Public Concerns and Liability Perspectives in the Shipowner's Country: Insights from the Oil Spill Incident Involving a Japanese-owned Carrier Near Mauritius

Public Concerns and Liability Perspectives in the Shipowner's Country: Insights from the Oil Spill Incident Involving a Japanese-owned Carrier Near Mauritius | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Public Concerns and Liability Perspectives in the Shipowner's Country: Insights from the Oil Spill Incident Involving a Japanese-owned Carrier Near Mauritius Chia-Hsuan Hsu, Kota Mameno, Rintaro Yamaguchi, Masashi Soga, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6720728/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Oil spills caused by ships are a major threat to environmental sustainability, severely damaging marine ecosystems, undermining local economies, and risking public health. Although many spills involve non-local shipping companies, the perspectives of citizens in shipowner countries remain underexplored. Understanding these public concerns is essential for developing sustainable governance, especially regarding liability management and ecological restoration. This study focuses on the MV Wakashio oil spill, where a Japanese-controlled bulk carrier grounded on a coral reef near Mauritius. A nationwide survey of 1,400 Japanese respondents was conducted using systematic sampling. Best-worst scaling (BWS) identified key concerns, and cluster analysis categorized respondents into perspective-based segments. Results showed that health impacts were the top concern, closely followed by the protection of coral reefs and fisheries—critical components of marine sustainability. Cluster analysis revealed three main segments: Ecology, Economy, and Health. Women prioritized health and environmental issues, while men were more concerned with economic aspects. Over 50% of respondents believed that the captain and crew should bear primary liability. Segment differences indicated that individuals with strong ecological concerns attributed greater responsibility to prevent environmental degradation. These findings stress the importance of integrating public views into sustainable policy frameworks, highlighting that long-term marine conservation and responsible shipping practices must be central to oil spill governance and global sustainability efforts. bulk carrier oil spill incidents liability attribution public perspective Best-Worst Scaling governance insights MV Wakashio Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction On July 25, 2020, the Japanese-controlled bulk carrier MV Wakashio ran aground on a coral reef near Mauritius, leading to an estimated 1,000 tonnes of oil spilling into the ocean starting on August 6. This incident marked the first recorded case of a Very Low Sulfur Fuel Oil spill (Lewis 2020; Scarlett et al. 2021). Following the spill, numerous studies employed modeling and satellite imagery to monitor the oil's movement and assisted the government in mitigating potential risks (Gurumoorthi et al. 2021; Rajendran et al. 2022; Prasad et al. 2022; Rao et al. 2022). Boswell (2022) indicated that this event caused significant harm to Mauritius, where the population heavily depends on natural resources (Boswell 2022). Although the precise environmental and societal impacts of this case remain understudied, previous reviews of oil spill incidents have highlighted their severe effects on coastal ecosystems, local economies, and public health (Chang et al. 2014). Oil spills pose a significant threat to marine ecosystems and their inhabitants. Floating oil on the water’s surface can directly harm or kill plankton, which are vital primary producers and consumers in marine ecosystems (Corner 1979; Jiang et al. 2010). When oil accumulates on shores, it can severely impact coastal and intertidal organisms, including marine mammals, reptiles, and birds that frequently surface to breathe, making them vulnerable to oil’s toxic effects (Heubeck et al. 2003; Peterson et al. 2003; Ruberg et al. 2021). Additionally, oil spills affect various marine organisms at different levels, including benthic species, invertebrates, and fish (Saadoun 2015; Adzigbli and Yuewen 2018). The oil spill incident in Mauritius significantly impacted two major marine and coastal ecosystems: coral reefs and mangroves (Lewis 2020). Coral reefs were severely damaged, as spills are known to disrupt these ecosystems (Loya and Rinkevich 1980; Guzman et al. 2020). Mangroves, reported as the primary ecosystem affected by the spill (Lewis 2020), experienced lasting consequences, with residual oil pollution still detectable in sediments even three years later (Scarlett et al. 2024). Given Mauritius’s reliance on marine resources, particularly tourism and fisheries, this incident likely had profound economic repercussions (Sobhee 2006). Marine oil spills have a profound impact on economies that depend on marine resources. For fisheries, the 2002 Prestige oil spill off the Galician coast of Spain caused a 66% loss in species richness in certain areas, significantly affecting offshore fish and crustacean fisheries (de la Huz et al. 2005; Sánchez et al. 2006). Similarly, the 2011 Penglai 19-3 oil spill in China resulted in substantial economic losses in both fisheries and aquaculture (Pan et al. 2015). In locations where fisheries and tourism coexist, such as Korea, the 2007 Hebei-Spirit oil spill severely impacted both sectors (Cheong 2012). Tourism-dependent economies have also suffered greatly from oil spill incidents, as seen with the 2007 Don Pedro merchant ship spill near the island of Ibiza (Cirer-Costa 2015) and the Prestige oil spill, which affected Spain and France (Garza-Gil et al. 2006). Beyond economic damage, the health risks for local residents are a critical issue, directly disrupting daily life (Chang et al. 2014). Human exposure to oil spills can lead to severe health problems, including acute physical effects and psychological consequences (Aguilera et al. 2010; Laffon et al. 2016). Residents in affected communities face risks from direct contact with crude oil, inhaling toxic fumes carried by the wind, and consuming contaminated seafood (McCoy and Salerno 2010). Studies indicate genotoxic effects, such as DNA damage, in individuals exposed to oil-contaminated environments (Laffon et al. 2006; Hildur et al. 2015). Physical symptoms can include vomiting, diarrhea, stomach pain, and constipation, while psychological effects may manifest as post-traumatic stress disorder, depression, suicidal thoughts, and anxiety (Kim et al. 2013; Choi et al. 2016; Anderson et al. 2024). These health impacts highlight the far-reaching consequences of oil spills on both individual well-being and community stability. After the oil spill incident, which resulted in these severe issues, the question of who should be held liable is seldom discussed in academic papers. However, understanding the main liability for such incidents involves reviewing the event and implementing measures to prevent future occurrences (Azik 2023). Potentially liable parties may include the captain and crew, the operating company, the ownership company, the government under whose flag the ship is registered, and more. For example, the Exxon Valdez oil tanker spill in 1989 resulted from the captain’s neglect of duties and lack of safety awareness, for which he bears inescapable liability (Carson et al. 2003). Similarly, the operating or ownership company of the spill incident is undeniably liable, especially those using “flag of convenience ships” which may not guarantee high safety standards, as evidenced by the incident of the "Prestige" (Zhang et al. 2021). Additionally, shipowner countries may also bear liability due to inadequate regulations and complex scenarios (Chen et al. 2017; Azik 2023). Thus, clarifying the complex liabilities of oil spill incidents is crucial, and incorporating the public's perspectives could provide valuable insights. Accordingly, this study focuses on the MV Wakashio incident to explore public perspectives, with particular emphasis on the viewpoints of citizens in the shipowner country, Japan. The research objectives are: (1) Which attributes were of greatest concern to the Japanese after the oil spill incident? (2) What did the segments identified through cluster analysis represent? (3) From the Japanese perspective, who was considered liable for this incident, and did this vary across the identified cluster segments? It is by no means our aim to judge who is responsible or liable; we merely explored the respondents' perspectives of liability instead of legal and moral responsibility. These questions are crucial for shaping the future governance of oil spill incidents, particularly in relation to donation recruitment and liability considerations. 2. Method and materials We conducted an online questionnaire survey to understand Japanese people's perspectives on the oil spill incident caused by a Japanese company. The survey was administered from November 12 to 16, 2020, the same year the MV Wakashio Oil Spill occurred. The survey target was the nationwide registered prospective respondents of a survey company (Cross Marketing Inc.), considered to represent Japan’s general public. We recruited the respondents by considering age, gender, and residential prefectures. This study was approved by the Institutional Review Board (IRB) of the University of Tokyo under approval number H-20100. Ultimately, we obtained 1,400 valid responses. The questionnaire began with a brief introduction to the incident: “On July 25, 2020, the cargo ship MV Wakashio, owned and managed by a subsidiary of Nagashiki Shipping Co., Ltd. and operated by Mitsui O.S.K. Lines, ran aground off the coast of Mauritius, an island nation in the Indian Ocean. On August 6, 2020, the ship's heavy oil tank was damaged, causing approximately 1,000 tons of the roughly 4,000 tons of heavy oil onboard to spill into the sea.” Table 1. Demographic variables and descriptive information (n = 1400). Characteristics (n; %) Sex male (677; 48.4%), female (720; 51.4%), Others (3, 0.214%) Age 18~29 (248; 17.7%), 30s (288; 20.6%), 40s (288; 20.6%), 50s (288; 20.6%), 60s (288; 20.6%) Knowledge of Mauritius: Did you know where Mauritius is located? Yes, I knew before the incident (253; 18.1%), Yes, I knew after the incident (269; 19.2%), No, I did not know (878; 62.7 %) Knowledge of the incident in Mauritius: Did you know about the ship stranding and heavy oil spill-off around Mauritius? I knew ship stranding and oil spill-off (956; 68.3%), I knew about ship stranding but I didn't know about an oil spill. (55; 3.93%), No, I did not know (389; 27.8 %) 2.1 Best–Worst Scaling (BWS) Design and Attributes Our main approach is Best-Worst Scaling (BWS), specifically the object case (Case 1) developed by (Finn and Louviere 1992), which effectively highlights clear differences in public preferences. The object case of BWS offers several advantages over conventional preference evaluation methods (Lusk and Briggeman 2009; Louviere et al. 2015) and has been widely applied in environmental conservation and management