Time dynamics of emergency response network for hazardous chemical accidents: A case study in China

Journal Pre-proof Time dynamics of emergency response network for hazardous chemical accidents: A case study in China Lei Du, Yingbin Feng, LiYaning Tang, Wei Lu, Wei Kang PII:

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DOI:

https://doi.org/10.1016/j.jclepro.2019.119239

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JCLP 119239

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Journal of Cleaner Production

Received Date: 5 August 2019 Revised Date:

5 November 2019

Accepted Date: 8 November 2019

Please cite this article as: Du L, Feng Y, Tang L, Lu W, Kang W, Time dynamics of emergency response network for hazardous chemical accidents: A case study in China, Journal of Cleaner Production (2019), doi: https://doi.org/10.1016/j.jclepro.2019.119239. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Time Dynamics of Emergency Response Network for Hazardous Chemical Accidents: A Case Study in China Lei Dua, Yingbin Fengb, LiYaning Tangb, Wei Lua*, Wei Kanga a

School of Economics and Management, Harbin Engineering University, No.145, Nan Tong Street, Harbin, Heilongjiang Province, China.

b

School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW2751, Australia.

Abstract Since currently hazardous chemical accidents occur frequently around the world, how to build up an effective emergency response network (ERN) for hazardous chemical accidents become an urgent issue. Given the truth that previous static ERN research could not reflect the dynamic interaction patterns among actors in emergency response process, this paper studied the emergency response network for hazardous chemical accidents from the perspective of temporal dynamics. Jiangsu Xiangshui 3.21 chemical plant explosion accident, a major hazardous chemical accident just happened in March 2019, was selected as a case in this paper. Based on the time slices of the accident emergency response process, the dynamic evolution of emergency response network, organization functions, and organization positions over time were analyzed by social network analysis (SNA). The results revealed the dynamics of emergency response network in the context of centralized administrative systems. Emergency response network became more decentralized over time, rather than centralized; central organization functions varied depending on the response needs in each period; and positions of participants in network changed as time passed. The results also showed that under the centralized administrative systems, public-sector organizations were the core of emergency response network; whilst non-profit organizations and private organizations were largely marginalized. A hybrid network with centralization and decentralization complement each other may be more effective. ERN for hazardous chemical accidents should be considered in a time-dynamic way and collaborations among public sector organizations, NPOs and private organizations should be paid more attention to improve the emergency response efficiency. These findings may expand knowledge for researchers and practitioners to understand emergency response network of hazardous chemical accidents from dynamic perspectives, having an important significance for reducing and controlling economic losses, social risks and environmental damage caused by hazardous chemical accidents. Keywords: Time Dynamic, Emergency Response Network (ERN), Hazardous Chemical Accident, Centralized Administrative System, Social Network Analysis (SNA)

*

Corresponding author. E-mail address: [email protected] (W. Lu)

1

Time Dynamics of Emergency Response Network for Hazardous Chemical Accidents: A Case

2

Study in China

3

Abstract

4

Since currently hazardous chemical accidents occur frequently around the world, how to build up an

5

effective emergency response network (ERN) for hazardous chemical accidents become an urgent

6

issue. Given the truth that previous static ERN research could not reflect the dynamic interaction

7

patterns among actors in emergency response process, this paper studied the emergency response

8

network for hazardous chemical accidents from the perspective of temporal dynamics. Jiangsu

9

Xiangshui 3.21 chemical plant explosion accident, a major hazardous chemical accident just happened

10

in March 2019, was selected as a case in this paper. Based on the time slices of the accident

11

emergency response process, the dynamic evolution of emergency response network, organization

12

functions, and organization positions over time were analyzed by social network analysis (SNA). The

13

results revealed the dynamics of emergency response network in the context of centralized

14

administrative systems. Emergency response network became more decentralized over time, rather

15

than centralized; central organization functions varied depending on the response needs in each period;

16

and positions of participants in network changed as time passed. The results also showed that under

17

the centralized administrative systems, public-sector organizations were the core of emergency

18

response network; whilst non-profit organizations and private organizations were largely marginalized.

19

A hybrid network with centralization and decentralization complement each other may be more

20

effective. ERN for hazardous chemical accidents should be considered in a time-dynamic way and

21

collaborations among public sector organizations, NPOs and private organizations should be paid

22

more attention to improve the emergency response efficiency. These findings may expand knowledge

23

for researchers and practitioners to understand emergency response network of hazardous chemical

24

accidents from dynamic perspectives, having an important significance for reducing and controlling

25

economic losses, social risks and environmental damage caused by hazardous chemical accidents.

26 27

Keywords: Time Dynamic, Emergency Response Network (ERN), Hazardous Chemical Accident,

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Centralized Administrative System, Social Network Analysis (SNA) 1

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1. Introduction

30 31

Hazardous chemical accidents refer to uncontrolled fire, explosion or toxic release involving one or

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more dangerous chemical in the process of disposal, storage and transportation (Lee, et al., 2019).

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These accidents cause unrecoverable damage to human life and property (Dtt & Ltb, 2018). Statistics

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show that in the past two decades, there were more than a thousand hazardous chemicals accidents in

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the world with losses exceeding US$100 million (Wang et al., 2018b). For example, the explosion in

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Toulouse, southern France in 2001 resulted in 29 deaths and about 2500 injuries, causing losses of

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more than €2.3 billion (approximately equivalent to US$2.59 billion); and more recently in 2018,

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Tianjin Port 8.12 Fire and Explosion Accident in China led to 165 fatalities and 798 injuries, with

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direct economic losses of CNY6.866 billion (approximately equivalent to US$1 billion). More

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seriously, hazardous chemicals are flammable, explosive, toxic, and hazardous (Lee, et al., 2019).

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Once hazardous chemical accidents are not responded properly, they would pose considerable

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environment risks, such as pollution incidents (Yu, et al., 2016). In 2005, the explosion of

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petrochemical company in Jilin Province, China, caused the hazardous chemical benzene leakage into

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Songhua river, which directly pollutes the major water resource of local residents. Hence, an effective

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emergency system in response to hazardous chemical accidents is needed.

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For existing emergency response systems, research has revealed that there were still some

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challenges such as poor communication and improper command (Pasman & Suter, 2005). Scholars

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were trying to seek ways to improve the systems (Waugh & Streib, 2006; Abbasi & Abbasi, 2005).

