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Abstract

This paper analyses the EU-MRV dataset containing CO2 emission data from vessels calling at European harbours. The data is provided in an aggregated form for 2018 and we decided to focus on ferries. We augmented the original dataset with data from the Copernicus Marine Environment Monitor- ing Service, the IMO Global Integrated Shipping Information System, and an open repository of ferry data. An analysis of various energy efficiency indicators revealed some clustering in the vessel population and the key factors were year of build, vessel length, service speed, and fuel type. Georeferencing data provided additional information on the continental patterns of the Ro-Pax emissions.
EU-MRV: an analysis of 2018’s Ro-Pax CO2data
Gianandrea Mannarini, Lorenzo Carelli, Amal Salhi
Ocean Predictions and Applications Division
Fondazione CMCC (Centro Euro-Mediterraneo sui Cambiamenti Climatici)
Lecce, Italy
Abstract—This paper analyses the EU-MRV dataset containing
CO2emission data from vessels calling at European harbours.
The data is provided in an aggregated form for 2018 and we
decided to focus on ferries. We augmented the original dataset
with data from the Copernicus Marine Environment Monitor-
ing Service, the IMO Global Integrated Shipping Information
System, and an open repository of ferry data. An analysis of
various energy efficiency indicators revealed some clustering in
the vessel population and the key factors were year of build,
vessel length, service speed, and fuel type. Georeferencing data
provided additional information on the continental patterns of
the Ro-Pax emissions.
Index Terms—Global warming, Environmental monitoring,
Marine transportation.
I. INTRODUCTION
For decades the Intergovernamental Panel on Climate
Change1has been warning about the current and future
impacts of climate change, at a global, regional, and local
scale, including air and marine heat waves, alterations in rain
patterns and the availability of fresh water, ocean acidification
and sea level rise, habitat and biodiversity loss, and a decrease
of both ocean and crop productivity2. The ultimate driver
of such rapid, unprecedented changes is the concentration of
greenhouse gases (GHG) in the atmosphere, emitted through
the combustion of fossil fuels, such as coal, oil, and natural
gas. Carbon dioxide (CO2) is the GHG responsible for most
of the warming observed since the beginnings of the industrial
age [1].
The European Union (EU) has set ambitious targets to
curb CO2emissions and thus limit global warming and its
detrimental consequences. In 2015 the European Parliament
(EP) and the Council approved regulation number 757 on
Monitoring, Reporting, and Verification of CO2emissions
from vessels calling at harbours not only from EU Member
States (MS) but within the European Economic Area (EEA)
[2], [3]. The regulation regards vessels exceeding 5,000 gross
tonnes, prescribes consistent and comparable CO2monitor-
ing methods, and defines procedures for CO2reporting and
the verification of reports. Since 2018, annually-aggregated
emissions reports have been mandatory for each category of
vessels, and these have been provided through a dedicated
system supported by the European Maritime Safety Agency
Funding through GUTTA project (contract no 10043587), part of IT-HR
Interreg programme, is acknowledged.
1https://www.ipcc.ch/
2https://www.ipcc.ch/working-group/wg2/
(EMSA): THETIS-MRV. These data were first published as
scheduled on June 30, 2019.
Two other systems have come into existence since the
EU-MRV has been in place: the Data Collection System
(DCS) by the International Maritime Organization (IMO) [4]
and the Chinese Regulation on Data Collection for Energy
Consumption of Ships3. They differ in terms of the parameters
monitored, type of reporting, and verification protocols. A
legislative process is currently ongoing at the EP in order to
align EU-MRV with IMO-DCS.
The THETIS-MRV dataset includes nearly 12,000 vessels,
of various types including: container ships, tankers, chemical
tankers, oil tankers, bulk carriers, and vehicle carriers. Ro-Pax
ships, also known as “Ro-Ro passenger” vessels or simply
“ferries”, are also part of the EU-MRV fleet. According to
Interferry, ferries globally transport over two billion passengers
every year4. In addition, they are generally considered a cost-
efficient means of displacing heavy-duty vehicles in coastal
areas. Furthermore, in THETIS-MRV, ferries represent 3% of
all vessels while accounting for 10% of all CO2emissions5.
