Ship Inspection Strategies: Effects on Maritime Safety and Environmental Protection
ABSTRACT Global trade depends for a large part on maritime transport, and safe ships are needed not only to protect precious cargo but also to prevent environmental damage. Flag state and port state authorities spend much effort in ship safety inspections to ensure a minimum safety level and to prevent casualties. This paper investigates the safety gains of current inspection rules as well as options for further improvement. The analysis is based on a dataset of over four hundred thousand ship arrivals originating from some important trading nations between 2002 and 2007, complemented with data on port state control and industry inspections and casualties. The results indicate considerable potential safety gains of incorporating estimated future casualty risks more explicitly in port state control strategies to select ships for safety inspection.
- SourceAvailable from: Kevin X. LiMaritime Policy & Management 01/2013; 40(3):261–277. · 0.74 Impact Factor
- Journal of the American Dietetic Association 06/2011; 111(6):816-8. · 3.80 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Purpose – The purpose of this paper is to measure a port's green performance. The literature is reviewed and a survey is carried out to identify major green port performance indicators and to evaluate three major ports' overall green performance in Asia. Indicators located in the critical quadrants with a high degree of importance and low degree of performance are identified and resources are suggested that can be employed to improve the ports' overall sustainability performance effectively. Design/methodology/approach – Port performance indicators are reviewed to select the green-related ones by a session of brain storming with academicians from China, Hong Kong, and Taiwan in the shipping discipline. Selected indicators are used to design an analytic hierarchy process (AHP) questionnaire. The weight and degree of performance of each of the 17 green indicators among three major container ports are calculated by the data obtained from the AHP round survey respondents. Findings – Avoiding pollutants during cargo handling and port maintenance, noise control, and sewage treatment were perceived to be the three critical indicators by respondents in two of the three ports investigated. Among the three investigated ports, Shanghai port had the highest number of critical indicators to be improved. Air pollutants avoidance, encouraging the use of low-sulphur fuel, and using electrically powered equipment were three of the five critical indicators found in Shanghai port, but not in the other two investigated ports in this study. Originality/value – The theoretical implications of this research are the development of a conceptual framework to measure the degree of importance of a set of green port performance indicators, and to provide a decision support system to help port authorities to evaluate their performance regarding the 17 green port performance indicators compared with that of other ports.International Journal of Physical Distribution & Logistics Management 01/2013; 43(5):427-451. · 1.04 Impact Factor
Ship Inspection Strategies:
Effects on Maritime Safety and Environmental Protection
Christiaan Heij1, Govert E. Bijwaard2, Sabine Knapp3
Econometric Institute Report 2010-33
Global trade depends for a large part on maritime transport, and safe ships are needed not
only to protect precious cargo but also to prevent environmental damage. Flag state and port
state authorities spend much effort in ship safety inspections to ensure a minimum safety
level and to prevent casualties. This paper investigates the safety gains of current inspection
rules as well as options for further improvement. The analysis is based on a dataset of over
four hundred thousand ship arrivals originating from some important trading nations between
2002 and 2007, complemented with data on port state control and industry inspections and
casualties. The results indicate considerable potential safety gains of incorporating estimated
future casualty risks more explicitly in port state control strategies to select ships for safety
maritime safety; inspection strategy; risk factor; hazard rate; port state control
1 Corresponding author. Address: Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam,
P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. Phone: +31-10-4081259; Fax: +31-10-4089162; E-mail address:
2 Netherlands Interdisciplinary Demographic Institute (NIDI), Lange Houtstraat 19, P.O. Box 11650, 2502 AR The Hague,
The Netherlands. E-mail address: firstname.lastname@example.org
3 Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR
Rotterdam, The Netherlands. E-mail address: email@example.com
Economic development and trade depend crucially on an efficient shipping industry, which
carries a high percentage of traded resources and manufactured goods. In 2008, international
seaborne trade reached over eight billion tons of goods loaded and a total of 32.7 trillion ton-
miles (i.e., tons of cargo multiplied by the average transport distance).4 Crude oil and oil
products account for about two-thirds of the total cargo carried, and other important cargoes
are 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
a high death toll, and tanker accidents may cause severe and extensive oil pollution.5
The shipping industry’s main regulatory bodies are the International Maritime Organization
(IMO) and the International Labor Organization (ILO), which are responsible for more than
fifty international conventions regulating all aspects of ship operations and measures to
protect the marine environment, including recent regulations for emissions from ships. Knapp
and Franses (2009) analyze the effectiveness of these conventions and note a change of
emphasis over time, as the focus of attention for technical safety measures in earlier times has
been shifted nowadays towards environmental aspects and the human factor of ship
operations. For example, the International Convention for the Prevention of Pollution from
Ships (MARPOL) now covers a wide range of environmental areas, such as 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. Changes to the legislative framework in shipping have been
characterized by reactive rather than preventive actions, as they tend to follow major
incidents. Further, flag states differ in their enforcement of minimum safety standards, which
has led to loopholes in the regulatory system. This lack of harmonized efforts to enforce
safety standards has created substandard shipping, estimated as about five to ten percent of
the world fleet.6 Prompted by a series of tanker accidents in the 1970’s, and to improve the
enforcement of international conventions, the concept of port state control (PSC) emerged.
