categorized into three groups: mainstream research, high-potential research, and emerging research. These groups illustrate variations in the prominence and maturity of topics across different research areas.

Mainstream research focused on simulating the disaster evolution process for urban infrastructure. Keywords include “failure” (2014–2017), “inoperability” (2014–2018), “critical infrastructure” (2014–2016), “numerical simulation” (2014–2016), “network” (2014–2016), and “dependency risk graphs” (2014–2016). It is noticeable that these studies were at the early stage of urban infrastructure cascading disaster research. During this phase, numerical simulations were performed on the evolution of social disasters, such as the functional and physical failures of infrastructure networks. The research focused on infrastructures with network characteristics, including water distribution systems (WDSs), power systems, and communication systems, which play a vital role in human life, industrial production, and organizational functions [63,84,85]. Their dependencies intensify failure propagation, leading to cascading failures. Consequently, modeling and analyzing disaster propagation paths in interdependent infrastructures became a significant research focus [64]. Furthermore, identifying risk categories through dependency risk maps emerged as a key research element, enabling the analysis of dynamic and complex cascading disaster pathways to enhance disaster mitigation efforts [86,87]. However, there remains a notable gap in analyzing multiple contributing factors in disaster modeling, highlighting the need for future research to address cascading disasters triggered by diverse or coupled factors.

High-potential research includes resilience assessments and enhancements, with the strongest cited keywords including “reliability” (2016–2018), “simulation” (2017–2019), “systems” (2018–2020), “framework” (2019–2020), and “damage” (2019–2020). Reliability describes the capacity of infrastructure to withstand, adapt, and recover from disasters [58,88,89]. In response to the growing impact of increased infrastructure dependencies on safety and reliability, scholars proposed policies aimed at improving infrastructure resilience through infrastructure system reliability assessments [58,88,89]. These assessments have been enhanced using various simulation frameworks, including the multi-criteria framework [90], the economic input–output model [91], the optimization simulation framework [92], and the Sendai framework [93]. Furthermore, given the vast destructive potential of cascading disasters, there has been an international emphasis on developing strategic guidance for managing urban infrastructure cascading disasters. Examples include the United Nations Sendai Framework for Disaster Risk Reduction and Urban Planning in the field of UN-Habitat [93,94]. The analysis concludes that the international efforts to address disasters are evolving, with policy guidance continuously improving. Researchers and scholars are encouraged to adopt a system-thinking approach, integrating various types of infrastructure in their analyses to enhance system reliability and infrastructure adaptability to disasters.

Emerging research features interdependent urban infrastructure networks in cascading disasters due to extreme weather. The main keywords include “climate change” (2020–2021), “Bayesian networks” (2020–2021), “cascading failures” (2021–2023), and “complex network theory” (2021–2023). In recent years, the increasing frequency of climatic disasters has disrupted critical infrastructure services, triggering cascading effects across various sectors and leading to significant economic losses [95]. Such disasters escalate in socio-physical interdependent systems, resulting in severe social crisis [96]. As a result, understanding complex coupled systems in the real world and analyzing external shock-induced cascading failures has become a hot research topic. For example, Wu et al. [97] developed a coupled extreme weather–human–infrastructure system to investigate the relationship between natural disasters, critical infrastructures, and humans. Complex network theory is widely used in research in this field; Hassan [98] integrated complex network theory with data analysis to evaluate the state of the transportation system before and after the disruption. Moreover, advancements in deep learning and intelligence techniques have provided valuable tools for assessing infrastructure resilience. Scholars have applied Bayesian network models to assess infrastructure vulnerability, simulate the evolution of cascading disasters, and quantify the probability of service disruptions [99,100]. In the future, Bayesian networks, complex network theory, and other methods are expected to be increasingly employed to analyze the interplay of disaster-causing factors within multidimensional complex infrastructure systems.

4. Conceptual Research Framework of Urban Infrastructure Cascading Disasters

Based on the analysis results, a conceptual framework for research on urban infrastructure cascading disasters is illustrated in Figure 10. It consists of three parts: key factors and impacts of cascading disasters; methodological approaches for modeling and analysis; and disaster progression, response, and recovery strategies.