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Network is considered as an effective method to deal with complexity and uncertainty of accidents

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(Comfort et al., 2012). A network can be defined as multi-organizational arrangements for solving

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goals that a single organization cannot achieve or achieve efficiently (Gulati & Gargiulo, 1999;

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Agranoff & McGuire, 2001), which is composed of two or more organizations and various

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relationships among them (Borgatti et al., 2013). In a networked emergency response system, more

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joint decision-making and coordination efforts can be made among multiple organizations to produce

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more unified and effective actions in a timely manner (Kapucu & Hu, 2014; Alkhatib et al, 2019).

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Characteristics, structure and performance of emergency response networks (ERNs) have been 2

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extensively studied in the existing literature from static perspective (e.g. Kapucu et al., 2010; Abbasi

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et al., 2018). However, research on static emergency response network cannot reveal how the network

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evolves and how organizations change their roles at different periods. Dynamic should be taken into

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account (Abbasi & Kapucu, 2012). In ERN research, dynamic can be understood as the network must

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adapt rapidly to changing conditions for maintaining its performance over time, such as variations in

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network composition and structure when participants enter, exit, and change their positions within

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network (Kapucu et al., 2010). Thus, dynamic should be integrated into emergency networks to

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present a more realistic hazardous chemical accidents response process for researchers and

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practitioners, which is new attempt in the field of emergency management.

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Meanwhile, only by fully understanding institutional systems in which actors are embedded, the

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evolution and dynamics of emergency response network could be comprehensively examined

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(Seekins, 2009). As Liao (2012) suggested, network in emergency response might vary in different

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administrative systems. This is because the emergency network in response process is largely

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determined by the pre-accident organizational relationships (Waugh & Streib, 2006; Col, 2007). The

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inter-organizational relationships prior to accidents under decentralized administrative systems are

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significantly different from those in centralized administrative systems (Guo & Kapucu, 2015). For

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example, under a centralized administrative system, China’s organizational relationships is centralized

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command and control, while in the United States with a decentralized administrative system,

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collaboration and leadership are the focus of its organizational relationships. Previous studies, such as

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Abbasi & Kapucu (2012), Wolbers et al. (2013), and Abbasi et al. (2018), have paid more attentions

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to the emergency response networks under decentralized administrative systems, especially the

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federal administrative system of the United States, while studies in the context of centralized

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administrative systems were lacking. Thus, a room should be provided to systematically analyze the

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emergency response network under centralized administrative systems, which contributes to the

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emergency management literature.

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Considering above criticism and gaps, the aim of this research was to study the emergency response

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network for hazardous chemical accidents under centralized administrative systems from the

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perspective of temporal dynamics. Specifically, this paper attempts to answer the following questions: 3

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•

accidents?

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•

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How do the organization functions vary in different phases in response to hazardous chemical accidents?

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How does the emergency response network evolve in response to hazardous chemical

•

How do the organization positions change in different phases in response to hazardous chemical accidents?

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The structure of this paper is organized as follows. Section 2 reviews the literature of composition,

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relationship, structure and dynamic of ERN. The next section describes the research design, case

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selection, social network analysis, and explains the methods of how to extract data to form an ERN

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and how to analyze the ERN. Section 4, the results of ERN evolution, organization functions variation,

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and organization positions change are described respectively. The research findings and their

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implications are discussed in Section 5. Section 6 summarizes the key findings and discusses the

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policy recommendations, research limitations and future research directions.

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2. Literature review

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2.1 Composition and Relationship of Emergency Response Network

102 103

Emergency response network is generally composed by multi-organizations, including public-

104

sector organizations, private organizations, non-profit organizations, and individual volunteers.

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Kapucu (2006) identified 73 federal agencies, 1176 non-profit organizations and 149 business

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organizations involved in the emergency response to the 9/11 terrorist attacks. Among them, the

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Federal Emergency Management Agency, the American Red Cross, and the New York City

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Emergency Management Office were key organizations for this emergency response (Kapucu, 2006).

109

Similarly, Guo & Kapucu (2015) and Opdyke et al. (2017) demonstrated that government

110

organizations had the highest actor centralities in network through analyzing the organizations

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participated in freezing rain and snow disaster in China and the Philippine typhoon emergency

4

112

response, respectively. In addition, some researchers focused on isolated and peripheral organizations

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within ERN. For example, Gillespie & Murty (1994) measured the centrality of isolated and

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peripheral organizations in disaster response; and Curtis (2018) studied the isolated organization in

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Hurricane Katrina's post-disaster response network. They reached the same conclusion that isolated

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and peripheral organizations hindered the effectiveness of response.

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Not only have the organizations involved in emergency response network been identified, but also

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inter-organizational relationships have been studied. Comfort & Haase (2006) analyzed the

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interactions among organizations in Hurricane Katrina ERN and found that the Federal Emergency

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Management Agency, the Governor of Louisiana, the New Orleans City Police Department, and the

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Mayor of New Orleans interacted more with other organizations. Hu et al. (2015) applied SNA to

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measure the degree centrality of organizations to understand how organizations can effectively

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coordinate their activities during disasters. The research results suggested that inter-organization

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relationships of ERN should present as collaboration among diverse actors.

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2.2 Structure of Emergency Response Network

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Studies of network structure reveal how network organizations link to each other (Malone &

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Kinnear, 2015). Different types of link patterns determine different network structure (Abbasi, 2014).

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Some research showed that different network structures may have different effects on ERN

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effectiveness or performance. For example, Hossain et al. (2015) studied the Australian forest fire

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ERN and pointed out that the decentralized network structures were generally high performance,

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while the performance of the centralized network structures was low on average. Similarly, by

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comparing the vertical and horizontal ERNs in two counties, Kapucu & Garayev (2016) found that

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horizontal network structures were easier to achieve a streamlined, efficient, and effective emergency

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response. Jung & Park (2016) pointed out that traditional bureaucratic emergency structures could

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hinder inter-organizational collaboration. Tang et al. (2017), however, suggested that hierarchical

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ERN structure can facilitate rapid response and effective coordination in the time-critical situation.

139

Thus, structure can be considered as one of the key factors that facilitate or hinder the network 5

140

performance (McGuire & Silvia, 2010).