4 See UNCTAD (2009).
5 See, for instance, Talley, Din, and Kite-Powell (2001, 2008).
6 Peijs (2003): Ménage a trios (speech at Mare Forum, Amsterdam).
The effectiveness of PSC inspections has been studied in the literature for some time.7 This
paper adds a new dimension to this analysis, by expressing the effect of PSC inspections on
safety in terms of reduced casualty risk. By combining casualty and inspection data, the risk
reducing effect of PSC inspections is estimated by means of duration analysis. The paper
further investigates the potential safety gains that can be obtained by incorporating the ship-
specific risk of future accidents explicitly in designing ship inspection strategies. Fixing
inspection rates at their historical levels, a risk-driven inspection strategy aiming to maximize
survival gains is compared to currently operating strategies in terms of the achieved
reductions in casualty risk. The innovative aspect of this paper lies in its unique combination
of inspection and casualty data, which allows the evaluation of inspections in terms of
survival gains. The empirical analysis covers general cargo vessels, dry bulk carriers,
container vessels, tankers, and passenger ships; it excludes other ship types, like offshore
supply vessels and fish factories.
The paper has the following structure. Section 2 provides information on current PSC
inspections. Section 3 discusses the data and the method to estimate the survival gains of
inspections. These gains are evaluated in Section 4, for both the currently employed strategies
and an alternative, risk-based strategy. Section 5 concludes.
2. Ship inspections
Port state control (PSC) 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 is
found to fail the minimum standards is detained, and the deficiencies have to be rectified
before the ship is released. In some cases, a ship can even be banned from re-entering ports if
it has been detained several times. Ship owners wish to avoid detention, as it bears high
economic costs and it may increase future inspections rates. PSC inspections are focused not
only on safety measures, but progressively also on environmental protection.
Today, there are ten PSC regimes operative that cover all coastal states and that enforce
international standards. The regimes are grouped by regions and countries which have agreed
to conduct inspections in a harmonized way, based on so-called Memoranda of
Understanding. All regimes use the same kind of information to decide, with varying levels
7 See, for instance, Payoyo (1994), Knapp and Franses (2007a-c, 2008), and Carriou, Mejia, and Wolff (2008).
of sophistication, whether a ship should be inspected or not. 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
particular data like the type, age, and size of the vessel, its flag state, its classification society,
and sometimes its Document of Compliance company8. In all regimes, high risk vessels are
tankers and passenger vessels, due to the potentially high costs in case of an incident. We
refer to Knapp and Franses (2007b) for an in-depth comparison of PSC inspection regimes
and target factors.9
As will be discussed in the next section, our dataset is obtained by combining data from a
subset of the ten PSC regimes, corresponding to some major trading nations. The regimes in
this subset provide a good reflection of the selection strategies that are used throughout the
industry. Further, as will also be described in the next section, we consider an alternative
selection strategy for inspections that amounts to a refined version of the current targeting
strategies. This alternative strategy is based on survival gains, obtained from the duration
analysis of Bijwaard and Knapp (2009) to estimate the risk of total loss accidents in terms of
ship particulars, economic indices, and event history including past inspections, industry
inspections, accidents, and changes of flag state and classification society. This means that a
wider information set is employed as compared to current strategies, which may help to
improve the estimated risk profiles of ships as a tool in making inspection decisions.