sectors in recent years (Kubo et al. 2019; Schuster et al. 2024; Mameno et al. 2024). In our questionnaire survey, respondents were asked to identify the most and least supported scenarios. Drawing on previous studies about the impact of oil spill accidents on human lives and coastal ecosystems (Chang et al. 2014), we selected five potentially affected attributes: mangroves, coral reefs, fisheries, tourism, and health (Table 1). The survey design plays a critical role in the results, as the object case of BWS involves multiple-choice questions. We constructed five choice sets with four attributes each by applying a balanced incomplete block design (BIBD; (Raghavarao and Padgett 2005), for details; Appendix S1). Table 2. Five attributes of Best-worst Scaling and their descriptions. Attributes Description Coral reef Negative impact on coral reefs: the area where the cargo ship ran aground is home to coral reefs that are inhabited by many of the world's largest living creatures. However, there are concerns that the coral has been scraped off by the bottom of the ship that ran aground this time and that the coral has been smothered by the turbidity of the seawater caused by the accident, leading to the degradation of the coral reefs. Mangrove Negative impact on mangrove forests: In the area where the cargo ship ran aground, mangrove forests are widespread in the coastal area. However, it has been pointed out that the spilled heavy oil could adhere to the mangrove forests, causing them to die. In addition, as mangrove forests provide a home for a wide variety of living creatures, there is concern that the accident could hurt the creatures that live there. Health Adverse health effects: It has been pointed out that the heavy oil spill caused by the grounding of the cargo ship could hurt the health of local residents and other people. In particular, there is concern that people who inhale or come into contact with heavy oil may suffer from headaches, dizziness, and breathing difficulties. Fishery Negative impact on the fishery industry: It has been pointed out that the cargo ship running aground and spilling fuel oil may cause a decrease in fish catches in the waters around Mauritius and a consequent decrease in fishermen's income and an increase in unemployment. There is also concern that the decrease in catches will make it difficult to secure a stable supply of food in Mauritius. Tourism Negative impact on the tourism industry: It has been pointed out that the cargo ship stranding and fuel oil spill could harm the tourism industry in Mauritius. In particular, there are concerns about the loss or restriction of marine recreational opportunities, a decrease in the number of tourists, especially foreigners, due to the deterioration of the tourism image caused by this accident, and a consequent decrease in the income of tourism operators and an increase in unemployment. 2.2 Best–Worst Scaling (BWS) Analysis Counting analysis was applied to answer the BWS question. Counting analysis is a useful calculation while being a highly accurate approximation of the estimation results in the parametric methods (Marley and Louviere 2005). We analyzed an individual respondent's best–worst score (BW score) of attributes; that is the difference in the number of times they were selected as the most needed support in each choice set and the number of times each choice was chosen as the least needed support, on each respondent: Our analysis also calculated the standardized BW score. The score was determined by dividing the average BW score by the number of respondents and the number of represented times of each attribute (i.e., 4 times): Then, we compared each attribute to understand which one Japanese individuals care about the most. The comparison analysis was conducted using the Kruskal-Wallis test. 2.3 Cluster analysis Hierarchical cluster analysis was employed to examine the preference heterogeneity among Japanese individuals concerning the attributes identified through BWS (Auger et al. 2007). Each subject’s BW scores were used as input for the analysis, representing their choices on different attributes. The study applied the Ward approach for hierarchical clustering, which minimizes the variance within clusters by merging the closest points at each step, forming compact and homogeneous groups. The resulting dendrogram visually represents the hierarchical clustering process, showing how preferences group together based on similarity. Besides, the chi-square test was used to understand the difference among different demographic variables. This approach allowed the study to identify distinct segments within the Japanese population, providing insights into how different groups prioritize various aspects of oil spill management and liability. 2.4 Descriptive Analysis of Liability To understand the Japanese public's perspective on who is liable for this incident, we included a general question to capture their opinions. Since the aim was to gather an overarching viewpoint, this question was not analyzed by categories or segments. The question was: “Who do you think is liable for this accident? (You may choose multiple options.)” The response options were: captains & crew, operating company, ownership company, Japanese government, nobody, and others. We used descriptive statistics to analyze the percentage distribution of responses. The analysis was conducted using R software (R Core Team 2022). 2.5 Cluster Scenarios and Liability To investigate whether individuals focusing on different segments have varying perspectives on the liability for this incident, we employed the Kruskal-Wallis test and the Bonferroni-Dunn posthoc test to identify differences among liability groups. A non-parametric approach was chosen instead of one-way ANOVA with Tukey’s HSD test because the variables did not meet the normality assumption ( p < 0.05, Shapiro–Wilk normality test). The analysis was performed using R software (R Core Team 2022). 3. Result 3.1. Best-worst attributes According to the results of the BWS analysis, Health had the highest standardized BW score (0.353), followed by Coral (0.114), Fishery (0.0704), Mangrove (-0.178), and Tourism (-0.359) (Fig. 1). Notably, both Mangrove and Tourism received negative standardized BW scores. These results indicate that subjects considered the health of residents in Mauritius to be the most important and deserving of support, whereas the tourism industry was viewed as comparatively less of a concern. 3.2. Best-worst cluster segments To compare preference heterogeneity, the standardized BW scores for each segment were calculated. Coral had the highest standardized BW score in one of the three segments, followed by Mangrove; this segment was referred to as the "Ecology" segment and accounted for 28.9% of the participants. Subjects in another segment prioritized Fishery as the most important attribute to support; this segment, comprising 23.9% of the Subjects, was termed the "Economy" segment. The largest segment, representing 47.2% of Subjects, was labeled the "Health" segment, as Health received the highest standardized BW score within this group. All the segments and their corresponding BW score of attributes are shown in Fig. 2. The significant differences among attributes in each segment are presented in Table 3. In terms of demographic variable differences among the segments, Age showed no significant variation across the segments ( p = 0.07, χ2 = 5.37, Kruskal-Wallis test). Gender exhibited a significant difference between the Economy and Health segments ( p < 0.05, z = -2.92, Bonferroni-Dunn test), although neither segment differed significantly from the Ecology segment ( p = 0.54, z = 1.34, with Economy; p = 0.37, z = -1.54, with Health, Bonferroni-Dunn test). Knowledge of Mauritius showed significant differences between the Ecology and Health segments ( p < 0.01, z = -3.57, Bonferroni-Dunn test), but neither differed significantly from the Economy segment ( p = 0.07, z = -2.28, with Ecology; p = 1, z = -0.85, with Health, Bonferroni-Dunn test). Finally, Knowledge of the incidents revealed a significant difference between the Ecology segment and the other two segments ( p < 0.01, z = 2.94, with Economy; p < 0.01, z = 3.01, with Health, Bonferroni-Dunn test), while no significant difference was observed between the Economy and Health segments ( p = 1, z = -0.4, Bonferroni-Dunn test). All the results of the demographic variable differences are presented in Table 3. Table 3. Results of the comparison tests of BW scores using Kruskal-Wallis and Bonferroni-Dunn tests for demographic variables across the three clustered segments. Different letters indicate significant differences. Ecology Economy Health Kruskal-Wallis Chi-squared Variable Standardized B-W score Rank Standardized B-W score Rank Standardized B-W score Rank Coral reef 0.660 i 1 -0.0149 ii 2 -0.153 iii 3 601.2 *** Mangrove 0.274 i 2 -0.354 ii 5 -0.365 ii 4 482.4 *** Health -0.258 iii 4 -0.0425 ii 3 0.926 i 1 1103 *** Fishery -0.199 iii 3 0.496 i 1 0.0193 ii 2 423.5 *** Tourism -0.476 ii 5 -0.0843 i 4 -0.427 ii 5 125.9 *** Demographic n=404 n=335 n=661 Age 18~29: 54; 30s: 86; 40s: 93; 50s: 87, 60s: 84 a 18~29: 73; 30s: 70; 40s: 67; 50s: 63; 60s: 62 a 18~29: 121; 30s: 132; 40s: 128; 50s: 138; 60s:142 a 5.367 Sex Male: 201; Female: 200; Others: 3 ab Male: 182; Female: 153 a Male: 182; Female: 153 b 8.860 * Knowledge of Mauritius Before: 92; After: 86; No: 226 a Before: 66; After: 52; No: 217 ab Before: 95; After: 131; No: 43 b 12.99 ** Knowledge of the accident in Mauritius Both: 302; Only stranding: 15; No: 87 a Both: 218; Only stranding: 11; No: 106 b Both: 436; Only stranding: 29; No: 196 b 11.58 ** 3.3. Attribution of Liability According to the descriptive statistics, the majority of subjects (57.8%) believed that the captains and crew should primarily bear liability for this incident, followed by the operating company (46.6%), the ownership company (36.6%), nobody (23.1%), the Japanese government (9.4%), and others (1.29%) (Figure 3). These results indicate that most subjects (over 50%) held the captains and crew accountable for the incident. 