STEP 4: ASSESSING THE COSTS AND BENEFITS
According to the FSA guidelines (IMO, 2018a), the purpose of Step 4 is to identify and compare the benefits and costs associated with the implementation of each RCO identified and defined in Step 3. A cost-benefit assessment may consist of the following stages:
• “Consider the risks assessed in Step 2, both in terms of frequency and consequence, in order to define the base case in terms of risk levels of the situation under consideration.
• Arrange the RCOs, defined in Step 3, in a way to facilitate understanding of the costs and benefits resulting from the adoption of an RCO.
• Estimate the pertinent costs and benefits for all RCOs.
• Estimate and compare the cost-effectiveness of each option, in terms of the cost per unit of risk reduction by dividing the net cost by the risk reduction achieved as a result of implementing the option.
• Rank the RCOs from a cost-benefit perspective in order to facilitate the decision-making recommendations in Step 5 (e.g., to screen those which are not cost-effective or impractical).” The cost and benefit values associated with an RCM have to be combined with the risk reduction to assess the costs and the benefits per percentage of risk reduction (IMO, 2018a).
Until recently, this step was focusing mainly on human safety. There are several indices, which express cost-effectiveness depending on the safety of life such as Gross Cost of Averting a Fatality (Gross CAF) and Net Cost of Averting a Fatality (Net CAF) as described in the FSA guidelines. The numerator of the Net CAF integrates the benefit, whereas the Gross CAF does not. Hence, Net CAF is much more adapted to environmental issues, as other benefits such as avoiding environmental damages could be considered.
Since 2006, the FSA framework has opened up to the analysis of risk evaluation criteria for accidental releases to the environment, and specifically for releases of oil. Discussions on this matter were sparked to a significant extent by EU research project SAFEDOR (Skjong et al., 2005), which defined the criterion of CATS (Cost to Avert one Tonne of Spilled oil) as an environmental criterion equivalent to CAF (Eide et al., 2009). Even though the FSA guidelines only include provisions to assess the environmental damages from oil spills (Kontovas and Psaraftis, 2011), other risk acceptance criterions have been developed and considered for FSA application through recent years (Vanem, 2012). These criterions are mainly focused on air An interdisciplinary approach to the management of whale-ship collisions emission, but encourage researches to build relevant criterions for risk assessments, as advocated by the FSA guidelines (IMO, 2018a).
In order to assess risk reduction measures related to ship collisions with whales, by using the FSA framework, there is a need to define an index “in terms of the cost per risk reduction unit by dividing the net cost by the risk reduction achieved as a result of implementing the option”. For ship strikes, our study, therefore, proposes a similar cost-effectiveness index, named Net Cost to Avert a Whale Fatality (NCAWF), as follows: 6)78# = Δ:; Δ< Δ= (
THE COST-EFFECTIVENESS CRITERION
One of the underlined principles of FSA is that the decision-makers (Step 5) should be provided with recommendations of measures to reduce the risk that are cost-effective. In order to do so, a cost-effectiveness criterion should be used. To recommend an RCO for implementation, the cost-effectiveness index must be less than the cost-effectiveness criterion; otherwise, the RCO is rejected by the IMO. The cost-effectiveness criterion definition varies depending on the risk evaluated. It usually takes into account the following approaches (IMO, 2006b, 2004b):
• “Observations of the willingness to pay to avert a fatality.
• Observations of past decisions and the costs involved with them.
• Consideration of societal indicators.”
The dominant yardstick in all FSA studies that have been submitted to the IMO so far is the so-called “$3m criterion”. This criterion is to cover human fatalities from accidents and implicitly, also, injuries or ill health from them. This criterion was calculated using the third approach (IMO, 2000; Lind, 1996; UNDP, 1990). Indeed, the human safety criterion was inspired by the Life Quality Index, which takes its origin in a combination of life expectancy, wealth, and health indicators (Nathwani et al., 1997). For environmental safety, the second and third approaches are usually used (Kontovas and Psaraftis, 2008; Vanem, 2012). For example, the criterion for the oil spill issue was calculated in function of the rescue and clean-up costs of historical events (2nd approach), whereas for carbon dioxide, its calculation was in function of the IPCC 2030 target (3rd approach) (Eide et al., 2009; SAFEDOR, 2005).