4.1. Key Factors and Impacts of Disasters Under Multiple Dependencies

This part examines catastrophic factors, infrastructure dependencies, and the impacts of cascading disasters. The causal factors of infrastructure cascading failures can be divided into three main types, including internal failures, human-made attacks, and natural disasters. These factors can act independently or interact with one another, leading to the escalation and evolution of cascading disasters [14,23,101]. For example, since 2021, heavy rainfall in many regions of China has triggered severe flooding, dam collapse, urban flooding, the destruction of houses, and damage to public roads. Notably, the 7.20 Zhengzhou flooding event, combined with inadequate disaster response capacity, resulted in widespread transportation paralysis [102]. Similarly, natural disasters have led to infrastructure failures, like the 2021 massive blackout in Australia, where extreme weather disrupted new energy sources [103]. Malicious attacks have also contributed to infrastructure failures, as exemplified by the 2015 cyberattack on Ukraine’s power grid, which led to large-scale blackouts [104,105]. More critically, the formation of cascading disasters is often driven by the coupling of multiple disaster-causing factors [12,106,107]. K. Tierney et al. [108] argue that cascading disasters are caused by the integration of ground shaking, tsunamis, nuclear fuel center technology, and societal panic, as exemplified by the Fukushima nuclear accident; Judy Lawrence et al. [109] argue that cascades are the result of interactions between natural and socio-economic systems that are coupled to each other. This complex interplay heightens the vulnerability of urban infrastructure systems, making them susceptible to cascading failures. Understanding the underlying causes of such disasters is crucial for developing effective risk prevention and mitigation strategies [15,110].

Urban infrastructures are interconnected across single or multiple dimensions, which influence the path of disaster propagation [84,110,111]. Existing classifications of infrastructure interdependencies mainly include cyber, functional, physical, spatial, logical, and organizational dimensions. Researchers usually examine the path of failure propagation by analyzing these dependencies [111]. However, the dependencies are not confined to a single relationship; they also involve complex interconnections, including organizational and managerial aspects, beyond mere functional or material exchanges [110]. For example, Aros-Vera, Felipe et al. [112] proposed a ‘four-dimensional dependency framework’ (physical, cyber, geographic, and logical dependencies); Changdeok Gim et al. [113] proposed the need to focus on the complex relationship between socio-ecological and technological dependencies based on a case study of water and energy systems and institutions in Arizona; using the example of urban water–energy–food systems, V. de Gooyert et al. [114] distinguish seven types of socio-technical infrastructural interdependencies that may influence urban sustainability transitions: functional, evolutionary, spatial, life cycle, policy/process, market, and cultural/normative interdependencies. Although scholars have realized that it is imperative to consider multi-dimensional interdependencies among infrastructures, the interdependencies are limited to study. Hence, future research needs to adopt a more comprehensive approach to understand infrastructure dependencies and consider multidimensional coupled dependencies to analyze the path of disaster evolution.

Cascading disasters disrupt urban infrastructure services, leading to failures like power outages, transportation paralysis, crippling networks, and water shutdowns [19]. For instance, the 2015 explosions in Tianjin Binhai New Area resulted in 165 fatalities, extensive damage to 304 buildings and 12,428 vehicles, and significant environmental contamination [23]. Given the suddenness, public significance, and destructive nature of cascading disasters, delays in disaster response can exacerbate public unrest and trigger adverse social events through the rapid spread of public opinion. The 2021 winter blizzard in Texas also sparked public criticism due to the government’s inadequate response [115]. Therefore, mitigating the adverse impacts of disasters and ensuring timely and effective responses are vital for disaster prevention, emergency preparedness, and resilience enhancement.

