Ship inspection strategies: Effects on maritime safety and environmental protection

Transportation Research Part D 16 (2011) 42–48

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Ship inspection strategies: Effects on maritime safety and environmental protection Christiaan Heij a,*, Govert E. Bijwaard b, Sabine Knapp a,1 a b

Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands Netherlands Interdisciplinary Demographic Institute, Lange Houtstraat 19, P.O. Box 11650, 2502 AR The Hague, The Netherlands

a r t i c l e Keywords: Maritime safety Inspection strategy Risk factor Hazard rate Port state control

i n f o

a b s t r a c t Global trade largely depends on maritime transport, and appropriate ships are needed to protect cargo but to minimize environmental damage and to this end, flag and port state authorities expend considerable effort in ship safety inspections. This paper investigates the safety gains of current inspection rules as well as options for further developments. The analysis is based on a dataset of over 400,000 ship arrivals covering 2002–2007, and is complemented with data on port state control and industry inspections and casualties. The results indicate considerable potential for safety gains from incorporating casualty risks more explicitly in port state control strategies to select ships for safety inspections. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction Economic development and trade depend to a large degree on efficient shipping, which carries a large percentage of raw material and manufactured goods that are traded. In 2008, international seaborne trade amounted to over eight billion tons of goods loaded and 32.7 trillion ton-miles (United Nations Conference on Trade and Development, 2009). Crude oil and oil products account for about 65% of all cargo carried, and with other important cargoes being dry bulk and containers. Maritime transport is relatively safe, but the personal, economic, and environmental costs of accidents can be huge. Loss of passenger ships at sea may involve high death tolls, and tanker accidents can cause severe and extensive oil pollution (Talley et al., 2001, 2008). The shipping industry’s main regulatory bodies are the International Maritime Organization (IMO) and the International Labor Organization (ILO). There has been a change of emphasis over time in the focus of these agencies, with the former attention on technical safety measures shifting towards environmental concerns and the human factors of ship operations (Knapp and Franses, 2009). For example, the International Convention for the Prevention of Pollution from Ships (MARPOL) now covers a wide range of environmental areas, including prevention of pollution from oil chemicals and other hazardous substances, ballast water treatment, the reduction of harmful paints, the reduction of emissions from ships, and ship recycling. Because of the very high costs of accidents, flag state authorities and coastal states try to follow preventive strategies. Flag states differ in their enforcement of minimum safety standards that has led to loopholes in the regulatory system. The lack of harmonization has created substandard shipping, estimated at about 5% to 10% of the world fleet.2 Prompted by a series of tanker accidents in the 1970s, and to assist in the enforcement of international conventions, the concept of port state control

* Corresponding author. E-mail address: [email protected] (C. Heij). 1 The views expressed in this article represent those of the author and do not necessarily represent those of the Australian Maritime Safety Authority (AMSA). 2 Peijs (2003): Ménage a trios (speech at Mare Forum, Amsterdam). 1361-9209/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.trd.2010.07.006

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C. Heij et al. / Transportation Research Part D 16 (2011) 42–48 Table 1 Arrivals and inspections per regime and half-year. General cargo Number of ships

a

Dry bulk

Container 2136

Tanker 3604

Passenger 197

Total 14,115a

3267

1447

Inspections per ship None (% of ships) One (% of ships) Two or more (% of ships)

15,723 46.2 15,300 44.9 3046 8.9

10,401 48.8 10,686 50.2 206 1.0

6380 42.4 5916 39.4 2735 18.2

10,171 49.2 8305 40.2 2199 10.6

251 38.9 232 36.0 162 25.1

42,926 46.8 40,439 44.1 8348 9.1

Arrivals All Eligible (% of all arrivals)

122,808 34,069 27.7

52,496 21,293 40.6

117,012 15,031 12.8

107,658 20,675 19.2

8716 645 7.4

408,690 91,713 22.4

Inspections per arrival type Inspections of all arrivals (% of all arrivals) (% of eligible arrivals)

22,330 18.2 65.5

11,141 21.2 52.3

12,889 11.0 85.7

13,791 12.8 66.7

859 9.9 133.2

61,010 14.9 66.5

Inspections of eligible arrivals (% of all eligible arrivals) (% of all inspections)

18,266 53.6 81.8

10,822 50.8 97.1

8586 57.1 66.6

10,434 50.5 75.7

392 60.8 45.6

48,500 52.9 79.5

The ship type is not fixed for 3464 vessels because of classification differences between regimes for general cargo, dry bulk, and container ships.