141 142

2.3 Dynamic of Emergency Response Network

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Dynamic is an important factor in predicting the evolution of ERN. Some scholars, represented by

145

Abbasi & Kapucu, are shifting from the somewhat static conceptualizations provided by most

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network studies to dynamic perspective. Abbasi & Kapucu (2012) investigated dynamic changes of

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the inter-organizational response network of Hurricane Charley, one of the major hurricanes that

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struck Florida in 2004. Subsequently, Abbasi (2014) quantitatively examined the link formation

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patterns among participants involved in a real emergency collaboration network, which helped predict

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more precisely the changes of network structure and the evolution mechanism of ERN over time.

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Further, Abbasi & Kapucu (2016) presented a longitudinal analysis of the evolution of Inter-

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organizational Disaster Coordination Networks (IoDCNs) by adding time dimension. This research

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suggested the network structure was not fixed and may vary in different periods depending on needs.

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Similarly, through studying the temporal dynamics of network structure in four-time durations of

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Australian extreme bushfire response, Abbasi et al. (2018) found the network structure gradually

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dispersed over time, rather than centralized.

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In addition, other scholars have also given modest attention to the dynamic of emergency response

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network. For example, Wolbers et al. (2013) sought to understand how Netherlands Tunnel Event

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response network interactions unfolded over time by the toolset of time slices, two-mode analysis, and

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information pathways. Cooper (2015) examined the member dynamic of an inter-organizational

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national disaster management network in the Caribbean. Hteina et al. (2018) compared the emergency

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response network structure between Myanmar flood in 2015 and 2016, and found that the overall

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network structures changed from a military centre in 2015 to a multi-focus and multi-centre

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interactions in 2016. All these studies demonstrated that emergency response network would adjust its

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relationship and structure over time, showing dynamic characteristics. However, these research were

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conducted in the context of decentralized administrative systems, the principles of hierarchy were

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always neglected. 6

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3. Methodology

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3.1 Research Design

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To address the highly context-dependent research questions, the method of case study was chosen

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(Yin, 2013). As Passenier et al. (2012) suggested, the case itself can function as a good tool to depict

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dynamics of inter-organizational interaction processes during emergency response. In this study, the

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case of Jiangsu Xiangshui 3.21 chemical plant explosion accident (hereafter Xiangshui 3.21 accident)

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in China was selected. Specifically, the research was designed into three steps (see Fig.1), which can

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serve as a theoretical framework for the time dynamic analysis of emergency response network. The

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first step was to identify the participating organizations, organizational relationships, and organization

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emergency support functions (ESFs). Document analysis was conducted to identify network actors

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and their functions and capture the mutual communication and interactive information among actors

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from government documents, situational reports, and news reports (Sams et al., 2011). The second

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step was to visualize networks. Having actors and links among them, data matrix was constructed.

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Based on actual interactions, data matrix was divided by different time slices to present the dynamics

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of ERN. UCINET software, a social network analysis software, was used to visualize the ERN. The

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third step was to measure networks. The evolution processes of networks, functions, and organization

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positions were analyzed by SNA according time slices, enabling researchers to understand the

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dynamic of ERN more effectively. In term of network evolution, density, connectedness, and other

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measures were introduced to facilitate the analysis on the change of network compactness over time;

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in term of network node functions and positions, centrality measures including degree centrality and

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betweenness centrality were used to investigate how the functions and positions of organizations

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within the network vary in different response durations.

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Fig. 1. Research Strategy

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3.2 Case Selection

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At 14:48 pm on March 21, 2019, a chemical plant explosion accident occurred in Jiangsu Tianjiayi

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Chemical Co., Ltd., Xiangshui County, Jiangsu Province (see Fig. 2). The Accident caused severe

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property damages, 566 injuries, and 78 fatalities. After the explosion, Ministry of Emergency

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Management (MEM) immediately activated the emergency plan to coordinate the resources of various

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agencies, who had responsibility for emergency response actions in their respective jurisdictions and

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professional areas. This case was selected for this study because:

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•

it is a major hazardous chemical accident, which just happened in March 2019;

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•

it clearly presented how emergency response network was generated in the process of multiorganizational coordination;

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emergency network was highly dynamic in response functions because the response phase of the hazardous chemical accident was lengthy (Wang, et al., 2018c); and 8

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•

the interactions among the core organizations were recorded by news reports and network archival records, which enhanced the reliability and validity of the data.

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Fig. 2. The map of Jiangsu Xiangshui 3.21 chemical plant explosion accident

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3.3 Data Collection

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Data mainly source from government documents, situational reports and news reports. Government

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documents (e.g. National Emergency Plan, National Emergency Plan for Safety Production Accidents

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and Disasters, Emergency Plan for Catastrophic Production Safety Accidents in Jiangsu Province,

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Emergency Plan for Hazardous Chemical Accidents) clarified the function of each organization in the

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emergency response. Situational reports (e.g live news conferences, official Wechat of Yancheng

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announcements, and official Wechat of MEM.) documented interactions of network organizations in

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details. News reports, such as China Daily and Xinhua Daily, are important news media in China.

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This multi-source hybrid data sources can capture organizational interactions and provide reliable

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structured data, which has also been used in recent research on emergency management network (e.g.,

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Kapucu et al., 2010; Abbasi et al., 2018).

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Data were collected by manual inspection and information retrieval of second-hand materials (e.g.,

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government documents, situational reports, and news reports) related to Xiangshui 3.21 accident

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during 3/21/2019 to 3/27/2019. The data collection involved three steps. First, document analysis

9

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method was used to identify key organizations and organization functions involved in response

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operations from all documents and news reports related to Xiangshui 3.21 accident. For example,

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emergency organization MEM and NHC were obtained from the news reports in “official Wechat of

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MEM” (https://www.wxnmh.com/user-113955.htm); ESFs were collected from the document of

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“National

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(http://www.gov.cn/yjgl/2006-01/23/content_21262.htm). All collected data were inspected to remove

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duplicates. Consequently, 50 organizations engaged in ERN were identified, including 44 public-

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sector organizations (28 government organizations, 12 public institutions and state-owned enterprises,

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4 army organizations), 4 non-profit organizations (NPOs), and 2 private organizations respectively, as

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shown in Appendix A. Twelve ESFs were listed in Table 1.