3.4 Comparison of cluster segments in liability dimensions In the Captain and Crew liability dimension, the Ecology segment scored significantly higher than both the Economy segment ( p < 0.001, Bonferroni-Dunn test) and the Health segment ( p < 0.01, Bonferroni-Dunn test), while there was no significant difference between the Economy and Health segments ( p = 0.06, Bonferroni-Dunn test). In the Operating Company liability dimension: the Economy segment was significantly lower than the Ecology segment ( p < 0.05, Bonferroni-Dunn test) and the Health segment ( p < 0.05, Bonferroni-Dunn test), with no significant difference between the Ecology and Health segments ( p = 1.000, Bonferroni-Dunn test). For the Ownership liability dimension, the Ecology segment differed significantly from the Economy segment ( p < 0.05, Bonferroni-Dunn test), but there were no significant differences between the Health segment and either the Ecology ( p = 0.48, Bonferroni-Dunn test) or Economy segments ( p = 0.45, Bonferroni-Dunn test). Regarding the Nobody liability dimension, the Economy segment scored significantly higher than both the Ecology ( p < 0.001, Bonferroni-Dunn test) and Health segments ( p < 0.01, Bonferroni-Dunn test), and the Health segment scored significantly higher than the Ecology segment ( p < 0.05, Bonferroni-Dunn test). No significant differences were observed among the segments in the Japanese Government and Others liability dimensions ( p = 0.73, for Japanese Government; p = 0.06, for Others, Kruskal-Wallis test). Figure 4 illustrates the results of the comparison of cluster segments across liability dimensions. Notably, the ranking of liability dimensions was consistent across all segments. The ratios and significance values are provided in Table 4. Table 4. The ratios and significance of liability dimensions across different segments. Ecology Economy Health Kruskal-Wallis Liability Ratio Median Ratio Median Ratio Median Captain/Crew 0.67 a 1 0.49 b 0 0.57 b 1 25.39*** Operating Company 0.5 a 0.5 0.4 b 0 0.48 a 0 8.77 * Ownership Company 0.42 a 0 0.33 b 0 0.38 a,b 0 6.32* Nobody 0.16 a 0 0.32 b 0 0.23 c 0 26.73*** Japanese Government 0.09 a 0 0.1 a 0 0.09 a 0 0.63 Others 0.02 a 0 0 a 0 0.02 a 0 5.74 Note: 1) In each attribute, the alphabets (a-c) indicate statistical differences in the scores among the groups based on the results of the Bonferroni-Dunn tests. 2) *,**, and *** denote p values of < 0.05, < 0.01 and < 0.001, respectively. 4. Discussion Oil spill incidents undoubtedly impact the environment, the health of local residents, and the economy (Chang et al. 2014; Jabbar et al. 2018). However, this study seeks to understand the perspectives and opinions of citizens from the shipowners’ country—a relatively underexplored area. Our aim is not to diminish the importance of any particular context of damage but to uncover public perceptions and identify potential gaps in the information they have received. Studies employing social science methods to examine public viewpoints on oil spills remain limited, with most research focusing primarily on health-related issues (e.g., (Ha et al. 2012; Kim et al. 2013)). By addressing this gap, this study provides valuable insights to help governments formulate future policies on fundraising strategies and liability attributions. Based on the analysis of BWS, we found that Japanese people are primarily concerned about the health problems of residents affected by oil spill incidents. We believe this is because health is most directly related to human well-being. Furthermore, food, as a daily necessity, is directly tied to health, especially when oil pollution has an impact (McCoy and Salerno 2010). Many research studies have also highlighted health problems, both physical and psychological, following oil spill incidents (Ha et al. 2008; Aguilera et al. 2010). Our findings revealed that women were significantly more concerned about health issues than men (Table 3). This result was consistent with previous studies, which show that women are more concerned than men about health in other contexts, such as food choices and environmental issues (Wardle et al. 2004; Xiao and McCright 2012). For instance, during the COVID-19 pandemic, studies indicated that women primarily focused on health-related issues, while men were more concerned about economic matters (van der Vegt and Kleinberg 2020). This alignment with our findings suggests a consistent gender difference in prioritizing health. However, the reasons why women expressed greater concern about health in the context of this oil spill incident require further investigation. The second most notable attribute was coral, categorized under the ecological segment. Coral reefs are often perceived as iconic symbols of natural beauty, which explains why many studies have evaluated their aesthetic value (Vercelloni et al. 2018; Pert et al. 2020). When people think of Mauritius, they frequently associate it with the image of beautiful coral reefs (Seveso et al. 2021), and indeed, numerous past oil spill incidents have significantly impacted coral reef ecosystems (Loya and Rinkevich 1980; Fernandes et al. 2022). However, mangroves, despite being the most affected ecological feature during this incident (Lewis 2020; Seveso et al. 2021), were the second most negatively focused attribute in this context. We believe this is because many individuals within the ecological segment lack a clear understanding of Mauritius, including its location (56% did not know, and 21.3% became aware only after the incident; Table 3), its environmental characteristics, and the actual extent of the oil spill’s impact (21.5% were unaware of the oil spill; Table 3). As a result, the public relied on stereotypes, imagining that coral reefs would suffer the most severe impacts from the incident and should therefore be the primary focus of attention. This perception may stem from a lack of information and aligns with the concept of "Perceived Risk," which highlights how risk perception is often shaped by affective factors (e.g., feelings and emotions) and contextual factors (e.g., the availability and quality of information) (Slovic et al. 2016). The discrepancy between perceived and actual impacts underscores how public concerns are often guided more by personal preferences and imagined scenarios than by objective evidence. These findings suggest that the public's attention tends to gravitate toward what they find aesthetically appealing or emotionally evocative rather than ecological realities. Addressing this gap requires improving risk communication and providing accurate, accessible information to ensure that public awareness aligns more closely with actual ecological priorities (Frewer 2003). Contradictorily, tourism emerged as the least important attribute in the BWS analysis, despite coral reefs being one of the most valuable coastal ecosystems for tourism (Cesar et al. 2003). Historically, many oil spill incidents have significantly damaged the tourism industry (e.g., (Hegazy et al. 2014)). Interestingly, the third most concerning attribute in this study was fishery, which was the only positive attribute within the economy segment (Figure 2). Although the fishery attribute ranked higher than mangroves, it was still rated lower than the top positive attributes, health and coral. This suggests that while some individuals do care about the local economy, their concern is predominantly limited to fisheries, as tourism—another key economic driver—was rated the lowest overall. Cluster analysis further revealed that subjects may not view tourism as a critical industry for Mauritius or believe that the oil spill would significantly affect it, as tourism was not perceived as a positive attribute (Figure 1). In reality, tourism is a vital sector for Mauritius, much like fisheries, and both contribute significantly to the local economy, which directly affects human livelihoods (Sobhee 2006). In contrast to our findings, Dominguez-Péry et al. (2021) examined social media during the MV Wakashio oil spill (the same case as this study) and found that the public's primary concerns were economic, highlighting the significance of financial impacts in public discourse. The discrepancy in concern between the public in Mauritius and citizens from the shipowner’s country raises an important question: Why did the Japanese respondents show less concern for the economic impacts of the oil spill? This warrants further investigation to better understand the factors influencing public perceptions of economic consequences in such incidents. As mentioned above, however, we speculate that the general lack of information and knowledge about the island nation may have hindered the Japanese respondents' understanding of reality. In addition, the respondents may have simply thought that the economic loss was recoverable and could not be the most serious dimension in Mauritius, which enjoys the status of an upper-middle-income country with a per capita income of more than US$10,000. Most subjects' perspectives indicated that the captain and crew should bear the liability for this incident. This is reasonable because investigators who interviewed the crew revealed that, at the time of the grounding, the crew had been celebrating a crew member's birthday aboard the ship, which had sailed near the shore to pick up a Wi-Fi signal. Previous incidents, such as those caused by human factors (e.g., (Carson et al. 2003)), also involved the captain neglecting their duties. However, the 1992 Protocol to the International Convention on Civil Liability for Oil Pollution Damage specifies that liability lies with the shipowner, excluding the responsibilities of the shipowner's employees, operators, charterers, and other related parties. Nevertheless, Cheong (2011) argues that exempting these parties from liability may weaken their awareness of oil pollution prevention, which could hinder efforts to reduce oil pollution incidents on ships. The operating company and ownership company should, of course, bear responsibility and liability for the incident. The operating company should be held responsible for shortcomings in the management and training of the crew (Celik and Topcu 2009), while the ownership company should bear liability under the "limitation of liability" provisions outlined in the 1992 Protocol. These entities ranked second and third in terms of the subjects' perspectives in this study (Figure 3). Additionally, the government of the ownership country may also be held accountable for failing to enforce proper governance or for implementing inadequate regulations (Chen et al. 2017; Zhang et al. 2021). In this case, Japan, where the shipowner is based, is a party to the International Convention on Limitation of Liability for Maritime Claims (LLMC) Protocol 96, which caps liability at 46.5 million Special Drawing Rights (SDRs) (Carey 2024). The issue of civil liability and compensation for oil pollution damage at sea has long posed challenges for governments, highlighting the importance of identifying responsible parties and establishing effective compensation mechanisms to minimize environmental harm (Azik 2023). Notably, 23.1% of respondents believed that nobody should be held liable, possibly reflecting either a lack of information or indifference, as approximately 28% of the subjects were unaware of the oil spill incident (Table 1). To further analyze segments in different liability dimensions, the results reveal that the Ecology segment has significantly higher scores in Captain and Crew, Operating Company, and Ownership dimensions (Figure 4). Inspecting the demographic information of the Ecology segment, we see that these subjects have the highest proportion of subjects who were familiar with the incident (Table 3). This might explain why members of the Ecology segment felt that these dimensions should be held liable, particularly for the Captain and Crew. On the other hand, the Economy segment significantly had the highest proportion of respondents who thought nobody should be liable, reflecting the highest percentage of individuals who were unaware of the incident (Table 3). These findings also highlight the importance of providing comprehensive information about the incident. When people receive incomplete or uncertain information, they may rely on their experiences or emotions to make decisions, which can lead to imprecise conclusions and challenges in risk communication (Morgan 1990; Granger Morgan 2002). 