4.2. Methodological Approaches for Modeling and Analysis

Methodological approaches can be classified into infrastructure dependency modeling, cascading disaster modeling, and resilience enhancement modeling. Multidimensional dependencies exist between infrastructures in the exchange of products, information, or services. In the event of a disaster, these dependencies become fault transmission paths, contributing to the propagation of cascading failures [53]. Quantifying infrastructure interdependencies and building analytical models are essential for the rapid recovery of infrastructure operations after a disaster. Several methods are commonly used to model infrastructure dependencies, including Markov chains, Petri Nets, game-theoretic equilibrium modeling, matrix modeling, and empirical modeling. The Markov chain approach captures the interdependence between critical infrastructures and predicts the resilience under cascading failures, which provides a modeling basis for improving the resilience of infrastructures [30]. PetriNets can simultaneously model an infrastructure’s internal and external dependency, enabling multiple infrastructure interdependence analyses [84]. The game-theoretic equilibrium model integrates interdependencies with human community impact analysis, broadening the scope of post-disaster impact assessments [14]. The matrix model simulates dependencies among infrastructures by measuring the strength of dependencies between nodes, networks of nodes, and across networks [111,116]. Meanwhile, the empirical model utilizes data from typical disaster cases to investigate the types and characteristics of interdependencies, though its reliance on empirical data may limit objectivity [117]. As the research focus is gradually shifting to multiple types of infrastructures, clarifying and modeling both internal and external dependencies help inform decision-makers and mitigate the cascading effects of infrastructure failures.

By simulating the dynamic evolution of disasters, cascading disaster modeling helps decision-makers make timely emergency response decisions and enhances the efficiency of disaster management. Currently, cascading catastrophe classical modeling includes scenario modeling, Bayesian networks, high-level architecture (HLA) co-simulations, the susceptible–infected–recovered (SIR) model, causal loop diagrams (CLDs), and integrated simulation platforms. Scenario modeling helps identify essential disaster scenario layers for cascading disaster impacts [118]. Bayesian networks facilitate the planning and real-time forecasting of cascading disaster scenarios and inform disaster management and operations [119,120]. HLA co-simulations capture the dynamic evolution of disasters and map the paths of cascade failure propagation within and between urban infrastructure systems [76]. The SIR model is used to develop recovery strategies by modeling cascading failures in healthcare infrastructure [121]. CLDs visualize cascading effects across infrastructure and different sectors, identify nonlinear critical feedback loops, and provide insights for post-disaster recovery decisions [122]. Integrated simulation platforms assess the impact of disasters on infrastructure networks [123]. However, breakthroughs in machine learning techniques, such as optimizing cascading disaster prediction models with the help of machine learning (ML) optimization models to improve the accuracy of scenario analysis, have greatly improved the accuracy of predictions [124]. These modeling approaches offer a systematic methodology for exploring the dynamic evolution of cascading disasters, revealing patterns of disaster propagation, and informing emergency management.

Improving the adaptive capacity of urban infrastructure is essential for enhancing the resilience of cities, with standard modeling approaches including a multi-modal recovery model, an optimal allocation model, and the input–output model. The multimodal recovery model specifies the scope of resilience analysis, establishes a management life cycle, defines physical function modeling, and designs interfaces for interdependent infrastructure to recover from infrastructure catastrophes [125]. The optimal allocation model provides optimal strategies for infrastructure resource allocation after disasters [126]. The input–output model assesses infrastructure recovery by considering facility-level dependencies [53]. It can be seen that quantitative theoretical modeling has emerged as a focal point for identifying optimal strategies to enhance infrastructure resilience.

4.3. Disaster Progression, Response, and Recovery Strategies

Based on the life cycle of disaster management, existing research is analyzed across various perspectives, namely pre-disasters, disaster responses, and post-disaster recovery. This includes the formation and modeling of cascading disasters, responding to disasters, and recovering from disasters.

From a pre-disaster perspective, current studies primarily focused on mechanisms underlying the formation of cascading disasters, emphasizing identifying causative factors, modeling infrastructure dependencies, and developing prediction simulation methods for cascading disasters. These elements are interrelated. However, existing studies often focus on single disaster-causing factors, with limited exploration of disasters caused by the coupling of multiple factors [23]. To better predict the dynamics evolution of disaster events, it is essential to systematically analyze multiple disaster-causing factors, clarify their interrelationships, and understand the mechanisms driving disaster dynamics across interconnected infrastructure systems [13,117].

