*Article* **Scientific Mapping of Coastal Governance: Global Benchmarks and Trends**

**Alejandro Vega-Muñoz <sup>1</sup> , Guido Salazar-Sepúlveda <sup>2</sup> , Nicolás Contreras-Barraza 3,\* and Lorena Araya-Silva <sup>1</sup>**


**Abstract:** This research panoramically and empirically reviews the scientific production on coastal governance studies, mapping global networks of countries, organizations, authors, themes, and journals as referents for this topic. The articles were examined through a bibliometric/scientometric approach based on 2043 articles corpus stored in the Web of Science (JCR), applying the bibliometric laws of Price, Lotka, and Zipf to add further validity to the use of VOSviewer for data and metadata processing. The results highlight an uninterrupted exponential increase in publications since 1991, with a high concentration in 29 countries (21%), 461 organizations (18%), 99 authors (1.45%), and 4 growing journals (1%). The emerging topics observed in the literature are related to coastal sustainability and coastal management. Complementing previous studies on coastal zone management and marine territorial planning, we add coastal systems governance as a topic.

**Keywords:** coastal management; fishing areas; spatial planning; coastal environmental; coastal geopolitics; blue economy; bibliometrics

## **1. Introduction**

This article aims to provide an empirical and panoramic overview of the worldwide scientific production on coastal governance. We understand coastal governance to be a system that integrates and manages the complex contexts that exist in the coastal zone to support decision making, based on policies, programs, and regulations, encouraging the participation of stakeholders in achieving sustainable development objectives. Coastal governance implies confronting problems, most of which are difficult problems without a technical solution; at this point, the political process of decision making is a key point in the system and relates to other systems of knowledge, in adjustment with a national or international normative framework. All these considerations put coastal governance into a policy framework: the action of the state and the diversity of actors. Thus, coastal governance encompasses not only the actions of the national/local state but also those of other actors, such as communities, businesses, and civil society organizations, starting with policy problems related by these different actors, their solutions, the decision making, the implementation, and the achievement of those solutions [1–4].

Coastal governance is a topic that has become increasingly important due to the growing interaction between productive activities, commercial flows, habitability in coastal zones, and their effects on the ecosystem [5–10], which has forced the incorporation of participatory and collaborative processes in territorial management and governance [11–16]. The growing interest from the scientific community in the coastal governance phenomenon is reflected in the increase in the articles published in journals indexed in the Web of Science (WoS) databases in the last fifteen years, highlighting research generated in countries, such as Australia [14,17], Canada [18,19], and the USA [20,21].

**Citation:** Vega-Muñoz, A.; Salazar-Sepúlveda, G.; Contreras-Barraza, N.; Araya-Silva, L. Scientific Mapping of Coastal Governance: Global Benchmarks and Trends. *J. Mar. Sci. Eng.* **2022**, *10*, 751. https://doi.org/10.3390/ jmse10060751

Academic Editors: Yannis N. Krestenitis, Yui-yip Lau and Tomoya Kawasaki

Received: 30 March 2022 Accepted: 25 May 2022 Published: 30 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

To achieve the research aims, bibliometric/scientometric methods were used to address questions, such as: What is the evolution of scientific production in recent decades on research on coastal governance? What is the geographical and organizational distribution of research on coastal governance? Which authors are the main researchers on coastal governance? Which journals on coastal governance tend to be more influential for scientific production?

#### *1.1. Fisheries Management and Coastal Governance*

Climate change, marine habitat pollution, and indiscriminate fishing are rapidly affecting the marine environment; therefore, governments are intensifying measures to mitigate these effects and promote sustainable development [22–25].

Among these measures, Marine Protected Areas (MPAs) have had a positive effect on conservation processes around the world, through co-management between public and private entities, considering a collaborative strategy, technological introduction, aquaculture development, and tourism promotion [7,8,26]. The challenges presented by Marine Protected Areas are to have guidelines for conflict resolution, organizations' rights recognition, and integration of values and local culture, seeking a balance between the different stakeholders and their own interests and vulnerabilities [11,13,27,28]. Among the weaknesses encountered are clashes between the organizations and the public sector, the lack of rules for co-management between the state and the beneficiaries, instabilities in power groups, ideological, cultural, and political differences, and disagreements between national and local actors [25,29].

Another approach for coastal territories' sustainability has been the implementation of integrated management focuses [12,30,31]. This approach promotes management decentralization, municipal governance power, cooperation with local academic institutions, and civil society participation to achieve resource conservation and preserve the local identity and cultural heritage [32–34]. However, the challenges include securing property rights in small-scale coastal fisheries, stopping illegal fishing, and limiting environmentally damaging fishing equipment, fostering the organizations' empowerment, a balance in co-management, and the internalization of biodiversity conservation [35–37].

Finally, ecosystem-based assessments help to reduce uncertainty in resource inventories and provide ecological functional indicators, allowing for flexible policy integration, social and ecological justice, collaborative governance, and local autonomy over coastal resource management [38–43]. To achieve effective long-term protection, conventional and centralized conservation approaches are not sufficient [9,44]. Challenges in marine ecosystem planning include resource scarcity management, scientific uncertainty, policy design to promote species conservation and recovery, coordination among the activities and practices of the various actors in planning, sensitivity to climate-change-driven species redistribution, and improving social connectedness [4,14,45–49].

#### *1.2. Policies of Coastal Governance*

Researchers have used various terms, such as agency, incentives, governance, environmental management, and systemic complexity, to refer to the policy concept. Agency has been treated as the work of actors in the creation, maintenance, and disruption of organizational practices [10,50], associated with the collaboration and participation of the actors involved in these processes, identifying collaborative structures and substructures, generally referred to as collaborative networks whose policy outcomes will depend on the characteristics of these structures and substructures.

Co-management has been used by authors, such as Marin et al. [51] and Linke et al. [52], when referring to the multiplicity of actors that optimize the management of resources and social capacities that allow the forms and functions of adaptive organizational systems to be modified in contexts of adaptive and sustainable governance. Co-management recognizes the multiplicity and types of actors that relate and interrelate [10,51,53] in the planning and implementation processes related to adaptive strategies and interactions that tend

to occur at the local level [53], noting the relevance of participatory processes to balance sustainability with survival, community development, and urbanization [54].

Other studies have noted the agency difficulties, such as the institutional fragmentation and constant change processes [15], the identification of dense collaborative substructures, with an increasing specificity and complexity of collaborative links [10], the integrated agency paradox [50], the diversity of rationalities involved [19], as well as the coexistence of conflicting preferences in coastal management in the face of various climate scenarios [54].

Economic incentives and coastal economic, social, and territorial transformations are other aspects that research has examined, addressing the economic incentives and fiscal reform effects on land development [55], the local public finance effects, urbanization, economic policy transformation [56], compensation policies in land acquisition processes, and new problems, such as displaced people [57]. This shows the relevance of financial management in policies involving coastal land use.

The governance concept has been linked to participatory and collaborative policy processes, as evidenced when referring to the work of actors and the legitimacy of practices in territorial management [50] or polycentric multistakeholder governance at various scales that exceed the state [58]. This can be related to local governance levels [53], governance practices [59], the regional approach to maritime governance in the pan-European context [15], ecosystem services governance [19], marine/maritime spatial planning (MSP), as an approach involving an adaptive ecosystem-based approach [42], and Integrated Management (IM) of coastal and marine activities [14].

Likewise, challenges emerge for environmental governance and restoration, marine and coastal environments [60,61], and environmental governance [17]. Forms of rescaled environmental governance have been identified [17], which, in some cases, would lead to their identification as pendulum swings [17]. These challenges could give way to adaptive governance, sustainable resource management [52], and a blue economy (BE) [62].

This has been associated with a systemic spatial complexity and the interests of different actors, identifying multifaceted problems that require complex arrangements [63], a conflicting spatial competition between economic, maritime, and biodiversity activities that can lead to the fragmentation of policies, private initiatives, and regulations [15]. The vulnerability of coastal regions is an open question for coastal use, linked to coastal tourism governance and other land uses [64,65]. A particular challenge has also been observed in local governments in the face of little or no adequate international governance in response to climate change, ecosystem integration, and the establishment of systemic governance [16].

Environmental management has gained relevance in studies associated with policies, identifying terms related to: watershed approach [17], the combination of complex nature and coastal uses [64], ecosystem-based management (EBM), as an integral approach to improve the environment and collaborate on public policies and public administration [10], ecosystem service use improvement to increase human well-being [19], long-term uncertainty in the face of climate change risks and its impacts [54], the coastal zone's characterization as a complex socioecological system [66], the focus on poor people in access to coastal resources [67], territorial use rights for small-scale fisheries [68], ecosystem service classification and adaptation to vulnerability [69], and finally, the linkage with the Sustainable Development Goals [62].

#### **2. Materials and Methods**

The method used was oriented to scientific measurement, based on documented scientific research, according to bibliometric laws. Scientometrics as a kind of meta-analysis [70] focuses on studying what, how much, when, who, and where knowledge is produced [71], and it is a method recently used in subjects related to this study [72–81].

A set of articles were extracted from the Web of Science Core Collection (WoS), using the databases: the Science Citation Index Expanded (SCIE) with 178 indexing categories and the Social Sciences Citation Index (SSCI) with 58 indexing categories; both indexes were included in the Journal Citation Report (JCR-WoS) and conformed to high-quality journals, whose impact is calculated annually based on the average citations received. These articles were identified with the search vector TS = (Coastal AND Governance), using the topics field label (TS), Boolean operator (AND), and simultaneously incorporating the concepts of coastal and governance. Time restrictions were left free, to the date of extraction (29 September 2021) from the year 1900 for SCI-E and 1956 for SSCI, the restrictions on access to the WoS database. [82].