Emergency

Plan

for

Safety

Production

Accidents

and

Disasters”

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Table 1

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Organization Emergency Support Functions (ESFs) Number ESF1 ESF2 ESF3 ESF4 ESF5 ESF6 ESF7 ESF8 ESF9 ESF10 ESF11 ESF12

Function Command and Coordination Search and Rescue Medical Treatment Health and Epidemic Prevention Information Release Communications and Transportation Order Maintenance Vigilance Guard Monitoring and Evaluation Social Mobilization Material Support Logistic Support

243 244

After identifying organizations involved in ERN, the next step was to extract relationships among

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network organizations. In an accident, participants (individuals or organizations) inevitably need to

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interact with each other through sharing information or requesting resource (Kapucu, 2008). The

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focus of organization interactive activities in ERN in this study was command transmission,

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information sharing, resource mobilization, personnel rescue, and humanitarian assistance. If

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organizations engage in any above activities, it can be drawn that there are interactive relationships

250

among organizations. Specifically, the interactions among organizations were extracted by manually

10

251

mining and reviewing sentences in second-hand materials related to Xiangshui 3.21 accident. For

252

example, resource mobilization interactions between “Jiangsu Fire Rescue Command Center” and

253

“Fire Prevention of Yancheng, Nanjing and Lian Yungang City” was extracted according to the

254

sentence of “the Command Center of Jiangsu Fire Rescue immediately dispatches Fire Prevention of

255

Yancheng,

256

(http://www.sohu.com/a/302880942_100191048). As a result, a total of 71 links were identified in

257

this study.

Nanjing

and

Lian

Yungang

City”

in

news

report

258

The last step was to produce a data matrix. The data about inter-organizational relationships

259

obtained in the second step were transformed into a network matrix. It amounts to translating

260

qualitative text data into a computer-readable 0-1 matrix format. “1” represents there are interactive

261

activities among organizations; while “0” represents interactive activities among organizations are

262

absent (Scott, 2017). Based on the data matrix, the ERN of Xiangshui 3.21 accident was visualized

263

(see Fig. 3).

264

265 266

Fig. 3. The Whole Emergency Response Network of Xiangshui 3.21 Accident

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(Note: Organizations full name can be found in Appendix A.) 11

268 269

3.4 Social Network Analysis

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Social network analysis (SNA) is a set of mathematical method and tool to visualize and analyze

272

networks. In SNA, individuals or organizations are called by ‘actors’ or ‘nodes’, and interactions or

273

connections are called by ‘ties’ or ‘links’ (Abbasi & Kapucu, 2012). Identifying key nodes and

274

measuring network links are focuses of SNA (Scott, 2013). SNA has been proven to be an effective

275

theoretical lens and analytical tool for studying organizational network and its dynamic in disasters

276

(Wolbers et al., 2013). Thus, SNA was used in this study to investigate how the emergency response

277

network, the organization functions, and the organization positions evolve during a hazardous

278

chemical accident. Density, connectedness, component and centrality were the main metrics used in

279

this paper.

280 281

3.4.1 Density

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Density is the proportion of existing ties to all possible ties, calculated as n(n-1)/2, n represents the

284

number of nodes in network. It measures the extent of average connection among network nodes,

285

indicating the cohesiveness of network (Provan & Milward, 1995). In an emergency situation, all

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respondents (individuals or organizations) need to be supported by each other (Abbasi et al., 2018). If

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network has high density, actors can interact more frequently to achieve more effective coordination

288

(Abbasi & Kapucu, 2016).

289 290

3.4.2 Connectedness

291 292

Connectedness is the percentage of actors that can reach other actors. A network is connected if

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each organization can connect to other organizations; conversely, the network is considered to be

294

disconnected. In a high separated network, to organize participants through network form is difficult

295

(Krackhardt, 1994). 12

296 297

3.4.3 Component

298 299

Component refers to sub-networks that have no connection or interaction with other sub-networks

300

in the whole network (Abbasi & Kapucu, 2012). The higher the number of components in network,

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the more unconnected sub-networks there are. During emergency response, if the number of

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components in network is 1, it indicates the network is completely connected and interactions among

303

organizations emerged in network are sufficient.

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3.4.4 Centrality

306 307

Centrality is defined as the compactness of a particular node. Centrality measurement indicators

308

include degree centrality, betweenness centrality, closeness centrality and effect centrality (Abbasi &

309

Kapucu, 2016). This study adopted degree centrality and betweenness centrality, which have been

310

proved to be useful to identify the importances and the positions of nodes in network (Abbasi et. al.,

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2018).

312

Degree centrality. Degree centrality is computed by the number of other nodes connected directly

313

to a node (Scott, 2013). The higher degree centrality is, the more connections or interactions among

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actors have (Borgatti et al, 2013). Actor with higher degree centrality is often regarded to have a

315

greater influence in network (Wasserman, 1994). More importantly, in directed networks, the link

316

direction between each pair of nodes is critical. The degree centrality measurement of in-degree

317

(incoming links to a node) and out-degree (outgoing links from a node to others) should be considered

318

(Scott, 2013).

319

Betweenness centrality. Betweenness centrality is another node centrality measure, which indicates

320

the potential influence of a note that connects other nodes without connection in network (Freeman,

321

1979). A node with high betweenness centrality reflects it has higher control over information

322

between nodes, playing an important “intermediary” role in ERN (Robinson, 1975).

323 13

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4. Results

325 326

In order to investigate the dynamic changes of ERN in different time periods, time slices were used

327

to divide the entire duration into smaller time intervals. The length of time interval can be determined

328

depending on the need of the researcher or the change of the actual interactions over time (Wolbers,

329

2013). This study divided time slice based on actual interactions, which can help better understand

330

how ERNs evolve over time in different periods. Thus, three important time points of Xiangshui 3.21

331

accident have been identified: (t1) the time of accident occurrence on March 21, 2019; (t2) the time to

332

extinguish the fires on March 22, 2019; (t3) the time of on-site search and rescue completion on

333

March 25, 2019. Based on these, emergency response process of Xiangshui 3.21 accident from March

334

21 to March 27 was chopped into three time slices (T1: 21/03-22/03; T2: 23/03-25/03; T3: 26/03-

335

27/03). Each time slice contains nodes and interactions among nodes occurred in a given time period.