5. Conclusion Our study offers valuable insights into public perspectives in shipowner countries. Oil spill incidents pose significant environmental, economic, and health issues, yet public perspectives, particularly from the shipowner’s country, remain underexplored. This study highlights the dominance of health concerns, especially among women, and the disconnect between perceived and actual ecological impacts, with coral reefs prioritized over more affected features like mangroves. Economic attributes, such as fisheries, received moderate attention, while tourism was undervalued despite its critical importance to Mauritius. The results emphasize the need for improved risk communication to bridge information gaps and better align public awareness with ecological and economic realities. While most respondents believed the crew and captain should bear liability, the reality of liability attribution is more complex, involving the captain, crew, operating and ownership companies, and the oversight responsibilities of the shipowner’s government under frameworks like the LLMC Protocol 96. Our research provides valuable insights for policymakers to understand the perspectives of people in ownership countries, offering strategies for effective fundraising strategies and a reference for liability attribution. Declarations A statement on participant consent： All participants provided informed consent to participate in this study. The study was approved by the Institutional Review Board (IRB) of the University of Tokyo under approval number H-20100. 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Mar Policy 62:18–24 Pert PL, Thiault L, Curnock MI, et al (2020) Beauty and the reef: Evaluating the use of non-expert ratings for monitoring aesthetic values of coral reefs. Sci Total Environ 730:139156 Peterson CH, Rice SD, Short JW, et al (2003) Long-term ecosystem response to the Exxon Valdez oil spill. Science 302:2082–2086 Prasad SJ, Nair TMB, Joseph S, Mohanty PC (2022) Simulating the spatial and temporal distribution of oil spill over the coral reef environs along the southeast coast of Mauritius: A case study on MV Wakashio vessel wreckage, August 2020. J Earth Syst Sci 131.: https://doi.org/10.1007/s12040-021-01791-z Raghavarao D, Padgett LV (2005) Block Designs: Analysis, Combinatorics And Applications. World Scientific Publishing, Singapore, Singapore Rajendran S, Aboobacker VM, Seegobin VO, et al (2022) History of a disaster: A baseline assessment of the Wakashio oil spill on the coast of Mauritius, Indian Ocean. Mar Pollut Bull 175:113330 Rao VT, Suneel V, Alex MJ, et al (2022) Assessment of MV Wakashio oil spill off Mauritius, Indian Ocean through satellite imagery: A case study. J Earth Syst Sci 131.: https://doi.org/10.1007/s12040-021-01763-3 R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://wwwR-project.org/ Ruberg EJ, Williams TD, Elliott JE (2021) Review of petroleum toxicity in marine reptiles. Ecotoxicology 30:525–536 Saadoun IMK (2015) Impact of oil spills on marine life. Emerging pollutants in the environment-current and further implications 10:60455 Sánchez F, Velasco F, Cartes JE, et al (2006) Monitoring the Prestige oil spill impacts on some key species of the Northern Iberian shelf. Mar Pollut Bull 53:332–349 Scarlett AG, Nelson RK, Gagnon MM, et al (2021) MV Wakashio grounding incident in Mauritius 2020: The world’s first major spillage of Very Low Sulfur Fuel Oil. Mar Pollut Bull 171:112917 Scarlett AG, Nelson RK, Gagnon MM, et al (2024) Very low sulfur fuel oil spilled from the MV Wakashio in 2020 remains in sediments in a Mauritius mangrove ecosystem nearly three years after the grounding. Mar Pollut Bull 209:117283 Schuster ALR, Crossnohere NL, Campoamor NB, et al (2024) The rise of best-worst scaling for prioritization: A transdisciplinary literature review. J Choice Model 50:100466 Seveso D, Louis YD, Montano S, et al (2021) The Mauritius oil spill: What’s next? Pollutants 1:18–28 Slovic P, Fischhoff B, Lichtenstein S (2016) Facts and fears: Understanding perceived risk. The perception of risk. https://doi.org/10.4324/9781315661773-18/facts-fears-understanding-perceived-risk-paul-slovic-baruch-fischhoff-sarah-lichtenstein Sobhee SK (2006) Fisheries biodiversity conservation and sustainable tourism in Mauritius. Ocean Coast Manag 49:413–420 van der Vegt I, Kleinberg B (2020) Women worry about family, men about the economy: Gender differences in emotional responses to COVID-19. arXiv [cs.CL] Vercelloni J, Clifford S, Caley MJ, et al (2018) Using virtual reality to estimate aesthetic values of coral reefs. R Soc Open Sci 5:172226 Wardle J, Haase AM, Steptoe A, et al (2004) Gender differences in food choice: the contribution of health beliefs and dieting. Ann Behav Med 27:107–116 Xiao C, McCright AM (2012) Explaining gender differences in concern about environmental problems in the United States. Soc Nat Resour 25:1067–1084 Zhang W, Li C, Chen J, et al (2021) Governance of global vessel-source marine oil spills: Characteristics and refreshed strategies. Ocean Coast Manag 213:105874 Mauritius arrests captain of Japanese ship that spilled oil. In: The Asahi Shimbun. https://www.asahi.com/ajw/articles/13648341. Accessed 13 Mar 2025a International Convention on Civil Liability for Oil Pollution Damage (CLC). https://www.imo.org/en/About/Conventions/Pages/International-Convention-on-Civil-Liability-for-Oil-Pollution-Damage-(CLC).aspx. Accessed 13 Mar 2025b Additional Declarations No competing interests reported. Supplementary Files AppendixS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6720728","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462113569,"identity":"484015d4-d400-49c2-a852-a96a267a9463","order_by":0,"name":"Chia-Hsuan Hsu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACxgYeMC3HJn/8+IePDWCxxgPEaDHml+BJY5zZwCABEsOrhYEBoiVx5gwGM2ZesBYGBrxamNt7jz38UXGHccPthrTHtjts6nTbDwNtqbGJxumwnnPpxjxnnjEb3Dl43Dj3TJqE2ZlEoJZjabkNuLTMyDGTZmw7zGZwICFBOrftsITZAaAWxobDeLVI/mw7zAPUYiBtCdJy/iFhLRK8QJWSMxLA1kmY3SBkS88ZM2meM4cN+HnOJBv2tqVJbrsBtCUBj18M23vMJH9UHK5vY28/+OBnmw2/2fn0hw8+1Njg1oJdIgGHchCQxyM3CkbBKBgFowACANk5ZczJQtNQAAAAAElFTkSuQmCC","orcid":"","institution":"National Institute for Environmental Studies","correspondingAuthor":true,"prefix":"","firstName":"Chia-Hsuan","middleName":"","lastName":"Hsu","suffix":""},{"id":462113570,"identity":"afe63e3e-c109-449c-bcef-3f49f76e74b1","order_by":1,"name":"Kota Mameno","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Kota","middleName":"","lastName":"Mameno","suffix":""},{"id":462113573,"identity":"6c061b7c-190c-43dd-890c-df954412d668","order_by":2,"name":"Rintaro Yamaguchi","email":"","orcid":"","institution":"National Institute for Environmental Studies","correspondingAuthor":false,"prefix":"","firstName":"Rintaro","middleName":"","lastName":"Yamaguchi","suffix":""},{"id":462113576,"identity":"aa832c30-8ca3-4053-9480-b58096df3937","order_by":3,"name":"Masashi Soga","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Masashi","middleName":"","lastName":"Soga","suffix":""},{"id":462113578,"identity":"a97fe593-80ad-46e4-af9c-9f472423e31a","order_by":4,"name":"Hiroya Yamano","email":"","orcid":"","institution":"National Institute for Environmental Studies","correspondingAuthor":false,"prefix":"","firstName":"Hiroya","middleName":"","lastName":"Yamano","suffix":""},{"id":462113580,"identity":"481c54db-0d53-4bc6-8410-d5c4d4c02c9c","order_by":5,"name":"Takahiro Kubo","email":"","orcid":"","institution":"National Institute for Environmental Studies","correspondingAuthor":false,"prefix":"","firstName":"Takahiro","middleName":"","lastName":"Kubo","suffix":""}],"badges":[],"createdAt":"2025-05-22 03:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6720728/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6720728/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83807128,"identity":"e951743d-c140-4c6e-a586-c63894c602f3","added_by":"auto","created_at":"2025-06-03 05:32:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55996,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the Best-Worst Scaling analysis and the ranking of attributes. The error bars represent the standard error.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6720728/v1/884e109334fbf70cc47c3c9d.png"},{"id":83807521,"identity":"624afb0d-0ea5-451d-83f3-f7dc77a5d93a","added_by":"auto","created_at":"2025-06-03 05:40:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89896,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of the cluster analysis are based on Best-Worst Scaling attributes, with the three segments categorized as Ecology, Economy, and Health. The error bars represent the standard error.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6720728/v1/c60ea3b22889f198eb36ee33.png"},{"id":83807131,"identity":"d0af8421-e4c1-4a64-89b9-8c6121ba68ce","added_by":"auto","created_at":"2025-06-03 05:32:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":176200,"visible":true,"origin":"","legend":"\u003cp\u003eLiability attribution for this incident from the subjects’ perspective.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6720728/v1/212610b8abc7d15349ae34bf.png"},{"id":83807130,"identity":"3a0fbb34-7e1f-4760-9b9f-524ed930d449","added_by":"auto","created_at":"2025-06-03 05:32:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":208748,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the different cluster segments across various liability dimensions. Differing letters indicate statistically significant differences and error bars represent standard error.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6720728/v1/5fd3eff87da06c78a78a6306.png"},{"id":83808311,"identity":"759b077a-e138-4d0c-a15f-d7d3b2fbf22b","added_by":"auto","created_at":"2025-06-03 06:04:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1231425,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6720728/v1/61ec6b9f-ece1-4bfc-b2a6-dd5fa638715c.pdf"},{"id":83807132,"identity":"dc6cecd8-3415-4363-96d1-77382de6a4a3","added_by":"auto","created_at":"2025-06-03 05:32:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4649067,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6720728/v1/4a64669c38161566153e645b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Public Concerns and Liability Perspectives in the Shipowner's Country: Insights from the Oil Spill Incident Involving a Japanese-owned Carrier Near Mauritius","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOn July 25, 2020, the Japanese-controlled bulk carrier MV Wakashio ran aground on a coral reef near Mauritius, leading to an estimated 1,000 tonnes of oil spilling into the ocean starting on August 6. This incident marked the first recorded case of a Very Low Sulfur Fuel Oil spill (Lewis 2020; Scarlett et al. 2021). Following the spill, numerous studies employed modeling and satellite imagery to monitor the oil\u0026apos;s movement and assisted the government in mitigating potential risks (Gurumoorthi et al. 2021; Rajendran et al. 2022; Prasad et al. 2022; Rao et al. 2022). Boswell (2022) indicated that this event caused significant harm to Mauritius, where the population heavily depends on natural resources (Boswell 2022). Although the precise environmental and societal impacts of this case remain understudied, previous reviews of oil spill incidents have highlighted their severe effects on coastal ecosystems, local economies, and public health (Chang et al. 2014).