The annual growth of research publications on the recovered article set was examined based on the Price Law [83,84], searching for possible exponential growth, based on the annual number of published articles and the adjusted coefficient of determination (R-squared) for the exponential growth rate of these publications over time. For data and metadata analysis, VOSviewer (CWTS—Leiden University, Leiden, The Netherlands) [85] was used, as well as the bibliometric laws of: (1) Lotka to identify the prolific authors set with the highest number of published articles on the coastal governance topic, a set estimated by the square root of the total authors contributing to the article set analyzed [86]; the author identification process by VOSviewer, based on the database extracted from WoS, incorporated the total of authors registered as data and metadata for each article analyzed; and (2) Zipf, which recognizes the exponential decrease in the use frequency of words inside a knowledge corpus [87–89]; therefore, there are words that are used very frequently and others that are present in the literature rarely. This law was applied in this case for the empirical determination of the keywords plus metadata incorporated in the database extracted from WoS with the highest occurrence frequency in the total set of articles studied [90].

#### **3. Results**

The 2043 articles on coastal governance, extracted on 29 September 2021, presented a temporal exponential growth, with an R<sup>2</sup> of 89.96% between 1991 and 2021, excluding, in this adjustment, one isolated article published in 1978 (see Supplementary Material Table S1: CGv2.txt to be read with bibliometric software and a spreadsheet\_CG.xlsx for standard use). This accounted for an overall critical researcher mass, interested in increasing the knowledge corpus on this topic. In addition to this, the temporal distribution of the 2043 articles published on coastal governance in 386 JCR-WoS journals is presented in Figure 1. *J. Mar. Sci. Eng.* **2022**, *10*, x FOR PEER REVIEW 5 of 17

At the global level, the contribution of countries or territories to the scientific production documented on coastal governance studies in JCR-WoS journals varied from one

Above the average global contribution, 29 countries or territories were identified as contributing 28 or more articles, and 461 organizations contributed 3 or more articles (see Figure 2). Other background information provided by the descriptive analysis was the high atomization of scientific production by territory and institution, corroborated by mode 1 (see Table 1, mode) and the variation coefficient that gave us a value of 2.38 for the territory and 1.76 for the organizations, confirming high variability. The distribution of the data was represented by a leptokurtic curve (see Table 1, kurtosis), which indicated that most of the data were concentrated around the mean and with a higher concentration

levels. In this way, it was possible to identify the global referents for exceeding the average

above the mean, as indicated by the skewness coefficient (see Table 1, skewness).

**Table 1.** Descriptive statistics of territorial and organizational contribution to coastal governance

**Statistics Country/Territory Organization** Mean 27.02 2.28 Standard error 5.52 0.08 Median 5 1 Mode 1 1 Standard deviation 64.41 4.01 Variation coefficient 2.38 1.76 Kurtosis 26.69 64.76 Skewness coefficient 4.69 6.74 Minimum 1 1 Maximum 503 69 Count 136 2587

**Figure 1.** Publication time series and trend on coastal governance. **Figure 1.** Publication time series and trend on coastal governance.

*3.1. Global Scientific Benchmarks in Coastal Governance Studies*

of the geographical set.

studies (1978–2021).

**Rank A**

6

8

Peoples R

Nether-

5 Germany 153 7% 20 5

7 France 132 6% 20 7

lands <sup>121</sup> 6% <sup>28</sup> <sup>8</sup>

<sup>10</sup> Sweden <sup>100</sup> 5% <sup>39</sup> <sup>10</sup> UC Santa Bar-

tuaries).

#### *3.1. Global Scientific Benchmarks in Coastal Governance Studies*

At the global level, the contribution of countries or territories to the scientific production documented on coastal governance studies in JCR-WoS journals varied from one country or territory to another, with their contribution commonly being at low-production levels. In this way, it was possible to identify the global referents for exceeding the average of the geographical set.

Above the average global contribution, 29 countries or territories were identified as contributing 28 or more articles, and 461 organizations contributed 3 or more articles (see Figure 2). Other background information provided by the descriptive analysis was the high atomization of scientific production by territory and institution, corroborated by mode 1 (see Table 1, mode) and the variation coefficient that gave us a value of 2.38 for the territory and 1.76 for the organizations, confirming high variability. The distribution of the data was represented by a leptokurtic curve (see Table 1, kurtosis), which indicated that most of the data were concentrated around the mean and with a higher concentration above the mean, as indicated by the skewness coefficient (see Table 1, skewness). *J. Mar. Sci. Eng.* **2022**, *10*, x FOR PEER REVIEW 6 of 17

**Figure 2.** Co-authorship networks. (**a**) Co-authorship at a national level. (**b**) Co-authorship at an organizational level (1978–2021). **Figure 2.** Co-authorship networks. (**a**) Co-authorship at a national level. (**b**) Co-authorship at an organizational level (1978–2021).


Figure 2a highlights countries/territories, such as the USA, Australia, England, and Canada, with over 250 published articles (see Table 2, rank A). Figure 2b highlights or-**Table 1.** Descriptive statistics of territorial and organizational contribution to coastal governance studies (1978–2021).

3 England 283 14% 35 3 Univ Sweden <sup>42</sup> 2% <sup>72</sup> 4 Canada 251 12% 25 4 Univ Tasmania Australia 41 2% 18 Figure 2a highlights countries/territories, such as the USA, Australia, England, and Canada, with over 250 published articles (see Table 2, rank A). Figure 2b highlights orga-

> <sup>1</sup> Names used according to the data in the WoS database. <sup>2</sup> Considering 12 other forms of NOAA affiliation, 17 additional articles were reported in this period (NOAA Fisheries, NOAA Int Sect Off Gen Counsel, NOAA Marine Natl Monuments Program, NOAA NCEI, NOAA NMFS, NOAA NOS, NOAA Ocean Initiat Program, NOAA Papahanaumokuakea Marine Natl Monument, NOAA Scientist Emeritus, NOAA Southwest Fisheries Sci Ctr, NOAA Star, and NOAAs Off Natl Marine Sanc-

land USA <sup>39</sup> 2% <sup>33</sup>

land Australia <sup>37</sup> 2% <sup>17</sup>

bara USA <sup>33</sup> 2% <sup>71</sup>

USA 37 2% 41

9 Spain 116 6% 27 9 Duke Univ USA 35 2% 41

Stockholm

Univ Rhode Is-

Univ Queens-

Univ Washington

nizations, such as James Cook University, the University of British Columbia, Stockholm University, and the University of Tasmania, with over 40 articles (see Table 2, rank B).

**Table 2.** Main global references to territorial and organizational contribution on coastal governance studies (1978–2021).


<sup>1</sup> Names used according to the data in the WoS database. <sup>2</sup> Considering 12 other forms of NOAA affiliation, 17 additional articles were reported in this period (NOAA Fisheries, NOAA Int Sect Off Gen Counsel, NOAA Marine Natl Monuments Program, NOAA NCEI, NOAA NMFS, NOAA NOS, NOAA Ocean Initiat Program, NOAA Papahanaumokuakea Marine Natl Monument, NOAA Scientist Emeritus, NOAA Southwest Fisheries Sci Ctr, NOAA Star, and NOAAs Off Natl Marine Sanctuaries).

Table 2 details the contribution to the 2043 documents on coastal governance. The top ten countries contributing to the production of these papers participated in authorship or co-authorship in over 5% of the total articles published, with the USA (25%) and Australia (16%) being the major contributors overall. This is reflected at the level of the top ten organizational contributions, with five affiliation organizations from the USA (Univ Rhode Island, NOAA, Univ Washington, Duke Univ, and UC Santa Barbara) and three affiliation organizations from Australia (James Cook Univ, Univ Tasmania, and Univ Queensland), with James Cook Univ as the organization with the highest global contribution in Australia, with 3%, given its participation in 69 articles.

Finally, with respect to individual authors who manage to be referents for their level of scientific production on coastal governance (1978–2021), of the 6826 authors identified according to Lotka's law, the prolific authors should be approximately 83 (= sqrt (6826)); we chose to recognize 99 of them with a production of five or more articles (up to 20 were observed) for the period studied. These 99 authors were adjusted to 98 by merging the WoS records of the authors: Bennett, Nathan J. (10 articles), and Bennett, Nathan James (6 articles).

Figure 3 allows us to identify, in the larger nodes, the authors who, among 98 cases, presented a much higher number of publications. The authors who were at the second concentration level (sqrt (98) ≈ 10) are detailed below. When considering 10 authors, this was equivalent to a scientific production level of 10 publications within the article set analyzed (see Table 3); therefore, 12 authors were considered to include all authors who had published at least 10 articles.

tion in Australia, with 3%, given its participation in 69 articles.

Table 2 details the contribution to the 2043 documents on coastal governance. The top ten countries contributing to the production of these papers participated in authorship or co-authorship in over 5% of the total articles published, with the USA (25%) and Australia (16%) being the major contributors overall. This is reflected at the level of the top ten organizational contributions, with five affiliation organizations from the USA (Univ Rhode Island, NOAA, Univ Washington, Duke Univ, and UC Santa Barbara) and three affiliation organizations from Australia (James Cook Univ, Univ Tasmania, and Univ Queensland), with James Cook Univ as the organization with the highest global contribu-

Finally, with respect to individual authors who manage to be referents for their level of scientific production on coastal governance (1978–2021), of the 6826 authors identified according to Lotka's law, the prolific authors should be approximately 83 (= sqrt (6826)); we chose to recognize 99 of them with a production of five or more articles (up to 20 were observed) for the period studied. These 99 authors were adjusted to 98 by merging the WoS records of the authors: Bennett, Nathan J. (10 articles), and Bennett, Nathan James (6

Figure 3 allows us to identify, in the larger nodes, the authors who, among 98 cases, presented a much higher number of publications. The authors who were at the second concentration level (sqrt (98)10) are detailed below. When considering 10 authors, this was equivalent to a scientific production level of 10 publications within the article set analyzed (see Table 3); therefore, 12 authors were considered to include all authors who had

**Figure 3.** Co-authorship network, main researchers. **Figure 3.** Co-authorship network, main researchers.

articles).

published at least 10 articles.