336 337

4.1 Networks Change during Emergency Response

338 339

In order to investigate the dynamic evolution of emergency response network, this study used

340

UCINET to measure the ERNs in all durations (T1-T3, T1, T2, T3) of Xiangshui 3.21 accident. Table

341

2 reports the number of organizations (nodes), the frequency of organizational interactions (links),

342

network density, connectedness, the number of components, and centrality.

343 344

Table 2

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Emergency Response Network Statistics and Measures

# of Organizations (Nodes) #of Interactions (Links) Density (%) Connectedness (%) # of Component In-Degree Centrality (%) Out-Degree

All Terms (T1-T3) 50 71 2.9 4.5 2 17.7 7.5

Term1 (T1) 45 64 3.3 4.8 2 17.6 8.3

Term2 (T2) 20 25 7.9 14.2 2 20.5 9.4

Term3 (T3) 10 10 2.0 8.9 3 12.3 12.3

346 347

As shown in Table 2, the whole ERN for the Xiangshui 3.21 accident was shaped by 50 14

348

organizations and 71 links among organizations from March 21, 2019 to March 27, 2019. The

349

network density and connectedness cohesion are 2.9% and 4.5% respectively, indicating the whole

350

emergency network of this accident was sparse. The in-degree and out-degree centralizations are

351

17.868% and 7.455%, respectively. This reflects that there were more organizations in the central

352

position to receive requests from other nodes for resource and information than central organizations

353

sent requests to other nodes in the whole network.

354

In addition, the results of time-phased statistics show that the ERN changed over time, from

355

centralized to decentralized in general. However, there is no specific ordered trend for some of the

356

network measures analysed in this study (e.g. density, connectedness, components, centralizations),

357

except for organizations (nodes) and interactions (links). According to Table 2, the number of

358

organizations and their interactions were shown in decreasing order across time. In T1 stage, the

359

number of organizations and inter-organizational interactions were the highest, with 64 interactions

360

among 45 different organizations; while in T3 of the accident, the number of organizations (10) were

361

the least, consequently communication and cooperation among organizations would likely decrease.

362

In term of density, surprisingly, the response network with the highest density (7.89%) was observed

363

at T2 period, while the network has the sparsest structure with the lowest density (2.00%) in T3

364

duration. The same trend as density can be found in connectedness. The emergency network was

365

higher connected in the period of T2 (14.2%) than T1 (4.8%) and T3 (8.9%) periods. Networks at T1,

366

T2, and T3 periods were fragmented, showing several organizations were not mutually reachable. In

367

term of component, the result shows that there were unconnected sub-networks at all periods, because

368

the number of components is not one. To explore what extent is the ERN centralized, in-degree and

369

out-degree centralization measures were used in this study. In term of in-degree, the network structure

370

was the most centralized in T2 (20.499%) period, while becoming relatively decentralized in T1

371

(17.614%) and T3 (12.346%) period. A higher value of in-degree centralization in T2 period shows

372

that the network organization received the most requests (possibly information or resources requests)

373

from other organizations. In term of out-degree, the network was also more centralized in T2 (9.418%)

374

period than in T1 (8.316%) and T3 (12.346%) periods. This reflects that many organizations within

375

network sent requests to others for information or resources in T2 period, but few in T1 and T3 period. 15

376 377

4.2 Organization Functions Change during Emergency Response

378 379

In order to explore the evolution of organizational functions over time, 2-mode network topology

380

diagrams between participating organizations and ESFs in each time slice were generated in UCINET

381

software, as shown in Fig.4.

382

383 384

Fig. 4. Organization-Function Relation 2-Mode Network Topology Diagram

385

(Note: The size of nodes set by degree centrality)

386 387

According to the Fig. 4, the network in T1 period was denser than networks in other periods, which

388

is the most similar to the whole network; while in T2 period, the network links between the

389

participating organizations and the ESFs decreased; and in T3 period, there were only a few isolated

390

pairs of connections in network. This indicates that the number of participating organizations involved

391

in the same ESFs changed over time, showing a decreasing trend as a whole. For example, for ESF on 16

392

search and rescue, 14 organizations such as ME, JSPG, CPLA-JSMR were involved in T1 period;

393

while in T2 period, only 8 organizations such as HARC, CCJSFR and AO-XS participated in.

394

Moreover, the types of organizations involved in the same ESF at different durations also varied as

395

time passed. For example, the function of logistics support in T2 period involved government

396

organizations (JSPG, JSCA), state-owned companies (SGCC) and non-profit organizations (RCSC),

397

but in T3 period, private companies (JSYCWTP, CIDG) became a new organization type to play this

398

emergency function.

399 400

To further identify the critical ESFs in different time periods, the degree centralities of ESFs involved in this study were calculated, as shown in Table 3.

401 402

Table 3

403

ESFs Degree Centrality (%) Measures

404

ESFs All Terms (T1-T3) Term1 (T1) ESF1 10(5) 16(1) ESF2 16(2) 14(2) ESF3 11(4) 11(4) ESF4 8(8) 6(8) ESF5 17(1) 14(2) ESF6 10(5) 10(6) ESF7 6(10) 5(10) ESF8 5(11) 5(10) ESF9 10(5) 8(7) ESF10 7(9) 6(8) ESF11 5(11) 5(10) ESF12 16(2) 11(4) Note: Numbers in parentheses represent the rank of the ESFs.

Term2 (T2) 10(1) 8(2) 5(4) 2(8) 8(2) 5(4) 1(11) 4(6) 2(8) 2(8) 4(6)

Term3 (T3) 1(5) 2(4) 4(2) 5(1) 4(2)

405 406

According to Table 3, there are differences in the number of ESFs involved in different time slices.