(PSC) emerged. We examine the effect of PSC inspections on safety in terms of reduced casualty risk by combining casualty and inspection data and we investigate the potential safety gains that can come from explicitly incorporating the ship-specific risk of future accidents explicitly in designing ship inspection strategies. 2. Ship inspections Port state control is the right of a port or coastal state to conduct safety inspections and to enforce the international measures on ships that visit its port. An inspected ship that fails the minimum standards is detained, and deficiencies have to be rectified before it is released. In some cases, a ship can be banned from re-entering ports if it has been detained several times. Ship owners wish to avoid detention because of the high economic costs and the possibility of increased future inspections. PSC inspections are focused not only on safety, but also progressively on environmental protection. There are 10 PSC regimes that cover all coastal states and that enforce international standards. The regimes are grouped by regions and countries that have agreed to conduct inspections in a harmonized way. Each regime uses only its own past inspection data to target ships for inspections, thereby ignoring not only the inspection outcomes of other regimes but also industry vetting inspections, which are primarily performed on dry bulk carriers and tankers. Apart from previous inspection results in the same region, other risk factors include ship specific features such as the type, age, and size of a vessel, its flag state, its classification group, and sometimes its Document of Compliance company DoC.3 In all regimes, high-risk vessels are tankers and passenger vessels, due to the potentially high costs in case of an incident PSC regimes differ in their selection of target factors (Knapp and Franses, 2007; Talley et al., 2005). To improve the estimated risk profiles of ships, the information set is expanded in this paper to include data on past inspections, industry inspections, accidents, and changes of flag state and classification state. 3. Data and methods 3.1. Arrival and inspection data The arrival dataset consists of daily arrival information of 14,115 ships between 2002 and 2007; some 400,000 arrivals. These data are obtained from port states that in 2008 accounted for 17% of goods loaded, 15% of goods unloaded, and 12.5% of the world merchant trade value.4 The dataset provides a representative spread of all trade segments and ship types. For each ship arrival, the information consists of the arrival date of the vessel in the respective port state, together with some basic ship particulars that apply at the time of arrival (ship type, age, gross tonnage, flag state, classification society, 3 The DoC company is the designated company for the safety management of the vessel. There are about 5000 DoC companies and it is difficult to obtain adequate data on them. 4 The data providers wish to remain anonymous. Readers interested in more details can contact the authors but provision of additional details is dependent on the approval of the data providers.