\* Co-author with the highest publications in their network. \*\* Co-author with the highest average citations compared to other co-authors in their network.

#### *3.2. Scientific Trends in Coastal Governance Studies*

Researchers from new countries are joining the discussion on coastal governance given the large number of new territorial affiliations shown in Figure 4 in yellow, orange, and red.

**Table 3.** Authors with the highest production in the co-authorship main researcher network.

Armitage, Derek Red 20 \* 12 Univ Waterloo, Canada Bennett, Nathan J. Yellow greenish 16 \* 60 Univ British Columbia, Canada

Ratana Violet <sup>14</sup> \* <sup>46</sup> Mem Univ, Canada Ban, Natalie C. Yellow greenish 12 25 Univ Victoria, Canada Gelcich, Stefan Yellow greenish <sup>12</sup> <sup>73</sup> \*\* Pontificia Univ Católica Chile,

Glaser, Marion Red 12 21 \*\* Univ Bremen, Germany Haward, Marcus Green 12 \* 27 Univ Tasmania, Australia Aswani, Shankar Green 10 28 \*\* Rhodes Univ, South Africa Berkes, Fikret Yellow greenish 10 39 Univ Manitoba, Canada Cohen, Philippa J. Orange 10 \* 32 James Cook Univ, Australia Fletcher, Stephen Lavender 10 \* 24 Univ Plymouth, England Schlueter, Achim Red 10 13 Jacobs Univ Bremen, Germany White, Alan T. Blue 10 \* 66 Tetra Tech, Indonesia \* Co-author with the highest publications in their network. \*\* Co-author with the highest average

Researchers from new countries are joining the discussion on coastal governance given the large number of new territorial affiliations shown in Figure 4 in yellow, orange,

**Citations Institutional Affiliation**

Chile

**Author Network Articles Average**

citations compared to other co-authors in their network.

*3.2. Scientific Trends in Coastal Governance Studies*

Chuenpagdee,

and red.

**Figure 4.** Temporal national co-authorship network. **Figure 4.** Temporal national co-authorship network.

*J. Mar. Sci. Eng.* **2022**, *10*, x FOR PEER REVIEW 9 of 17

For the 2974 keywords plus identified by Clarivate (WoS proprietary company), 1887 plus keywords only had one occurrence, and, in general, its exponential decrease fits a power regression (see Appendix A). There were 53 most relevant keywords plus For the 2974 keywords plus identified by Clarivate (WoS proprietary company), 1887 plus keywords only had one occurrence, and, in general, its exponential decrease fits a power regression (see Appendix A). There were 53 most relevant keywords plus according to Zipf's Law with 33 or more occurrences (sqrt (2974) = 54), from 33 (state, adaptive governance, and areas) to 590 (governance) occurrences. We highlighted several concepts used more recently, as shown in Figure 5 in red (values expressed in average years of publication), among them, some specific terms, such as (number of occurrences in parentheses): challenges (113), climate change (218), communities (46), ecosystem services (68), impact (56), perceptions (44), risk (49), sea-level rise (60), and vulnerability (125). according to Zipf's Law with 33 or more occurrences (sqrt (2974) = 54), from 33 (state, adaptive governance, and areas) to 590 (governance) occurrences. We highlighted several concepts used more recently, as shown in Figure 5 in red (values expressed in average years of publication), among them, some specific terms, such as (number of occurrences in parentheses): challenges (113), climate change (218), communities (46), ecosystem services (68), impact (56), perceptions (44), risk (49), sea-level rise (60), and vulnerability (125).

More recently used concepts (mean publication year: 2017), such as ecosystem services (68 occurrences), communities (46), sea-level rise (60), protected areas (86), climate

(113), risks (56), and impacts (49), appear in the figure; the frame label size reflects the occurrence level, and the lines indicate the simultaneous use in articles or co-occurrence. Figure 6 shows the concentration of publications on coastal governance studies between 2002 and 2021, the period in which 2008 (98%) of the 2043 articles were published, in the journals Marine Policy and Ocean Coastal Management. However, there is a notable increase in the number of publications in the fully open access journals Frontiers in Marine Science and Sustainability. Table 4 shows some of the characteristics of these four journals.

**Figure 5.** Keywords plus temporal co-occurrence graph. **Figure 5.** Keywords plus temporal co-occurrence graph.

Environ. Stud.

Total, selec-

Total, journals \*

More recently used concepts (mean publication year: 2017), such as ecosystem services (68 occurrences), communities (46), sea-level rise (60), protected areas (86), climate change (218), and a set of concepts thematically related to vulnerability (125), challenges (113), risks (56), and impacts (49), appear in the figure; the frame label size reflects the occurrence level, and the lines indicate the simultaneous use in articles or co-occurrence.

Figure 6 shows the concentration of publications on coastal governance studies between 2002 and 2021, the period in which 2008 (98%) of the 2043 articles were published, in the journals Marine Policy and Ocean Coastal Management. However, there is a notable increase in the number of publications in the fully open access journals Frontiers in Marine Science and Sustainability. Table 4 shows some of the characteristics of these four journals. *J. Mar. Sci. Eng.* **2022**, *10*, x FOR PEER REVIEW 10 of 17

**Figure 6.** Journal publication trends (last 20 years). **Figure 6.** Journal publication trends (last 20 years).



\* Data updated from WoS on 27 March 2022. \*\* Articles published with open access (reading access fees covered by the authors).

tion 4 journals <sup>49</sup> <sup>67</sup> <sup>77</sup> <sup>85</sup> <sup>119</sup> <sup>198</sup> <sup>397</sup> 441 journals 198 224 315 316 356 692 1,409 \* Data updated from WoS on 27 March 2022. \*\* Articles published with open access (reading access fees covered by the authors). From Table 4, we can observe that these main journals that cover coastal governance studies belong mainly to the first quartile in their respective WoS categories, given that their impact factors (2020) fluctuated between 3251 and 4912, and the set of articles with open access was close to 50% of the total publications [91,92]. The highest impact factor (2020) corresponded to the journal "Frontiers in Marine Science", and thematically, the categories converged mainly to Environmental Studies and Environmental Sciences, al-

> From Table 4, we can observe that these main journals that cover coastal governance studies belong mainly to the first quartile in their respective WoS categories, given that

> open access was close to 50% of the total publications [91,92]. The highest impact factor (2020) corresponded to the journal "Frontiers in Marine Science", and thematically, the categories converged mainly to Environmental Studies and Environmental Sciences, although with double indexing variants that fluctuated between science, technology, and

international politics.

**4. Discussion**

though with double indexing variants that fluctuated between science, technology, and international politics.

#### **4. Discussion**

This article allows for the analysis of coastal systems studies from a management and political decision-making perspective and is, therefore, in line with previous bibliometric work by Birch et al. [93] on coastal zone management and Chalastani et al. [94] on marine spatial planning. However, given the breadth of the term governance, it can also approximate specific bibliometric analyses on estuarine system aspects [95], flood vulnerability [96], and coastal biogeochemistry [97].

The analysis was based on a set of 2043 articles analyzed from 386 journals, and their data and metadata were above the range (318 to 1316 articles from 118 to 309 journals) of other related bibliometrics identified [94–96]; in the case of Birch et al. [93], their sampling was even broader (5461 articles from 891 journals) by covering a topic as extensive as coastal zone management. The work by Gattuso et al. [97] seems, to us, to be an exception by adding terms in an inclusive way (with OR Boolean operators) to cover the broad spectrum of 14,743 articles in coastal biogeochemistry. Moreover, the authors' coverage analyzed (6826) was higher than those reported in other papers [94,95], although this figure depends on the co-authorship customs of each discipline and journal. Finally, VOSviewer was used as data and metadata analysis software, the same choice as other related bibliometrics [94,95], although, in general, this type of analysis is supported by specialized software [94,96] or specific Rstudio packages [93,95]. Moreover, we see the use of bibliometric laws, in this case, Price [86,87], Lotka [89], and Zipf [90], as a value in favor of this article to provide a methodological contribution to coastal governance studies.

The results reported with publication time series were subjected to growth adjustments, with R2 values from 83.89% to 94.49%, showing adjustments with respect to quadratic [93,95] or exponential functions [94]; in this respect, we were inclined to an exponential adjustment reporting values in that range [98]. Moreover, the use of Lotka's law [89] allowed us to incorporate, as an additional value, objective, and replicable criteria for the selection of prolific authors, which was also achieved for the establishment of relevant plus keywords by means of Zipf's law [90]. The countries with the highest production (USA, Australia, England, and Canada) were also reflected in the results reported in other related bibliometrics [93–95,97], and in the case of the reference journals identified in Table 4, there was agreement with the case of Marine Policy [93,94], Ocean & Coastal Management [93,94], and Frontiers in Marine Science [94]. The same was also true in the temporal publications that we presented in this article, although in some cases, this information was only partial [93]. Thus, our article complements previous studies on coastal zone management and marine territorial planning by providing knowledge on coastal governance research, so far identified in bibliometric studies only at the marine governance [93,94] and ocean governance [94] levels.

#### **5. Conclusions**

From the present coastal governance bibliometric study, we can conclude that, based on the research questions and on empirical evidence gathered from three decades of study, coastal governance is evolving positively at an exponential growth rate, allowing the generation of an ever-greater volume of knowledge on this topic.

As for the territorial contribution to scientific production, the 2043 articles studied were the result of the interconnected effort made by authors from 136 countries/territories; several of these were new contributors, but in total, they contributed an average of 27 articles in these thirty years, and in 10 cases, they made at least 100 contributions, with authors from the USA, Australia, England, and Canada standing out with more than 250 articles.