407

The whole process of emergency response involved 12 ESFs; while T1, T2 and T3 time slice involved

408

12, 11 and 5 ESFs, respectively. This shows that with the advance of emergency response, the number

409

of ESFs involved decreased. In addition, the central functions of emergency response in each time

410

slice were significantly different from the whole network. In the whole ERN, the key ESFs mainly

411

included information release, search and rescue, logistic support, medical treatment, etc., which

412

embodies the goal of minimizing casualties and maintaining social stability in dealing with accident

17

413

(Haghi et al., 2017; Cao et al., 2018). As for each time slice, in T1 period, there was a need for

414

organizations to coordinate emergency resources by command, and then to launch search and rescue

415

for injured people and damaged properties. In T2 period, the smooth transportation of emergency

416

materials and casualties became a new important emergency function except for command and

417

coordination, search and rescue, and medical treatment. In T3 period, search and rescue and medical

418

treatment were not the central functions of emergency response. Emergency attention has turned to

419

environmental monitoring and evaluation, information release, and health and epidemic prevention. It

420

can be found that the central functions of emergency response changed over time. Few emergency

421

response functions were at the central position during the whole emergency response process. Even

422

critical emergency response functions in one period may not be included in another period.

423 424

4.3 Organization Positions Change during Emergency Response

425 426

In order to identify the key organizations during emergency response, degree measure was used in

427

this study. The ERNs to Xiangshui 3.21 accident in different time periods were visualized, as shown

428

in Fig. 5.

429

18

430 431

Fig. 5. Four Emergency Response Networks in Different Time Periods

432

(Note: The size of nodes set by degree centrality)

433 434

According to the colors and size of nodes in Fig. 5., the organization positions and organization

435

types in ERN can be identified. In the whole response network, YCMG, JSPG, MEM, NHC, XSCG

436

were the top five prominent and influential organizations, all of which are national agencies and

437

metropolitan and provincial government organizations. They played a significant role in coordinating

438

emergency resources. In addition, as presented in Fig. 5., the central organizations changed as the

439

network evolved over time. In T1 phase, YCMG, JSPG, MEM, NHC, XSCG occupied the core

440

position and were the high centralized actors of emergency network; while in T2 phase, CCJSR and

441

HARC became the new central nodes in network. In T3 phase, government organizations, state-owned

442

companies and private companies established collaborative interactions and played crucial roles in 19

443

ERN. Thus, compared to other types of organizations, public-sector organizations were placed in a

444

more central position throughout the whole accident.

445

To trace the position dynamic changes of network participants during Xiangshui 3.21 accident

446

response, detailed degree (in-degree, out-degree) and betweenness centrality measures were listed in

447

Table 4-6.

448 449

Table 4

450

Top 5 Provider Organizations in ERN (Normalized In-Degree Centrality %)

451

Organizations All Term (T1-T3) Term1 (T1) Term2 (T2) YCMG 22(1) 22(1) 20(3) JSPG 16(2) 18(2) 30(1) XSCG 14(3) 16(3) NHC 14(4) 16(3) 15(4) MEM 12(5) 13(5) 30(1) ME 15(4) CPLA-JSMR 15(4) YCHURCB JSEM JSEED CDIGC CIDG Note: Numbers in parentheses represent the rank of the organizations.

Term3 (T3) 30(1) 20(3) 30(1) 20(3) 20(3) 20(3) 20(3)

452 453

Table 4 shows the top five organizations with high in-degree centrality in each ERN at different

454

periods. High in-degree centrality means the organization receives more requests from other

455

organizations for resources, information or other assistance. According to Table 4, MEM was the only

456

one organization that remains in the top 5 providers (receiving more links) during all three time

457

periods. This indicates MEM organization was in core position of the network and it had a greater

458

influence on controlling vital emergency resources and information. In addition, YCMG, NHC and

459

JSPG were important resource providers in T1 and T2 periods, but not within T3 period. In T3 phase,

460

core providers changed toward YCHURCB, ME, JSEM, JSEED, CDIGC and CIDG organizations.

461

This implies that some organizations act as active providers for two or more durations, but few remain

462

active for the entire duration.

463

20

464

Table 5

465

Top 5 Seeker Organizations in ERN (Normalized Out-Degree %)

466

Organizations All Terms (T1-T3) Term1 (T1) Term2 (T2) XSPH 12(1) 13(1) MEM 10(2) 9(2) 20(1) CCJSFR 8(3) 9(2) 20(1) JSEED 6(4) 7(4) 15(3) HARC 6(4) 7(4) 15(3) FP-YC,NJ,LY 6(4) 7(4) 15(3) ME 6(4) 7(4) YCSPH 6(4) 7(4) JSHC 6(4) 7(4) 15(3) NHC 6(4) 7(4) AO-XS 6(4) 7(4) 15(3) RCSC-XSB 6(4) 7(4) YCMB 6(4) 7(4) XSMB 6(4) 7(4) YCHC 6(4) 7(4) YCEEB 6(4) 7(4) JSAPC 6(4) 7(4) YCFPH 6(4) 7(4) CDIGC 6(4) CIDG 6(4) JSEM JSYCWTP Note: Numbers in parentheses represent the rank of the organizations.

Term3 (T3) 20(3) 20(3) 20(3) 20(3) 30(1) 30(1) 20(3) 20(3)

467 468

Table 5 lists the top five seekers (sending more links) with higher out-degree centrality in

469

descending order. Organizations with high out-degree centrality means they often seek information,

470

resources, and supports from other organizations in emergency response process. Based on this, MEM

471

can be regarded as the most active seeker in the whole emergency response process. CCJSFR was also

472

an active seeker, with third and first highest out-degree in T1 and T2 periods respectively. However, it

473

did not send any requests to other organizations in T3 period. A similar situation applies to HARC

474

organization. This is because in T3 phase, search and rescue work was over, the central ESFs were

475

shifted to aftermath of accident. This reflects there is a high correlation between the organization

476

positions and the core emergency response functions. Active seekers at a certain period did not

477

maintain the fixed positions in the subsequent periods, only a few organizations remained active at

478

two or more durations.

479

21

480

Table 6

481

Top 5 Organizations with High Normalized Betweenness Centrality (%)

482

Organizations All Terms (T1-T3) Term1 (T1) Term2 (T2) MEM 5.8(1) 4.4(1) 11.9(1) JSPG 4.6(2) 3.8(2) 5.6(2) YCMG 4.1(3) 3.4(3) 4.4(3) XSCG 1.8(4) 1.9(4) NHC 1.1(5) 1.1(5) HARC 4.4(3) CPLA-JSMR 3.5(5) JSEED JSEM ME Note: Numbers in parentheses represent the rank of the organizations.