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Document of Compliance company, country of location, changes of ship particulars over time), and the decision whether or not the ship is inspected. This information is complemented by port state control inspection data from various other PSC regimes as well as industry inspections,5 see Bijwaard and Knapp (2009) for a more detailed description of these additional data. The combined inspection data show which arrivals result in inspections and detentions, together with the number of deficiencies found at each inspection. Table 1 shows summary statistics, differentiated by five ship types; general cargo, dry bulk, container, tanker, and passenger. General cargo, tanker, and container vessels have the largest number of ships, arrivals, and inspections. The number of inspections is 61,010, and the overall inspection rate (that is, the number of inspections divided by the number of arrivals) is about 15%, with relatively the highest rates occurring for dry bulk carriers and general cargo vessels. We distinguish between arrivals that occur within the same PSC region over the 12 different half-year periods from 2002 to 2007. The reason for these distinctions is that a PSC inspection reduces the subsequent risk of an incident, but inspections of the same ship at close intervals will not all lead to the same survival effect. Shipping experts differ in their opinion on how long the effect of PSC inspections last, but it is generally agreed that the effect is lost after a year. Industry vetting inspections are performed more frequently on tankers and dry bulk carriers, and depending on the PSC regime, high-risk vessels can become eligible for inspections after a period of 6 months; in the case of passenger ships 3 months (Knapp and Franses, 2007). We assume that the effect lasts for half a year, after which a new inspection is assumed to produce a survival gain that does not depend on previous inspections. For every given PSC region and every half-year, the set of arrivals of a ship is compressed into a single arrival that we call the eligible arrival of this ship in this region for this half-year, in the sense that this arrival is the single candidate for inspection of the ship. By construction, subsequent inspections of eligible arrivals of the same ship in the same PSC region always occur in different 6-month periods.6 It may, however, be that ships that enter various regions in the same half-year are inspected more than once. We will allow this to reflect the current practice that PSC regimes disregard inspections by other regimes. In the majority of cases, ships are inspected at most once per half-year. Table 1 shows that, on average, only 9% of the ships encounter more than one inspection every half-year, whereas 44% are inspected once and 47% are not inspected. The compression of multiple arrivals within a half-year provides 91,713 eligible arrivals. When restricted to eligible arrivals, there are 48,500 inspections; 79.5% of inspections in the full database. Multiple inspections occur most frequently for passenger and container ships, with inspection omission rates 54.4% and 34.4%. For eligible arrivals, the inspection rate per halfyear ranges between 50% and 60% across all ships. The restriction to eligible arrivals is needed for the evaluation of the survival gain strategy proposed in this paper. Without this restriction, the same ship could in principle be selected many times within the same half-year, and it would be unrealistic to assume that the same survival gain is realized at all of these inspections. 3.2. Casualty data and risk factors Casualty data were obtained from Lloyd’s Register Fairplay. The severity of casualties has been classified according to IMO definitions, ranging from ‘less serious’ to ‘very serious’ and ‘total loss’. The risk analysis is restricted to total loss accidents, because of their large impact in terms of economic and environmental costs. The main determinants of loss risk identified by Bijwaard and Knapp (2009) are past incidents, past PSC and industry inspection outcomes, ship economic cycles,7 and ship particulars: age, size, flag, classification society, country of location of the DoC company, and changes of ship particulars over time. For simplicity, we use terms like casualty, accident, and risk to denote (the risk of) total losses. The effect of the risk factors on casualty risk is modelled using a hazard rate. Let S(t) be the survival function; the probability that the ship will survive for at least t periods from its construction. The hazard rate is defined as k(t) = dln(S(t))/dt.8 A high hazard rate corresponds to a large risk that a ship will not survive long. The hazard rate model takes the form

kðtÞ ¼ kb ðtÞ  expðb1 X 1 ðtÞÞ      expðbk X k ðtÞÞ where the baseline hazard kb(t) models the age effect9 and where (X1, . . . , Xk) denote the other risk factors. This is called a proportional hazard model, as each risk factor has a proportional effect on the hazard rate. The effect of a PSC inspection is measured by X1, with X1 = 1 in case of an inspection and X1 = 0 with no inspection. k1 (k0) is the hazard rate after (without) an inspection, with k1 = exp(b1)  k0, and the risk reduction factor of an inspection is (k0  k1)/k0 = 1  exp(b1). 5 These industry inspections include vetting inspections of RightShip for dry bulk carriers, of the Chemical Distribution Institute for chemical tankers, and of the Oil Companies International Marine Forum for oil and chemical tankers. 6 The precise selection of the eligible arrival of a given ship in a given region and half-year is made as follows. If the ship was inspected once during the halfyear, then the arrival where this inspection occurred is the eligible arrival. If the ship was not inspected in the half-year, the arrival with the largest survival gain is the eligible arrival. If the ship was inspected more than once during the half-year, the arrival with inspection with the largest survival gain is the eligible arrival. In case of multiple arrivals of the same ship, the survival gains at different arrivals are usually close together, in which case the eligible arrival can be considered as a randomly chosen arrival of the ship in the half-year under consideration. 7 Monthly average earnings data per ship type, except vessels, are obtained from the Shipping Intelligence Network from Clarksons.  Rfor passenger  t 8 The survival function is obtained from SðtÞ ¼ exp  0 kðsÞds (see, Van den Berg, 2001 for further explanation of hazard models). 9 A piecewise constant specification is used with six age groups, and kb(t) = exp(aj) in age group j (j = 1, . . . , 6).

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C. Heij et al. / Transportation Research Part D 16 (2011) 42–48 Table 2 Summary of hazard models. General cargo Risk reduction factor of PSC inspection 2003 0.16a 2004 0.28 2005 0.38 2006 0.34 2007 0.31 Some other risk factors (results for 2007) Vetting inspectionb n/a Serious casualtyb Very serious casualtyb + PSC detentionb PSC deficienciesc + Gross tonnage (log) + Age 0–4 years BM Age 5–10 years – Age 11–15 years – Age 16–20 years Age 21–25 years + Age over 25 years +

Dry bulk

Container

Tanker

Passenger

0.65 0.63 0.64 0.53 0.51

0.67 0.50 0.53 0.38 0.33

0.32 0.30 0.34 0.24 0.08a

n/a n/a 0.71 0.67 0.73

–

n/a +

– + +

n/a

+

+

+ BM – –

+ BM

+ BM –

BM –

+ +

+ +

+ +

+

Notes: +/ denotes a risk increasing/decreasing effect, significant at 5%; BM is the benchmark age class; n/a is when the variable does not apply for the ship type. a Not significant at 5%. b Takes the value 1/0 if the event did/did not occur within one year before arrival. c Number of deficiencies found at PSC inspections in a period of one year before arrival.

Separate hazard models are estimated for each ship type and year from 2003 to 2007, using an expanding estimation window that stretches up to the year of arrival. Table 2 summarizes the main results.10 The risk reduction factors are considerable and differ by ship type. The largest reductions, corresponding to the largest risk reduction factors, are for passenger vessels and dry bulk carriers, and while reductions for container ships are large in initial years they become smaller later. The table also shows the effects of other risk factors for the model of 2007. Industry vetting inspections reduce the casualty risk. Past casualties, detentions, and deficiencies increase the risk, although not all of these are significant for all ship types. Further, on average, larger ships have a higher risk, with the same applying for ships less than 5 years old and for those over 20 years old. 3.3. Calculation of survival gains and the survival gain strategy The hazard model forms our basis for the evaluation of the benefits of inspections in terms of reduced casualty risk. Because the beneficial effects of an inspection fade out over time, we take the effect over a period of half a year into account (see Section 3.1). Now, consider a ship that at the current time (t) is in port. Using the hazard model that applies for the current year and the ship’s data on all the risk factors of the model, we calculate the hazard rate k0 (k1) that applies without (with) an inspection, where k1 = exp(b1)  k0. We denote the associated conditional survival probability of this ship for the next half-year by S0 (S1), where

S0 ¼ Probðsurvival till t þ 1=2Þ=Probðsurvival till tÞ  Z ¼ exp 

tþ1=2

  Z t   Z k0 ðsÞds exp  k0 ðsÞds ¼ exp 

0

0

tþ1=2

 k0 ðsÞds

t

If we use the notation a = exp(b1), then k1 = ak0; as b1 < 0, it follows that 0 < a < 1, and

 Z S1 ¼ exp  t

tþ1=2

  Z k1 ðsÞds ¼ exp a t

tþ1=2

 k0 ðsÞds ¼ Sa0

Because shipping is not a high risk industry, survival probabilities are large. Knapp et al. (2010) find base risks (1  S0) that range between 1% and 4% a year, 0.5–2% per half-year. Because S0 is close to unity, the first order Taylor expansion Sa0 = (1 + (S0  1))a  1 + a(S0  1) provides an accurate approximation of S1. The survival gain for the next half-year, caused by inspecting the ship is

S1  S0 ¼ Sa0  S0  ð1  aÞð1  S0 Þ ¼ ð1  expðb1 ÞÞð1  S0 Þ 10 The hazard models contain more factors than are shown in Table 2, including indicators for accidents in the previous half-year, flag, classification society, DoC company, and earnings (Knapp et al., 2010). For passenger ships, the model could not be estimated with sufficient accuracy for 2003 and 2004, because of data limitations.

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Table 3 Descriptive statistics of inspected ships (subset of half-yearly eligible arrivals).