\u003c/p\u003e\n\u003cp\u003eOil spills pose a significant threat to marine ecosystems and their inhabitants. Floating oil on the water\u0026rsquo;s surface can directly harm or kill plankton, which are vital primary producers and consumers in marine ecosystems (Corner 1979; Jiang et al. 2010). When oil accumulates on shores, it can severely impact coastal and intertidal organisms, including marine mammals, reptiles, and birds that frequently surface to breathe, making them vulnerable to oil\u0026rsquo;s toxic effects (Heubeck et al. 2003; Peterson et al. 2003; Ruberg et al. 2021). Additionally, oil spills affect various marine organisms at different levels, including benthic species, invertebrates, and fish (Saadoun 2015; Adzigbli and Yuewen 2018). The oil spill incident in Mauritius significantly impacted two major marine and coastal ecosystems: coral reefs and mangroves (Lewis 2020). Coral reefs were severely damaged, as spills are known to disrupt these ecosystems (Loya and Rinkevich 1980; Guzman et al. 2020). Mangroves, reported as the primary ecosystem affected by the spill (Lewis 2020), experienced lasting consequences, with residual oil pollution still detectable in sediments even three years later (Scarlett et al. 2024). Given Mauritius\u0026rsquo;s reliance on marine resources, particularly tourism and fisheries, this incident likely had profound economic repercussions (Sobhee 2006).\u003c/p\u003e\n\u003cp\u003eMarine oil spills have a profound impact on economies that depend on marine resources. For fisheries, the 2002 Prestige oil spill off the Galician coast of Spain caused a 66% loss in species richness in certain areas, significantly affecting offshore fish and crustacean fisheries (de la Huz et al. 2005; S\u0026aacute;nchez et al. 2006). Similarly, the 2011 Penglai 19-3 oil spill in China resulted in substantial economic losses in both fisheries and aquaculture (Pan et al. 2015). In locations where fisheries and tourism coexist, such as Korea, the 2007 Hebei-Spirit oil spill severely impacted both sectors (Cheong 2012). Tourism-dependent economies have also suffered greatly from oil spill incidents, as seen with the 2007 Don Pedro merchant ship spill near the island of Ibiza (Cirer-Costa 2015) and the Prestige oil spill, which affected Spain and France (Garza-Gil et al. 2006). Beyond economic damage, the health risks for local residents are a critical issue, directly disrupting daily life (Chang et al. 2014).\u003c/p\u003e\n\u003cp\u003eHuman exposure to oil spills can lead to severe health problems, including acute physical effects and psychological consequences (Aguilera et al. 2010; Laffon et al. 2016). Residents in affected communities face risks from direct contact with crude oil, inhaling toxic fumes carried by the wind, and consuming contaminated seafood (McCoy and Salerno 2010). Studies indicate genotoxic effects, such as DNA damage, in individuals exposed to oil-contaminated environments (Laffon et al. 2006; Hildur et al. 2015). Physical symptoms can include vomiting, diarrhea, stomach pain, and constipation, while psychological effects may manifest as post-traumatic stress disorder, depression, suicidal thoughts, and anxiety (Kim et al. 2013; Choi et al. 2016; Anderson et al. 2024). These health impacts highlight the far-reaching consequences of oil spills on both individual well-being and community stability.\u003c/p\u003e\n\u003cp\u003eAfter the oil spill incident, which resulted in these severe issues, the question of who should be held liable is seldom discussed in academic papers. However, understanding the main liability for such incidents involves reviewing the event and implementing measures to prevent future occurrences (Azik 2023). Potentially liable parties may include the captain and crew, the operating company, the ownership company, the government under whose flag the ship is registered, and more. For example, the Exxon Valdez oil tanker spill in 1989 resulted from the captain\u0026rsquo;s neglect of duties and lack of safety awareness, for which he bears inescapable liability (Carson et al. 2003). Similarly, the operating or ownership company of the spill incident is undeniably liable, especially those using \u0026ldquo;flag of convenience ships\u0026rdquo; which may not guarantee high safety standards, as evidenced by the incident of the \u0026quot;Prestige\u0026quot; (Zhang et al. 2021). Additionally, shipowner countries may also bear liability due to inadequate regulations and complex scenarios (Chen et al. 2017; Azik 2023). Thus, clarifying the complex liabilities of oil spill incidents is crucial, and incorporating the public\u0026apos;s perspectives could provide valuable insights. \u003c/p\u003e\n\u003cp\u003eAccordingly, this study focuses on the MV Wakashio incident to explore public perspectives, with particular emphasis on the viewpoints of citizens in the shipowner country, Japan. The research objectives are: (1) Which attributes were of greatest concern to the Japanese after the oil spill incident? (2) What did the segments identified through cluster analysis represent? (3) From the Japanese perspective, who was considered liable for this incident, and did this vary across the identified cluster segments? It is by no means our aim to judge who is responsible or liable; we merely explored the respondents\u0026apos; perspectives of liability instead of legal and moral responsibility. These questions are crucial for shaping the future governance of oil spill incidents, particularly in relation to donation recruitment and liability considerations.\u003c/p\u003e"},{"header":"2. Method and materials","content":"\u003cp\u003eWe conducted an online questionnaire survey to understand Japanese people\u0026apos;s perspectives on the oil spill incident caused by a Japanese company. The survey was administered from November 12 to 16, 2020, the same year the MV Wakashio Oil Spill occurred. The survey target was the nationwide registered prospective respondents of a survey company (Cross Marketing Inc.), considered to represent Japan\u0026rsquo;s general public. We recruited the respondents by considering age, gender, and residential prefectures. This study was approved by the Institutional Review Board (IRB) of the University of Tokyo under approval number H-20100. Ultimately, we obtained 1,400 valid responses.\u003c/p\u003e\n\u003cp\u003eThe questionnaire began with a brief introduction to the incident: \u0026ldquo;On July 25, 2020, the cargo ship MV Wakashio, owned and managed by a subsidiary of Nagashiki Shipping Co., Ltd. and operated by Mitsui O.S.K. Lines, ran aground off the coast of Mauritius, an island nation in the Indian Ocean. On August 6, 2020, the ship\u0026apos;s heavy oil tank was damaged, causing approximately 1,000 tons of the roughly 4,000 tons of heavy oil onboard to spill into the sea.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eTable 1. Demographic variables and descriptive information (n = 1400).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e(n; %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003emale (677; 48.4%), female (720; 51.4%), Others (3, 0.214%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e18~29 (248; 17.7%), 30s (288; 20.6%), 40s (288; 20.6%),\u003c/p\u003e\n \u003cp\u003e50s (288; 20.6%), 60s (288; 20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eKnowledge of Mauritius: Did you know where Mauritius is located?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003eYes, I knew before the incident (253; 18.1%),\u003c/p\u003e\n \u003cp\u003eYes, I knew after the incident (269; 19.2%),\u003c/p\u003e\n \u003cp\u003eNo, I did not know (878; 62.7 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eKnowledge of the incident in Mauritius: Did you know about the ship stranding and heavy oil spill-off around Mauritius?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003eI knew ship stranding and oil spill-off (956; 68.3%),\u003c/p\u003e\n \u003cp\u003eI knew about ship stranding but I didn\u0026apos;t know about an oil spill. (55; 3.93%),\u003c/p\u003e\n \u003cp\u003eNo, I did not know (389; 27.8 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cem\u003e2.1 Best\u0026ndash;Worst Scaling (BWS) Design and Attributes\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eOur main approach is Best-Worst Scaling (BWS), specifically the object case (Case 1) developed by (Finn and Louviere 1992), which effectively highlights clear differences in public preferences. The object case of BWS offers several advantages over conventional preference evaluation methods (Lusk and Briggeman 2009; Louviere et al. 2015) and has been widely applied in environmental conservation and management sectors in recent years (Kubo et al. 2019; Schuster et al. 2024; Mameno et al. 2024).\u003c/p\u003e\n\u003cp\u003eIn our questionnaire survey, respondents were asked to identify the most and least supported scenarios. Drawing on previous studies about the impact of oil spill accidents on human lives and coastal ecosystems (Chang et al. 2014), we selected five potentially affected attributes: mangroves, coral reefs, fisheries, tourism, and health (Table 1). The survey design plays a critical role in the results, as the object case of BWS involves multiple-choice questions. We constructed five choice sets with four attributes each by applying a balanced incomplete block design (BIBD; (Raghavarao and Padgett 2005), for details; Appendix S1).\u003c/p\u003e\n\u003cp\u003eTable 2. Five attributes of Best-worst Scaling and their descriptions.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAttributes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoral reef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative impact on coral reefs: the area where the cargo ship ran aground is home to coral reefs that are inhabited by many of the world\u0026apos;s largest living creatures. However, there are concerns that the coral has been scraped off by the bottom of the ship that ran aground this time and that the coral has been smothered by the turbidity of the seawater caused by the accident, leading to the degradation of the coral reefs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMangrove\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative impact on mangrove forests: In the area where the cargo ship ran aground, mangrove forests are widespread in the coastal area. However, it has been pointed out that the spilled heavy oil could adhere to the mangrove forests, causing them to die. In addition, as mangrove forests provide a home for a wide variety of living creatures, there is concern that the accident could hurt the creatures that live there.