At the organizational level, there were 2587 contributors with 2 to 3 articles on average, but with peaks reaching 69 publications in the case of James Cook University (Australia), followed by the University of British Columbia (Canada), Stockholm University (Sweden),

and the University of Tasmania (Australia). This serves as context for the identification of 12 prolific authors with a contribution of the publication of 10 to 20 articles, some linked to the universities of James Cook, British Columbia, and Tasmania, and with links to the reference countries Australia (two cases), Canada (five), England (one), and Germany (two) but also highlighting authors from Chile, Indonesia, and South Africa.

This research was published mainly in a group of four journals, which gathered a third of the scientific production analyzed in coastal governance (683 articles), marking a growing trend in publications on this topic, a situation that is also being recognized by other contemporary bibliometrics in the marine and coastal management fields, with 50% of articles now published in open access format. New thematic trends include emerging concepts related to coastal sustainability (ecosystem services, communities, sea-level rise, protected areas, and climate change) and coastal management (vulnerability, challenges, risks, and impacts).

Potential limitations were the lack of greater depth in specific coastal governance areas, such as fisheries and policy areas, aspects that we considered in a general way. However, we opted for taking a less classical and more panoramic viewpoint in search of new trends, such as those observed.

In terms of future research, it seems relevant, from a scientometric perspective, to delve into the marked trend towards open access publications in the four most prolific journals and how this could improve their citation value rates, author prominence, and core and peripheral mobility. From the thematic implication of coastal governance, there are scale challenges in analyses at the ocean governance and port governance levels that can be explored and related to our findings. Moreover, the disciplinary interfaces with the economy in terms of the blue economy [76] and, at the geopolitical level, their impacts are other variants to be explored [99].

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/jmse10060751/s1, Table S1: CGv2.txt, Table S2: spreadsheet\_CG.xlsx.

**Author Contributions:** Conceptualization, N.C.-B. and A.V.-M.; methodology, A.V.-M.; software, A.V.-M. and G.S.-S.; validation, N.C.-B.; formal analysis, A.V.-M. and N.C.-B.; data curation, A.V.-M.; writing—original draft preparation, L.A.-S., N.C.-B. and G.S.-S.; writing—review and editing, A.V.-M.; project administration, A.V.-M.; funding acquisition, N.C.-B., G.S.-S. and A.V.-M. All authors have read and agreed to the published version of the manuscript.

**Funding:** The article processing charge (APC) was partially funded by Universidad Católica de la Santísima Concepción (Code: APC2022) and by Universidad Andres Bello (Code: C.C. 21500). Additionally, the publication fee (APC) was partially financed by the Universidad Autónoma de Chile, through the publication incentive fund 2022. (Code: C.C. 456001).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The analyzed dataset has been included as Supplementary Materials.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A. Zipf Law**

This appendix presents the outcomes of fitting keyword plus usage occurrence to a power regression (*y* = *ax<sup>β</sup>* ).

**Table A1.** Model Summary \*.


\* The independent variable is ID.



\* The independent variable is ID.

**Table A3.** Coefficients \*.


\* The dependent variable is ln(occurrences).

#### **References**


## *Article* **Risk Assessment of Navigation Safety for Ferries**

**Wen-Kai K. Hsu 1,\* , Jun-Wen Chen <sup>1</sup> , Nguyen Tan Huynh 1,2 and Yan-You Lin <sup>3</sup>**


**Abstract:** This study aims to discuss a risk assessment of navigation safety for ferries. In this research, the risk factors (RFs) for the navigation safety of ferries are first investigated from relevant literature and ferry operational features. A fuzzy AHP (Analytic Hierarchical Process) approach is then proposed to weight those RFs, after which a continuous risk-matrix model is then developed to determine the RFs' risk levels. Finally, to validate the practical application of the proposed model, ferries traveling across the Taiwan Strait were empirically investigated. The results may provide practical information for ferry operators to improve their safety performances. Further, the proposed risk assessment approach may provide references for related research in the safety management of short-distance passenger ships.

**Keywords:** ferry; navigation; safety; risk; fuzzy AHP

#### **1. Introduction**

Throughout the history of human development, sea transport has been widely exploited for the movement of passengers and cargo in many nations, especially in archipelagic countries. Although sea passenger transport has gradually diminished over the past two decades [1], in part because of rapid developments in aviation and road transport, passengers continue to use cruises or ferries as the main means of transport for different purposes [2].

Generally, cruise ships operate long-distance international routes, and their main functions are to provide passengers with leisure travel and sightseeing needs. Therefore, in design, the size of a cruise ship is usually larger and the requirements for entertainment facilities and comfort are generally more important than speed, whereas a ferry, also known as a traffic ship, is a regular multifunction ship for passengers and cargo. Its main function is to carry passengers, goods, and vehicles (including land vehicles and trains) between islands across short distances. Furthermore, the ferry is also known as a mass transportation system for islands and cities located by the water. For transportation between two points, the cost of a ferry is significantly lower than that of building bridges or tunnels. Nonetheless, one of the disadvantages of ferry transport is that it could be easily suspended due to weather conditions.

In practice, the primary requirement of passenger transport by ferry is travel speed [3]. Thus, in ship design, a ferry's tonnage is relatively small compared to that of a cruise vessel. In addition, the requirement for speed is much more crucial than comfort and entertainment facilities. Furthermore, in terms of safety facilities for maritime navigation, the requirements of cruise ships are much greater than those of ferries. Generally, cruise ships not only have diversified professionals and a variety of life-saving equipment but also have a certain number of specifications for the prevention of maritime accidents and for personnel training [4]. By contrast, for ferries, except for basic rescue and escape equipment,

**Citation:** Hsu, W.-K.K.; Chen, J.-W.; Huynh, N.T.; Lin, Y.-Y. Risk Assessment of Navigation Safety for Ferries. *J. Mar. Sci. Eng.* **2022**, *10*, 700. https://doi.org/10.3390/ jmse10050700

Academic Editor: Dracos Vassalos

Received: 31 March 2022 Accepted: 15 May 2022 Published: 20 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

the safety management activities are relatively inadequate compared with cruise ships. Furthermore, due to the features of short-distance traffic, in practice, operators may easily neglect the SOPs (standard operational procedures) for safety navigation. As a result, although governments have enforced stricter regulations, many fatal ferry accidents still occur relatively frequently.

Globally, calamitous accidents with many casualties and injuries pertaining to ferry transport have been reported. For instance, at least 60 people drowned after an overloaded ferry capsized in the river in the DR Congo in February 2021 [5]. Another deplorable incident happened in Bangladesh in April 2021 when an overcrowded ferry collided headon with a cargo ship, leading to a total of 34 deaths [6]. In addition, the number of reported ferry accidents raises concerns about navigation safety management for ferry transportation. For example, South Korea documented at least 110 ferry accidents between 2015 and 2019, although its government has implemented coastal ferry safety innovative strategies since September 2014 to avoid maritime disasters, such as the sinking of the MV Sewol, resulting in a death toll of 304 passengers and crew members in April 2014 [7]. For Taiwanese maritime navigation, a total of 583 ship accidents occurred between 2014 and 2019 for some key reasons: collision (33.22%), striking (15.18%), machinery failure (10.16%), grounding (8.56%), and fire/explosion (1.37%) [8].

Additionally, the proportion of navigation accidents is currently on the rise in several countries. More specifically, about 37.5% of accidents involving passenger vessels, including ferries, were recorded in Bangladesh between 2008 and 2019 [9]. Furthermore, the potential risks concerning the safety of ferries are expected to increase thanks to the expansion of sea traffic, the expansion of the offshore fishing industry, and wind farms. It is argued that a single accident by ferry transportation can cause mass mortalities and property loss since the ferry typically carries a lot of people and freight on board [7]. In the relevant research, most studies only focused on the identification of the safety factors of ship navigation, e.g., [10,11]. A few articles further evaluated the risk levels of those factors. In practice, the different risk levels of safety factors should have different corresponding strategies so as to improve the efficiency of safety management for ship navigation [12].

To fill the literature gap, this paper aims to assess the risks to navigation safety for ferries. In this study, the risk factors (RFs) affecting ferry navigation safety are first investigated. Since the RFs' assessments are highly professional problems, a fuzzy Analytic Hierarchical Process (AHP) approach is then used to weight those RFs, by which a continuous risk matrix is constructed to rank the RFs' risks. Finally, ferry operators traveling across the Taiwan Strait were empirically examined to validate the application of the proposed risk-matrix model. The rest of the paper is organized as follows: Sections 2 and 3 explain the risk factors in ferry navigation and the research methods used in this study, respectively. Section 4 discusses the research results. Finally, we provide some conclusions, limitations, and suggestions for further research.

#### **2. Literature Review**

#### *2.1. An Overview of Ferry Transportation in Taiwan*

Recent years have seen an increase in cross-Taiwan Strait communications. Travel between Mainland China and Taiwan has increased at an 8 percent average annual growth rate between 2010 and 2018 [13]. As shown in Figure 1, currently there are four major ferry routes: (1) Tapie-Pingtan managed by the Lina Wheel (LW), (2) Keelung-Matsus/Dongyin served by Taiwan Horse Star (THS), (3) Kaohsiung-Penghu operated by Tai-hua Wheel (THW), and (4) Taichung-Pingtan operated by the Strait (ST). The specifications of each ferry are also exhibited in Table 1. In addition, there are a few minor ferry routes between Hualien and Suao, including Taitung port, Orchid Island, and Green Island.

**Figure 1.** Ferry routes between Taiwan and archipelagic islands. **Figure 1.** Ferry routes between Taiwan and archipelagic islands.


**Table 1.** The ship profiles of main ferries in Taiwan. **Table 1.** The ship profiles of main ferries in Taiwan.

#### *2.2. The Risk Factors of Navigation Safety*  According to the European Union's 2008 Safety Research Plan: Safer EURORO Re-*2.2. The Risk Factors of Navigation Safety*

port [14], the RFs for ferry safety were classified into four dimensions, including humanware, hardware, software, and environment [14]. Based on this framework, numerous navigation-related studies have been conducted. Organizational factors, environmental conditions, human mistakes in safety management, and other possible RFs for marine transportation have been identified in recent research [15–17]. People are often injured or killed, and the environment is often polluted as a result of these RFs. As a result, maritime operators and academics have paid particular attention to how to deal with these RFs in order to ensure maritime navigation safety [18]. According to the European Union's 2008 Safety Research Plan: Safer EURORO Report [14], the RFs for ferry safety were classified into four dimensions, including humanware, hardware, software, and environment [14]. Based on this framework, numerous navigation-related studies have been conducted. Organizational factors, environmental conditions, human mistakes in safety management, and other possible RFs for marine transportation have been identified in recent research [15–17]. People are often injured or killed, and the environment is often polluted as a result of these RFs. As a result, maritime operators and academics have paid particular attention to how to deal with these RFs in order to ensure maritime navigation safety [18].

Accordingly, we depend on previous research and IMO criteria for maritime navigation safety in this work. Based on Safer EURORO's framework [14], this research focuses on the following four key safety evaluation factors for ferry transport: crew factor, ship hardware, ship management, and company management.

#### 2.2.1. Crew Factor (CF)

Relevant studies have indicated that human error is the primary cause of marine accidents, including personal knowledge, skills, talents, attitude, working drive, and awareness [19]. For example, human error was shown to be the root cause of more than 80% of shipping-related incidents [20]. Human error was to blame for 79% of European maritime disasters between 1981 and 1992 [21]. As a result, human error is responsible for over 79 percent of towing vessel groundings [22], almost 26 percent of fire and explosion incidents [23], and approximately 30 percent of onboard fires/explosions. There are internal and external components to the errors in terms of the crew members that could be differentiated. Internal human error can be attributable to work stress, knowledge, self-discipline, or crews' perceived fatalism [24,25]. Conversely, external human error could be caused by the working environment (i.e., unclean workplace, noise, or pilotage-related deficiencies [18] or a harsh natural environment [16], which makes crew members lack foresight and concentration in their duties. Likewise, other onboard mistakes by crew members that could affect maritime operational safety include misjudgment and misunderstanding [21], inadequate technical knowledge [16], a lack of knowledge about the ship system [3], fatigue, poor rescue communication [16,21], or a lack of awareness of survival procedures [10]. It is argued that a crew member's ability to respond professionally to shipping accidents is able to restrict a mass loss of property and life [11]. To sum up, passenger ferry safety assessments must take into account the importance of the crew factor.

#### 2.2.2. Ship Hardware (SH)

One of the most important variables in marine navigation safety is the condition of a ship's mechanical equipment. Related studies indicated that ship accidents caused by mechanical failure range from 10% to 51% of total accidents [8]. In addition, ship structure is shown to be a critical factor in marine transportation's overall safety. The general engineering and technical system, strength and stability, power and propulsion, and maneuverability are the four pillars of shipbuilding excellence [11]. Furthermore, studies have shown the importance of onboard equipment, such as excellent radio communication, nautical lights and searchlights, and the radar system [16], in ensuring ship navigational safety. Vessel operators should pay more attention to some ship equipment failures to reduce the potential risks, such as broken mooring lines, rusted bolts, damaged gas detectors, and crippled exhaust fans [19]. Additionally, other onboard rescue equipment, such as lifeboats, lifejackets, fire extinguishers, and seat belts [8,10,15,18], and communications systems, such as the Automatic Identification System (AIS), Very High Frequency (VHF) radios, and even the Ship Security Alert System (SSAS) for security, have also been demonstrated to be an indispensable part of marine navigational safety practices.

#### 2.2.3. Ship Management (SM)

Ship management is an essential part of maintaining and operating boats in a safe and efficient manner, as well as for minimizing the risk of accidents and mishaps. Since 1998, the maritime sector has used the International Safety Management (ISM) code to standardize ship management. This code mandates that ship operators follow standard operating procedures (SOPs) in order to optimize operational efficiency and minimize risk. In addition to crews' abilities and expertise, the process of managing crew members onboard is widely considered to be an important aspect of increasing ship safety operations, such as crew working hours, workloads, and job allocations [26].

Furthermore, organizing regular exercise and periodic training programs are manifested to be a crucial part of marine risk-prevention strategies for major shipping lines such

as COSCO and Yang Ming. On top of that, working on vessels requires a "team effort"; in other words, a "one-man-show" cannot operate the whole vessel effectively and efficiently. It is evident that good interpersonal relationships among seafarers, which enable them to coordinate and cooperate in the workplace, are important for performing operations smoothly and safely on board. Comprehensive maritime accident analyses also found that the lack of team training and poor communication between crews and third parties are prone to major accidents [18]. such as COSCO and Yang Ming. On top of that, working on vessels requires a "team effort"; in other words, a "one-man-show" cannot operate the whole vessel effectively and efficiently. It is evident that good interpersonal relationships among seafarers, which enable them to coordinate and cooperate in the workplace, are important for performing operations smoothly and safely on board. Comprehensive maritime accident analyses also found that the lack of team training and poor communication between crews and third parties are prone to major accidents [18].

Furthermore, organizing regular exercise and periodic training programs are manifested to be a crucial part of marine risk-prevention strategies for major shipping lines

*J. Mar. Sci. Eng.* **2022**, *10*, x FOR PEER REVIEW 5 of 17

#### 2.2.4. Company Management (CM) 2.2.4. Company Management (CM)

The process of company management is an important aspect that is crucial to optimizing ferry navigation and improving ferry operators' business performances. For the role of company management in ferry navigation safety, the responsibilities delegated to an executive cover two categories: technical management and crew management. Arguably, technical management services, such as arranging and supervising dry dockings, repairs, alterations, and maintenance, ensure that the vessel's machinery maintains a particular standard of operation and safety. In recent years, crew management has received more attention as an imperative facet of estimating the risks of maritime transportation. In practice, crew management for shipping companies mainly includes the development of safety procedures [3], crew manpower planning [26], and safety training systems [11]. On top of this, regulatory actions [19]; certification counterfeiting; poor inspection [18], incentive and punishment mechanisms [26]; and crew recruitment processes [3] are also a few of the numerous factors that affect ship navigation safety. The process of company management is an important aspect that is crucial to optimizing ferry navigation and improving ferry operators' business performances. For the role of company management in ferry navigation safety, the responsibilities delegated to an executive cover two categories: technical management and crew management. Arguably, technical management services, such as arranging and supervising dry dockings, repairs, alterations, and maintenance, ensure that the vessel's machinery maintains a particular standard of operation and safety. In recent years, crew management has received more attention as an imperative facet of estimating the risks of maritime transportation. In practice, crew management for shipping companies mainly includes the development of safety procedures [3], crew manpower planning [26], and safety training systems [11]. On top of this, regulatory actions [19]; certification counterfeiting; poor inspection [18], incentive and punishment mechanisms [26]; and crew recruitment processes [3] are also a few of the numerous factors that affect ship navigation safety.

#### *2.3. Risk Matrix 2.3. Risk Matrix*

To improve maritime safety, the International Maritime Organization (IMO) proposed the Formal Safety Assessment (FSA) procedures to assess safety risks [27]. The process, shown in Figure 2, includes five steps: (1) hazard identification, (2) risk analysis, (3) risk control options, (4) cost-benefit assessment, and (5) recommendations on decisions [28]. In this article, the hazard is defined as any accident that endangers the navigation safety of a ferry. Since the FSA procedure includes complete and concrete implementation steps, it was widely applied in many workplaces of safety management, including maritime transportations [25,26,29], container terminals [28], airfreight transportations [12], etc. To improve maritime safety, the International Maritime Organization (IMO) proposed the Formal Safety Assessment (FSA) procedures to assess safety risks [27]. The process, shown in Figure 2, includes five steps: (1) hazard identification, (2) risk analysis, (3) risk control options, (4) cost-benefit assessment, and (5) recommendations on decisions [28]. In this article, the hazard is defined as any accident that endangers the navigation safety of a ferry. Since the FSA procedure includes complete and concrete implementation steps, it was widely applied in many workplaces of safety management, including maritime transportations [25,26,29], container terminals [28], airfreight transportations [12], etc.

**Figure 2. Figure 2.**  The Formal Safety Assessment (FSA) procedures. The Formal Safety Assessment (FSA) procedures.

In the FSA procedures, the hazard identification in Step 1 is to define the risk factors (RFs), and a risk matrix is usually employed to analyze the RFs in Step 2 (i.e., Risk Analysis). Traditionally, the risk matrix is constructed based on the consequence and likelihood of the RF (Duijm, 2015). The consequence refers to the extent of loss to an organization when a particular RF is incurred and can be generally divided into 1~4 (or 1~5) levels, such as very

serious, major, moderate, minor, etc. The likelihood refers to the number of occurrences of a specific RF within a certain period. Again, it is divided into 1~4 (or 1~5) levels, such as: often occurs, common, less frequently occurs, and rarely occurs. as very serious, major, moderate, minor, etc. The likelihood refers to the number of occurrences of a specific RF within a certain period. Again, it is divided into 1~4 (or 1~5) levels, such as: often occurs, common, less frequently occurs, and rarely occurs. as very serious, major, moderate, minor, etc. The likelihood refers to the number of occurrences of a specific RF within a certain period. Again, it is divided into 1~4 (or 1~5) levels, such as: often occurs, common, less frequently occurs, and rarely occurs.