Results of inspections Number of inspections Overlap of actual and SGS inspections (%) Survival gain (half-year; average %) Detained (%)a Total deficiencies (average)a Zero deficiencies (%)a Future risk of inspected ships Total loss (within one year, %) Very serious casualty (within one year, %) Total lossb Inspected Very serious casualtyb Inspected Characteristics of inspected ships Size (gross tonnage/1000; average) Average group age Age (average) Flag black (%) Flag grey (%) Flag white (%) Flag undefined (%) Class IACS (%) Class non-IACS (%) Class unknown (%) a

b

General cargo

Dry bulk

Actual

SGS

Actual

SGS

Actual

Container SGS

Actual

Tanker SGS

Actual

Passenger SGS

18,266 56

18,266

10,822 54

10,822

8586 57

8586

10,434 51

10,434

392 67

392

0.25

0.43

0.14

0.24

0.14

0.24

0.15

0.26

0.22

0.29

1.42 0.71 78

1.79 0.81 76

4.19 2.10 54

4.91 2.39 51

1.08 0.50 83

1.38 0.55 83

0.93 0.56 81

1.10 0.64 79

0.51 0.56 81

0.76 0.61 80

0.08 0.04

0.13 0.03

0.04 0.05

0.05 0.05

0.01 0.05

0.00 0.05

0.04 0.12

0.07 0.12

0.00 0.00

0.00 0.00

2 1 6 4

2 0 6 4

32 15 13 7

32 23 13 5

7 4 12 5

7 5 12 5

25.47

26.16

43.43

45.55

34.57

3.07 13.06 45.78 9.15 43.87 1.20 89.74 9.96 0.31

3.72 16.58 45.62 9.78 42.28 2.33 88.80 10.63 0.57

2.72 11.29 49.71 12.26 37.28 0.75 95.37 4.54 0.09

2.90 12.23 51.08 12.77 34.01 2.13 95.08 4.79 0.13

2.49 9.96 31.35 4.82 63.50 0.33 88.11 11.68 0.21

7 4 24 13

7 7 24 13

0 n/a 0 n/a

0 n/a 0 n/a

34.96

40.34

51.10

48.46

45.31

2.76 11.39 28.97 5.03 65.54 0.47 85.46 14.15 0.38

2.49 9.89 23.39 7.68 67.60 1.33 85.05 14.50 0.45

2.82 11.69 21.31 6.03 71.44 1.23 84.31 14.91 0.78

2.71 12.49 10.71 5.36 82.14 1.79 85.71 13.52 0.77

3.15 15.68 9.44 5.87 82.40 2.30 85.97 12.76 1.28

For SGS, this is computed for the subset of SGS inspections that are also actual inspections because the relevant information is missing for ships not actually inspected. Over all years and for the subset of half-yearly eligible arrivals.

As compared to the probability (1  S0) of an accident in the next half-year if the ship is not inspected, the gain is (S1  S0)/(1  S0)  1  exp(b1). This implies that the risk reduction factors in Table 2 can also be interpreted with high accuracy, as factors by which the probability of an accident in the next half-year is reduced by an inspection. The survival gains form the basis of the alternative inspection strategy; the survival gain strategy (SGS). For every PSC regime, ship type, and half-year, we determine the number of actual inspections, restricted to the set of eligible arrivals. SGS selects an equal number of ships for inspection, but it selects the vessels with the largest survival gains for a given PSC regime, ship type, and half-year. As S1  S0  (1  a)(1  S0), the survival gain is almost proportional to the half-year casualty risk (1  S0), thus the SGS tends to select the highest risk vessels for inspection.