In the literature, there is currently no cost-effectiveness criterion to assess risks to whales. In our opinion, a combination of the second and third approach could be used. Indeed, for societal indicators (3rd approach), the cost of losing a whale can be looked into. Several approaches can be used and combined to achieve this assessment. First, contingent studies on the willingness to pay to protect whales can be done (Boxall et al., 2012; Hageman, 1985; Loomis and Larson, 1994; Rudd, 2007; Wallmo and Lew, 2012). These studies are nevertheless costly and time-consuming (Loomis and White, 1996). One way to overcome these constraints is through a benefit transfer study using willingness to pay value from original studies (Lew, 2015; Loomis and Richardson, 2008; U.S. EPA, 2014; Wilson and Hoehn, 2006). Unfortunately, these kinds of studies suffer from different biases that tend to cause variation in results, related to factors such as methodology, location, species concerned, resident status, payment vehicle and frequency (Amuakwa-Mensah, 2018). Second, whales are since a few decades considered as biodiversity services as non-consumable direct use-value (e.g., whale watching). Using whale watching revenues (Cisneros-Montemayor et al., 2010; O’Connor et al., 2009), the calculation of the lifetime value of a whale can lead to the assessment of the cost of losing a whale (Knowles and Campbell, 2011). Finally, a market approach has emerged recently (Gerber et al., 2014a), although highly discussed (Babcock, 2013; Gerber et al., 2014b; Smith et al., 2014).
STEP 5: RECOMMENDATION FOR DECISIONMAKING
The final Step of FSA aims at giving recommendations to the decision makers for safety improvement, taking into consideration the findings during all four previous steps. The RCOs that are being recommended should reduce the risk to the “desired level” and be cost-effective – efficient (Kontovas, 2005). To this extent, there is a need to define the desired or acceptable level of risk and clear cost-effectiveness criteria. According to the guidelines, the purpose of this Step is to define recommendations, which should be presented to the decision-makers in an auditable and traceable manner. The recommendations would be based upon the comparison and ranking of all hazards; the comparison and ranking of risk control options as a function of associated costs and benefits; and the identification of those risk control options which keep the risk as low as reasonably practicable (see the notion of ALARP in Section 2.3).
DAMAGE AND COST INFORMATION
For our analysis, we gathered information on the ship speed, length, and associated damages for the collision events in the UD. In the case where the ship’s speed or length was not available An interdisciplinary approach to the management of whale-ship collisions in the original dataset, we used online databases such as MarineTraffic and VesselFinder to extract the ship’s particulars. As ship speed during a strike is sometimes not provided in the UD, and as ship speed for a given type of ship does not change dramatically over time (1970-2010), when needed, we used average operational speeds based on AIS data for similar ships, as presented in IMO (2014). We believe, though that more information on the exact speed during collisions is needed to get better insights. When the damage status was not available, other sources of information were checked to recover damage information related to the UD, such as IWC archives, IMO Global Integrated Shipping Information System archives, scientific publications, and journal articles. Besides, the type and the cost of damages were included in the UD.
PROBABILITY AND DAMAGE COSTS
Vanderlaan and Taggart (2007) proposed a methodology to define the “probability of lethal whale injury based on ship speed” when struck. The methodology used the IWC ship strike database to derive the probability of lethal injury and has, since then, been widely used as a basis for risk assessment studies (Martin et al., 2015; Nichol et al., 2017). We, therefore, follow the same reasoning to derive the probability of ship damage as a result of a whale-ship collision, depending on ship length and speed. Only events for which information on ship speed, length, and damages were reported are included in the analysis.
The probability of ship damages subsequent to a collision with a whale as a function of the ship speed or length, and their ratio, was calculated by performing a logistic regression analysis, with bootstrapping, using “R” (R Development Core Team, 2008). A lack of observations limited the needed degrees of freedom and prevented a logistic regression of both the speed and length variable (Peduzzi et al., 1996). As a result, we used the ratio of the variables as a proxy. Note that the logistic regression is the appropriate regression analysis when the dependent variable, in our case, the damage to the ship, is dichotomous (binary). Bootstrapping is a type of resampling where large numbers of smaller samples of the same size are repeatedly drawn, with replacement,from a single original sample – in our analysis, 1,000 iterations were performed (Haman and Avery, 2019; Venables and Ripley, 2002). To illustrate our results, we then compute the probability of damages on four typical ships, which are often involved in collisions (oil tanker, bulk carrier, container and cargo-ferry ships).