In the FSA procedures, the hazard identification in Step 1 is to define the risk factors (RFs), and a risk matrix is usually employed to analyze the RFs in Step 2 (i.e., Risk Analysis). Traditionally, the risk matrix is constructed based on the consequence and likelihood of the RF (Duijm, 2015). The consequence refers to the extent of loss to an organization when a particular RF is incurred and can be generally divided into 1~4 (or 1~5) levels, such

In the FSA procedures, the hazard identification in Step 1 is to define the risk factors (RFs), and a risk matrix is usually employed to analyze the RFs in Step 2 (i.e., Risk Analysis). Traditionally, the risk matrix is constructed based on the consequence and likelihood of the RF (Duijm, 2015). The consequence refers to the extent of loss to an organization when a particular RF is incurred and can be generally divided into 1~4 (or 1~5) levels, such

In the traditional risk matrix, based on the levels of both consequence and likelihood, a two-dimensional panel with a risk value is used to rank the RFs' levels. The panel is divided into several colored areas to characterize the levels. Moreover, a risk value is yielded by the product of the two levels. For example, Figure 3 shows a 4 × 4 risk matrix that ranks the RFs into three levels. The RFs located in the green area with risk values between 1 and 2 are classified as L (low-risk) levels. In contrast, the RFs situated in the yellow and red regions are classified as M (medium-risk) and H (high-risk) levels, respectively. Since the levels of both consequence and likelihood are discontinuous, the risk value is discrete and, as a result, the panel becomes a discrete risk matrix. In the traditional risk matrix, based on the levels of both consequence and likelihood, a two-dimensional panel with a risk value is used to rank the RFs' levels. The panel is divided into several colored areas to characterize the levels. Moreover, a risk value is yielded by the product of the two levels. For example, Figure 3 shows a 4 × 4 risk matrix that ranks the RFs into three levels. The RFs located in the green area with risk values between 1 and 2 are classified as L (low-risk) levels. In contrast, the RFs situated in the yellow and red regions are classified as M (medium-risk) and H (high-risk) levels, respectively. Since the levels of both consequence and likelihood are discontinuous, the risk value is discrete and, as a result, the panel becomes a discrete risk matrix. In the traditional risk matrix, based on the levels of both consequence and likelihood, a two-dimensional panel with a risk value is used to rank the RFs' levels. The panel is divided into several colored areas to characterize the levels. Moreover, a risk value is yielded by the product of the two levels. For example, Figure 3 shows a 4 × 4 risk matrix that ranks the RFs into three levels. The RFs located in the green area with risk values between 1 and 2 are classified as L (low-risk) levels. In contrast, the RFs situated in the yellow and red regions are classified as M (medium-risk) and H (high-risk) levels, respectively. Since the levels of both consequence and likelihood are discontinuous, the risk value is discrete and, as a result, the panel becomes a discrete risk matrix.


*J. Mar. Sci. Eng.* **2022**, *10*, x FOR PEER REVIEW 6 of 17

*J. Mar. Sci. Eng.* **2022**, *10*, x FOR PEER REVIEW 6 of 17

**Figure 3.** The traditional risk matrix. **Figure 3.** The traditional risk matrix. **Figure 3.** The traditional risk matrix.

In practice, the discontinuity of a risk matrix may limit its applicability with respect to accuracies due to the consistency of the measurement data, risk-matrix grading [28], etc. To improve the shortcomings of discontinuity, the concept of a continuous risk matrix was thus proposed as shown in the curve in Figure 4 [12]. In practice, the discontinuity of a risk matrix may limit its applicability with respect to accuracies due to the consistency of the measurement data, risk-matrix grading [28], etc. To improve the shortcomings of discontinuity, the concept of a continuous risk matrix was thus proposed as shown in the curve in Figure 4 [12]. In practice, the discontinuity of a risk matrix may limit its applicability with respect to accuracies due to the consistency of the measurement data, risk-matrix grading [28], etc. To improve the shortcomings of discontinuity, the concept of a continuous risk matrix was thus proposed as shown in the curve in Figure 4 [12].

**Figure 4.** The continuous risk matrix. **Figure 4.** The continuous risk matrix. **Figure 4.** The continuous risk matrix.

#### **3. Research Method 3. Research Method**

**3. Research Method** In this paper, the risk factors (RFs) for accidents endangering the navigation safety of a ferry are initially identified. A fuzzy AHP (Analytic Hierarchy Process) is then used to assess those RFs' weights, including both consequence and likelihood. Based on those weights, a continuous risk matrix is developed to assess the RFs' risk levels. Furthermore, the ferries traveling across the Taiwan Strait were empirically investigated, following which, practical management policies for ferry navigation safety are suggested. In this paper, the risk factors (RFs) for accidents endangering the navigation safety of a ferry are initially identified. A fuzzy AHP (Analytic Hierarchy Process) is then used to assess those RFs' weights, including both consequence and likelihood. Based on those weights, a continuous risk matrix is developed to assess the RFs' risk levels. Furthermore, the ferries traveling across the Taiwan Strait were empirically investigated, following which, practical management policies for ferry navigation safety are suggested. In this paper, the risk factors (RFs) for accidents endangering the navigation safety of a ferry are initially identified. A fuzzy AHP (Analytic Hierarchy Process) is then used to assess those RFs' weights, including both consequence and likelihood. Based on those weights, a continuous risk matrix is developed to assess the RFs' risk levels. Furthermore, the ferries traveling across the Taiwan Strait were empirically investigated, following which, practical management policies for ferry navigation safety are suggested.

#### *3.1. The Risk Factors (RFs)*

A total of sixteen RFs are generated based on the ferry's navigational characteristics and literature review in Section 2.2. Each RF is based on one of four factors: crew factor, ship hardware, ship management, and company management.

(1) Crew factor (CF)

In this paper, the CF is defined as the crew's personal perspectives and attitudes regarding ferry safety practices, including safety knowledge, ability to manage shipping, personal self-discipline for work, fatalism cognition, etc. [10,11,16,18,19,21,24–27].

(2) Ship hardware (SH)

In this paper, the SH is defined as the equipment and facilities onboard a ship, including the usability of fundamental navigation equipment for ferry navigation, as well as the availability of emergency rescue systems, safety monitoring systems, and emergency alert navigational aids [3,8,10,11,15,16,18,19,27].

(3) Ship management (SM)

In this paper, the SM is defined as the management of crew operations in navigation, including compliance with standard operating procedures (SOPs), safety drillings, and crew coordination and collaboration. Furthermore, it also includes the development and implementation of different safety management processes onboard [14,16,18,27].

(4) Company Management (CM)

In this paper, the CM is defined as the safety management systems of a shipping company, including the process of crew recruiting, the crew assessment system, technical management, the mechanisms for rewarding and punishing employees, etc. [3,10,11,18,19,26].

Based on the above definitions, a two-layer structure of RFs was created hierarchically. To improve the practical validity of the RFs, three experienced crews working onboard ferries in Taiwan were invited to revise them. Furthermore, they were also asked to check the interdependencies among the RFs. After two rounds of revisions, the final hierarchical structure of the RFs, as shown in Table 2, contained 4 constructs of RFs for the first layer and 16 for the second layer.


**Table 2.** The risk factors (RFs) for ferry navigation safety.

#### *3.2. The Continuous Risk Matrix*

Based on Figure 4, both weights of consequence and likelihood are employed to construct the traditional risk matrix following which, the RF's risk level is ranked.

#### 3.2.1. The Expert Questionnaire and Research Sample

Since this study proposes a fuzzy AHP approach to weight the RFs, a pair-wise comparison questionnaire with a nine-point rating scale was designed to measure the respondents' perceived scores for each RF, including consequence and likelihood. According to the hierarchical structure of the RFs in Table 2, an expert questionnaire with 4 criteria and 16 sub-criteria was created.

In this study, the top four ferry operators in Taiwan (Taiwan ferries case), as shown in the last row of Table 1, were empirically examined to validate the research. Each ferry operator was asked to provide 4-8 senior crews as respondents. Since the survey items are highly professional, all surveyed subjects must have sufficient work experience in navigation safety. Furthermore, to enhance the validity and reliability of the survey, an assistant was assigned to assist each respondent with completing the questionnaire. Finally, we successfully surveyed 22 respondents. Furthermore, since each crew was asked to answer both the perceived consequence and likelihood of the RFs, the total samples number 44. For verifying the consistency of the 44 measures, both consistency index (CI) and consistency ratio (CR) are used to test the consistency of each sample's pairwise comparison matrix:

$$\text{CI} = \frac{\lambda\_{\text{max}} - n}{n - 1} \tag{1}$$

and

$$\text{CR} = \frac{\text{CI}}{\text{RI}} \tag{2}$$

where *λ*max is the maximum eigenvalue for each matrix, *n* is the number of criteria in the matrix, and RI represents a randomized index as shown in Table 3 (e.g., Hus, et al., 2016). Theoretically, Saaty suggested that the CR ≤ 0.1 is an acceptable range [12,30].

**Table 3.** The values of the RI corresponding to a variety of *n*.


In this paper, the software package Expert Choice 11.5 is first used to find the CI for each sample, then, its CR can be obtained by Equation (2). Results showed six samples' CI or CR > 0.1, which meant that they were inconsistent. Therefore, the questionnaire respondents were asked to modify their answers until their scales fitted the consistency tests.

The respondents' profiles are shown in Table 4. Evidently, all respondents have at least 5 years of work experience and possess workplace safety licenses. The experiential qualifications of the respondents can support the reliability of the survey results.

**Table 4.** Profiles of the respondents.



**Table 4.** *Cont.*

#### 3.2.2. The Weights of the RFs

From the sample data in the Taiwan ferries case, we have 44 positive reciprocal matrixes for each pair-wise comparison of the RFs in each layer, including 22 matrixes for consequence measures and 22 matrixes for likelihood measures. To consider the linguistic fuzziness of respondents when answering the survey, the fuzzy AHP approach was proposed to weight both the consequence and likelihood of the RFs [12,28]. For ease of explanation, we take the RFs in the CF construct with consequence measures as an example to detail the process of the fuzzy AHP approach. As shown in Table 2, the RFs in the CF construct include CF1, CF2, CF3, and CF4.

(1) The integration of multi-respondents' opinions.

In this paper, the geometric mean of the measuring scores from multi-respondents is first found. A triangular fuzzy number parameterized by the geometric mean and two extreme values: the minimum and maximum of the measuring scores is then constructed to integrate the multi-respondent's positive reciprocal matrixes into a fuzzy matrix [30].

(2) The integrated fuzzy positive reciprocal matrix

Suppose *A*e is the integrated fuzzy positive reciprocal matrix with *n* RFs as:

$$
\widetilde{A} = \begin{bmatrix} \widetilde{a}\_{ij} \end{bmatrix}\_{n \times n} = \begin{bmatrix} 1 & \widetilde{a}\_{12} & \dots & \widetilde{a}\_{1n} \\ \widetilde{a}\_{21} & 1 & \dots & \widetilde{a}\_{2n} \\ \vdots & \vdots & & \vdots \\ \widetilde{a}\_{n1} & \widetilde{a}\_{n2} & \dots & 1 \end{bmatrix} \tag{3}
$$

where the element <sup>e</sup>*aij* is a triangular fuzzy number with parameters:

$$
\widetilde{a}\_{ij} = \begin{cases}
[\![l\_{ij} \; m\_{ij} \; \!u\_{ij}] \; \!] \; \text{if } i > j \\
[\![\![ \; \mathbf{1} \; \mathbf{1} \; \mathbf{1} ] \; \text{if } i = j \\
[\![\![ \frac{1}{\!u\_{ji}} \; \frac{1}{\!m\_{ji}} \; \mathbf{1} \; \frac{1}{\!l\_{ji}} \; \text{if } i < j
\end{cases}
$$

If we have *m* positive reciprocal matrix from *m* respondents, then based on step (1), those *<sup>m</sup>* matrixes can be aggregated into a fuzzy matrix *<sup>A</sup>*<sup>e</sup> with elements <sup>e</sup>*aij* as:

$$\widetilde{a}\_{ij} = [l\_{ij}, m\_{ij}, u\_{ij}] = \left[ \min\_{1 \le k \le m} \left\{ a\_{ij}^{(k)} \right\}, \left( \prod\_{k=1}^m a\_{ij}^{(k)} \right)^{1/m}, \max\_{1 \le k \le m} \left\{ a\_{ij}^{(k)} \right\} \right],$$
 
$$i = 1, 2, \dots, n, \ j = 1, 2, \dots, n \text{ and } k = 1, 2, \dots, m.$$

For the data of the CF construct example, we had 22 matrixes. Based on Equation (4), those matrixes are integrated into a fuzzy positive reciprocal matrix, termed *A*e0, as:

*A*e0 = [1.000, 1.000, 1.000] [0.500, 0.740, 1.500] [0.571, 0.862, 1.250][0.667, 1.149, 2.000] [0.667, 1.351, 2.000] [1.000, 1.000, 1.000][0.800, 1.118, 1.750][1.500, 1.589, 2.000] [0.800, 1.160, 1.750] [0.571, 0.894, 1.250][1.000, 1.000, 1.000][1.250, 1.387, 1.750] [0.500, 0.871, 1.500] [0.500, 0.629, 0.667][0.571, 0.721, 0.800][1.000, 1.000, 1.000] (4) (3) The integrated crisp positive reciprocal matrix

In this paper, a weighted geometric mean method is used to defuzzify the *<sup>A</sup>*<sup>e</sup> = [e*aij*] *n*×*n* into a crisp positive reciprocal matrix *A* = [*aij*] *n*×*n* , in which, the fuzzy element <sup>e</sup>*aij* = [*lij*, *<sup>m</sup>ij*, *<sup>u</sup>ij*] in *<sup>A</sup>*<sup>e</sup> is defuzzified [12,30]:

$$a\_{\rm ij} = \sqrt[4]{l\_{\rm ij} \cdot 2m\_{\rm ij} \cdot u\_{\rm ij}} \text{ } i = 1, 2, \dots, n \text{ } j = 1, 2, \dots, n \tag{5}$$

Based on Equation (5), the example matrix *A*e0 was defuzzified as:

$$A\prime = \begin{bmatrix} 1.000\ 0.801\ 0.854\ 1.152\\ 1.249\ 1.0001.150\ 1.659\\ 1.172\ 0.869\ 1.000\ 1.432\\ 0.868\ 0.603\ 0.698\ 1.000\ \end{bmatrix}$$

Note, that it is easy to test that the matrix *A*0 still retains the features of a positive reciprocal matrix [30]. Thus, the simplified method: NGMR (Normalization of the Geometric Mean of the Rows) can be used to find the priority weights of the matrix *A*0 (Satty, 2003).

(4) The RFs' weights

Theoretically, the weights of the RFs can be determined from the eigenvectors of the matrix *A* = [*aij*] *n*×*n* . Let *W* = [*w*1, *w*2, . . . , *wn*] *T* represents the vector of the RFs' weights. Then the *W* can be found by the eigenvector and eigenvalue of *A* as Saaty [31]:

$$\begin{cases} \displaystyle \begin{aligned} A\mathcal{W} &= \lambda \mathcal{W} \\ \displaystyle \sum\_{i=1}^{n} w\_{i} &= 1, i = 1, 2, \dots, n. \end{aligned} \tag{6} $$

If *A* is a positive reciprocal matrix, Saaty (2003) proposed the simplified method NGMR to find the approximated eigenvectors of *A*. Let *W* = [*w*1, *w*2, . . . , *wn*] *T* be the eigenvector of *A*, then it can be found by the normalized geometric means of *aij* as:

$$\boldsymbol{W} = \begin{bmatrix} \boldsymbol{w} \\ \boldsymbol{w}\_2 \\ \vdots \\ \boldsymbol{w}\_n \end{bmatrix} = \begin{bmatrix} \left(\prod\_{j=1}^n a\_{1j}\right)' / \sum\_{i=1}^n \left(\prod\_{j=1}^n a\_{ij}\right)^{1/n} \\ \left(\prod\_{j=1}^n a\_{2j}\right)' / \sum\_{i=1}^n \left(\prod\_{j=1}^n a\_{ij}\right)^{1/n} \\ \vdots \\ \left(\prod\_{j=1}^n a\_{nj}\right)^{1/n} / \sum\_{i=1}^n \left(\prod\_{j=1}^n a\_{ij}\right)^{1/n} \end{bmatrix}, i = 1, 2, \dots, n, j = 1, 2, \dots, n. \tag{7}$$

Since the matrix *A*0 is a positive reciprocal matrix, based on Equation (7), we have:

$$W\prime = \begin{bmatrix} w\_1'\\ w\_2'\\ w\_3'\\ w\_4' \end{bmatrix} = \begin{bmatrix} 0.2319\\ 0.3060\\ 0.2706\\ 0.1915 \end{bmatrix} \prime$$

Further, substituting Equation (7) into Equation (6), we have

$$
\lambda I = (AW)\mathcal{W}^{-1} \tag{8}
$$

where *λI* ≈ (*λ*1.*λ*2, . . . , *λn*). Finally, the approximated maximum eigenvalue *λ*max of matrix *A* can be found by averaging the (*λ*1.*λ*2, . . . , *λn*) as:

$$
\lambda\_{\text{max}} \approx \frac{1}{n} \cdot \sum\_{i}^{n} (\lambda\_1 + \lambda\_2 + \dots + \lambda\_n) \tag{9}
$$

For the example matrix *A*0, based on Equations (8) and (9), we had:

$$
\begin{bmatrix} \lambda\_1\\ \lambda\_2\\ \lambda\_3\\ \lambda\_4 \end{bmatrix} = \left( \begin{bmatrix} 1.000\,0.801\,0.854\,1.152\\ 1.249\,1.000\,1.150\,1.659\\ 1.172\,0.869\,1.000\,1.432\\ 0.868\,0.603\,0.698\,1.000 \end{bmatrix} \cdot \begin{bmatrix} 0.2319\\ 0.3060\\ 0.2706\\ 0.1915 \end{bmatrix} \right)^{-1} \begin{bmatrix} 4.003\\ 0.3060\\ 0.2706\\ 0.1915 \end{bmatrix}^{-1} = \begin{bmatrix} 4.003\\ 4.002\\ 4.000\\ 4.002 \end{bmatrix}
$$

⇒ *λ*max ≈ 4.002

(5) The consistency test

Based on the *λ*max (= 4.002) in Equation (9), both the indexes of CI and CR can be obtained from Equations (1) and (2) in Section 3.2.1 to test the consistency of the matrix *A*0. The results show: CI = 0.001 and CR = 0.001. Likewise, we tested the consistencies of the RFs in the other constructs in the Taiwan ferries case. The results shown in Table 5 indicate that all the CI and CR values are less than 0.1, implying that all the positive reciprocal matrixes in the Taiwan ferries case are consistent.


**Table 5.** The consistency tests for the samples in the Taiwan ferries case.

Note: Boldfaced values represent the CI and CR for the example of the CF construct.

(6) The global weights of the RFs

Based on Equation (7), the local weights of the RFs can be found. Then, the global weights of the RFs can be obtained by multiplying the RFs' local weights by their corresponding constructs' global weights. As a result, in the Taiwan ferries case, the results of the RFs' global weights for consequence and likelihood are shown in the last column of Tables 6 and 7, respectively.

**Table 6.** The consequence weights of risk factors (RFs).



**Table 6.** *Cont.*

Note: The boldfaced values represent the RFs with higher weights.


**Table 7.** The likelihood weights of risk factors (RFs).

Note: The boldfaced numbers represent the RFs with higher weights.

Table 6 indicates that for the RFs' consequence weights, CF (32.53%) has the highest weight in the first layer of RFs, followed by SW (28.65%), SH (19.60%), and CM (19.22%). In the second layer, the RFs with higher weights are CF2 (9.95%), CF3 (8.80%), and SM3 (8.80%). Meanwhile, Table 7 shows that in the first layer of RFs, the RF with the highest likelihood weight is CF (29.48%), followed by SH (25.62%), SM (22.55%), and CM (22.35%). In the second layer, the RFs with higher weights are CF2 (8.81%) and CF3 (8.43%).