Term3 (T3) 1.4(4) 0(5) 5.6(1) 4.2(2) 4.2(2)

483 484

Betweenness centrality is a measure of the extent to which network participants play bridge roles

485

between pairs of organizations, reflecting the ability of information control. Table 6 lists the top five

486

organizations with the highest betweenness centrality in different time periods. According to Table 6,

487

MEM, JSPG, YCMG, XSCG and NHC placed in the bridge positions in the whole network, providing

488

the shortest communication path for other organizations. The top 5 bridge organizations in T1, T2,

489

and T3 periods were also listed. Among them, MEM had the highest betweenness centrality in T1 and

490

T2 periods, while in T3 period, JSEED is the most influential bridge organization. Thus, organizations

491

with the highest bridge-capacity in a certain period did not remain unchanged at all period.

492 493

5. Discussion and Implications

494

By evaluating the network of emergency response to hazardous chemical accidents in the context of

495

centralized administrative systems, the dynamics of emergency response network, organization

496

functions, and organization positions are now discussed.

497 498

5.1 Evolution of Emergency Response Network for Hazardous Chemical Accidents

499 500

The measurement results of nodes, links, density, connectedness, components, and centralizations

501

interactions showed that the network evolved over time. The results also revealed that the emergency

22

502

response network changed from centralized to decentralized in general, showing the dynamic

503

characteristic. This is reasonable because in the early stages of the accident, more organizational

504

participations and interactions were needed to respond effectively and rapidly; while as time goes on

505

and the urgency of accidents decreases, new organizations entered without establishing as many

506

connections with others as possible in early period, and even some organizations withdrew from

507

emergency networks. This suggests that emergency response needs to be considered in a time-

508

dynamic way and emergency response strategy should be adjusted when nodes or interactions among

509

organizations increase or decrease. This highlights the significance of analyzing dynamic emergency

510

response networks rather than static networks.

511 512

5.2 Variation of Organization Functions in Different Phases

513 514

While measuring the degree centrality of organization functions, it was found that the central

515

emergency support functions varied in different periods. In the early response stage, organizations

516

core emergency functions were focused more on search and rescue, and resource allocation; in the

517

mid-term of emergency response, medical treatment, communications, and transportation were more

518

important; while in the late stage of emergency response, environmental monitoring, health and

519

epidemic prevention, and logistical support became new crucial emergency functions. Changes in

520

emergency core functions show a corresponding nature with the urgent issues faced by accidents in a

521

specific duration, which can be regarded as a demand function. This is in line with Galaskiewicz

522

(1985) who incorporated resource dependency into the analysis of network structure variation.

523 524

5.3 Changes of Organization Positions in Different Phases

525 526

This paper found that the central nodes and bridge organizations in emergency response network

527

changed over time, except for MEM organizations. MEM, a newly established Ministry in the 2018

528

China national institutional reform, is endowed with great unified power for the overall planning,

529

management, and coordination of the response mechanism when disasters occur. In emergency 23

530

response to Xiangshui 3.21 accident, MEM has always been a leading provider, seeker, and

531

intermediary, which greatly changes China's past dispersed emergency management model

532

(Emergency Interim Working Party) and effectively improves the quality of China emergency

533

management. This proves that the ERN within centralized administrative systems may be more

534

effective than ERN within decentralized administrative systems in an emergent situation. This is

535

because ERN under centralized administrative systems can achieve rapid coordinated response

536

through integration of emergency services and resources in a short time, which is consistent with the

537

conclusions of Tang et al. (2017). However, ERN in decentralized administrative systems have

538

drawbacks of fragmentation of authorities, poor communication, and inadequate resource allocation,

539

which have been fully exposed in Hurricane Katrina emergency response (Leah & Robbin, 2015).

540

These problems of ERN in decentralized administrative systems may be improved by drawing on the

541

strengths of the emergency response mechanism in centralized administrative systems.

542

In addition, this paper also found that no matter how the core organizations changed, their

543

organizational type was centred on public-sector organizations (government organizations, public

544

institutions, and state-owned companies). This implies that the emergency management system within

545

centralized administrative systems mainly relies on public-sector organizations’ resources and

546

capabilities; while few relies on NPOs and private organizations. This is because in centralized

547

systems, administrative structure is government-centered; while market and society are centered on

548

government organizations, presenting the characteristic of “strong government-weak society”. This

549

may limit the emergency response capability of private organizations and NPOs to play. Therefore,

550

the most effective ERN may not be highly dispersed or rigidly centralized, but rather a hybrid network

551

with centralized and decentralized forms complementing each other.

552 553

5.4 Implications

554 555

The results in 5.1 and 5.2 opened up a new way to promote the efficiency of emergency response to

556

hazardous chemical accidents from the perspective of time dynamics. To identify the crucial

557

emergency response functions firstly based on different emergency demands in different time phases 24

558

is necessary. In the early response phase, the basic search and rescue was the core emergency function;

559

while in the late phase of emergency response, environmental monitoring was the critical emergency

560

function. As a high-risk industry with fragile ecological environment, hazardous chemical industry

561

presents numerous risks that can damage the environment. Any improper coordination and

562

communication in emergency response may cause widespread environmental pollution incidents.

563

Thus, organizations involved in response should timely and dynamically adjust their emergency

564

strategies for different emergency response tasks in different periods.

565

Moreover, the research result of 5.3 implied that the emergency management for hazardous

566

chemical accidents in centralized administrative systems should actively encourage private

567

organizations and NPOs to participate in and establish the collaborative relationships with public-

568

sector organizations. This is due to the hazardous chemical accidents have a tendency to produce off-

569

site effects, which may cause unrecoverable damage to adjacent populations, environments, and

570

ecosystems. A single public-sector organization cannot respond to such serious consequences of

571

hazardous chemicals accidents. Only joint decision-making and coordination efforts among public-

572

sector organizations, private organizations and NPOs can minimize the potential damages of life,

573

property and environment caused by accidents. Especially, private organizations and NPOs can

574

provide diversified resources and services with lower cost and higher quality, which bridges the

575

critical gap of public-sector organizations’ capabilities to meet the emergency response needs in

576

emergencies.