4. Survival gains of inspection strategies The effect of PSC inspections can be evaluated in terms of reductions in casualty risk. To prevent over-estimation of the survival gains obtained by multiple inspections of the same ship within a brief period, we focus on eligible arrivals. This ensures that successive inspections of the same ship in the same PSC regime never occur within the same half-year. For each regime, ship type, and half-year, the inspection rate is fixed at the historical rate that applied for the set of eligible arrivals for this regime, ship type, and half-year. For each vessel type and each year, the average survival gain per inspection is found by dividing the sum of the survival gains of all inspections by the number of inspections. This average provides an indication of the survival gain that has been achieved by current inspection strategies. As an alternative, the average survival gain is computed for SGS that applies the same inspection rates as the current strategies per regime, ship type, and half-year, but it selects eligible arrivals for inspection that have the largest survival gains. Table 3 summarizes the results of the actual inspection strategies and those of SGS. The table shows that there are good opportunities for improving the effectiveness of inspections in terms of greater safety. The average survival gain of SGS is a factor of about 1.7 higher than that of current strategies, except for passenger vessels where this factor is about 1.3. In absolute terms, the improvement of SGS over current strategies consists, per inspection, of a further risk reduction of 0.18% for general cargo, of about 0.10% for dry bulk, container, and tanker, and of 0.07% for passenger vessels. We can also compare the

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C. Heij et al. / Transportation Research Part D 16 (2011) 42–48

.4

All Elig

.3 .2

.28 .24

SGS

.20

All

.16

Elig

.12

2

3

4

5

6

7

8

1

2

3

.35 SGS

.30 .25

All

.20 Elig

.15 .10 2

.28 .24

SGS

.20 .16

All

.12

Elig

5

6

7

8

1

2

3

4

5

6

7

8

Year

.6

Tanker

1

4

Year

Survival Gain (average %)

Survival Gain (average %)

.40

Container

.32

.08

.08 1

Year

.05

Survival Gain (average %)

Dry Bulk

SGS

Survival Gain (average %)

Survival Gain (average %)

General Cargo

.5

.1

.36

.32

.6

3

4

5

Year

6

7

8

Passenger

SGS

.5 .4 .3 Elig

.2

All

.1 1

2

3

4

5

6

7

8

Year

Fig. 1. Average percentage survival gain for the 6 months following an inspection, for all actual inspections for all arrivals (All), all actual inspections for eligible arrivals (Elig), and for SGS inspections eligible arrivals (SGS) for 2002–2007 (2004–2007 for passenger ships).

survival gains (S1  S0) with the un-inspected casualty risk (1  S0), which is, on average, 1.88% for general cargo, 1.47% for dry bulk, 0.76% for container, 1.23% for tanker, and 0.48% for passenger vessels.11 For example, inspections of general cargo vessels reduce on average the risk within a half-year from 1.88% to 1.63%; SGS inspections achieve a further reduction to 1.45%. Even though the risks and risk reductions may appear small in absolute terms, the value benefits of inspections in terms of saved potential accident costs are substantial. Accidents involving total loss do not only involve the loss of ship and cargo, but may also result in loss of life, environmental damage, and damage to third parties. It is not easy to quantify the monetary value of total loss, but using Knapp et al. (2010), we obtain median values of $6.3 million for general cargo, $3.8 million for dry bulk, $4.4 million for container, $9.7 million for tanker, and $16.4 million for passenger ships.12 For example, the extra survival gain of 0.18% of SGS as compared to current inspections of general cargo vessels amounts to extra savings of about $11 thousand per inspection. Whereas Table 3 shows average survival gains, these gains are split up over the years in Figure 1. Apart from the actual strategy for eligible arrivals (‘Elig’) and SGS, both shown in Table 3, the figure contains also the survival gains for the actual strategy for all arrivals in the database, including multiple inspections of the same ship within the same regime and half-year (‘All’). The figure indicates that the restriction to eligible arrivals is not a severe one for the current strategies, although in most cases, the overall average gains are somewhat larger than those of the subset of eligible arrivals are. This means that the multiple inspections of current strategies may be well motivated, as they often involve ships with relatively high risks. SGS outperforms both versions of the current strategies by a considerable margin. The gains show time patterns that differ between the ship types. The trend is steadily upwards for general cargo and passenger vessels, but fluctuates for container ships, and for dry bulk carriers and tankers there is an initial rise followed by a fall. Returning to Table 3, for each vessel type, SGS inspections coincide with actual inspections in slightly more than half of the cases. Compared to current inspection strategies, SGS inspections are somewhat more successful in targeting ships that will be detained. Further, the average number of deficiencies is somewhat larger and the proportion of inspections finding no deficiencies is slightly smaller. In most cases, the accident rates of vessels that would have been selected by SGS is higher than that of the actual inspected ships. This suggests that the information present in the hazard rate factors does help to