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdverse health effects: It has been pointed out that the heavy oil spill caused by the grounding of the cargo ship could hurt the health of local residents and other people. In particular, there is concern that people who inhale or come into contact with heavy oil may suffer from headaches, dizziness, and breathing difficulties.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFishery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative impact on the fishery industry: It has been pointed out that the cargo ship running aground and spilling fuel oil may cause a decrease in fish catches in the waters around Mauritius and a consequent decrease in fishermen\u0026apos;s income and an increase in unemployment. There is also concern that the decrease in catches will make it difficult to secure a stable supply of food in Mauritius.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTourism\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative impact on the tourism industry: It has been pointed out that the cargo ship stranding and fuel oil spill could harm the tourism industry in Mauritius. In particular, there are concerns about the loss or restriction of marine recreational opportunities, a decrease in the number of tourists, especially foreigners, due to the deterioration of the tourism image caused by this accident, and a consequent decrease in the income of tourism operators and an increase in unemployment.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cem\u003e2.2 Best\u0026ndash;Worst Scaling (BWS) Analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eCounting analysis was applied to answer the BWS question. Counting analysis is a useful calculation while being a highly accurate approximation of the estimation results in the parametric methods (Marley and Louviere 2005). We analyzed an individual respondent\u0026apos;s best\u0026ndash;worst score (BW score) of attributes; that is the difference in the number of times they were selected as the most needed support in each choice set \u0026nbsp;and the number of times each choice was chosen as the least needed support, on each respondent:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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width=\"662\" height=\"48\"\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis also calculated the standardized BW score. The score was determined by dividing the average BW score by the number of respondents \u0026nbsp;and the number of represented times of each attribute (i.e., 4 times):\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"755\" height=\"73\"\u003e\u003c/p\u003e\n\u003cp\u003eThen, we compared each attribute to understand which one Japanese individuals care about the most. The comparison analysis was conducted using the Kruskal-Wallis test.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.3 Cluster analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eHierarchical cluster analysis was employed to examine the preference heterogeneity among Japanese individuals concerning the attributes identified through BWS (Auger et al. 2007). Each subject\u0026rsquo;s BW scores were used as input for the analysis, representing their choices on different attributes. The study applied the Ward approach for hierarchical clustering, which minimizes the variance within clusters by merging the closest points at each step, forming compact and homogeneous groups. The resulting dendrogram visually represents the hierarchical clustering process, showing how preferences group together based on similarity. Besides, the chi-square test was used to understand the difference among different demographic variables. This approach allowed the study to identify distinct segments within the Japanese population, providing insights into how different groups prioritize various aspects of oil spill management and liability.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.4 Descriptive Analysis of Liability\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTo understand the Japanese public\u0026apos;s perspective on who is liable for this incident, we included a general question to capture their opinions. Since the aim was to gather an overarching viewpoint, this question was not analyzed by categories or segments. The question was: \u0026ldquo;Who do you think is liable for this accident? (You may choose multiple options.)\u0026rdquo; The response options were: captains \u0026amp; crew, operating company, ownership company, Japanese government, nobody, and others. We used descriptive statistics to analyze the percentage distribution of responses. The analysis was conducted using R software (R Core Team 2022).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.5 Cluster Scenarios and Liability\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTo investigate whether individuals focusing on different segments have varying perspectives on the liability for this incident, we employed the Kruskal-Wallis test and the Bonferroni-Dunn posthoc test to identify differences among liability groups. A non-parametric approach was chosen instead of one-way ANOVA with Tukey\u0026rsquo;s HSD test because the variables did not meet the normality assumption (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Shapiro\u0026ndash;Wilk normality test). The analysis was performed using R software (R Core Team 2022).\u003c/p\u003e"},{"header":"3. Result","content":"\u003ch2\u003e\u003cem\u003e3.1. Best-worst attributes\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eAccording to the results of the BWS analysis, Health had the highest standardized BW score (0.353), followed by Coral (0.114), Fishery (0.0704), Mangrove (-0.178), and Tourism (-0.359) (Fig. 1). Notably, both Mangrove and Tourism received negative standardized BW scores. These results indicate that subjects considered the health of residents in Mauritius to be the most important and deserving of support, whereas the tourism industry was viewed as comparatively less of a concern.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.2. Best-worst cluster segments\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTo compare preference heterogeneity, the standardized BW scores for each segment were calculated. Coral had the highest standardized BW score in one of the three segments, followed by Mangrove; this segment was referred to as the \u0026quot;Ecology\u0026quot; segment and accounted for 28.9% of the participants. Subjects in another segment prioritized Fishery as the most important attribute to support; this segment, comprising 23.9% of the Subjects, was termed the \u0026quot;Economy\u0026quot; segment. The largest segment, representing 47.2% of Subjects, was labeled the \u0026quot;Health\u0026quot; segment, as Health received the highest standardized BW score within this group. All the segments and their corresponding BW score of attributes are shown in Fig. 2. The significant differences among attributes in each segment are presented in Table 3.\u003c/p\u003e\n\u003cp\u003eIn terms of demographic variable differences among the segments, Age showed no significant variation across the segments (\u003cem\u003ep\u003c/em\u003e = 0.07, \u0026chi;2 = 5.37, Kruskal-Wallis test). Gender exhibited a significant difference between the Economy and Health segments (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, z = -2.92, Bonferroni-Dunn test), although neither segment differed significantly from the Ecology segment (\u003cem\u003ep =\u003c/em\u003e\u0026nbsp; \u0026nbsp;0.54, z = 1.34, with Economy; \u003cem\u003ep =\u003c/em\u003e 0.37, z = \u0026nbsp;-1.54, with Health, Bonferroni-Dunn test). Knowledge of Mauritius showed significant differences between the Ecology and Health segments (\u003cem\u003ep \u0026lt;\u003c/em\u003e 0.01, z = -3.57, Bonferroni-Dunn test), but neither differed significantly from the Economy segment (\u003cem\u003ep =\u003c/em\u003e 0.07, z = -2.28, with Ecology; \u003cem\u003ep =\u003c/em\u003e 1, z = -0.85, with Health, Bonferroni-Dunn test). Finally, Knowledge of the incidents revealed a significant difference between the Ecology segment and the other two segments (\u003cem\u003ep \u0026lt;\u003c/em\u003e 0.01, z = 2.94, with Economy; \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.01, z = 3.01, with Health, Bonferroni-Dunn test), while no significant difference was observed between the Economy and Health segments (\u003cem\u003ep =\u003c/em\u003e 1, z = -0.4, Bonferroni-Dunn test). All the results of the demographic variable differences are presented in Table 3.\u003c/p\u003e\n\u003cp\u003eTable 3. Results of the comparison tests of BW scores using Kruskal-Wallis and Bonferroni-Dunn tests for demographic variables across the three clustered segments. Different letters indicate significant differences.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eKruskal-Wallis Chi-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandardized B-W score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eStandardized\u003c/p\u003e\n \u003cp\u003eB-W score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandardized B-W score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCoral reef\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.660 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.0149 \u003csup\u003eii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.153 \u003csup\u003eiii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e601.2\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMangrove\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.274 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.354 \u003csup\u003eii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.365 \u003csup\u003eii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e482.4\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.258 \u003csup\u003eiii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.0425 \u003csup\u003eii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.926 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1103\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFishery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.199 \u003csup\u003eiii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.496 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0193 \u003csup\u003eii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e423.5\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTourism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.476 \u003csup\u003eii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e-0.0843 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.427 \u003csup\u003eii\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.9\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003en=404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003en=335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003en=661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e18~29: 