3. Data and methods
3.1. Arrival and inspection data
The arrival dataset consists of daily arrival information of 14,115 individual ships over the
period 2002 till 2007, and it contains over 400,000 arrivals in total. These data are obtained
from a number of port states that cover a considerable portion of world seaborne trade,
8 The DoC company is the designated company for the safety management of the vessel. It is still difficult to
obtain adequate data on DoC companies, and there are many companies in operation (about five thousand).
9 See Talley, Din, and Kite-Powell (2005) for a more detailed analysis of the inspection program of one of the regimes.
amounting (in 2008) to 17% of goods loaded, 15% of goods unloaded, and 12.5% of the
world merchant trade value.10 The dataset provides a representative spread of all trade
segments and of all 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, 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,11 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 of the data, differentiated for the five considered ship types,
that is, general cargo, dry bulk, container, tanker, and passenger. General cargo, tanker, and
container vessels have the largest number of ships, arrivals, and inspections. The total number
of inspections is 61,010, and the overall inspection rate (that is, the total number of
inspections divided by the total number of arrivals) is about 15%, with relatively the highest
rates occurring for dry bulk carriers and general cargo vessels.
Insert Table 1 about here.
For the purpose of our analysis later in this paper, it is relevant to distinguish between arrivals
that occur within the same PSC region in the twelve different half-year periods from 2002 to
2007. The reason for this distinction is the following. A PSC inspection reduces the risk of a
casualty later on, but inspections of the same ship that follow each other too fast will not all
lead to the same survival gain. Shipping experts differ in their opinion on how long the effect
of PSC inspections last, but it is generally agreed that all effect is lost after a period of one
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
10 The data providing regimes wish to remain anonymous for the purposes of this paper. Readers interested in
more details can contact the authors; provision of additional details depends on approval of the data providers.
11 These industry inspections include vetting inspections of RightShip for dry bulk carriers, of the Chemical
Distribution Institute (CDI) for chemical tankers, and of the Oil Companies International Marine Forum (OCIMF) for oil and
inspections after a period of six months (in the case of passenger ships three months).12 We
will assume that the effect lasts sufficiently strongly 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 half-years.13 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 happen, in
order to respect the current practice that PSC regimes disregard inspections of the other
regimes. Evidently, the effectiveness of PSC inspections could be enhanced by following an
integrated approach covering all regimes, but the coordination of inspections between all the
regimes falls outside the scope of this paper.
In the far 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 per half year,
whereas 44% of the ships are inspected once and 47% are not inspected. The compression of
multiple arrivals within a half-year leads to a total number of 91,713 eligible arrivals. When
restricted to eligible arrivals, the total number of inspections is 48,500, and this amounts to
79.5% of all inspections in the full database. This means that we exclude 20.5% of the
inspections by omitting multiple inspections. As such multiple inspections occur most
frequently for passenger and container ships, the inspection omission rates for these two ship
types are higher (respectively 54.4% and 34.4%). For eligible arrivals, the inspection rate per
half-year ranges between 50% and 60% for all five ship types.
The restriction to eligible arrivals is essential for the evaluation of the survival gain strategy
analyzed later 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 these inspections. As we will see in Section 4, the average
12 See Knapp and Franses (2007b) for further details.
13 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 actually inspected exactly once during the half-year, then the arrival where this inspection
occurred is the eligible arrival. If the ship was actually not inspected in the half-year, the arrival with the largest
survival gain is the eligible arrival. Finally, if the ship was actually inspected more than once during the half-
year, the arrival with inspection with the largest survival gain is the eligible arrival. The calculation of the
survival gain of an inspection is explained in Section 3.3. In case of multiple arrivals of the same ship, the
survival gains at different arrivals are commonly very close together, in which case the eligible arrival can be
considered as a randomly chosen arrival of the ship in the considered half-year.
survival gain of the actual inspections on all (eligible and non-eligible) arrivals is about the
same as that on the subset of eligible arrivals, so that eligible arrivals are representative in
this respect. This result holds true because multiple inspections are relatively rare.
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 total loss risk
identified by Bijwaard and Knapp (2009) are past incidents, past PSC and industry inspection
outcomes, ship economic cycles,14 and ship particulars: age, size, flag, classification society,
country of location of the DoC company, and changes of ship particulars over time.
The effect of the risk factors on total loss casualty risk is modelled by means of a hazard rate.
Let S(t) be the survival function, that is, the probability that the ship will survive for at least t
periods from its creation. The hazard rate, denoted by (t), is defined by (t) = –dln(S(t))/dt.15
A large hazard rate corresponds to a large risk that the ship will not survive for long. Our
hazard rate model has the form
(t) = b(t) exp(1X1(t)) … exp(kXk(t)),
where the baseline hazard b(t) models the age effect16 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. Of particular interest for our analysis is the effect of a
PSC inspection (measured by, say, X1, with X1 = 1 in case of an inspection and X1 = 0 if no
inspection is performed). Let 1 (0) be the hazard rate after (without) an inspection, then 1
= exp(1)0, and the risk reduction factor of an inspection is (0 – 1)/0 = 1 – exp(1).
14 Monthly average earnings data per ship type (except passenger vessels) are obtained from the Shipping
Intelligence Network from Clarksons.
15 Here, ‘ln’ denotes the natural logarithm, and the survival function is obtained by S(t) = exp( –
We refer to Van den Berg (2001) for further explanation of hazard rates and duration models.
16 We use a piecewise constant specification with six age groups, and b(t) = exp(j) in age group j (j = 1, …, 6).
Separate hazard models for total loss accidents are estimated for each ship type and for each
year from 2003 to 2007, using an expanding estimation window that stretches up to the year
of arrival. Table 2 summarizes the estimation results of main importance for our analysis.17
The risk reduction factors are considerable and differ per ship type. The largest reductions
(corresponding to the largest risk reduction factors) are realized for passenger vessels and dry
bulk carriers, and the reductions for containers are large in initial years but become smaller
later on. The table shows also the effects of other risk factors for the model of the final year
2007. Industry vetting inspections reduce the casualty risk. Past casualties, detentions, and
deficiencies increase the risk, although not all of these effects are significant for all ship
types. Further, on average, larger ships carry higher risk, and the same applies for new ships
(less than five years old) and for relatively old ships (more than twenty years old).
Insert Table 2 about here.
3.3. Calculation of survival gains and the survival gain strategy
The hazard model for total loss accidents forms our basis for the evaluation of the benefits of
inspections in terms of reduced casualty risk. As discussed in Section 3.1, because the
beneficial effects of an inspection fade out over time, we take the effect over a period of half
a year into account. 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 0 (1) that applies without (with) an inspection, where 1
= exp(1)0. We denote the associated conditional survival probability of this ship for the
next half-year by S0 (S1), where
S0 = Prob(survival till t + 1/2) / Prob(survival till t)
) / exp(–
) = exp(–
If we use the notation = exp(1), then 1 = 0; as 1 < 0, it follows that 0 < < 1, and
S1 = exp(–
) = exp(–
) = S0.
Because shipping is not a very risky industry, survival probabilities are large. Knapp,
Bijwaard and Heij (2010) find base risks of total loss (1 – S0) that range roughly between 1-
17 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. For passenger ships, the model
could not be estimated with sufficient accuracy for 2003 and 2004, because of data limitations. Full details of
the estimated hazard models per ship type and per year are presented in Knapp, Bijwaard and Heij (2010).
4% per year, so about 0.5-2% per half-year, so that S0 will mostly be larger than 98%.
Because S0 is close to 1, the first order Taylor expansion S0 = (1 + (S0 – 1)) 1 + ( S0 – 1)
provides an accurate approximation of S1. The survival gain for the next half-year, caused by
inspecting the ship now, is
S1 – S0 = S0 – S0 (1 – )(1 – S0) = (1 – exp(1))(1 – S0).