577 578

6. Conclusion

579 580

This research studied the emergency response network for hazardous chemical accidents under

581

centralized administrative systems from the perspective of temporal dynamics. Organizations emerged

582

in response to the Xiangshui 3.21 Accident were identified and the dynamic of emergency response

583

network, organization functions, and organization positions in centralized administrative systems were

584

analyzed through SNA. The results showed that emergency response network evolved from

585

centralized to decentralized; the organization central emergency response functions varied depending 25

586

on demands; and organization positions changed over time. The results also revealed that under

587

centralized administrative systems, the core emergency response organizations were mostly public-

588

sector organizations, with few private organizations and NPOs, which is conducive to the integration

589

of resources and information in a minimal time frame, but not to multi-organizational cooperation.

590

The findings imply that the centralized emergency response network may not adequately respond to

591

hazardous chemical accidents. A hybrid network may be more effective because centralization and

592

decentralization complement each other.

593

Based on above conclusions, two policy recommendations were proposed to improve the

594

emergency response efficiency for hazardous chemical accidents. Firstly, it is suggested to consider

595

the ERN for hazardous chemical accidents in a time-dynamic way. In the process of emergency

596

response, changes in the functions and positions of organization involved in ERN in different stages

597

should be identified first; and then emergency resources should be dynamically adjusted to provide

598

more support for core emergency organizations and core emergency response functions. Secondly,

599

more attention should be paid to the collaborations among public sector organizations, NPOs and

600

private organizations in the emergency response process under centralized administrative systems.

601

Related policies could be changed to strengthen the collaborative relationships among public sector

602

organizations, NPOs and private organizations and to promote the development of NPOs and private

603

organizations under centralized administrative systems, thereby integrating emergency resources for

604

effective emergency response services.

605

This study presents critical theoretical and practical contributions to the field of emergency

606

management. Firstly, this study presents a new attempt to investigate the time dynamics of

607

emergency response network. It provides a theoretical framework for analyzing the time dependence

608

of emergency response network. Compared with the traditional static social network analysis method,

609

time dimension was taken into account in the social network analysis of emergency response network

610

by developing time slices and two-mode network toolset. This research also enriches the emergency

611

management theory system by introducing the contextual factor of centralized administration into

612

emergency response network research. Secondly, this research helps the practitioners to make

26

613

effective decisions based on real-time information. For example, emergency responses can be made

614

based on the immediate needs in different periods. Through in-depth study on the participants’

615

interaction patterns, functions and positions at critical moments, the unsuccessful response phase can

616

be tracked to detect poor performances.

617

There are still some limitations. In this study, data were collected manually based on the document

618

analysis. It is however acknowledged that some organizations and their interactions may not be

619

available if they were not recorded by data sources. In the future, various data collection technologies

620

(e.g. data crawler and big data analytics) and methods (e.g. questionnaire survey and interviews) are

621

recommended to address this problem. Secondly, single case study was used in this paper. More cases

622

could be used to investigate whether new inferences can be made on the dynamic of ERN. Future

623

research can focus on multi-case comparative study. For example, what are changes and differences

624

of ERN before and after the establishment of the MEM in China can be carried out in the following

625

study. In addition, a hierarchical, centralized command and control emergency response network in

626

centralized administrative systems can be compared with other emergency response networks in

627

decentralized administrative systems.

628 629

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Appendix A.

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Organizations Participated in Emergency Response Network Organization Attribute Government Organizations

Organization Name Jiangsu Ecological Environment Department State Council Ministry of Emergency Management of the People's Republic of China Ministry of Public Security of the People's Republic of China Fire Prevention of Yancheng, Nanjing and Lian Yungang City 31

Organization Abbreviation JSEED SC MEM MPS FP-YC,NJ,LY

Public Institutions and State-owned Companies

Army Organizations

Non-profit Organizations Private Organizations

Ministry of Ecology of the People's Republic of China Command Center of Jiangsu Fire Rescue Yancheng Propaganda Department Yancheng Ecological Environment Bureau Yancheng Municipal government Yancheng Municipal postal Administration National Health Commission of the People's Republic of China National Health Emergency Response Team Jiangsu Environmental Monitoring Jiangsu Provincial Government National Medical and Mental Health Emergency Expert Group Jiangsu Health Commission Yancheng Health Commission Xiangshui Health Commission Headquarters for Accident Rescue and Command Yancheng Public Security Bureau Xiangshui Fire Squadron Xiangshui County Government Jiangsu Communications Administration Xiangshui County Traffic and Transportation Bureau Xiangshui County Education Bureau Jiangsu Armed Police Corps Yancheng Municipal Housing and Urban-Rural Construction Bureau China Center for Disease Control and Prevention Peking Union Medical College Hospital Yancheng City First People's Hospital Yancheng City Second People's Hospital Xiangshui People's Hospital Jiangsu Province Blood Center State Grid Corporation of China State Grid Jiangsu Electric Power Corporation Jiangsu Meteorological Bureau Yancheng Meteorological Bureau Xiangshui Meteorological Bureau China Development Investment Group Corporation Jiangsu Military Region of the Chinese People's Liberation Army Yancheng Military Subarea Xiangshui Army Office Binhai, Fuyang, Sheyang Army Office Red Cross Society of China Red Cross Society of China Jiangsu Branch Red Cross Society of China Xiangshui Branch Xiangshui Volunteer Association Jiangsu Yulang Chemical Wastewater Treatment Plant City Investment Development Group

749

32

ME CCJSFR YCPD YCEEB YCMG YCMPA NHC NHERT JSEM JSPG NMMHEEG JSHC YCHC XSHC HARC YCPSB XSFS XSCG JSCA XSTTB XSEB JSAPC YCMHURCB CCDCP PUMCH YCFPH YCSPH XSPH JSBC SGCC SG-JSEPC JSMB YCMB XSMB CDIGC CPLA-JSMR YCMS AO-XS AOBH,FY,SY RCSC RCSC-JSB RCSC-XSB XSVA JSYCWTP CIDG

1. Dynamic of emergency response network for hazardous chemical accident was studied. 2. Emergency response network evolves from centralized to decentralized over time. 3. Functions of core organizations in network vary depending on response needs. 4. Positions of participants in network change with time. 5. Effective emergency network may be a centralized and decentralized hybrid network.

Declaration of interests

√ □The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: