Tenth AIMS International Conference on Management
January 6-9, 2013
An Expected Consequence Approach to Inter-continental Transportation
of Crude Oil
Atiq Siddiqui
Manish Verma
atiq.siddiqui@mun.ca
mverma@mun.ca
Memorial University of Newfoundland
1. Introduction
Maritime transportation is one of the most important modes carrying crude oil and petroleum products trade
(Rodrigue et. al. 2009). This enormous global transportation operation is accompanied by incidents leading to
oil spills resulting in significant environmental, social and economic consequences. The most prominent of such
an incident is the Exxon Valdez case (Alaska, USA in 1989) which led to a cleanup cost of over 2 billion dollars
alone. Fortunately such catastrophic episodes are infrequent, though there are numerous occurrences of smaller
spills which are also a source of concern. The latter phenomenon is also underlined by the latest figures released
by International Tanker Owner Pollution Federation viz. around 10,000 spills from 1974 to 2008 (ITOPF 2009).
In response to the catastrophic accidents such as Exxon Valdez, several legislations have been adopted e.g.
MARPOL by International Maritime Organization (IMO) that covers pollution of the marine environment from
operational/accidental causes (IMO 2011), the Erika packages for maritime safety (EU Legislations 2011) by the
European Union, and the 1990 Oil Pollution Act (OPA) in the United States (Douligeris et. al. 1997). Such risk
control measures have, in part, been supported by the five-step Formal Safety Assessment (FSA) methodology
developed by the IMO, that makes use of accident frequency of extremely remote, remote, reasonably probable,
and, frequent levels; and the four consequence levels (i.e. minor, significant, severe, and catastrophic) to
categorize various risk scenarios (Kontovas & Psaraftis 2009, IMO 2002). Recent trend suggests increased FSA
compliance in risk assessment research to ensure practicability e.g. see Hu et al. (2007), Safdor (2008a, 2008b)
and Martins & Maturana (2010), however, mainly applied in the ship design and personal training areas.
Although risk assessment within maritime transportation has been active research area, and we review it in the
following section, it is fair to say that most of the works deal with localized setting and hence have limited use
for intercontinental routing of crude oil tankers. Through this work we aim to close the gap by proposing a novel
risk assessment methodology for global transportation of crude oil.
The rest of the paper is organized as follows: Section 2 reviews the relevant literature, followed by the risk
assessment methodology in Section 3, and a discussion on parameter estimation in Section 4. The proposed
methodology is used to solve a realistic problem instance in Section 5, followed by the conclusion in Section 6.
2. Literature Review
Hazardous materials (hazmat) are integral to our industrial lifestyle, and almost always are consumed away from
the point of production. It is interesting to note that although hazmat transportation has been a very busy
research area over the past two decades, the focus has been mostly on highway and railroad transportation
(Erkut et. al. 2007). This is all the more surprising given the widespread use of maritime links to transport a
whole variety of hazmat, including chemicals and petroleum products. The existing works can be grouped under
two threads: assessment models; and, parameter estimation.
Assessment
In a study related to modeling the operational decisions, Iakovou (2001) proposed an analytical framework that
enables risk analysis and routing decisions. Most of the published works around this thread involve the
development and use of U.S. Natural Resource Damage Assessment Model for Coastal and Marine
Environment (NR-DAM/CME), originally proposed in Grigalunas et al. (1988). Douligeris et al. (1997)
proposed a framework to assess risk of oil transportation in the Gulf of Mexico, where the cost of risk for each
route is the weighted sum of the cost of risk of the surrounding zones. Subsequently, Yudhbir and Iakovou
(2001) built on the conceptual framework outlined by Wheeler (1993), and proposed an event-tree approach to
assign cost of risk based on the spill-size scenario.
Parameter Estimation
Soares and Teixeira (2001) discuss the different sources of risk while presenting statistics for various ship types,
while Fowler and Sørgård (2000) propose a marine accident risk model, developed under the aegis of the
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January 6-9, 2013
European Commission, to analyze accident related factors. Hu et al. (2007) make use of the FSA based risk
model to identify and rank sources of risk. In a recent high level oil tanker (60,000 DWT) FSA IMO study
(Safdor 2008b, Safdor 2008a), the analysis suggests the safety level of modern ships to lie within the ALARP
(As Low As Reasonably Practicable) tolerable limits set by IMO. Another area of research within this stream
focused on spill related cost estimation, wherein Etkin (1999, 2000), Vanem et al. (2008), and Shahriari and
Frost (2008) are amongst some of the notable contributors. Yamada (2009) made use of the IOPCF (2008)
database to propose a non-linear regression model between total cost and weight of oil spill. This effort was
followed by Kontovas et al.(2010), who considered current prices and removed outliers thereby improving the
correlation coefficient between the dependent and independent variables. It is important to note that each of the
work has its own limitations stemming from factors such as dependency on limited/incomplete databases and
restricted modeling assumptions.
To the best of our knowledge, none of reviewed works proposes a risk assessment methodology that
incorporates geographical differences in terms of accident rates and expected cost in order to determine the costof-risk applicable to the determination of the intercontinental routes for crude oil tankers. Focusing this void, we
next outline the methodology necessary to assess transport risk (as a dollar amount) on international maritime
links.
3. Risk Assessment Methodology
In this section, we first analyze the empirical oil spill data to understand the nature of oil tanker accidents, which
is then used to outline the proposed assessment methodology.
Tanker Accidents
We first analyze the oil-spill statistics made available by Environment Canada (ESTC 2011) and ITOPF (2009).
While the former database lists only 743 incidents all of size 136 tonnes and above which are obtained of the
period of 1978-2010; the ITOPF database provided details on 9640 incidents over a period of twenty-five years
(i.e., 1974-2008). On further analysis of the ITOPF database, we noticed that 7845 incidents were less than or
equal to 7 tonnes, and 1795 over 7 tonnes (including 460 over 700 tonnes). Though 81% of the spills were less
than 7 tonnes, the exact quantity spilled is not specified perhaps because spills in this category mainly result
from operational factors and not much emphasis is placed on good reporting (Huijer 2005). It was reported that
a total of 5.71 million tonnes was lost in spills, but one could deduct that fewer than 7% spills exceeded 5000
tonnes, and that the average spill size was approximately 3,181 tonnes (Figure 1).
0.02
0.018
0.90
Relative Frequency
0.016
Relative Frequency
0.80
0.70
0.60
0.50
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0.40
0
5000
0.30
25000
45000
More
Spill Size (Tonnes)
0.20
0.10
0.00
7
21000
53000
85000
117000
149000
Spill Size (Tonnes)
Figure 1 Relative Frequency of Spill Size
Since we intend to propose a methodology that is in line with the FSA framework, and also to tide over the
limitations associated with detailed data unavailability, we group spills into two categories as presented in
Figure 2. Based on the FSA levels and associated characteristics, it is clear that operational related spills will not
result in voyage termination, whereas the remaining three levels would. Consequently, we designate them as
minor (m) and major (M), and propose them as surrogates for minor, and significant to catastrophic FSA levels,
respectively. The aforementioned implies that on any given link for a specified route, a crude oil tanker could be
in one of the following three states: passes it safely; meets with an accident resulting in a minor spills; and,
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Tenth AIMS International Conference on Management
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meets with an accident resulting in a major spill (and hence voyage termination). We make use of the three
possible states to develop the mathematical expression for measuring risk in section 3.2.
FSA Levels
Minor
Characteristics
Proposed Categories
Operational spillages, locally containable spills or
local damages resulting in small spills
Minor (m)
( 7 tonnes)
An accident requiring termination of voyage where
a significant-catastrophic spill has occurred
Major (M)
(> 7 tonnes)
Significant
Severe
Catastrophic
Figure 2 FSA Levels and Spill Categories
Risk Model
We propose an (undesirable) expected consequence approach, defined as the probability of accident times the
resulting consequence to measure total transport risk incurred by oil tanker haulage. Modeling with this
traditional risk approach, consider a tanker route-link l of known length. If plM and plm are the probabilities of
a tanker meeting with an accident, resulting in major ( S lM ) or minor spills ( Slm ) respectively, on link l, then the
transport risk posed by this tanker over link l can be represented by:
(1)
Riskl  plM SlM AClM  plm Slm AClm
*
where, ACl is the adjusted per unit oil-spill cost for link l, which we elaborate in section 4. It should be clear
that transport risk (or just risk) for a route composed of links l and l+1 is a probabilistic experiment, since the
expected consequence for link l+1 depends on whether the tanker meets with an accident on link l. The expected
consequence for link l+1 is (1  plM )( plM1SlM1 AClM1  plm1Slm1 AClM1 ) . To generalize, if there are s tanker route-links
over a route R, the corresponding expected consequence would be expressed as follows:
k
s 

Risk R  Risk1    Riskk  (1  p Mj 1 ) 
k 2 
j 2

(2)
Equation (2) implies that an oil tanker continues to travel as long as it does not meet with an accident causing
major spill. We now outline the technique for estimating various parameters used in eqn. (2).
4. Parameter Estimation
Accident Probability
Estimating tanker accident probabilities is challenging because of scarce and disparate data, inaccurate
information about type, size and route of vessels. This is especially more relevant on a global scale since exact
data reporting does not receive equal attention across different jurisdictions. For this case we propose an
estimation technique which processes network wide coarse historical data in a meaningful manner to deduct
results for a specific link. We parsed oil-spill statistics from 1974-2010, then the 1188 data points belonging to
the major category (i.e., exceeding 7 tonnes) are geographically identified on Marsden Squares (a global grid
formed by ten-degrees divides of longitude and latitude). Such representation provides an idea about the
different hot spots in the world, as well as, lets us assume homogeneous attributes within a given square. For
example, for the given period, a total of 135 major marine accidents happened in the square at the intersection of
60 degree longitude and 30 degree latitude. Clearly any route using this Marsden square is riskier than a square
with lower number of accidents.
If a Marsden square is treated as a link of any route, then (3) can be used to estimate the probability of a
marine accident resulting in major spill. For example, the indicated probability for link l is:
plM 
number of tanker accidents resulting in major spill on link l
total number of tanker voyages through link l
(3)
To estimate the denominator in (3), we make use of the global oil flow information from the ITOPF data
(figure 4). Subsequently, the flow density information and vessel capacity could be used to approximate the
number of tanker voyages through a specific Marsden square. For example for the year 2005, the total number
of voyages through the 300 million tonnes link is approximately equal to 1154, which is 300 million tonnes
divided by the average capacity of a VLCC tanker (i.e., 260,000 tonnes). Note that the total imports decreased
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Tenth AIMS International Conference on Management
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by 9.5% in 2010, and hence the number of voyages between the given supply-demand pair was only 1044.
Other flow densities can be converted into number of tanker voyages similarly.
Type
1
2
3
4
5
6
Annual Flow
(Million Tonnes)
0-10
100-200
10-50
200-300
50-100
>300
Figure 4 Oil Movement in 2005 (www.itopf.com)
For estimating the probability of accidents resulting in minor spill, since there is no information on the
location and size of these spills, we cannot adopt the approach outlined for major spills. Thus, we make use of
the historical split of 0.81 and 0.19 (Figure 1) to estimate this probability i.e. plm  0.81 plM / 0.19 . For example
in Marsden Square 103 the estimated plM is 5.07E-03, therefore the plm is estimated to be 2.18E-02.
Cost of an Oil Spill
As model presented in equations (1) and (2) uses spill size for both the major and the minor spill sizes, we resort
to average spill sizes in both cases i.e. for the major spill size we use SlM  3181 tonnes, as determined from the
data analysis presented in section 3.1, while as no reliable data is available for calculating minor spill size we
used the upper conservative limit of the category i.e. Slm  7 tonnes . We also looked at the full loss scenario
where we used given total cargo size as the value for SlM . To calculate the cost of spill for these given spill
sizes we resorted to the two models available in the literature i.e. The Etkin's (1999, 2000) and Kontovas et al.
(2010) models (Table 1). The Etkin (1999, 2000) is a linear model that incorporates factors such as oil-type,
spill size, spill location, spill strategy, and distance from shoreline. It is limited as it fails to capture the nonlinear relationship between spill-size and per unit spill cost, and does not estimate the total cost. On the other
hand Kontovas et al.(2010) belongs to the non-linear category, wherein the regression model is used to estimate
total cost based only on spill sizes.
Table 1 Modified Spill-Cost Expressions
Riskl  plM ( SlM AClM )  plm ( Slm AClm ) where:
Etkin (1999, 2000)
ACli  2.5Cli  ( SLO  OT  CLS  SSl )
Riskl  { plM  ( SlM )0.7280  plm  ( Slm ) 0.7280 }  51432
Kontovas et al. (2010)
5. A Problem Instance
The proposed assessment methodology was applied to a realistic size problem instance involving delivery of
light-crude oil from a supply point in the Persian Gulf to a demand location in the Gulf of Mexico (Figure 4).
There are two routes between the supply-demand locations, one through the Suez Canal and the other via the
Cape of Good Hope referred to as the North and South route, respectively. The customer has placed a demand
for 260,000 tonnes of light-crude oil, and the supplier has to dispatch a VLCC tanker that has an average speed
of 15 knots. The other details for the two routes are presented in table 2, whereas the estimated probabilities in
table 3.
Table 2 Attributes for the Two Routes
North
South
18
20
Distance (nautical miles)
9421
12096
Travel Time @ 15 knots
26 days, 4 hrs.
33 days, 13.5 hrs.
Number of Marsden Squares
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Table 3 Probabilities of Major and Minor Accidents on North and South Routes
North Route
South Route
Links/
Marsden Square
plm
plM
Links/
Marsden Square
plm
plM
1
2.18E-02
5.07E-03
1
2.18E-02
5.07E-03
2
2.18E-02
3.76E-05
2
2.18E-02
3.76E-05
3
7.95E-02
0.00E+00
3
3.53E-03
0.00E+00
4
7.95E-02
2.78E-03
4
3.53E-03
0.00E+00
5
7.95E-02
3.76E-03
5
3.53E-03
0.00E+00
6
7.95E-02
4.81E-03
6
3.53E-03
0.00E+00
7
7.95E-02
3.38E-03
7
3.92E-02
0.00E+00
8
7.95E-02
3.76E-04
8
3.92E-02
4.96E-04
9
7.95E-02
3.54E-03
9
3.53E-03
8.27E-04
10
9.17E-02
2.27E-03
10
3.53E-03
0.00E+00
11
9.17E-02
0.00E+00
11
3.53E-03
0.00E+00
12
9.17E-02
0.00E+00
12
3.53E-03
0.00E+00
13
9.17E-02
0.00E+00
13
3.53E-03
0.00E+00
14
9.17E-02
0.00E+00
14
3.53E-03
0.00E+00
15
9.17E-02
0.00E+00
15
3.53E-03
0.00E+00
16
9.17E-02
1.93E-02
16
3.53E-03
0.00E+00
17
2.33E-02
8.12E-04
17
3.92E-02
1.85E-03
18
2.33E-02
4.65E-03
18
3.92E-02
1.40E-03
19
3.92E-02
8.12E-04
20
3.92E-02
4.65E-03
Four links (or squares) are common to both routes, and while the route through the Suez Canal traverses
eighteen, the South route crosses twenty. It is important to note that six of the eighteen Marsden squares on the
North route, and twelve of the twenty on the South route did not witness a tanker accident resulting in major
spill. On the other hand, the remaining twelve squares on the North route appear to be riskier than the remaining
eight on the South route, which could be relevant in the determination of cost of spill.
As indicated earlier, we made use of the cost of spill models proposed in literature to estimate risk (in dollars)
for the two routes. Note that Etkin’s model (Etkin 2000) requires information on oil-type, location, shoreline
distance and the cleanup strategy, in addition to the spill size, and hence we introduce the relevant parameters.
Since we are dealing with light-crude oil, a correction factor of  62%  Cli and the most expensive cleanup
strategy is assumed, and we note that other scenarios can be generated similarly. Modified spill-cost expressions
from Table 1, together with route attributes from Table 3, were used to estimate the route risk (Table 4). For
each resulting dollar risk value, minor spill size ( slm ) was 7 tonnes, whereas major spill size ( SlM ) assumed two
distinct values: 3181 tonnes based on the historical database; and, 260K tonnes implying total loss from the
VLCC tanker. Hence, for each of the two major spill values, Table 4 depicts the results generated from using the
two spill-cost models, on each of the two routes.
Table 4 Risk (Millions of Dollars) on the Two Routes
Models
Etkin
Kontovas et al.
SlM size
North Route
South Route
Average
0.9248
0.2023
Total Loss
70.7110
15.6300
Average
1.1970
0.3442
Total Loss
23.0730
6.8789
With both models, we notice that the risk value for the North route which goes through the Suez Canal is
considerably higher than that for the South route which is 2675 nautical miles longer. Other factors being
constant, longer route would have resulted in higher risk, but not in this instance since as indicated earlier, the
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Tenth AIMS International Conference on Management
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links with non-zero probability of accident with major spills on the North route is much riskier than the links
with similar attributes on the South route. This is an important observation since decisions based purely on cost
could result in much higher expected damage and/or cleanup cost. On the other hand, the route through the Cape
of Good Hope would be preferred by a risk-averse decision maker, only if the expected decrease in insurance
premium offsets the increase in operational cost including higher in-transit inventory. In addition, the outlined
methodological steps enable a better understanding of the inherent risk, which could be pertinent for
ascertaining the incremental impact on insurance premiums for the given routes.
6. Conclusions
In this work, we have outlined an assessment methodology for estimating risk from intercontinental
transportation of crude oil. The expected consequence approach for assessing oil-tanker risk required
determining accident probabilities and consequence on various links of a given route. Subsequently the
methodology was used to study and analyze a realistic size problem instance involving maritime transportation
of crude oil from the Persian Gulf to the Gulf of Mexico.
For a given route, risk associated with oil spill depends on both the density of traffic and the cleanup costs in
different regions along the given route. This observation has a two-fold implication: first, risk-averse decision
makers will not necessarily chose the shortest (or cheapest) paths; and second, an understanding of the inherent
route risk could potentially facilitate oil-tanker operators negotiating insurance premiums. Furthermore, route
risk should be given consideration together with operational cost and the scheduling constraints in developing
routing plans for tankers, since they indirectly impact the bottom line of the firm. Finally, the predictive ability
of the indicated approach will improve significantly, if it is tested on good and detailed data.
7. References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Douligeris, C., Iakovou, E. & Yudhbir, L. 1997, "Maritime Route Risk Analysis for Hazardous
Materials Transportation", IFAC Transportation Systems - TS'97: A proceedings volume from the 8th
IFAC/IFIP/IFORS symposium, Chania, Greece, 16-18 June 1997IFAC, 1997, pp. 574.
Environmental Science Technology Centre (ESTC) 2011, 07/13/2011-last update, Environmental
Science Technology Centre (ESTC) Tanker Spill Database [Homepage of Environment Canada],
[Online]. Available: http://www.etc-cte.ec.gc.ca/databases/TankerSpills/Default.aspx [2011, 07/13].
Erkut, E. & Verter, V. 1995, "Hazardous Materials Logistics: A Review" in Facility Location: A
Survey of Applications and Methods, ed. Z. Drezner, Springer-Verlag, pp. 506.
Erkut, E., Tjandra, S.A. & Verter, V. 2007, "Chapter 9 Hazardous Materials Transportation" in
Handbooks in Operations Research and Management Science, ed. Cynthia Barnhart and Gilbert
Laporte, Elsevier, , pp. 539-621.
Etkin, S. 2000, "Worldwide Analysis of Marine Oil Spill Cleanup Cost Factors", June 2000.
Etkin, S. 1999, "Estimating Cleanup Costs for Oil Spills", API Publ. American Petroleum Institute,
EU Legislations 2011, Waterborne Transport: Maritime Safety [Homepage of Europa], [Online].
Available: http://europa.eu/legislation_summaries/transport/waterborne_transport/index_en.htm; [2011,
07/14].
Fowler, T.G. & Sørgård, E. 2000, "Modeling Ship Transportation Risk", Risk Analysis, vol. 20, no. 2,
pp. 225-244.
French McCay, D., Rowe, J.J., Whittier, N., Sankaranarayanan, S. & Schmidt Etkin, D. 2004,
"Estimation of Potential Impacts and Natural Resource Damages of Oil", Journal of hazardous
materials, vol. 107, no. 1-2, pp. 11-25.
Grigalunas, T.A., Opaluch, J.J., French, D., Reed, M. & Knauss, D. 1988, "A Natural Resource
Damage Assessment Model for Coastal and Marine Environments", GeoJournal, vol. 16, no. 3, pp.
315-321.
Hu, S., Fang, Q., Xia, H. & Xi, Y. 2007, "Formal Safety Assessment Based on Relative Risks Model in
Ship Navigation", Reliability Engineering & System Safety, vol. 92, no. 3, pp. 369-377.
Huijer, K. 2005, Trend in Oil from Tanker Ships 1995-2004, ITOPF, London, UK.
Iakovou, E.T. 2001, "An interactive multiobjective model for the strategic maritime transportation of
petroleum products: risk analysis and routing", Safety Science, vol. 39, no. 1-2, pp. 19-29.
IMO 2011, International Convention for the Prevention of Pollution from Ships (MARPOL)
[Homepage
of
International
Maritime
Organization],
[Online].
Available:
http://www.imo.org/about/conventions/listofconventions/pages/international-convention-for-theprevention-of-pollution-from-ships-(marpol).aspx [2011, April/16].
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15. IMO 2002, Guidelines for Formal Safety Assessment (FSA) for Use in the IMO Rule-Making Process,
London.
16. IOPCF 2008, Annual Report 2008: International Oil Pollution Compensation Funds, IOPCF, Kent,
Great Britain.
17. ITOPF 2009, ITOPF Oil Spill Statistics [Homepage of The International Tanker Owners Pollution
Federation Limited], [Online]. Available: http://www.itopf.com/information-services/data-andstatistics/statistics/ [2010, 15.01.2010].
18. Kontovas, C.A. & Psaraftis, H.N. 2009, "Formal Safety Assessment: A Critical Review", Marine
Technology, vol. 46, no. 1, pp. 45-59.
19. Kontovas, C.A., Psaraftis, H.N. & Ventikos, N.P. 2010, "An Empirical Analysis of IOPCF Oil Spill
Cost Data", Marine pollution bulletin, vol. 60, no. 9, pp. 1455-1466.
20. Martins, M.R. & Maturana, M.C. 2010, "Human Error Contribution in Collision and Grounding of Oil
Tankers", Risk Analysis, vol. 30, no. 4, pp. 674-698.
21. Psarros, G., Skjong, R. & Vanem, E. 2011, "Risk Acceptance Criterion for Tanker Oil Spill Risk
Reduction Measures", Marine pollution bulletin, vol. 62, no. 1, pp. 116-127.
22. Rodrigue, J., Comtois, C. & Slack, B. 2009, The Geography of Transport Systems, 2nd edn, Routledge,
New York.
23. Safdor 2008a, Formal Safety Assessment, FSA – Crude Oil Tankers, Submitted by Denmark (MEPC
58/17/2), International Maritime Organization.
24. Safdor 2008b, Formal Safety Assessment, FSA – Crude Oil Tankers, Submitted by Denmark (MEPC
58/INF.2), International Maritime Organization.
25. Shahriari, M. & Frost, A. 2008, "Oil spill cleanup cost estimation—Developing a mathematical model
for marine environment", Process Safety and Environmental Protection, vol. 86, no. 3, pp. 189-197.
26. Soares, C.G. & Teixeira, A.P. 2001, "Risk Assessment in Maritime Transportation", Reliability
Engineering & System Safety, vol. 74, no. 3, pp. 299-309.
27. Vanem, E., Endresen, Ø. & Skjong, R. 2008, "Cost-Effectiveness Criteria for Marine Oil Spill
Preventive Measures", Reliability Engineering & System Safety, vol. 93, no. 9, pp. 1354-1368.
28. Wheeler, T.A. 1993, "Risk-Based Ranking of Dominant Contributors to Maritime Pollution Events",
Risk Analysis, vol. 13, no. 2, pp. 207-214.
29. Yamada, Y. 2009, "The Cost of Oil Spills from Tankers in Relation to Weight of Spilled Oil", Marine
Technology and SNAME News, vol. 46, no. 4, pp. 219-228.
30. Yudhbir, L. & Iakovou, E. 2001, "A Maritime Oil Spill Risk Assessment Model", API Publ. American
Petroleum Institute, pp. 235.
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