54; 30s: 86;\u003c/p\u003e\n \u003cp\u003e40s: 93; 50s: 87, 60s: 84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e18~29: 73; 30s: 70;\u003c/p\u003e\n \u003cp\u003e40s: 67; 50s: 63; 60s: 62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18~29: 121; 30s: 132;\u003c/p\u003e\n \u003cp\u003e40s: 128; 50s: 138; 60s:142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eMale: 201; Female: 200;\u003c/p\u003e\n \u003cp\u003eOthers: 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eMale: 182; Female: 153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale: 182; Female: 153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.860 \u0026nbsp;\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKnowledge of Mauritius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eBefore: 92; After: 86;\u003c/p\u003e\n \u003cp\u003eNo: 226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eBefore: 66; After: 52;\u003c/p\u003e\n \u003cp\u003eNo: 217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBefore: 95; After: 131;\u003c/p\u003e\n \u003cp\u003eNo: 43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.99 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKnowledge of the accident in Mauritius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eBoth: 302;\u003c/p\u003e\n \u003cp\u003eOnly\u0026nbsp;stranding: 15; No: 87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eBoth: 218;\u003c/p\u003e\n \u003cp\u003eOnly\u0026nbsp;stranding: 11; No: 106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBoth: 436;\u003c/p\u003e\n \u003cp\u003eOnly\u0026nbsp;stranding: 29; No: 196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.58 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cem\u003e3.3. Attribution of Liability\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eAccording to the descriptive statistics, the majority of subjects (57.8%) believed that the captains and crew should primarily bear liability for this incident, followed by the operating company (46.6%), the ownership company (36.6%), nobody (23.1%), the Japanese government (9.4%), and others (1.29%) (Figure 3). These results indicate that most subjects (over 50%) held the captains and crew accountable for the incident.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.4 Comparison of cluster segments in liability dimensions\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eIn the Captain and Crew liability dimension, the Ecology segment scored significantly higher than both the Economy segment (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, Bonferroni-Dunn test) and the Health segment (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Bonferroni-Dunn test), while there was no significant difference between the Economy and Health segments (\u003cem\u003ep\u003c/em\u003e = 0.06, Bonferroni-Dunn test). In the Operating Company liability dimension: the Economy segment was significantly lower than the Ecology segment (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Bonferroni-Dunn test) and the Health segment (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Bonferroni-Dunn test), with no significant difference between the Ecology and Health segments (\u003cem\u003ep\u003c/em\u003e = 1.000, Bonferroni-Dunn test).\u003c/p\u003e\n\u003cp\u003eFor the Ownership liability dimension, the Ecology segment differed significantly from the Economy segment (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Bonferroni-Dunn test), but there were no significant differences between the Health segment and either the Ecology (\u003cem\u003ep\u003c/em\u003e = 0.48, Bonferroni-Dunn test) or Economy segments (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.45, Bonferroni-Dunn test). Regarding the Nobody liability dimension, the Economy segment scored significantly higher than both the Ecology (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, Bonferroni-Dunn test) and Health segments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Bonferroni-Dunn test), and the Health segment scored significantly higher than the Ecology segment (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Bonferroni-Dunn test).\u003c/p\u003e\n\u003cp\u003eNo significant differences were observed among the segments in the Japanese Government and Others liability dimensions (\u003cem\u003ep\u003c/em\u003e = 0.73, for Japanese Government; \u003cem\u003ep\u003c/em\u003e = 0.06, for Others, Kruskal-Wallis test). Figure 4 illustrates the results of the comparison of cluster segments across liability dimensions. Notably, the ranking of liability dimensions was consistent across all segments. The ratios and significance values are provided in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4. The ratios and significance of liability dimensions across different segments.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKruskal-Wallis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaptain/Crew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.57 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.39***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOperating Company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.48 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.77\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOwnership Company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.38 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.32*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNobody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.16 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.32 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.73***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJapanese Government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: 1) In each attribute, the alphabets (a-c) indicate statistical differences in the scores among the groups based on the results of the Bonferroni-Dunn tests. 2) *,**, and *** denote \u003cem\u003ep\u003c/em\u003e values of \u0026lt; 0.05, \u0026lt; 0.01 and \u0026lt; 0.001, respectively.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOil spill incidents undoubtedly impact the environment, the health of local residents, and the economy (Chang et al. 2014; Jabbar et al. 2018). However, this study seeks to understand the perspectives and opinions of citizens from the shipowners\u0026rsquo; country\u0026mdash;a relatively underexplored area. Our aim is not to diminish the importance of any particular context of damage but to uncover public perceptions and identify potential gaps in the information they have received. Studies employing social science methods to examine public viewpoints on oil spills remain limited, with most research focusing primarily on health-related issues (e.g., (Ha et al. 2012; Kim et al. 2013)). By addressing this gap, this study provides valuable insights to help governments formulate future policies on fundraising strategies and liability attributions. \u003c/p\u003e\n\u003cp\u003eBased on the analysis of BWS, we found that Japanese people are primarily concerned about the health problems of residents affected by oil spill incidents. We believe this is because health is most directly related to human well-being. Furthermore, food, as a daily necessity, is directly tied to health, especially when oil pollution has an impact (McCoy and Salerno 2010). Many research studies have also highlighted health problems, both physical and psychological, following oil spill incidents (Ha et al. 2008; Aguilera et al. 2010).\u003c/p\u003e\n\u003cp\u003eOur findings revealed that women were significantly more concerned about health issues than men (Table 3). This result was consistent with previous studies, which show that women are more concerned than men about health in other contexts, such as food choices and environmental issues (Wardle et al. 2004; Xiao and McCright 2012). For instance, during the COVID-19 pandemic, studies indicated that women primarily focused on health-related issues, while men were more concerned about economic matters (van der Vegt and Kleinberg 2020). This alignment with our findings suggests a consistent gender difference in prioritizing health. However, the reasons why women expressed greater concern about health in the context of this oil spill incident require further investigation.\u003c/p\u003e\n\u003cp\u003eThe second most notable attribute was coral, categorized under the ecological segment. Coral reefs are often perceived as iconic symbols of natural beauty, which explains why many studies have evaluated their aesthetic value (Vercelloni et al. 2018; Pert et al. 2020). When people think of Mauritius, they frequently associate it with the image of beautiful coral reefs (Seveso et al. 2021), and indeed, numerous past oil spill incidents have significantly impacted coral reef ecosystems (Loya and Rinkevich 1980; Fernandes et al. 2022). However, mangroves, despite being the most affected ecological feature during this incident (Lewis 2020; Seveso et al. 2021), were the second most negatively focused attribute in this context. We believe this is because many individuals within the ecological segment lack a clear understanding of Mauritius, including its location (56% did not know, and 21.3% became aware only after the incident; Table 3), its environmental characteristics, and the actual extent of the oil spill\u0026rsquo;s impact (21.5% were unaware of the oil spill; Table 3). As a result, the public relied on stereotypes, imagining that coral reefs would suffer the most severe impacts from the incident and should therefore be the primary focus of attention.\u003c/p\u003e\n\u003cp\u003eThis perception may stem from a lack of information and aligns with the concept of \u0026quot;Perceived Risk,\u0026quot; which highlights how risk perception is often shaped by affective factors (e.g., feelings and emotions) and contextual factors (e.g., the availability and quality of information) (Slovic et al. 2016). The discrepancy between perceived and actual impacts underscores how public concerns are often guided more by personal preferences and imagined scenarios than by objective evidence. These findings suggest that the public\u0026apos;s attention tends to gravitate toward what they find aesthetically appealing or emotionally evocative rather than ecological realities. Addressing this gap requires improving risk communication and providing accurate, accessible information to ensure that public awareness aligns more closely with actual ecological priorities (Frewer 2003).\u003c/p\u003e\n\u003cp\u003eContradictorily, tourism emerged as the least important attribute in the BWS analysis, despite coral reefs being one of the most valuable coastal ecosystems for tourism (Cesar et al. 2003). Historically, many oil spill incidents have significantly damaged the tourism industry (e.g., (Hegazy et al. 2014)). Interestingly, the third most concerning attribute in this study was fishery, which was the only positive attribute within the economy segment (Figure 2). Although the fishery attribute ranked higher than mangroves, it was still rated lower than the top positive attributes, health and coral. This suggests that while some individuals do care about the local economy, their concern is predominantly limited to fisheries, as tourism\u0026mdash;another key economic driver\u0026mdash;was rated the lowest overall.