From a disaster response perspective, the system’s capacity to withstand damage is mainly evaluated through vulnerability, resilience, and robustness assessments. Vulnerability analysis identifies the weak parts of a system that may fail [99,127,128]. However, the inherent uncertainty of hazard events and the constant complexity of the infrastructure make it hard to accurately predict risks. Solely relying on vulnerability analyses is insufficient for creating infrastructure networks resilient to multiple risks. To address this, many scholars have conducted robustness assessments of coupled infrastructure systems, which enable the quantification of vulnerabilities and predictive analyses of system resilience post-disasters [110,129,130]. These assessments assist policymakers and regulators in making decisions about cascading catastrophes [131]. Beyond system assessments, case-based reasoning is employed to address complex disaster events, enhancing the efficiency of emergency responses to cascading disasters and offering practical solutions for managing emergencies [22,23].

From a post-disaster perspective, recovery efforts focus on constructing resilience and robustness enhancement models to accelerate recovery processes and minimize downtime [92,132,133]. These models typically include dynamic resilience assessment frameworks, multi-stakeholder consultation platforms, layered reliability frameworks, and IoT early warning systems [134,135,136,137]. They provide managers with a complete chain of decision-making tools from a multi-stage, multi-stakeholder, multi-level, and advanced warning perspective. Existing studies have analyzed the improvement of urban infrastructure to facilitate post-disaster recovery from the technical, socio-economic, and governance perspectives. From the technical perspective, existing studies have increasingly emphasized that infrastructure resilience can be improved by making an optimal decision regarding resource allocation, land use planning, and the prioritization of infrastructure restoration [110,117].

From the socio-economic and governance perspectives, the studies focus on analyzing the influence of socio-economic and governance factors on urban infrastructure. Inequality and limited access to resources result in marginalized groups being likely to live in flood-prone or structurally unsafe areas, while affluent groups are more likely to have access to insurance, disaster-resistant housing, and evacuation resources, which accelerates post-disaster recovery [138], and economically diversified cities are more resilient to the risk of industry-specific downturns, securing safe and reliable sources of funding for infrastructure stabilization [139]. Poor policy and institutional frameworks can undermine project effectiveness [140]; multilevel, collaborative governance makes infrastructure recovery more flexible and effective [141]. Thus, economic, social, and governance factors interact with each other. Poor governance exacerbates socio-economic disparities, which in turn affects infrastructure resilience. Governance determines systemic preparedness for disasters, and socio-economics affects the vulnerability of individuals and communities. Together, they determine the speed of recovery and the inclusiveness of urban infrastructure.

The proposed conceptual framework establishes a systematic and multidisciplinary research paradigm. Its contributions are threefold: Firstly, the framework advances theoretical integration and knowledge systematization. By synthesizing the interplay among disaster drivers (e.g., internal failures, anthropogenic threats, and natural hazards), infrastructure interdependencies (physical, functional, and organizational), and resilience strategies, it constructs a dynamic feedback theoretical system. This system elucidates nonlinear interactions within socio-technical infrastructure networks (e.g., cascading failures arising from coupled hazards like extreme weather and governance gaps) and provides a structured analytical tool for modeling emergent risks. Secondly, it introduces a methodologically innovative toolkit that bridges engineering rigor and socio-technical systems perspectives. The framework harmonizes traditional quantitative approaches (e.g., Markov chains and Bayesian networks) with emerging methodologies (e.g., agent-based simulations for human–infrastructure interactions), fostering interdisciplinary cross-fertilization. This integration enhances the explanatory power of models in addressing complex, real-world scenarios, such as simulating public opinion dynamics during delayed disaster responses or optimizing post-recovery resource allocation under equity constraints. Thirdly, the framework redefines resilience governance through a dynamic, lifecycle-oriented perspective. By unifying pre-disaster prevention (e.g., multi-hazard coupling simulations), real-time response (e.g., AI-driven critical node prioritization), and post-disaster recovery (e.g., participatory resilience co-design), it emphasizes technology–institution–society synergies. For instance, blockchain-enabled transparency in resource distribution and cross-sector resilience task forces exemplify its policy relevance, offering empirical grounding for formulating resilient city regulations (e.g., mandatory infrastructure dependency audits and climate-adaptive zoning codes).

