Maxime Sèbe
email: maxime.sebe@univ-brest.fr

Abdelhak Nassiri
email: abdelhak.nassiri@univ-brest.fr

Linwood Pendleton
email: linwood.pendelton@oceandata.earth

Caroline Roux

Using choice experiment designs to evaluate mitigation solutions to reduce whale-ship collisions Authors

Keywords: avoidance, choice experiment, preference, shipping industry, speed reduction, whale-ship collision

Whale-ship collisions represent a threat to some whale population survival. The shipping industry rarely adopts solutions to reduce the risk of collisions. This lack of compliance is partly due to the fact that previous work has failed to assess the economic and logistic constraints these solutions put on the shipping industry. Our work explored for the first time the logistical considerations affecting the adoption of whale-ship collision avoidance approaches by shipping companies. We used a choice experiment approach to assess the shipping industry's preferences for mitigation solutions, by questioning ship crews. Amongst other things, our results demonstrated a preference for avoiding a high-density whale area instead of reducing speed in it, and a requirement for upstream information to plan the journey depending on these areas. Our findings could be used as guidelines for the implementation of mitigation solutions depending on situational characteristics (e.g., travel distance, area's size) and provide insights for policy-making to reduce the risk of whale-ship collisions.

Introduction

the applied effectiveness is often significantly different from the theoretical effectiveness, because of the shipping industry's low compliance with the proposed solutions [START_REF] Chion | A voluntary conservation agreement reduces the risks of lethal collisions between ships and whales in the St . Lawrence Estuary (Que Canada): From co-construction to monitoring compliance and assessing effectiveness[END_REF][START_REF] Lagueux | Response by vessel operators to protection measures for right whales Eubalaena glacialis in the southeast US calving ground[END_REF].

Only a few attempts have been made to study the logistic and economic factors that may influence whether shipping companies adopt collision avoidance approaches or not. Mainly, these studies investigated the additional fuel cost incurred by the avoidance of an area or the reduction of speed in it. For example, Kite-Powell and Hoagland [START_REF] Kite-Powell | Economic aspects of right whale ship stike management measures, Final project report to the National Marine Fisheries Serivce[END_REF] defined the costs associated with the reduction of speed around the Boston harbors (USA). In this study, the reduction in speed led to the loss of some ports of call. Indeed, the short distance between the regional ports did not make it possible to make up for lost time by increasing ship speed. The economic losses for harbors were estimated to be between $300,000 (US$2005) and $4,800,000 (US$2005) per year, depending on the size of the harbor. A similar study, conducted by Nathan Associates Inc. [START_REF] Associates | Economic analysis of North Atlantic right whale ship strike reduction rule, A report prepared for the NOAA[END_REF], estimated the cost of collision reduction solutions at between $2,790,000 (US$2009) and $142,476,000 (US$2009) for the U.S. East Coast, based on different scenarios (e.g., speed reduction, dynamic management area). Recently, Gonyo et al. [START_REF] Gonyo | An economic analysis of shipping costs related to potential changes in vessel operating procedures to manage the co-occurrence of maritime vessel traffic and whales in the Channel Islands region[END_REF] estimated the inventory carrying costs and the transportation costs of avoidance and speed reduction in the Channel Islands region (U.S. West Coast). This study reported a decrease in costs for the re-routing solution (1.6%-3.4%) and an increase in costs for the speed reduction solution (1.3%-2.0%). In parallel with these studies of the total costs of mitigation solutions, other studies focused on the cost of setting up and maintaining technical solutions [START_REF] Silber | Report of a workshop to identify and asess technologies to reduce ship strikes of large whales[END_REF]. These costs are often relatively low compared to company revenues (e.g., REPCET; [START_REF] Jacob | Characteristics of maritime traffic in the Pelagos sanctuary and analysis of collision risk with large cetaceans, From the report of Quiet Oceans and Institut EcoOcéans[END_REF]), but they did not take into account operational costs associated with implementation (e.g., gas emission tax, fuel consumption, port of call loss; [START_REF] Stopford | Maritime Economics -3rd edition[END_REF]).

Thus, these studies often only partially reflect the economic dimension, which in turn would show an incomplete knowledge of the logistic dimension. Indeed, to the authors' knowledge, few studies have tackled the preferences of the shipping industries regarding whale-ship collision mitigation solutions depending on the logistical features required to implement the said solutions (e.g., [START_REF] Reimer | Mitigating vessel strikes: The problem of the peripatetic whales and the peripatetic fleet[END_REF]). Before looking at the costs of any solution, the study of the logistic dimensions could be decisive to propose viable solutions for shipping companies, depending on the organization of the shipping industry. Some solutions may be impossible to implement because of delays in arrival in ports of call [START_REF] Kite-Powell | Economic aspects of right whale ship stike management measures, Final project report to the National Marine Fisheries Serivce[END_REF], the inability of engines to adapt to a solution (slow steaming; [START_REF] Psaraftis | Speed models for energy-efficient maritime transportation: A taxonomy and survey[END_REF]) or because of specific navigational rules (e.g., COLREG; [START_REF] Eriksen | The branching-course model predictive control algorithm for maritime collision avoidance[END_REF]).

Our study aims to give first insights into how logistical considerations affect the adoption of whale-ship collision avoidance approaches by the shipping industry. Because of a lack of economic and logistic data regarding the interactions between ships and whale-related collisions, we attempt to estimate the shipping industry's preferences for risk reduction solutions by using the Choice Experiment (CE) method. CE design is used to invite maritime professionals to reveal their preferences among different alternatives. We propose mitigation solutions to the shipping industry -through their crew -accounting for situational characteristics (e.g., travel distance, area's size) in order to determine those factors that are most consequential in determining the adoption of avoidance methods and thus reflect the most significant features to take into consideration when proposing these solutions. We used collected data to estimate Lancasterian utility function with mixed logit specifications, which takes into account the impact of unobservable heterogeneity of each attribute preference [START_REF] Kehlbacher | Stated attribute non-attendance in successive choice experiments[END_REF], and its importance, or conversely, its non-attendance [START_REF] Manski | Daniel McFadden and the econometric analysis of discrete choice[END_REF][START_REF] Mcfadden | Conditional logit analysis of qualitative choice behavior[END_REF]. This model relaxes the Independence of the Irrelevant Alternative hypothesis because the preferences of the five attributes are not supposed independent [START_REF] Hausman | Specification Tests for the Multinomial Logit Model[END_REF]. This allows highlighting the complementarity/substitutability relation between attributes.