#### 3.2.3. The Continuous Risk Matrix

In the theory of risk matrix, an RF with a higher consequence weight and higher likelihood weight should be ranked as a higher risk. Based on this conception, a risk value (RV) is thus constructed by the product of the two weights [12,25]. Let *C<sup>i</sup>* and *L<sup>i</sup>* be the consequence and likelihood weights of the *i*th RF, respectively. Then, the RV of the *i*th RF is found as:

$$\text{RV}\_{i} = \mathbb{C}\_{i} \* L\_{i}, \ i = 1, 2, \ldots, n \tag{10}$$

Finally, the RV can be normalized as:

$$\text{RV}\_{i} = \frac{\text{C}\_{i} \ast L\_{i}}{\sum\_{i=1}^{n} (\text{C}\_{i} \ast L\_{i})} \times 100\%, \ i = 1, 2, \dots, n \tag{11}$$

Based on Equation (11), and the RFs' weights of consequence and likelihood in Tables 6 and 7, the RVs for each RF can then be found in the fourth field of Table 8, named "RVs". The results show that the RF with the highest risk is CF2 (13.57%), followed by CF3 (11.48%), SM3 (9.42%), and CF1 (8.38%).


**Table 8.** The results of traditional risk matrix.

In this paper, a continuous risk matrix with four risk zones is constructed to rank the RFs' risk levels. As shown in Figure 5, the risk matrix consists of an x-axis representing consequence weights and a y-axis depicting likelihood weights. Based on Equation (11), the matrix can be divided into four risk zones by three decreasing curves with different RV means. Firstly, the middle curve with RV = 6.25% is obtained by averaging the RVs of all the RFs in Table 8. The curve is then used to divide all the RFs into two groups by their RVs. Group one contains 4 RFs (CF2, CF3, SM3, and CF1) and group 2 includes the remaining 12 RFs. Averaging the four RVs of the RFs in group one, we have the second curve with the mean RV =10.71%. Similarly, the third curve with TRV = 4.76% can be obtained by averaging the 12 RVs of the RFs in group two. RV means. Firstly, the middle curve with RV = 6.25% is obtained by averaging the RVs of all the RFs in Table 8. The curve is then used to divide all the RFs into two groups by their RVs. Group one contains 4 RFs (CF2, CF3, SM3, and CF1) and group 2 includes the remaining 12 RFs. Averaging the four RVs of the RFs in group one, we have the second curve with the mean RV =10.71%. Similarly, the third curve with TRV = 4.76% can be obtained by averaging the 12 RVs of the RFs in group two. The results, shown in the last field of Table 8 and visualized in Figure 5, indicate that two RFs (CF2 and CF3), are ranked as E level (extreme-risk), and two RFs (SM3 and CF1) as H (high-risk). Furthermore, seven RFs were classified as M level (medium-risk), and five RFs as L level (low-risk).

the matrix can be divided into four risk zones by three decreasing curves with different

The result of the risk matrix assessment for the Taiwan ferries case shows that four

Generally, crew members must be qualified ahead of being recruited to work onboard, so their proficiency in ferry passenger handling could be assured. However, in the past decade, navigation technology has progressed rapidly and new navigation safety rules have been updated accordingly. Under these circumstances, crew members need to continuously acquire new knowledge of shipping handling. This paper suggests that ferry companies should connect and cooperate with academic institutions to regularly train crew members in specialized fields of new knowledge, such as the search and rescue of victims in distress at sea, new government regulations on maritime navigational safety, and risk identification and assessment. This suggestion is expected to reduce accidents in the workplace and prompt human safety [28]. Furthermore, the results of training programs should also be used to appraise crew members' annual performances so that mari-

results, we conducted a post-interview with some practical experts among the surveyed respondents and proposed the following management suggestions for ferry operators:

**Figure 5.** The risk matrix for the Taiwan ferries case. **Figure 5.** The risk matrix for the Taiwan ferries case.

1. CF2 (crews' skills in shipping handling)

2. CF3 (Crews' self-discipline for work)

**4. Discussion** 

time personnel are motivated to join training programs in earnest.

The results, shown in the last field of Table 8 and visualized in Figure 5, indicate that two RFs (CF2 and CF3), are ranked as E level (extreme-risk), and two RFs (SM3 and CF1) as H (high-risk). Furthermore, seven RFs were classified as M level (medium-risk), and five RFs as L level (low-risk).

#### **4. Discussion**

The result of the risk matrix assessment for the Taiwan ferries case shows that four RFs are classified as E level (CF2 and CF3) and H level (SM3 and CF1). Based on these results, we conducted a post-interview with some practical experts among the surveyed respondents and proposed the following management suggestions for ferry operators:

#### 1. CF2 (crews' skills in shipping handling)

Generally, crew members must be qualified ahead of being recruited to work onboard, so their proficiency in ferry passenger handling could be assured. However, in the past decade, navigation technology has progressed rapidly and new navigation safety rules have been updated accordingly. Under these circumstances, crew members need to continuously acquire new knowledge of shipping handling. This paper suggests that ferry companies should connect and cooperate with academic institutions to regularly train crew members in specialized fields of new knowledge, such as the search and rescue of victims in distress at sea, new government regulations on maritime navigational safety, and risk identification and assessment. This suggestion is expected to reduce accidents in the workplace and prompt human safety [28]. Furthermore, the results of training programs should also be used to appraise crew members' annual performances so that maritime personnel are motivated to join training programs in earnest.

2. CF3 (Crews' self-discipline for work)

Maritime transportation has witnessed many accidents in the workplace due to crew members' health [28]. So, personal self-discipline for work should be paid more attention to, as evidenced by our article. Any level of alcohol or illegal drug consumption by crew members threatens the safety of ferry operations, other crew members, as well as passengers. Overworking and burnout, which is an alarming trend among employees in the shipping industry globally, also have adverse effects on ferry navigation safety. To tackle these circumstances, this paper suggests that ferry companies should frequently utilize recorded data to check crew operations on board after each voyage. Any abnormal actions occurring need to be reported immediately to minimize the likelihood of incidents. It is also advised that safety alert devices be installed to prevent captains from becoming overly fatigued as a result of overworking or sleeplessness or to detect the use of illicit drugs and alcohol.

#### 3. SM3 (Compliance with SOPs)

As mentioned earlier, many maritime accidents are attributable to crew or human error. To prevent such accidents, shipping companies should establish standard operational procedures (SOPs) for crew members to follow. Currently, compliance with SOPs is occasionally ignored. In Taiwan, overcrowding and overloading are also major problems causing navigational risks for ferry transport, which could result in crew members neglecting the company's SOPs. This paper suggests that overcrowding and overloading should be inspected carefully before commencing a voyage; that ferry owners should be heavily fined for overloading; and that carrying commercial cargo in passenger ferries should be strictly prohibited [9]. Furthermore, in practice, because safety regulations often change over time, this paper also suggests that ferry managers should update existing SOPs regularly and ask crew members to implement SOPs accurately in navigation operations to reduce accidents.

#### 4. CF1 (Emergency responses)

In practice, a crew's emergency response capabilities can be strengthened by sufficient safety knowledge and training. Based on the post-interviews, this study recommends the following for this RF:


#### **5. Conclusions**

In practice, ferries are the preferred form of transport for cargo and passengers between islands with relatively short distances. However, some operational safety standards in ferry transportation are easily ignored, thus they are prone to dangerous accidents. Therefore, ensuring ferry navigational safety has attracted much attention from academics, policymakers, and practitioners. The purpose of this paper was aimed at assessing the risks to navigation safety for ferries. In the article, sixteen risk factors (RFs) were first investigated for ferry navigation. A continuous risk matrix based on a fuzzy AHP approach was then developed to evaluate the RFs' risks. The risk assessment approach may provide references for related research in the safety management of short-distance passenger ships, including ferries and cruise ships.

To validate the practical application of the research, the main ferry operators in Taiwan were empirically investigated. The results identified four top-layer RFs including crews' skills in shipping handling, personal self-discipline for work, compliance with SOPs, and emergency responses. With regard to the results, some management policy improvements are suggested. These results provide helpful information for TNC to improve its navigational safety. Furthermore, the empirical results are representative and may also provide practical management references for foreign ferry companies.

Although this paper succeeds in assessing navigation safety for ferries, several potential limitations can be noted for further studies. First, this article uses fuzzy AHP to evaluate the weights of the RFs. One of the basic assumptions of AHP is that the criteria (i.e., RFs) must be independent of each other. However, the independence among the RFs in this study was just verified by practical experts. In the questionnaire design stage, they were interviewed to revise the RFs and the hierarchical structure based on their subjective judgments. Therefore, in theory, it may not be rigorous enough. Future research could consider adopting ANP (Analytic Network Process) or the AHP revision model to assess the RFs [30]. Secondly, this study investigated the ferry navigational safety in the Taiwan Strait as an empirical study. However, different ferry routes may differ in their environmental features. Thus, this paper's results may not be completely applicable to ferries in other areas. Lastly, in this study, 22 experts from Taiwanese ferries were empirically surveyed. This article also adopted an interview survey instead of a mailed survey to improve the validation of the survey. Therefore, the validity and reliability of the research results could be verified. However, to better confirm the empirical results, more representative samples may be needed in future research.

**Author Contributions:** Conceptualization, W.-K.K.H.; methodology, J.-W.C. and W.-K.K.H.; writing and revising, N.T.H. and W.-K.K.H.; investigation, Y.-Y.L.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Ministry of Science and Technology, R.O.C. grant number [MOST 110-2622-H-992-004].

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The study did not report any data.

**Conflicts of Interest:** The authors declare no conflict of interest.

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