\u003c/p\u003e\n\u003cp\u003eCluster analysis further revealed that subjects may not view tourism as a critical industry for Mauritius or believe that the oil spill would significantly affect it, as tourism was not perceived as a positive attribute (Figure 1). In reality, tourism is a vital sector for Mauritius, much like fisheries, and both contribute significantly to the local economy, which directly affects human livelihoods (Sobhee 2006). In contrast to our findings, Dominguez-P\u0026eacute;ry et al. (2021) examined social media during the MV Wakashio oil spill (the same case as this study) and found that the public\u0026apos;s primary concerns were economic, highlighting the significance of financial impacts in public discourse. The discrepancy in concern between the public in Mauritius and citizens from the shipowner\u0026rsquo;s country raises an important question: Why did the Japanese respondents show less concern for the economic impacts of the oil spill? This warrants further investigation to better understand the factors influencing public perceptions of economic consequences in such incidents. As mentioned above, however, we speculate that the general lack of information and knowledge about the island nation may have hindered the Japanese respondents\u0026apos; understanding of reality. In addition, the respondents may have simply thought that the economic loss was recoverable and could not be the most serious dimension in Mauritius, which enjoys the status of an upper-middle-income country with a per capita income of more than US$10,000. \u003c/p\u003e\n\u003cp\u003eMost subjects\u0026apos; perspectives indicated that the captain and crew should bear the liability for this incident. This is reasonable because investigators who interviewed the crew revealed that, at the time of the grounding, the crew had been celebrating a crew member\u0026apos;s birthday aboard the ship, which had sailed near the shore to pick up a Wi-Fi signal. Previous incidents, such as those caused by human factors (e.g., (Carson et al. 2003)), also involved the captain neglecting their duties. However, the 1992 Protocol to the International Convention on Civil Liability for Oil Pollution Damage specifies that liability lies with the shipowner, excluding the responsibilities of the shipowner\u0026apos;s employees, operators, charterers, and other related parties. Nevertheless, Cheong (2011) argues that exempting these parties from liability may weaken their awareness of oil pollution prevention, which could hinder efforts to reduce oil pollution incidents on ships.\u003c/p\u003e\n\u003cp\u003eThe operating company and ownership company should, of course, bear responsibility and liability for the incident. The operating company should be held responsible for shortcomings in the management and training of the crew (Celik and Topcu 2009), while the ownership company should bear liability under the \u0026quot;limitation of liability\u0026quot; provisions outlined in the 1992 Protocol. These entities ranked second and third in terms of the subjects\u0026apos; perspectives in this study (Figure 3). Additionally, the government of the ownership country may also be held accountable for failing to enforce proper governance or for implementing inadequate regulations (Chen et al. 2017; Zhang et al. 2021). In this case, Japan, where the shipowner is based, is a party to the International Convention on Limitation of Liability for Maritime Claims (LLMC) Protocol 96, which caps liability at 46.5 million Special Drawing Rights (SDRs) (Carey 2024). The issue of civil liability and compensation for oil pollution damage at sea has long posed challenges for governments, highlighting the importance of identifying responsible parties and establishing effective compensation mechanisms to minimize environmental harm (Azik 2023). Notably, 23.1% of respondents believed that nobody should be held liable, possibly reflecting either a lack of information or indifference, as approximately 28% of the subjects were unaware of the oil spill incident (Table 1).\u003c/p\u003e\n\u003cp\u003eTo further analyze segments in different liability dimensions, the results reveal that the Ecology segment has significantly higher scores in Captain and Crew, Operating Company, and Ownership dimensions (Figure 4). Inspecting the demographic information of the Ecology segment, we see that these subjects have the highest proportion of subjects who were familiar with the incident (Table 3). This might explain why members of the Ecology segment felt that these dimensions should be held liable, particularly for the Captain and Crew. On the other hand, the Economy segment significantly had the highest proportion of respondents who thought nobody should be liable, reflecting the highest percentage of individuals who were unaware of the incident (Table 3). These findings also highlight the importance of providing comprehensive information about the incident. When people receive incomplete or uncertain information, they may rely on their experiences or emotions to make decisions, which can lead to imprecise conclusions and challenges in risk communication (Morgan 1990; Granger Morgan 2002). \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study offers valuable insights into public perspectives in shipowner countries. Oil spill incidents pose significant environmental, economic, and health issues, yet public perspectives, particularly from the shipowner\u0026rsquo;s country, remain underexplored. This study highlights the dominance of health concerns, especially among women, and the disconnect between perceived and actual ecological impacts, with coral reefs prioritized over more affected features like mangroves. Economic attributes, such as fisheries, received moderate attention, while tourism was undervalued despite its critical importance to Mauritius. The results emphasize the need for improved risk communication to bridge information gaps and better align public awareness with ecological and economic realities. While most respondents believed the crew and captain should bear liability, the reality of liability attribution is more complex, involving the captain, crew, operating and ownership companies, and the oversight responsibilities of the shipowner\u0026rsquo;s government under frameworks like the LLMC Protocol 96. Our research provides valuable insights for policymakers to understand the perspectives of people in ownership countries, offering strategies for effective fundraising strategies and a reference for liability attribution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eA statement on participant consent：\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent to participate in this study. The study was approved by the Institutional Review Board (IRB) of the University of Tokyo under approval number H-20100.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdzigbli L, Yuewen D (2018) Assessing the impact of oil spills on marine organisms. J Oceanogr Mar Res\u003c/li\u003e\n\u003cli\u003eAguilera F, M\u0026eacute;ndez J, P\u0026aacute;saro E, Laffon B (2010) Review on the effects of exposure to spilled oils on human health. J Appl Toxicol 30:291\u0026ndash;301\u003c/li\u003e\n\u003cli\u003eAnderson C, Krishnamurthy J, McAdam J, et al (2024) Acute gastrointestinal symptoms associated with oil spill exposures among U.S. coast guard responders to the Deepwater Horizon oil spill. Ann Epidemiol 99:16\u0026ndash;23\u003c/li\u003e\n\u003cli\u003eAuger P, Devinney TM, Louviere JJ (2007) Using best\u0026ndash;worst scaling methodology to investigate consumer ethical beliefs across countries. J Bus Ethics 70:299\u0026ndash;326\u003c/li\u003e\n\u003cli\u003eAzik P (2023) A review of international conventions regarding the responsibilities caused by oil pollution of the seas. Isagoge - Journal of Humanities and Social Sciences. https://doi.org/10.59079/isagoge.v3i1.212\u003c/li\u003e\n\u003cli\u003eBoswell R (2022) Waking up to wakashio: Marine and human disaster in Mauritius. In: The Palgrave Handbook of Blue Heritage. 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Accessed 13 Mar 2025b\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"bulk carrier, oil spill incidents, liability attribution, public perspective, Best-Worst Scaling, governance insights, MV Wakashio","lastPublishedDoi":"10.21203/rs.3.rs-6720728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6720728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Oil spills caused by ships are a major threat to environmental sustainability, severely damaging marine ecosystems, undermining local economies, and risking public health. Although many spills involve non-local shipping companies, the perspectives of citizens in shipowner countries remain underexplored. Understanding these public concerns is essential for developing sustainable governance, especially regarding liability management and ecological restoration. This study focuses on the MV Wakashio oil spill, where a Japanese-controlled bulk carrier grounded on a coral reef near Mauritius. A nationwide survey of 1,400 Japanese respondents was conducted using systematic sampling. Best-worst scaling (BWS) identified key concerns, and cluster analysis categorized respondents into perspective-based segments. Results showed that health impacts were the top concern, closely followed by the protection of coral reefs and fisheries—critical components of marine sustainability. Cluster analysis revealed three main segments: Ecology, Economy, and Health. Women prioritized health and environmental issues, while men were more concerned with economic aspects. Over 50% of respondents believed that the captain and crew should bear primary liability. Segment differences indicated that individuals with strong ecological concerns attributed greater responsibility to prevent environmental degradation. These findings stress the importance of integrating public views into sustainable policy frameworks, highlighting that long-term marine conservation and responsible shipping practices must be central to oil spill governance and global sustainability efforts.","manuscriptTitle":"Public Concerns and Liability Perspectives in the Shipowner's Country: Insights from the Oil Spill Incident Involving a Japanese-owned Carrier Near Mauritius","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 05:32:16","doi":"10.21203/rs.3.rs-6720728/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d4b1bf6-28bb-41ce-9ffa-198449a5e98c","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-03T05:32:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 05:32:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6720728","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6720728","identity":"rs-6720728","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}