Methods

Choice Experiment Design

The CE methods are often used to assess the preferences of various stakeholders regarding environmental policies [START_REF] Garrod | Heterogeneity of preferences for the benefits of Environmental Stewardship: A latent-class approach[END_REF]. CE surveys present a series of alternatives -also known as choice sets -which encompass attributes describing a situation and a policy. The respondents must select the best alternative, in their opinion, allowing an implicit trade-off between the attributes [START_REF] Hanley | Choice modelling approaches: A superior alternative for environmental valuation?[END_REF][START_REF] Zander | Assessing the total economic value of threatened livestock breeds in Italy: Implications for conservation policy[END_REF]. The preference between attributes is then usually revealed through a willingness-to-pay value, which can be used as a monetary value, or as an indicator of the change in the utility [START_REF] Garrod | Heterogeneity of preferences for the benefits of Environmental Stewardship: A latent-class approach[END_REF][START_REF] Morey | Using attitudinal data to identify latent classes that vary in their preference for landscape preservation[END_REF]. Identifying relevant attributes that compose the alternatives proposed to respondents is crucial to designing a CE survey [START_REF] Hanley | Choice modelling approaches: A superior alternative for environmental valuation?[END_REF]. In our study, the attributes -and the values (referred to as levels in the CE terminology) that these take -were defined by consulting several experts on maritime traffic, whale conservation, and habitat modeling (Table 1). Using the CE method, we propose two mitigation solutions to the shipping industry by using a questionnaire distributed to maritime professionals. The first one is the avoidance solution (AS) of a high-density whale area -or a high probability of collision area. The AS is a binary attribute composed of two levels, namely to avoid or not to avoid the area. The second solution is a speed reduction solution (SRS) for the area. The SRS attribute is composed of four levels representing speed reductions of 0%, 20%, 30%, and 40%. These levels convey what can be seen in the literature with speed reduction proposals to 12kn, 14kn and 16kn [START_REF] Guzman | Potential vessel collisions with Southern Hemisphere humpback whales wintering off Pacific Panama[END_REF][START_REF] Wiley | Modeling speed restrictions to mitigate lethal collisions between ships and whales in the Stellwagen Bank National Marine Sanctuary, USA[END_REF]. It should be noted that both AS and SRS can be combined in the same alternative, similar to that which is found in the literature (e.g., [START_REF] Vanderlaan | Reducing the risk of lethal encounters: Vessels and right whales in the Bay of fundy and on the Scotian shelf[END_REF]).

To understand the contextual factors influencing the shipping industry preferences for mitigation solutions, situational characteristics are also included into the alternatives (i.e., each alternative is composed of three situational characteristics and two mitigation solutions; Fig. 1).

First, we offer different sizes -diameter -of the whale high-density area, hereafter referred to as the area of interest (AOI). The levels describing area sizes have been selected to reflect the range of possibilities between the current habitat suitability models and future predictive abilities (5-10 years; [START_REF] Hampton | Big data and the future of ecology[END_REF][START_REF] Bohorquez | Filling the data gap -A pressing need for advancing MPA sustainable finance[END_REF][START_REF] Madon | Strike-Alert : towards real-time, high resolution navigational software for whale avoidance[END_REF]).

For each choice alternative, one of the attributes represents the time of reception of the information (TRI) about the AOIs' characteristics (size and location). The TRI is crucial for the crew to take a decision on avoidance action possibilities. To define the levels of TRI, some logistical features were highlighted by informal interviews with captains, watch officers, and company environmental managers. These interviews underlined that, without a fleet center, the watch officers prepare the trip of the ship on the day of the journey or a day earlier. Then, the captain validates the journey one hour before departure. These times vary between companies, but the levels chosen for the study integrate this range of variability. Besides, one of the TRI levels was chosen to represent the possibility of receiving information at the last moment (1h before the arrival on the area), which can be the case with some observation networks'

technologies (e.g., REPCET). To be noted that Reimer et al., [START_REF] Reimer | Mitigating vessel strikes: The problem of the peripatetic whales and the peripatetic fleet[END_REF] defined that the TRI is a key factor in the decision process, and our study will quantify this factor.

Finally, the last attribute is the travel distance (TD), which is an essential parameter of the shipping industry, as it defines the type of navigation a ship operates [START_REF] Stopford | Maritime Economics -3rd edition[END_REF]. The travel distance levels selected represent a range of different types of navigation, from coastal navigation to longdistance travel. The survey protocol was presented to a panel of maritime professionals who helped select and validate the attributes and their levels. The resulting experimental design -matrix of all the attributes' levels (Table . 1) -in our study is composed of 4 × 4 × 4 × 2 × 4 = 512 alternatives.

We then followed the different steps that are recommended and applied in the majority of the literature that uses the multi-attribute choice method to build an optimal design (e.g., [START_REF] Hanley | Choice modelling approaches: A superior alternative for environmental valuation?[END_REF]). A Doptimal fractional factorial design of 16 alternatives was generated 1 by the SAS® software using the OPTEX procedure (Edition 9.3). This optimal design is composed of 8 sets of 2 alternatives. 1 This first design assumes all the parameters null. The questionnaire is composed of four parts. First, an introductory text explains the problem of whale-ship collisions. In this introduction, the logistical and economic issues that collision mitigation solutions could generate are also presented. At the end of this part, an explanation on how to fill in the CE questionnaire is provided.

The second part of the questionnaire seeks demographic information to weigh CE results. For example, questions about the respondent's job (captain or watch officer), the size of the vessel on which the respondent is deployed, and the ship category (tanker, bulk carrier, cargo shipwithout passengers, cargo -with passengers) were asked.

The third part of the questionnaire introduces the CE survey. For each choice set (8 choice sets in total), respondents were asked to assume that they had performed the two alternatives proposed. Based on this principle, respondents were asked to answer the following question: "In your opinion, which alternative represents the best compromise between the issues of maritime traffic and the protection of whales?".

For the various options proposed to be comparable in the multi-attribute choice surveys, it is usually necessary to introduce a reference option. Some authors use the current state ("status quo"; e.g., [START_REF] Boxall | Complexity in choice experiments : choice of the status quo alternative and implications for welfare measurement*[END_REF]) as a reference option. This state corresponds to a predefined and observable combination of the attributes, which is unique and common to all respondents [START_REF] Holmes | Choice Experiments[END_REF], and the other alternatives correspond to changes in these attributes. This approach is usually used when studies focus on a given geographical area or a market [START_REF] Beshears | How are preferences revealed?[END_REF][START_REF] Dhar | Consumer Preference for a No-Choice Option[END_REF][START_REF] Lew | Place of Residence and Cost Attribute Non-Attendance in a Stated Preference Choice Experiment Involving a Marine Endangered Species[END_REF]. In our study, the current state corresponds to the actual practices of each maritime professional. These practices are not identical between ship categories nor between professionals. In addition, these practices are not observable; therefore, they cannot be a common frame for all alternatives [START_REF] Campbell | Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider[END_REF][START_REF] Scarpa | Modelling attribute non-attendance in choice experiments for rural landscape valuation[END_REF]. To solve this issue, we opt for CE design, which consists of proposing several alternatives (two in our case)

with the possibility of rejecting them. The latter option is referred to as "no action" or "opt-out option" [START_REF] Campbell | Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider[END_REF][START_REF] Veldwijk | The Effect of Including an Opt-Out Option in Discrete Choice Experiments[END_REF]. In each choice set, two alternatives were, therefore, presented to answer the question: "Alternative 1" and "Alternative 2". In addition, the following opt-out alternative was proposed in each choice set: "None of the alternatives are realistic" (Fig. 2). Finally, in the fourth and last part of the questionnaire, a Likert scale is proposed to respondents in order to measure how important was each attribute in their choices [START_REF] Kehlbacher | Stated attribute non-attendance in successive choice experiments[END_REF][START_REF] Hensher | Non-attendance and dual processing of common-metric A latent class specification[END_REF]. The authors' contacts were also provided to the respondents so that they could share any comments on the questionnaire, or their activity.

Before administering the questionnaire to maritime professionals, we submitted a first draft to several commanders, watch officers, and environmental managers to identify clarity and terminology issues, or issues due to the authors' misunderstanding of the maritime traffic processes. Afterward, the questionnaire was also submitted for testing to researchers from the AMURE Economics Laboratory to identify flaws in the survey protocol. Answers and comments from both phases were integrated to build the final optimal design, and, therefore, the final questionnaire.

Maritime companies -mainly from the south of France -were contacted, and the questionnaire was administered via the Internet by using the LimeSurvey platform [START_REF]LimeSurvey: An Open Source survey tool /Lime[END_REF]. The environmental officers of the shipping companies transferred the LimeSurvey questionnaire to commanders and watch officers. Therefore, this administration protocol allowed us to define precisely the sample size.

Econometric Analysis of Choice Data

We used a questionnaire based on the discrete choice method to collect empirical data on maritime professionals decisions to reduce the risk of whale-ship collision. This CE protocol aims to identify the preferences of these maritime professionals between the proposed solutions.

We proposed eight choices sets, t={1,..,8}, to each respondent, i={1,...,n}, which were asked to choose among three alternatives, l={1,2,3} in each set. To study these choices, we assume as in

McFadden's pioneering paper [START_REF] Mcfadden | The Revealed Preferences of a Government Bureaucracy: Theory[END_REF] that the underlying preferences are characterized by a

V = ∑ V + 0 1 0 !
depends on the 5 attributes with + 0 being the value of each attribute k in each alternative j chosen by the respondent i facing a set t of choices.

The developments in the random utility models' applications over the past two decades included four points [START_REF] Manski | Daniel McFadden and the econometric analysis of discrete choice[END_REF][START_REF] Mcfadden | Conditional logit analysis of qualitative choice behavior[END_REF]. The first one is (1) the unobservable heterogeneity of preferences for each attribute [START_REF] Mcfadden | Conditional logit analysis of qualitative choice behavior[END_REF]. We select the specification of mixed logit of the form 0 = 2 0 + 3 0 , with 3 4 = 3 ! , … , 3 0 , … , 3 1 a Gaussian vector of zero mean and a var-cov matrix, Ω. This specification has a triple advantage of: testing the unobservable heterogeneity of preferences for each attribute j using the diagonal elements of Ω; identifying the interdependence of preferences between the different attributes j using the signs of the correlation coefficients deduced from this matrix Ω; and releasing the Independence of the Irrelevant Alternative (IIA) hypothesis [START_REF] Hensher | The implications on willingness to pay of respondents ignoring specific attributes[END_REF].

The second point deals with (2) the functional form of preferences. We opt for non-linear forms. We tested several forms by coding all quantitative attributes as qualitative explanatory variables (with modalities), and by applying linear, quadratic and logarithmic transformation.

Given the estimates, we selected the quadratic specification, which is the most adequate according to the BIC criterion (Tables available upon request). For each quantitative attribute noted + 0 , its utility is then specified in the form V + 0 = 0 + 0 + 07 + 0 7 , which can be concave/convex depending on the sign (-/+) of 07 where the first derivative V 8 9 = 0 + 2 07 + 0 informs on the variations in preferences according to the level of this attribute and to -0 2 07 ⁄ its inflection point 2 .

The third point (3) is to know if the level of importance or attention that respondents give to attributes when making their choice biases the protocol (Attribute Non-Attendance; ANA; [START_REF] Hess | Using conditioning on observed choices to retrieve individualspecific attribute processing strategies[END_REF][START_REF] Hess | It's not that I don't care, I just don't care very much: confounding between attribute non-attendance and taste heterogeneity[END_REF]). Two solutions are usually proposed. The first solution is to use adequate econometric specifications to take into account the importance of each attribute endogenously. This method, called inferred ANA, is applied when the authors fail to measure the importance given to each attribute by each individual when administering the survey. Scarpa et al. [START_REF] Scarpa | Modelling attribute non-attendance in choice experiments for rural landscape valuation[END_REF], Hensher and Greene [START_REF] Hensher | Non-attendance and dual processing of common-metric A latent class specification[END_REF], Hess et al. [START_REF] Hess | Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies[END_REF], Hole [START_REF] Hole | A discrete choice model with endogenous attribute attendance[END_REF], Campbell et al. [START_REF] Campbell | Non-attendance to attributes in environmental choice analysis: a latent class specification[END_REF], and Hensher et al. [START_REF] Hensher | Inferring attribute non-attendance from stated choice data: implications for willingness to pay estimates and a warning for stated choice experiment design[END_REF] used discrete distributions (models with classes), and Hensher et al. [START_REF] Hensher | Accounting for attribute non-attendance and common-metric aggregation in a probabilistic decision process mixed multinomial logit model: A warning on potential confounding[END_REF] proposed a more flexible specification allowing releasing the constraints of the parameters' equality between the classes [START_REF] Hole | Inferred vs. stated attribute non-attendance in choice experiments: A study of doctors' prescription behaviour[END_REF]. The second solution is to directly question respondents concerning the importance of each attribute (stated ANA; [START_REF] Alemu | Attending to the reasons for attribute non-attendance in choice experiments[END_REF]). Some authors added these questions after each set of alternatives [START_REF] Puckett | The role of attribute processing strategies in estimating the preferences of road freight stakeholders[END_REF], but this option has been criticized because it disrupts the individual's choice process and, consequently, complicates the questionnaire and the cognitive effort to complete it [START_REF] Hess | Using conditioning on observed choices to retrieve individualspecific attribute processing strategies[END_REF][START_REF] Caputo | Attribute non-attendance or attribute-level non-attendance? A choice experiment application on extra virgin olive oil[END_REF][START_REF] Colombo | What are the consequences of ignoring attributes in choice experiments? Implications for ecosystem service valuation[END_REF][START_REF] Hess | Asymmetric preference formation in willingness to pay estimates in discrete choice models[END_REF][START_REF] Carlsson | Dealing with ignored attributes in choice experiments on valuation of Sweden's environmental quality objectives[END_REF]. Also, this solution may lead to answers that are not consistent with the decision taken. For these reasons, in our study, we adopt the second solution by proposing respondents a 5-level Likert scale 3 for each attribute at the end of our questionnaire.