11

The un-inspected casualty risks are found by transforming annual risks for 2003–2007 (Knapp et al., 2010) into average half-yearly risks. These values are obtained from Knapp et al. (2010) using the (conservative) lower bound median values for expected cost savings in their Table 6 (say L) and the average of the yearly gains in their Table 5 (say G). As L = V  G, it follows that V = L/G. For example, for general cargo vessels, L = $21.0 thousand and G = 0.0167/5 = 0.00334, so that V = L/G is about $6.3 million. 12

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select ships of high risk in terms of detention, deficiencies, and future casualties. Finally, the bottom part of the table provides an indication of the average characteristics of inspected ships. As compared to current inspection strategies, SGS tends to select ships that are somewhat larger and older. The distribution over flags and classification societies is roughly similar to current practice. 5. Conclusions The practice of port state control inspections is that, although they share the same objectives, information is not yet shared between them as the regimes effectively disregard each other’s inspections as well as inspections performed by the industry, such as vetting. This paper proposes at an inspection strategy based on combined data on ship arrivals, inspections, and casualties. The strategy is explicitly risk-driven: inspect ships for which inspections produce the largest survival gains. It is found to improve considerably upon current practice, with survivals gains that are a factor of about 1.7 (1.3 for passenger ships) larger than the gains that are currently achieved. These results are only indicative, as the historical inspection and casualty data are obtained under the prevailing PSC regimes and the proposed rule has not been operative in practice. Nonetheless, the estimated gains are considerable and indicate that the incorporation of future casualty risk in making inspection decisions deserves the attention of port authorities. For practical implementation purposes, the proposed inspection strategy can be complemented with easy-to-use selection rules. For example, one of the PSC regimes currently classifies ships into a number of risk groups and applies target inspection rates that increase per risk group. In a similar way, the expected gained lifetimes due to an inspection can be classified into a limited number of groups, with higher inspection rates for groups with larger expected gains. Although this leads to some loss in gained survival rate, the advantage is that an element of non-predictability of inspections for low-hazard ships acts as an incentive for ship owners to maintain high safety levels. Another possible refinement consists of incorporating the costs involved in losing a ship, which depend on the value of the ship, its cargo, and environmental costs. Acknowledgements We thank the anonymous data providers for supplying us with information on ship arrivals. We thank Lloyd’s Register Fairplay for supplying casualty data. References Bijwaard, G.E., Knapp, S., 2009. Analysis of ship life cycles – the impact of economic cycles and ship inspections. Marine Policy 33, 350–369. Knapp, S., Bijwaard, G.E., Heij, C., 2010. Estimated cost savings in shipping due to inspections. Working Paper, Econometric Institute Report 2010-28, Erasmus University Rotterdam. Knapp, S., Franses, P.H., 2007. A global view on port state control – econometric analysis of the differences across port state control regimes. Maritime Policy and Management 34, 453–483. Knapp, S., Franses, P.H., 2009. Does ratification matter and do major conventions improve safety and decrease pollution in shipping? Marine Policy 33, 826– 846. Talley, W.K., Jin, D., Kite-Powell, H., 2001. Vessel accident oil-spillage: post US OPA-90. Transportation Research Part D 6, 405–415. Talley, W.K., Jin, D., Kite-Powell, H., 2005. The US Coast Guard vessel inspection program: a probability analysis. Maritime Economics and Logistics 7, 156– 172. Talley, W.K., Jin, D., Kite-Powell, H., 2008. Determinants of the severity of cruise vessel accidents. Transportation Research Part D 13, 86–94. United Nations Conference on Trade and Development, 2009. Review of Maritime Transport, New York. Van den Berg, G.J., 2001. Duration models: specification, identification and multiple durations. In: Heckman, J.J., Leamer, E. (Eds.), Handbook of Econometrics, vol. 5. Elsevier Science, Amsterdam, pp. 3381–3460.