PROBABILITY OF SHIP DAMAGE
Our study estimates the probability of ship damage as a result of a whale-ship collision by using a logistic regression model in line with Vanderlaan and Taggart (2007). Similarly to their study, the limitation of data, and the non-integration of relevant variables to shipping (e.g., thickness of the hull, material resistance; Zhang, 1999), or whales (e.g., size, direction) results in large confidence intervals. Nevertheless, our results represent the first step towards the integration of ship damages in whale-ship collision risk assessments.
Results show that Cargo-Ferries (A3) ships face the most significant risk for damage, especially passenger ferries and high-speed passenger ships. The literature revealed several events of severe impacts involving these ship categories. These events lead to a sudden loss of speed, damages requiring towage, or human injuries and fatalities (Laist et al., 2001; Ryu et al., 2010). Other ship categories exhibit lower probabilities of damages. Nevertheless, the overall probability of ship damage for large commercial ships seems to be around Pdamage ≈ 0.10, although again, we want to stress out that the dataset is very limited. This observation may indicate that some damages may go unnoticed, or are not linked to a ship-strike, even when the ship requires repairs. By using a logistic regression model, our study allows a straightforward assessment of the risk reduction induced by a particular collision mitigation solution: speed reduction. When implementing speed reduction, one can observe a reduction in the probability of whale lethal injury (Parrott et al., 2016; Vanderlaan and Taggart, 2007). Based on our study, the probability of damage can also be estimated to expose the risk reduction in ship damages for this mitigation solution. If a Cargo-Ferry of 165 meters length and navigating at a speed of 18.5kn (Pdamage = 0.119) is asked to reduce its speed to 12kn, it reduces the risk of damage by 11% (Pdamage = 0.106).
To be noted that at the same time, the probability of lethal injury to whales is reduced by 45% (from 0.937 to 0.507 based on the model derived by Vanderlaan and Taggart, 2007). Note that this study assesses the probability of damage but does not deal with the severity of the damages, as there are not sufficient data in the IWC database. The severity depends on several factors, such as the thickness of the hull, the material resistance, or the shape of the bow (Liu et al., 2018). Some hydrodynamic models were used to study the behavior of these parameters under different scenarios, i.e., ship-ship, ship-container, and ship-floating log collisions, or groundings (Zhang, 1999). Some researchers studied the hydrodynamics of a whaleship collision, but in order to assess damages to whales (Knowlton et al., 1998; Silber et al., 2010). To our knowledge, there are no similar studies on ship damages. The undertaking of such studies focusing on the damages to ships after whale-ship collisions would improve our understanding of these events and help improve the management of the risk that ships face as a result of ship strikes. Note that there is a parallel body of literature on dynamic models for wildlife-vehicle collisions (e.g., car and train), which could be applied to whale-ship collisions (Anderson et al., 2015; Visintin et al., 2018).
Table of contents :
LIST OF ANNEXES
LIST OF FIGURES
LIST OF TABLES
1. EVOLUTION OF HUMAN-INDUCED DIRECT THREATS TO WHALES
1.1. THE HARVESTING PHASE (1600-1986)
1.2. THE COMPETITIVE EXCLUSION PHASE (1800-)
1.3. THE CUMULATIVE EFFECT OF HUMAN-INDUCED DIRECT MORTALITY
2. WHALE-SHIP COLLISION
2.1. WHALE-SHIP COLLISION RISK
2.2. THE CURRENT STATE OF WHALE-SHIP COLLISION MANAGEMENT
3. FROM A BOTTOM-UP TO A TOP-DOWN STANDARDIZED MANAGEMENT OF WHALESHIP
3.1. WHALE-SHIP COLLISION MANAGEMENT: MONODISCIPLINARY, MULTIDISCIPLINARY,
INTERDISCIPLINARY, OR TRANSDISCIPLINARY?