The fourth and final point deals with (4) the impact on the evaluation of the respondents' heterogeneity. During prior discussions with the maritime professionals, the only notable heterogeneity observed were the differences between captains and watch officers, and between the types of ship (e.g., cargo, tanker). Consequently, we re-estimated our different models by stratifying our sample according to these two variables, but only by retaining the majority group 4 .

Few variations of the results are observed.

Based on these four points, in our study, each estimated model is based on a mixed logit model specification with a non-linear random utility function of the form U = V + ε with

Results

Respondents Characteristics

The survey was conducted in June 2019. In total, 67 respondents completed the questionnaire, which represents 19.7% of the sample size. The sample frame was composed of captains and watch officers of leading French shipping companies (N=6). The proportion of captains and watch officers that responded to the questionnaire was similar (Table 2). No crew navigating on ships below 100m in length answered the questionnaire, which is not surprising as few of these ships are represented in the shipping companies surveyed. Most of the respondents belonged to cargo ships of sizes ranging from 100m to 250m (Fig. 3). The distribution of responses to the 5-point Likert scale is given in Table 3. One can notice that the table is asymmetric and skewed to the right. This means that captains and watch officers give a high value, and, therefore more importance to all attributes. None of them give a value lower than 3 to all the attributes; 62.69% give at least the value 3 to the four discriminating attributes (TRI, AOI, AS, SRS). By only considering AS and SRS, 97% of the respondents give these mitigation solutions a value greater than or equal to 3, and 95.52% the value of 5 (Supplementary Material). These results indicate that prior discussions with maritime professionals helped build a robust questionnaire. Consequently, the attributes selected for this survey are relevant to the shipping industry and did not bias our study.

Model Results

Table 2 summarizes the best-fitted models. The model6 M1.a is the most relevant one, because its BIC value is the lowest (1042). On one hand, the model M1.a shows that the preferences of the interviewees are subject to non-observable heterogeneity (i.e., the values of the standard deviations are significant for all the attributes). On the other hand, the model demonstrates that preferences are variable according to the attribute level (Fig. 4). Regarding situational characteristics attributes, results show that the crew preference positively increases with distance travelled (TD) attribute (Fig. 4). In other words, the greater the distance, the less reluctant the crews of surveyed companies are to implement a whale-ship collision mitigation solution. Similarly, the preferences for the time of reception of the information (TRI) are positive up to an inflection point at 15.19h. This result means that the crew prefers to have upstream information about an area of interest (AOI) up to a point (15.19h) where there is no added value of having more time to prepare the journey. Not surprisingly, the crew prefers AOI limited in size. Each additional nautical mile to the AOI results in a loss of utility of 0.00345.

For the whale-ship mitigation solutions, we find that the shipping industry is well aware of the underlying issues and is not reluctant to either solution (the estimated parameters are positive and more than 99% reliable). However, their preferences for speed reduction sharply decreases when the reductions imposed are too high-exceeding the inflection point of 11.94% of speed reduction) -and become negative when they exceed 30.39%. We can use our estimates to compare the trade-offs between the preferences of the two mitigation solutions. In Figure 4, the curve of the utility function of the attribute SRS is higher than its value for the attribute AS for speed reductions between 7.9% and 16% (the two roots of polynomial _`_ = a b_ ⟺ 0.074 × _`_ -0.0031 × _`_ 7 = 0.391). This means that avoidance is preferable in these cases (when the speed reductions are too high above 16 and too weak below 7.9). Undoubtedly, avoidance is an opportunity for ships to take other alternative shipping routes that are just as beneficial to their commercial activity. Further, our mixed logit specification M1.b with a dependent multivariate Gaussian distribution allows estimating the correlation between attributes due to the unobservable heterogeneity of preferences It should be noted that, we refer to the results of this model without forgetting that its BIC value is higher than the one from the M1.a specification (hij k!.l = 1042 m hij k!.n = 1091). For mitigation solutions, these first results highlight a significant negative correlation (more than 99% reliable depending on the bilateral Student's t-test), which demonstrates a form of substitutability between the two solutions. In other words, maritime transport professionals prefer when they are given a choice between the two solutions.

Depending on the situational characteristics, they can decide on their own to avoid or reduce their speed.

Discussions and Conclusions

Shipping Industry's Preferences

In this research, we performed an empirical evaluation of the shipping industry's preferences for mitigation solutions using a Choice Experiment. We surveyed ships' crews, composed of captains and watch officers. The crew takes navigational decisions based on their training, international regulation, but also based on instructions of its companies regarding schedules and economic aspects [START_REF] Sèbe | A decision-making framework to reduce the risk of collisions between ships and whales[END_REF][START_REF]Lloyd's Register, FSA for vessels crossing TSS and precautionary areas in Singapore Strait, Part 1[END_REF]. Consequently, their choices reflect the overall policies and constraints of the shipping industry. Therefore, the crew preferences are reflective of shipping industry behavior regarding mitigation solutions.

The avoidance solution appears to be the ship crews' most appreciated solution -with some exceptions. This observation corroborates other findings of compliance in the literature regarding these kinds of solutions. The avoidance of an area of interest can be undertaken either by TSS or ATBA. Regarding TSS, Lagueux et al. [START_REF] Lagueux | Response by vessel operators to protection measures for right whales Eubalaena glacialis in the southeast US calving ground[END_REF] found a 96.2% rate of compliance with recommended routes in the right whales' southeast US calving ground (in Georgia and Florida). Similarly,

McKenna et al. [START_REF] Mckenna | Response of Commercial Ships to a Voluntary Speed Reduction Measure: Are Voluntary Strategies Adequate for Mitigating Ship-Strike Risk?[END_REF] showed compliance rates of between 79% and 89% with the TSS for blue whales protection off the coast of southern California. The compliance with ATBA also seems to align with our results; Vanderlaan and Taggart [START_REF] Vanderlaan | Efficacy of a voluntary area to be avoided to reduce risk of lethal vessel strikes to endangered whales[END_REF] found a 71% rate of compliance with the voluntary ATBA in Roseway Basin right whale habitat. Speed reductions are not favoured by our respondents nor do they enjoy high levels of compliance in the literature. Several studies found compliance below 33%, and in one case authors show a 1% rate of compliance [START_REF] Silber | The role of the International Maritime Organization in reducing vessel threat to whales: Process, options, action and effectiveness[END_REF][START_REF] Lagueux | Response by vessel operators to protection measures for right whales Eubalaena glacialis in the southeast US calving ground[END_REF][START_REF] Mckenna | Response of Commercial Ships to a Voluntary Speed Reduction Measure: Are Voluntary Strategies Adequate for Mitigating Ship-Strike Risk?[END_REF][START_REF] Freedman | The effectiveness of incentivized and non-incentivized vessel speed reduction programs: Case study in the Santa Barbara channel[END_REF][START_REF] Wiley | Effectiveness of voluntary conservation agreements: Case study of endangered whales and commercial whale watching[END_REF].

