Injury and loss concentration by sinkings in fishing fleets

Pérez-Labajos, C.A.; Blanco, Beatriz; Azofra, M.; Achútegui, Juan J.; Eguía, Emilio

doi:10.1016/j.ssci.2008.03.005

Cited by 17 publications

(8 citation statements)

References 22 publications

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“…Numerous studies have been carried out to analyse past shipping accidents. A major focus was placed on fishing accidents in these studies (e.g., Jin et al, 2001; Jin and Thunberg, 2005; Perez-Labajos et al, 2006; Perez-Labajos et al, 2009; Roberts et al, 2010). However, it should be noted that these results were only applicable to fishing vessels.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Shipping Accident Casualties using Zero-inflated Negative Binomial Regression Technique

Weng

Huang

2015

J. Navigation

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This study develops a Zero-Inflated Negative Binomial (ZINB) regression model to evaluate the factors influencing the loss of human life in shipping accidents using ten years' ship accident data in the South China Sea. The ZINB regression model results show that the expected loss of human life is higher for collision, fire/explosion, contact, grounding, hull damage, machinery damage/failure and capsizing accidents occurring in adverse weather conditions during night periods. Sinking can cause the highest loss of life compared to all other accident types. There are fewer fatalities and missing people when the ship involved in an accident is moored or docked. The results also reveal that the loss of human life is associated with shipping accidents occurring far away from the coastal area/harbour/ports. The results of this study are beneficial for policy-makers in proposing efficient strategies to reduce shipping accident casualties in the South China Sea. K E Y WO R D S 1. Zero Inflated Negative Binomial Model.2. Safety.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Shipping Accident Casualties using Zero-inflated Negative Binomial Regression Technique

Weng

Huang

2015

J. Navigation

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“…The first group focuses on the relationship between severity of injury in ship accidents and influencing factors (e.g., Jin, 2014; Weng et al, 2018a). The major focus of the second group is to assess the consequences of ship accidents such as injury or loss of human life (e.g., Jin et al 2001; Talley et al, 2006; Perez-Labajos et al 2009; Yip et al, 2015; Weng et al, 2016) and property damage cost (e.g., Talley et al, 2008; Weng et al, 2018b). Nevertheless, there is only a limited number of studies (Weng et al, 2019) focusing on the assessment of ship accident economic loss which is a major concern of insurance companies.…”

Section: Literature Reviewmentioning

confidence: 99%

Economic Loss Analysis of Fishing Boat Collisions Considering Spatial-Temporal Interaction Effects

Weng

2020

J. Navigation

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Considering unobserved spatial-temporal interaction effects, this study proposes a Bayesian spatial-temporal interaction model for predicting economic loss from fishing boat collisions using 10-year (2004–2014) collision records from six different areas in the waters of Fujian, China. Results show strong spatial heterogeneity and correlation effects in fishing boat collisions, while the economic loss from boat collisions gradually decreases with the time trend. Collision time, collision location, visibility and the involvement of LNG/LPG/chemical-carrying ships show similar marginal effects on the economic loss for the two collision types: fishing boat collisions and collisions involving no fishing boats. Navigational status and the involvement of cargo ships exhibit much bigger effects in fishing boat collisions compared with collisions involving no fishing boat. Unlike collisions involving no fishing boat, fishing boat collisions are associated with reduced economic loss in poor weather conditions characterised by strong wind/waves because in Fujian waters additional safety measures are adopted for fishing boats in such conditions. The proposed model is useful for policymakers in adopting safety enhancement strategies to decrease the economic loss resulting from fishing boat collisions.

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“…To analyze the concentration of AOVs, we have resorted to Gini indices and associated Lorenz curves since this is a tool that has also been used successfully in recent years in the field of marine accidents in fishing vessels (Perez-Labajos et al, 2009;Perez-Labajos, 2012). The present study, however, is the first time that this tool has been applied to the analysis of marine pollution.…”

Section: Gini Index Of Alleged Offending Vesselsmentioning

confidence: 99%

Illegal discharges in Spanish waters. Analysis of the profile of the Alleged Offending Vessel

Alonso¹,

Ortega

Labajos

2015

Marine Pollution Bulletin

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Injury and loss concentration by sinkings in fishing fleets

Cited by 17 publications

References 22 publications

Evaluation of Shipping Accident Casualties using Zero-inflated Negative Binomial Regression Technique

Evaluation of Shipping Accident Casualties using Zero-inflated Negative Binomial Regression Technique

Economic Loss Analysis of Fishing Boat Collisions Considering Spatial-Temporal Interaction Effects

Illegal discharges in Spanish waters. Analysis of the profile of the Alleged Offending Vessel

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