5. Research Gaps and Future Research Directions

Based on the results of the scientometrics analysis, key research gaps are identified, and potential future research directions are outlined as follows.

(1)

Incomplete exploration of coupling relationships between disaster-causing factors.

A large number of studies have focused on individual hazards, e.g., heavy rainfall, snowstorms, earthquakes, and aging infrastructure, analyzing their impacts on urban infrastructure and stimulating the formation of cascading disasters. While it is noted that the formation of urban infrastructure cascading disasters is a dynamic process involving numerous interrelated factors, there are relatively few quantitative studies exploring this perspective due to the complexity of interactions among causal factors. Therefore, future research should focus on exploring the coupling relationships between disaster-causing factors and elucidating the mechanisms behind urban infrastructure cascading disasters.

(2)

Complex cascading disasters evolution process simulation and analysis.

Among the previous studies, there have been a large number of models exploring the evolutionary paths of cascading disasters. However, existing studies concentrate on analyzing disasters within different types of infrastructure (i.e., transportation, energy, water supply, and telecommunications), often overlooking the broader social impacts, such as casualties, environmental pollution, and social conflicts. Therefore, future studies should examine the entire evolution process of cascading disasters, from infrastructure system failure to social crisis, and identify key factors that influence the progression of these disasters.

(3)

Lack of AI-driven prediction models in the pre-disaster phase.

Many studies have paid attention to developing models to assess and predict the cascading disasters in urban infrastructures. However, existing models typically rely on historical case data or expert judgment to predict the severity and trajectory of cascading disasters. The limitations of historical data and the inherent subjectivity of expert judgment can reduce the accuracy of these predictions. As a result, current models may struggle to precisely predict the impacts and development of cascading disasters nowadays. Future research could leverage AI and big data techniques to collect multi-source data, such as official documents, online news, and social media. Data-driven models built on such data can provide more accurate assessments and predictions of cascading disasters.

(4)

Obstacles to efficient collaborative responses.

Effective and rapid responses to cascading disasters in urban infrastructures depend on the collaborative participation of various stakeholders, including governments, non-profit organizations, the public, and social media. However, collaboration among these stakeholders can be inefficient when lacking well-defined systems and mechanisms, leading to passive participation. Despite this, collaborative response mechanisms have not been thoroughly explored in existing studies. Future studies can investigate multi-agent collaborative response mechanisms for cascading disasters, incorporating techniques like blockchain, and identify strategies to enhance the capabilities of effective collaboration in responding to cascading disasters in urban infrastructures.

(5)

Lack of systematic recovery planning in the post-disaster phase.

Improving infrastructure resilience and ensuring timely recovery from cascading disasters require a multifaceted approach, including optimizing resource allocation, land use, and infrastructure rehabilitation priorities. However, existing studies have yet to develop effective methods for generating optimal recovery strategies that account for disaster propagation and interdependence among infrastructures under the constraint of recovery times and costs. Future studies could leverage digital technologies such as distributed cloud platforms, machine learning, and geographic information systems (GISs) to build comprehensive disaster databases, design effective recovery decision-making frameworks, and create systematic recovery plans for cascading infrastructure disasters.

6. Conclusion, Implications and Limitations

6.1. Conclusions

Urban infrastructures are inherently multidimensional and interdependent systems that extend beyond engineered facilities, utilities, and networks. The increasing occurrence of floods, typhoons, power outages, and technical failures has heightened the vulnerability of these infrastructures to cascading disasters. This paper comprehensively examines the last 10 years of research on “urban infrastructure collateral disasters” via CiteSpace 6.2 R6, and the main findings are as follows.

There is a general upward trend in the number of publications in the field. However, the analysis of co-authors showed that close collaboration networks have yet to form. The United States leads in the number of publications and highly influences publishing countries, followed by China. While among the institutions, Beijing University of Aeronautics and Astronautics holds the highest influence. In addition, regarding journals, Reliability Engineering and System Safety boasts the largest number of publications in this field.