One exception that should be highlighted is in St. Lawrence Estuary where investigators found a 72% compliance rate with speed reductions. This case was remarkable for a high level of collaboration by stakeholders [START_REF] Parrott | Slow down and save the whales[END_REF]. Note the compliance often increase when a solution is mandatory (e.g., compliance from 9.8-23.2% to 75%; SRS; [START_REF] Lagueux | Response by vessel operators to protection measures for right whales Eubalaena glacialis in the southeast US calving ground[END_REF]) or with the application of incentives to comply with it (e.g., compliance from 13% to 77%; SRS; [START_REF] Mckenna | Response of Commercial Ships to a Voluntary Speed Reduction Measure: Are Voluntary Strategies Adequate for Mitigating Ship-Strike Risk?[END_REF]). Also, Weinrich et al. [START_REF] Weinrich | The effectiveness of dedicated observers in reducing risks of marine mammal collisions with ferries: A test of the technique[END_REF]noted that the avoidance solution was preferred in cases of last-minute decisions.

that the voyage plan "should be approved by the ships' [captain] prior to the commencement of the voyage" [START_REF] Imo | Guidelines for voyage planning -Resolution A[END_REF], and the captain should also validate significant modifications during the voyage (Varin, pers.

comm. 2019). It is then pivotal that watch officers know the information in advance in order to organize the journey and propose solutions to the captain, before departure, when the validated voyage plan does not constraint decisions. Our results are in line with those of Reimer et al. [START_REF] Reimer | Mitigating vessel strikes: The problem of the peripatetic whales and the peripatetic fleet[END_REF],

which show a preference for receiving information before leaving port, or within a few hours of arriving at the AOI (respectively 53% and 35% of the respondents). However, Reimer et al. [START_REF] Reimer | Mitigating vessel strikes: The problem of the peripatetic whales and the peripatetic fleet[END_REF] did not find a clear consensus on the best time to receive the information. In light of our results, this lack of consensus may be explained by the fact that Reimer et al. [START_REF] Reimer | Mitigating vessel strikes: The problem of the peripatetic whales and the peripatetic fleet[END_REF] did not make a distinction between captains and watch officers. Moreover, our study quantified the maximum reception time of information. As mentioned before, at one point -inflection point -there is no added value of having more time to prepare the journey (15.19h).

Also, not surprisingly, whatever the solution selected; the utility function increases with the distance covered meaning that collision mitigation measures are more likely to be complied with for longer voyages. This feature can be decisive for whale protection. For example, Mediterranean fin whales exhibit a coastal distribution in summer between France, Italy and Corsica, where whales are at risk of collisions with short-travel distance passenger ships [START_REF] Jacob | Characteristics of maritime traffic in the Pelagos sanctuary and analysis of collision risk with large cetaceans, From the report of Quiet Oceans and Institut EcoOcéans[END_REF]. For short-travel cruises, through preferences, our study shows that avoidance would meet more compliance from companies than the speed reduction solution (i.e., utility increases with shorter traveled distances). However, in winter, this population is partly suspected to inhabit more offshore waters, such as the ones of Western Sardinia [START_REF] Laran | Seasonal distribution and abundance of cetaceans within French waters -Part I: The North-Western Mediterranean, including the Pelagos sanctuary[END_REF]. In this area, ships travel longer distances, between, for example, France and Africa. Our results suggest that, if habitat modeling confirms fin whales' offshore winter distribution, the implementation of mitigation solutionseven speed reduction, to a lesser extend -should be effective thanks to potential high compliance. To be noted that the compliance depends on various factors such as enforcement [START_REF] Kirchler | Enforced versus voluntary tax compliance: The "slippery slope" framework[END_REF], and that our observations on the compliance are interpretations from the preferences highlighted in this study.

Implications For Conservation

Conservation scientists often propose the whale-ship collision mitigation solution that achieves the greatest benefit to whales when adopted. Speed reduction is one of the bestidentified solutions [START_REF] Vanderlaan | Vessel collisions with whales: The probability of lethal injury based on vessel speed[END_REF][START_REF] Parrott | Slow down and save the whales[END_REF] to reduce whale mortality due to collisions. This solution acts directly on the probability of mortality, whereas the avoidance solution acts on the probability of encounter -occurrence [START_REF] Vanderlaan | Reducing the risk of lethal encounters: Vessels and right whales in the Bay of fundy and on the Scotian shelf[END_REF]. Hence, the implementation of speed reduction guarantees having a positive impact on the risk of lethal collision, even in a data-poor environment. On the contrary, to be effective, avoidance solutions require an extensive understanding of whale habitat and distribution, which is not always the case. Therefore, conservation scientists are more prone to advocate for speed reductions, despite some recent reconsiderations on the subject [START_REF] Gonyo | An economic analysis of shipping costs related to potential changes in vessel operating procedures to manage the co-occurrence of maritime vessel traffic and whales in the Channel Islands region[END_REF][START_REF] Iwc | A Joint IWC-IUCN-ACCOBAMS workshop to evaluate how the data and process used to identify Important Marine Mammal Areas (IMMAs) can assist the IWC to identify areas of high risk for ship strike[END_REF].

While theoretically effective, the applied effectiveness of the speed reduction solution is often limited by a lack of compliance. In the literature, the risk reduction induced by solutions is often expressed by assuming full compliance from the shipping industry (e.g., [START_REF] Gonyo | An economic analysis of shipping costs related to potential changes in vessel operating procedures to manage the co-occurrence of maritime vessel traffic and whales in the Channel Islands region[END_REF][START_REF] Silber | Report of a workshop to identify and asess technologies to reduce ship strikes of large whales[END_REF][START_REF] Wiley | Modeling speed restrictions to mitigate lethal collisions between ships and whales in the Stellwagen Bank National Marine Sanctuary, USA[END_REF][START_REF] Vanderlaan | Reducing the risk of lethal encounters: Vessels and right whales in the Bay of fundy and on the Scotian shelf[END_REF]). However, this level of compliance is rarely met [START_REF] Mckenna | Response of Commercial Ships to a Voluntary Speed Reduction Measure: Are Voluntary Strategies Adequate for Mitigating Ship-Strike Risk?[END_REF]. According to the preferences highlighted in this paper, compared to speed reduction, the avoidance solution is more likely to be applied by the shipping industry, and therefore, might have a higher applied effectiveness.