The relative survival gain, as compared to the probability (1 – S0) of a total loss accident in
the next half-year if the ship is not inspected, is therefore (S1 – S0) / (1 – S0) 1 – exp(1).
This means that the risk reduction factors in Table 2 can also be interpreted, to a high degree
of accuracy, as the factors by which the probability of a total loss accident in the next half-
year is reduced by means of an inspection.
The survival gains form the basis of our alternative inspection strategy, which we call the
survival gain strategy (abbreviated henceforth by SGS). For every given PSC regime, ship
type, and half-year, we determine the actual number of inspections, restricted to the set of
eligible arrivals defined in Section 3.1. SGS selects an equal number of ships for inspection,
but it selects the ships with the largest survival gains for the given PSC regime, ship type, and
half-year. As we found above that S1 – S0 (1 – )(1 – S0), the survival gain is almost
proportional to the half-yearly casualty risk (1 – S0), so that SGS tends to select the highest
risk vessels for inspection.
4. Survival gains of inspection strategies
The effect of PSC inspections can now be evaluated in terms of the resulting reduction of
total loss casualty risk. To prevent over-estimation of the realized survival gains obtained by
multiple inspections of the same ship within a brief period of time, the attention is restricted
to eligible arrivals. This guarantees 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 ship type and each year, the average survival
gain per inspection is obtained by dividing the sum total of survival gains of all inspections
by the number of inspections. This average provides an indication of the survival gain that
has actually been achieved by current inspection strategies. As an alternative, the average
survival gain is also computed for SGS. SGS applies the same inspection rates as the actual
strategies per regime, ship type, and half-year, but it selects those of the eligible arrivals for
inspection that have the largest survival gains.
Insert Table 3 about here.
Table 3 summarizes the results of the actual inspection strategies and of SGS. The main
conclusion is that there exist good opportunities for improving the effect of inspections in
terms of gained 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 reduction of total loss risk of 0.18% for general cargo, of about 0.10% for dry bulk,
container, and tanker, and of 0.07% for passenger vessels. It is also of interest to compare the
survival gains (S1 – S0) in Table 3 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.18 For example, inspections of general cargo vessels reduce
the total loss risk within a half-year on average from 1.88% to 1.63%, whereas SGS
inspections achieve a further reduction to 1.45%.
Even though the risks and risk reductions may seem to be small in absolute terms, the
achieved benefits of inspections in terms of saved potential accident costs are huge. Total loss
accidents do not only involve the loss of ship and cargo, but they may also cause additional
costs in terms of loss of life, environmental damage, and damage to third parties. It is not
easy to quantify the monetary value involved in total loss accidents, but using results in
Knapp, Bijwaard and Heij (2010), we obtain the following median values (in 2008 USD): 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.19 For example, the extra survival gain of 0.18%
of SGS as compared to current inspections of general cargo vessels amounts to an estimated
extra saved value of about 11 thousand USD per inspection.
18 These un-inspected casualty risks are obtained from Knapp, Bijwaard and Heij (2010), by transforming yearly
risks for 2003-2007 into average half-yearly risks.
19 These values (say, V) are obtained from Knapp, Bijwaard and Heij (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 = VG, it follows that V = L/G. For example, for general cargo vessels, L = 21.0
thousand USD and G = 0.0167/5 = 0.00334, so that V = L/G is about 6.3 million USD.
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 (denoted by ‘Elig’ in Figure 1)
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 (denoted by ‘All’ in Figure 1). 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, it is quite fluctuating for container ships, whereas an initial rise is followed by a
decline for dry bulk carriers and tankers.
Insert Figure 1 about here.
To conclude our comparison of inspection strategies, we return to the discussion of further
results in Table 3. For each ship type, SGS inspections coincide with actual inspections in
slightly more than half of all cases. As 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 percentage of inspections
finding no deficiencies is slightly smaller. In most cases, the future total loss accident rate of
ships that would have been selected by SGS is higher than that of the actually inspected
ships. All these results show that the information present in the hazard rate factors does
indeed help to select ships that have a high risk (in terms of detention, deficiencies, and future
casualties). Finally, the bottom part of Table 3 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 in size and somewhat older. The distribution over flags
and classification societies is roughly similar to current practice.