The high-frequency co-keyword analysis demonstrates that hot topics in urban infrastructure cascading disasters include simulation analyses of cascading disaster propagation paths, the vulnerability of interdependent infrastructures, and the resilience of infrastructure using complex network theory. Then, eight cluster labels were obtained using keyword clustering analysis, which were classified into four categories to summarize hot research areas. The keywords burst analysis demonstrates mainstream research of the evolution process of cascading disaster models and cascading disaster risk predictions and assessments; high-potential research of enhancing infrastructure disaster resilience; and emerging research of multidimensional coupling of disaster-causing factor analysis.

A conceptual research framework was developed in this study. It mainly analyzes disaster-causing factors, infrastructure dependencies, and disaster effects. The disaster-causing factors are characterized by their complexity, diversity, and mutual coupling. The infrastructure dependencies are multidimensional, including logical, physical, and functional. The impacts of urban infrastructure cascading disasters are broad, including economic, human, and environmental aspects. In terms of methodological methods, the framework examines modeling approaches for disaster prediction, interdependence simulations, and infrastructure resilience improvements. Additionally, the studies on urban infrastructure cascading disasters are summarized across three phases: before, during, and after. Overall, this framework provides a comprehensive foundation for the study of cascading disasters and offers significant potential for further refinement in the future.

6.2. Research Implications

From a theoretical perspective, this paper uses CiteSpace 6.2 R6 to systematically review the research on urban infrastructure cascading disasters, thereby advancing the theoretical understanding of urban infrastructure cascading disasters and bridging the gap between quantitative-based reviews in the field. In addition, the developed research framework offers a new perspective for researchers, contributing to the ongoing development of theoretical knowledge.

From a practical perspective, this study offers the following key implications: First, the findings emphasize that the cascading and propagation of disasters primarily stem from multidimensional interdependencies among different infrastructure systems. Therefore, policymakers need to incorporate interdependency analysis into infrastructure planning to increase resilience and reduce systemic risk, e.g., by mandating cross-sectoral risk assessments for projects establishing resilience indicators for critical infrastructure (transportation, energy, water), and cities should establish resilient hubs (e.g., distributed energy/water systems) to isolate failures and reduce cascading impacts. Second, this study highlights that cascading disasters in urban infrastructure often arise from the interaction of multiple interrelated factors, including the coupling of internal system failures and external extreme weather events. There is thus a need to prioritize preventive resilience by addressing root causes (e.g., aging infrastructure and governance fragmentation) and climate adaptation measures. To minimize these risks, practitioners and policymakers should prioritize the prevention of internal failures, e.g., decision-making errors, while addressing external uncertainties, thereby reducing the likelihood of compounding risks. Third, the research reveals the transformative potential of advanced technologies in predicting and responding to cascading disasters. For their part, governments and relevant authorities need to establish smart systems for monitoring the operation of infrastructure; centralizing the management of asset status, dependencies, and climate vulnerability identification; and training municipal staff in the effective use of technology-assisted decision-making tools (e.g., the use of GIS to plan evacuation routes). Fourth, a socio-economic and policy governance perspective is needed to improve the resilience of urban infrastructure. The establishment of relevant industrial funds to provide stable economic support and the transformation of poor neighborhoods to reduce the disadvantages of unequal economic resources are needed. Using big data to drive the construction of a digital platform for urban infrastructure resilience and open data policies is also needed. Finally, to better realize the above policies, we propose a three-phase resilience enhancement pathway for cities and countries: a diagnostic phase (using tools such as network analysis to audit infrastructure dependencies and socio-economic data to map climate exposures (e.g., flood-prone areas); a design phase (where resilience planning is developed by project builders, policymakers, and infrastructure service communities); and an implementation phase (utilizing the dynamic resilience dashboard for real-time tracking of post-disaster recovery metrics).

6.3. Limitations

Despite the implications presented in this study, there are some limitations. For example, this study relied only on Web of Science as a data source, which may be slightly missing for the selection of the literature in this area, while future re-studies could use other databases such as Google Scholar and Scopus as a complement to achieve a high coverage of relevant publications.