The shipping industry's preferences, elicited by our choice experiment, advocate for the use of the avoidance solution instead of speed reduction, at least for professionals sharing our respondent characteristics. For our respondents, which navigate mainly Mediterranean and Atlantic waters, the primary preferences of the shipping industry and of the conservation scientists are therefore at odds. As a consequence, conservation scientists should integrate the shipping industry's preferences before presenting a mitigation solution to achieve more effective protection of whales. For short journeys, such as the ones in the summer habitat of Mediterranean fin whales, avoidance solutions should prevail, especially for ships providing cabotage services. This solution is logistically more efficient to offset time lost [START_REF] Brouer | The Vessel Schedule Recovery Problem (VSRP) -A MIP model for handling disruptions in liner shipping[END_REF]. According to our results, for offshore navigation, the type of mitigation solution matters less to the shipping industry, as longer distances allow for better offsetting of lost time.

However, to be able to offset the cost of any mitigation solutions, ships need to be advised in advance of the characteristics of the areas where whale collisions are likely. Whale habitat suitability models that are dynamically updated are needed provide this information with sufficient advanced notice. Whale habitat suitability models are becoming increasingly sophisticated with the integration of biotic (e.g., whale acoustic and observation) and abiotic (e.g., bathymetry; chlorophyll) parameters [START_REF] Becker | Predicting cetacean abundance and distribution in a changing climate[END_REF][START_REF] Mannocci | Extrapolating cetacean densities beyond surveyed regions: Habitat-based predictions in the circumtropical belt[END_REF][START_REF] Sigourney | Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty : insights from simulations and an application to fin whales (Balaenoptera physalus)[END_REF]. The emergence of Big data, using satellite information, opens-up the possibility of more frequent habitat updates or even near real-time updates [START_REF] Hampton | Big data and the future of ecology[END_REF][START_REF] Madon | Strike-Alert : towards real-time, high resolution navigational software for whale avoidance[END_REF][START_REF] Pimm | Emerging technologies to conserve biodiversity[END_REF]. Of course, the technology leading to a predictive tool is not yet operational, and, meanwhile, other promising options of alerting can be implemented such as acoustic networks (e.g., Boston harbor; [START_REF] Ritter | Collisions of vessels with cetaceans -The underestimated threat[END_REF]) or observation networks (e.g., REPCET; [START_REF] Couvat | Evaluation of the technical solutions and management measures implemented at the international level to reduce the risks of collisions between ships and large cetaceans[END_REF]). Reimer et al. [START_REF] Reimer | Mitigating vessel strikes: The problem of the peripatetic whales and the peripatetic fleet[END_REF] showed that crew's have a preference for receiving such updated information via their Automatic Identification System (AIS). The AIS presents the advantage of not being disruptive of the crews' activities. Nevertheless, several limitations to the incorporation of whales' areas of interest to the AIS have yet to be overcome [START_REF] Mcgillivary | Enhancing AIS to Improve Whale-Ship Collision Avoidance and Maritime Security, Ocean[END_REF].

While our study highlights the required features for mitigation solutions, the resulting compliance with them might still be low if their implementation is not standardized. This aspect has recently been highlighted for greenhouse gas emission reduction solutions. Shipping companies appear to approve speed reductions for decreasing these emissions, but request a clear regulation to act [START_REF] Psaraftis | Speed optimization versus speed reduction: Are speed limits better than a bunker levy?[END_REF]92]. A regulation would dictate the implementation of the solution to the entire fleet of a region, and avoid the loss of competitiveness that countries' unilateral implementation would bring [START_REF] Gritsenko | Governing shipping externalities: Baltic ports in the process of SOx emission reduction[END_REF]. This reasoning applies to whale-ship collision solutions, as expressed by a former captain at the International Conference on Marine Mammal Protected Areas [START_REF] Icmmpa | Proceedings of the fifth international conference in marine mammal protected areas[END_REF]. During a public conference, this former captain criticized the implementation of a mitigation solution (REPCET) to only French ships: "We can't have a rule that applies to a French vessel and not to an Italian vessel, this is discrimination. We need solutions at the International Maritime Organization level" (Varin, pers. comm. 2019).

The International Maritime Organization (IMO) represents then a promising way of implementing standardized approaches and evaluating the most efficient solutions. The IMO is a United Nations agency, which regulates all aspects of maritime safety and protection of the marine environment [START_REF] Silber | The role of the International Maritime Organization in reducing vessel threat to whales: Process, options, action and effectiveness[END_REF] and is recognized as the authority in international shipping [START_REF] Geijer | A network approach to migratory whale conservation: Are MPAs the way forward or do all roads lead to the IMO?[END_REF]. The IMO "produces conventions which become law when they are enacted by each maritime state" (p. 656; [START_REF] Stopford | Maritime Economics -3rd edition[END_REF]).

While the implementation of voluntary mitigation solutions often meets low compliance, the IMO's involvement through mandatory or recommended measures -mostly TSS and ATBAhas been proven to be effective in increasing the compliance [START_REF] Geijer | A network approach to migratory whale conservation: Are MPAs the way forward or do all roads lead to the IMO?[END_REF]. Furthermore, as the Roseway Basin seasonal ATBA exhibits, the IMO recommendations can be flexible [START_REF] Imo | Area to be Avoided "In Roseway Basin, South of Nova Scotia" -Any other business[END_REF]. This flexibility can be used to provide the best-suited solutions based on the preferences of the shipping industry and whale habitat dynamics.

Our study may also contribute to other IMO mechanisms that can be adapted to collision management, such as Particularly Sensitive Sea Areas (PSSA). A PSSA is "an area that needs special protection through action by IMO because of its significance for recognized ecological, socio-economic, or scientific attributes where such attributes may be vulnerable to damage by international shipping activities" [START_REF] Imo | Revised guidelines for the identification and designation of Particularly Sensitive Sea Areas -International Maritime Organization -ASSEMBLY/A 24/Res[END_REF]. Crew' preference can be used, for example, in the ongoing reflection for a PSSA in the Mediterranean [START_REF] Imo | Identification and protection of special areas and PSSAs. Information on recent outcomes regarding minimizing ship strikes to cetaceans[END_REF]. This PSSA should cover a large part of the Western Mediterranean, and could -according to guidelines -combine several solutions inside the PSSA. Depending on the different types of navigation within the potential PSSA, various solutions can be considered based on the preferences of the shipping industry to optimize their compliance.

Further Research

Further research is required to improve our understanding of the shipping industry's preferences. For example, our results highlight that the utility for small speed reductions, below 7.9%, is inferior to moderate speed reductions -7.9% to 16% -for which the utility is even superior to the avoidance solution. These preferences might be linked to how the ships and the industry operate [START_REF] Kite-Powell | Economic aspects of right whale ship stike management measures, Final project report to the National Marine Fisheries Serivce[END_REF][START_REF] Psaraftis | Speed models for energy-efficient maritime transportation: A taxonomy and survey[END_REF][START_REF] Eriksen | The branching-course model predictive control algorithm for maritime collision avoidance[END_REF]. For example, small changes in speed might be logistically challenging, as modifications might be required in the engine room. These modifications might be worth doing only for larger changes in speed [START_REF] Psaraftis | Speed models for energy-efficient maritime transportation: A taxonomy and survey[END_REF]. These results might also indicate that the shipping industry prefers to be given a choice, rather than to have a solution imposed.