3.2. LESSONS LEARNED FROM OTHER WILDLIFE-VEHICLE COLLISIONS
4. THESIS OBJECTIVES AND STRUCTURE
CHAPTER 1: A DECISION-MAKING FRAMEWORK TO REDUCE THE RISK OF COLLISIONS BETWEEN SHIPS AND WHALES
2. USING FORMAL SAFETY ASSESSMENT TO REDUCE THE RISK OF SHIP STRIKES
2.1. AN INTRODUCTION TO FSA
2.2. STEP 1: HAZARD IDENTIFICATION
2.3. STEP 2: RISK ANALYSIS
2.4. STEP 3: RISK CONTROL OPTIONS
2.5. STEP 4: ASSESSING THE COSTS AND BENEFITS
2.6. STEP 5: RECOMMENDATION FOR DECISION-MAKING
3. CONCLUSIONS AND FUTURE RESEARCH
CHAPTER 2: REDUCING WHALE-SHIP COLLISIONS BY BETTER ESTIMATING DAMAGES TO SHIPS
2. MATERIALS AND METHODS
2.1. DATA PREPARATION
2.2. DAMAGE AND COST INFORMATION
2.3. PROBABILITY AND DAMAGE COSTS
3.1. DESCRIPTIVE RESULTS
3.2. REGRESSION RESULTS
4.1. DESCRIPTIVE RESULTS
4.2. PROBABILITY OF SHIP DAMAGE
4.3. COSTS OF DAMAGE
4.4. IMPLICATION FOR WHALE-SHIP COLLISION MANAGEMENT
5. CONCLUSIONS AND PERSPECTIVES
CHAPTER 3: AN ASSESSMENT OF THE SHIPPING INDUSTRY’S PREFERENCES FOR WHALESHIP COLLISION MITIGATION SOLUTIONS
2. MATERIALS AND METHODS
2.1. CHOICE EXPERIMENT DESIGN
2.3. ECONOMETRIC ANALYSIS OF CHOICE DATA
3.1. RESPONDENTS CHARACTERISTICS
3.2. MODEL RESULTS
4. DISCUSSIONS AND CONCLUSIONS
4.1. SHIPPING INDUSTRY’S PREFERENCES
4.2. IMPLICATIONS FOR CONSERVATION
4.3. FURTHER RESEARCH
4.4. CONCLUDING REMARKS
CHAPTER 4: WHALE HUMAN-INDUCED DIRECT MORTALITY IN MEDITERRANEAN SUBPOPULATIONS: HOW MUCH IS TOO MUCH?
2. MATERIALS AND METHODS
3.1. DATA COLLECTION
3.2. SEVERITY OF THE HIDM IMPACT ON WHALES
4.2. DEALING WITH UNCERTAINTY
4.3. CONSERVATION IMPLICATIONS
4.4. FUTURE RESEARCH
CHAPTER 5: RISK EVALUATION CRITERION: EVALUATION OF MEASURES TO REDUCE SHIP STRIKES – A MEDITERRANEAN CASE STUDY
2. VALUING THE BENEFIT OF REDUCING WHALE-SHIP COLLISION RISK
2.1. THE VALUE OF PROTECTING WHALES
2.2. THE VALUE OF AVERTING A WHALE FATALITY
2.3. CASE STUDY: MEDITERRANEAN FIN WHALES
2.4. RESULTS: VALUE OF IMPLEMENTING RULES TO AVERT A MEDITERRANEAN FIN WHALE
3. POTENTIAL USES WITHIN MARITIME RISK ASSESSMENT: RISK EVALUATION
3.1. APPLICATION OF THE RISK EVALUATION CRITERION TO THE FSA
3.2. EVALUATION OF MEASURES TO REDUCE THE COLLISION RISK TO FIN WHALES IN THE
4.1. EXISTENCE VALUE LIMITATIONS
4.2. RISK EVALUATION CRITERION LIMITATIONS
5. CONCLUSIONS AND FUTURE RESEARCH
1.THE KEY FINDINGS OF THE THESIS
2. IMPLICATION FOR WHALE CONSERVATION AND POLICY-MAKERS
2.1. THE IMPORTANCE OF THE IMO FOR WHALE-SHIP COLLISION MANAGEMENT
2.2. THE IMPORTANCE OF THE ECONOMIC AND LOGISTIC ASSESSMENTS
3. LIMITATIONS AND PERSPECTIVES
3.1. LIMITATIONS TO THE IMPLEMENTATION OF SYSTEMIC APPROACHES FOR WHALE-SHIP