Maritime safety can be improved by well-targeted ship inspections. The current practice of
port state control inspections is that the various regimes share the same objectives and
employ the same type of information, with varying levels of sophistication. However,
information is not yet shared between regimes, and the regimes disregard each other’s
inspections as well as inspections performed by the industry, such as vetting inspections.
Based on a rich combined dataset, consisting of arrivals and inspections from several port
state control regimes, industry inspections, and casualties, this paper investigates the
effectiveness of currently employed inspection strategies in terms of reducing casualty risks.
The central tool to transform relevant ship particular information into an inspection decision
is the hazard rate, which expresses casualty risk in terms of a set of risk factors. The hazard
rate model is estimated using data from all regimes, and it is used to compute the survival
gains associated with inspections. These gains form the basis for an inspection strategy that is
explicitly risk-driven, by selecting the ships for inspection that have the largest survival gain.
This survival gain strategy (SGS) 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. In absolute terms, and as compared to current strategies, the
risk of total loss accidents within half a year after inspection is reduced by 0.18% for general
cargo, 0.10% for dry bulk, container, and tanker, and 0.07% for passenger vessels. The
corresponding potential savings in monetary terms are considerable, because total loss
accidents involve very high costs.
The reported SGS results are only indicative, as the historical inspection and casualty data are
obtained under the prevailing PSC regimes and the SGS 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, who share responsibility for selecting ships for inspections and for enforcing
minimum international safety standards. The effectiveness of the selection of ships for
inspection can be enhanced by integrating other inspection information, that is, the
inspections made in other regimes as well as industry vetting inspections, in addition to
casualty data. In the future, IMO’s planned port state control module of the Global Integrated
Ship Information System (GISIS) should preferably contain port state control inspections
from all regimes, which can be linked to the existing GISIS module on casualties to provide
an integrated database to target ships for inspections.
For practical implementation purposes, SGS 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 cost
considerations. SGS evaluates all gains in survival probability on an equal footing. However,
the costs involved in losing a ship depend on the value of the ship and its lost cargo and on
the associated environmental costs. Such cost information can be combined with casualty
risks to refine inspection rules and to modify inspection rates.
We thank the data providers, who wish to remain anonymous, for providing us with
information on ship arrivals. We thank Lloyd’s Register Fairplay for supplying the casualty
Bijwaard, G.E., Knapp, S., 2009. Analysis of ship life cycles – the impact of economic cycles
and ship inspections. Marine Policy 33, 350-369.
Carriou, P., Mejia, M.Q., Wolff, F.C., 2008. On the effectiveness of port state control
inspections. Transportation Research Part E 44, 491-503.
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
Knapp, S., Franses, P.H., 2007(a). Econometric analysis on the effect of port state control
inspections on the probability of casualty. Marine Policy 31, 550-563.
Knapp, S., Franses, P.H., 2007(b). A global view on port state control - econometric analysis
of the differences across port state control regimes. Maritime Policy and Management 34,
Knapp, S., Franses, P.H., 2007(c). Econometric analysis on the effectiveness across port state
control regimes – what are the areas of improvement for inspections? Ocean Economics
Review of China 1, 26-54.
Knapp, S., Franses, P.H., 2008. Econometric analysis to differentiate effects of various ship
safety inspections. Marine Policy 32, 653-662.
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.
Payoyo, P.B., 1994. Implementation of international conventions through port state control:
an assessment. Marine Policy 18, 379-392.
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 U.S. 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.
Table 1: Arrivals and inspections (per regime and per half year)
General CargoDry Bulk
14,115 (a)Total number of ships3267197
Inspections per ship
None (number of ships)
idem (% of ships)
idem (% of ships)
Two or more (number)
idem (% of ships)
idem (% of all arrivals)
Inspections per arrival type
Inspections of all arrivals (number)
idem (% of all arrivals)
idem (% of eligible arrivals)
Inspections of eligible arrivals (number)
idem (% of all eligible arrivals)
idem (% of all inspections)
Note: (a) In total 3464 ships changed of type during the observation period, and the actual number of ships of each
type is larger than shown in the table; nearly all changes occurred between general cargo, dry bulk, and container.