In addition, more ship and crew characteristics should be investigated to improve our results.

Most of the survey respondents in our study operate small to medium-size cargo ships, which are typical of short-haul trips (Fig. 2; [START_REF] Jacob | Characteristics of maritime traffic in the Pelagos sanctuary and analysis of collision risk with large cetaceans, From the report of Quiet Oceans and Institut EcoOcéans[END_REF]). Consequently, respondents' answers for longer journeys might be biased. Also, on one hand, more than 20% of the respondents operate passenger ships that are more likely to undergo damages following whale-ship collision [START_REF] Sèbe | Reducing whale-ship collisions by better estimating damages to ships[END_REF] and, therefore, the crew presumably might be more careful in avoiding obstacles, such as whales. On the other hand, passenger ships' contribution to deadly whale collisions are known to be higher than that of other ship categories [START_REF] Jacob | Characteristics of maritime traffic in the Pelagos sanctuary and analysis of collision risk with large cetaceans, From the report of Quiet Oceans and Institut EcoOcéans[END_REF], leading to a higher focus of conservationists on passenger ships. Hence, the crew of other categories of ships might be less aware of the whale-ship collision issue, because conservationists raised less awareness of the crew in the categories. These heterogeneity factorsamongst others -might influence our results and require further investigation. Future studies using applied route choice data might help refine our understanding of the shipping preferences, as it is done for fishery management [START_REF] Hynes | Discrete choice modelling of fi sheries with nuanced spatial information[END_REF][START_REF] Berman | Modeling Spatial Choice in Ocean Fisheries[END_REF].

Our survey also was carried out on a limited number of respondents -mainly due to the limited sample frame that represents captains and watch officers. To be noted, we used a Defficient design, which requires a smaller sample size to estimate all parameters at the level of statistical significance [START_REF] Rose | Sample size requirements for stated choice experiments[END_REF], as it has been shown in previous studies [START_REF] Puckett | Preferences for alternative short sea shipping opportunities[END_REF][START_REF] Remoundou | Valuing climate change mitigation : A choice experiment on a coastal and marine ecosystem[END_REF]. Contacting other shipping companies might be decisive to overcome the lack of heterogeneity in respondents.

Further research expanding the sample to areas outside the Mediterranean and Northeastern

Atlantic is required to have an overview of the shipping industry's preferences at the international level. Also, while we tested several specifications in our study, other specifications might be adapted to this kind of survey (e.g., endogenous NAA; [START_REF] Hole | Inferred vs. stated attribute non-attendance in choice experiments: A study of doctors' prescription behaviour[END_REF]).

CONCLUDING REMARKS

To sum up, our study highlighted some features to define best-suited whale-ship collisions mitigation solutions. In the end, our results could be used as guidelines to solution proposals based on the situational characteristics of a studied site; and help conservationists propose solutions that are the most likely to meet the shipping industry's compliance. In most situations, avoidance is the preferred operational solution, and might reach the highest applied effectiveness due to a potential high compliance. However, in order to improve the solutions' effectiveness, receiving information in advance about the area of interest location and size is required. Also, the spatial information regarding the area of interest location and size should be more precise (model resolution) to improve compliance. These features emphasize the need for improved habitat suitability modeling, or other tools to define the areas of interest; that currently often do not meet the required precision to help the shipping professionals to decide on the best option to avoid whales. The emergence of Big Data and predictive modeling is likely to improve these models, and improve collision management.

Figure 1 .

 1 Figure 1. Conceptual illustration of the questionnaire. Note that the AS can be combined with the SRS in the questionnaire. TD = Travel distance; TRI = Time of information reception; AOI = Size of the area of interest; AS = Avoidance solution; SRS = Speed reduction solution. The situational characteristic attributes are in black; mitigation solutions are in white. Conception: Authors.

Figure 2 .

 2 Figure 2. Example of a choice set. Respondents were presented with 8 unique choice sets, each composed of different alternatives. Conception: Authors.

  separable additive utility function U = V + ε with ε corresponding to its random component. The probability of choosing the j th alternative is then characterized by the logit specification Pr y = j| = e ∑ e ! ⁄ if the random component have cumulative density function of Gompertz #$% &'( ) = *+, -*+,$% &'( ).. The non-stochastic component of utility

V

  = β ! TD + β !7 TD 7 + β 7 TRI + β 77 TRI 7 + +β AOI + β 7 AOI 7 + +β B AS + β 1 SRS + β 17 SRS 7 . The vector β = β ! , … , β D , … , β 1 ′ follows a multivariate Gaussian law N b, Ω of density f β b, Ω ⁄ . The parameters of the model b, Ω are estimated by maximizing the expectation of the likelihood of the observations according to the density f . of the unobservable heterogeneity, L b, Ω = E LMN LL β N. This expectation is calculated by the simulation method 5proposed by Train[START_REF] Train | Discrete choice methods with simulation[END_REF] using 500 random draws of the vector of the parameters β O according to the density f . . The simulated likelihood L * b, Ω = the individual i chooses the alternative j among the set of options t conditional on the fact that the preferences of this individual are subject to an unobservable heterogeneity characterized by density distribution Z . .

Figure 3 .

 3 Figure 3. A Marimekko chart in which the percentage of ships in each size class are divided among the different types of vessels. Conception: Authors.

Figure 4 .

 4 Figure 4. Correlation and variation in the utility of the attributes. The AS attribute in the illustration is constant (a). Its variation is dependent on other attributes (see (b)). Conception: Authors.

Table 1 .

 1 List of attributes and levels

			Number of
	Type of attribute	Attributes a		Levels b
			levels
	Situational characteristics	Travel distance (TD)	4	100 nm; 300 nm; 500 nm; 700 nm
				24h before port departure; 12h before port
		Time of reception of the
			4	departure; 1h before port departure; 1h before
		information (TRI)
				arrival on AOI;
		Size of the area of interest		2.5 nm; 8 nm; 14 nm; 26 nm
			4
		(AOI)
	Mitigation solutions	Avoidance solution (AS)	2	Yes; No
		Speed reduction solution		No speed reduction (0%); 18kn to 14kn (20%);
			4
		(SRS)		23kn to 16kn (30%); 20kn to 12kn (40%)

a The variables names as used in the model are in parentheses

b

The percentages of reduction in speed are in parentheses. These percentages were not visible in the questionnaire

Table 2 .

 2 Respondents characteristics expressed in

	percentages
	Respondents' characteristics	Share
	Response rate	19.7%
	Captains	44.8%
	Watch officers	55.2%

Table 3 .

 3 Respondents' perception of the importance of the attributes

			Slightly	Moderately		Very
		Not important			Important
			important	important		important
	Travel Distance (TD)	16.42%	16.42%	17.91%	37.31%	11.94%
	Time of reception of the information
		4.48%	19.40%	13.43%	34.33%	28.36%
	(TRI)
	Area of interest size (AOI)	0.00%	1.49%	14.93%	43.28%	40.30%
	Avoidance (AS)	2.99%	5.97%	11.94%	40.30%	38.81%
	Speed reduction (SRS)	4.48%	2.99%	14.93%	26.87%	50.75%

Table 4 .

 4 Mixed logit results (online discrete choice experiment with 67 respondents, 1608 observations). In bold, the model selected for the study. Abbreviations: TD = Travel distance; TRI = Time of reception of the information; AOI = Area of interest; AS = Avoidance solution; SRS = Speed reduction solution. Subscript numbers correspond to the

	subscript in the utility function.
		ML, U linear		ML, U quadratic				ML with only significant parameters
	Attributes	Independant		(M0.a) Independant	(M0.b) Dependant Ω [	(M1.a) Independant	(M1.b) Dep. .Ω [
		2 \	] ^	2 \	] ^	2 \	] ^	2 \	] ^	2 \	] ^
		(pvalue)	(pvalue)	(pvalue)	(pvalue)	(pvalue)	(pvalue)	(pvalue)	(pvalue)	(pvalue)	(pvalue)
	TD	0.00172	-0.0022	0.00277	0.00249	0.00262	0.00301	0.00169	0.00261	0.00210	0.00299
		(0.000)	(0.000)	(0.044)	(0.000)	(0.068)	(0.000)	(0.001)	(0.000)	(0.000)	(0.000)
	TD 2*			-1.3e-06		-7.9e-07
				(0.486)		(0.689)
	TRI	0.0284	0.0507	0.133	0.0592	0.145	0.0496	0.141	0.0594	0.144	0.0516
		(0.011)	(0.001)	(0.000)	(0.000)	(0.000)	(0.005)	(0.000)	(0.000)	(0.000)	(0.004)
	TRI 2*			-0.0042		-0.0048		-0.00464		-0.00470
				(0.0059)		(0.003)		(0.000)		(0.000)
	AOI	-0.0345	0.0478	-0.0764	0.0576	-0.0712	0.0543	-0.0700	0.0584	-0.0810	0.0466
		(0.010)	(0.051)	(0.032)	(0.009)	(0.059)	(0.005)	(0.000)	(0.009)	(0.000)	(0.028)
	AOI 2			0.00041		-0.00037
				(0.751)		(0.785)
	AS (coding effect)	0.113	0.586	0.385	0.674	0.413	0.668	0.391	0.653	0.439	0.549
		(0.303)	(0.000)	(0.005)	(0.000)	(0.006)	(0.000)	(0.002)	(0.000)	(0.002)	(0.035)
	SRS	-0.0248	0.0627	0.0738	0.0847	0.0742	0.0354	0.0740	0.0863	0.0783	0.0420
		(0.013)	(0.000)	(0.005)	(0.000)	(0.008)	(0.039)	(0.001)	(0.000)	(0.002)	(0.029)
	SRS 2			-0.0029		-0.0033		-0.00310		-0.00326
				(0.000)		(0.000)		(0.000)		(0.000)
	BIC	1065		1056		1103		1042		1091

* Estimated parameters 2 \ of these variables are not significant in both model M0. a/b and M1. a/b. Therefore, there are removed in the last model.

The level of the attribute at which preferences change directions of variation, increasing first, and then decreasing.

Furthermore, disregarding the importance of each attribute in fact skews the results of the estimates and distorts the willingness to pay that can be deduced from them, in particular when the monetary attribute is ignored[START_REF] Scarpa | Modelling attribute non-attendance in choice experiments for rural landscape valuation[END_REF]. Note that there is no monetary attribute in our assessment and there is no question of willingness to pay.

The number of respondents is not large enough. However, this is not a sample. We cannot, therefore, venture to estimate our models for all the sub-groups, which therefore have very few respondents.

The estimation of this model is carried out using the Stata estimation procedure developed by Hole.

As mentioned in the previous section, other models have been estimated (Conditional Logit, Mixed Logit with different transformations of quantitative attributes). The results tables are large and provide little additional information. The BIC values are significantly higher (available to the readers upon request).

We warmly thank the shipping companies' environmental officers who allowed

Economically, the preference for avoidance appears to make sense. Gonyo et al. [START_REF] Gonyo | An economic analysis of shipping costs related to potential changes in vessel operating procedures to manage the co-occurrence of maritime vessel traffic and whales in the Channel Islands region[END_REF] studied the variation in costs between the avoidance and speed reduction solutions. While the implementation of the speed reduction solution decreases transportation cost -linked to fuel consumption -, the inventory-carrying costs do increase. In opposition, there is a slight decrease in the inventory carrying and the transportation costs for the avoidance solution. Additional transit time appears to be a driving factor in the lack of preference for speed reduction. Speed reduction will have positive economic impacts because fuel consumption per kilometres decreases with lower speeds [START_REF] Bialystocki | On the estimation of ship's fuel consumption and speed curve: A statistical approach[END_REF]. On the other hand, speed reduction results in longer transit times and may require increasing the speed outside the area of interest to offset lost time.

Because slow steaming speeds are different than normal operational speeds, adapting to speed reductions could require a reconfiguration of the engine to achieve an efficient lower power output [START_REF] Psaraftis | Speed models for energy-efficient maritime transportation: A taxonomy and survey[END_REF]. Furthermore, the distance between harbors might be too short to offset lost time, and supplementary constraints might appear (e.g., delays of arrival in port, disrupted land transportation; loss of docking; [START_REF] Associates | Economic analysis of North Atlantic right whale ship strike reduction rule, A report prepared for the NOAA[END_REF][START_REF] Kontovas | Reduction of emissions along the maritime inter modal container chain: Operational models and policies[END_REF][START_REF] Kontovas | The link between economy and environment in the postcrisis era: lessons learned from slow steaming[END_REF]). These parameters lead to an increase in inventory carrying costs (i.e., costs unrelated to fuel consumption). On the other hand, the avoidance solution could increase the transit time by increasing the distance traveled. However, a slight increase in operational speed can offset lost time, which explains the low additional costs.

Logistically, our results highlight the importance of receiving early information (TRI) about the characteristics of high-density whale areas, especially for watch officers. The sooner the information is received, the more it will impact the utility function positively. When we know the role of each crew member (e.g., captain, watch officer; Supplementary Material), we underline that the TRI is significant for the watch officers, but not for the captains. These results confirm the hypothesis collected during informal interviews regarding voyage planning; the watch officers formulate a voyage plan at least 12 hours before departure, and the captain validates it around one hour before departure. Our results confirm this feature, as the delay of reception of the information is more important to watch officers than for captains. Besides, regulations impose