The latter observation raises the question of carbon intensity
of vessels of this class: Can it be related to the navigational
domain? Or perhaps to the characteristics of the vessels? If so,
which characteristics and how? All this motivates a closer look
at the Ro-Pax data available in the first EU-MRV report with
reference to emissions from 2018. These data will represent
the baseline of all future reports.
Our methodology is presented in Sect. II. The results are
provided in Sect. III, and discussed in Sect. IV.
II. ME TH OD S
Although data fusion presents various challenges related to
data quality and consistency, as the data may have originally
been collected for different purposes [5], it can greatly increase
the value of information. For example, in [6] data fusion
of kinematical (automatic identification system, AIS) and
environmental data (sea state from model analyses) means that
vessel speed loss in waves can be estimated without needing
specific information about the vessels.
In order to enhance the information contents of the
THETIS-MRV dataset we fused it with several other datasets
(Sect. II-A) before analyzing it (Sect. II-B).
3http://bit.ly/ChinaDC-LR
4https://interferry.com/ferry-industry-facts/
5In version #160, the total emissions are 141,088,729.6 t CO2.
287
2020 21st IEEE International Conference on Mobile Data Management (MDM)
2375-0324/20/$31.00 ©2020 IEEE
DOI 10.1109/MDM48529.2020.00065
A. Datasets
The datasets used in this work are all open-access:
THETIS-MRV: this corresponds to version #160 (down-
loaded from EMSA6on February 28, 2020). Of all the vessels
in the dataset, just the “Ro-pax ships” were extracted, resulting
in 356 database entries. Each entry includes 61 fields, some
of which are lacking some values. The first field is the
IMO identification number of the vessel, which was used
as primary key when fusing THETIS-MRV with the other
datasets described below.
CMEMS The Copernicus Marine Environment Monitoring
System7provides sea state analysis fields from numerical
models run operationally for the various seas in Europe.
Related domains and spatial resolutions are specified in Tab. I.
Hourly-instantaneous significant wave height fields (Hs) for
year 2018 were downloaded (a volume of about 0.1 TB) and
aggregated on an annual basis. Tab. I also reports the counts of
the Ro-Pax vessels based on their average location (see method
explained in Sect. II-B1). Wherever a vessel’s location belongs
to a region with overlapping model outputs (cf. Fig. 1), it is
attributed to the one with the highest resolution (i.e., smallest
grid size).
GISIS is the Global Integrated Shipping Information Sys-
tem8which has been developed by the IMO and includes
details on the ship and shipping company. From the public
version of GISIS, we only extracted the year when the ship
was built from the ”date of build” parameter for each of the
356 THETIS-MRV Ro-Pax vessels.
Ferry-site9is an open repository with an emphasis on north
European ferries. Whenever multiple values are provided for
the parameters - length overall (LOA), service speed vserv,
and number of passengers nP- we took their highest value;
and lowest value for number of beds nBand cars nC. LOA
and vserv were validated against the MS version in the GISIS
database. Ferry-site also includes information about the route,
from which the ports of call were parsed and georeferenced.
Of the 356 THETIS-MRV Ro-Pax ferries, 342 were found also
on Ferry-site.
B. Analysis
The dataset resulting from fusing all the data presented in
Sect. II-A is called hereafter the “augmented dataset”. The
data was processed in three steps, as reported below.
1) Georeferencing: For a preliminary classification of
THETIS-MRV data with respect to the typical domain of
operation, we associated a single geographical location with
each Ro-Pax vessel. The rationale for this coarse assignment
was to relate it to the temporal coarse (annual average)
emission information available. Finer spatial analysis would
entail having finer temporal emission data, but such data are
not publicly available.
6https://mrv.emsa.europa.eu/#public/emission-report
7http://marine.copernicus.eu/
8https://gisis.imo.org/
9http://www.ferry-site.dk/
TABLE I
RO-PAX V ESS EL S IN EUR OPE AN S EAS W IT H THE G RI D SIZ E OF SE A STATE
MODEL OUTPUTS (1 NMI = 1852 M)
Domain short name Resolution # vessels
[nmi]
North West Shelf NWS 0.8 89
Baltic Sea Bal 1.0 76
Arctic Sea Arc 1.6 0
Black Sea BS 1.7 2
Mediterranean Sea Med 2.5 164
Iberian-Biscay-Irish Seas IBI 3.0 11
subtotal 342
outliers f4of Sect. II-B2 14
total (THETIS-MRV) 356
The representative location was computed as follows: the
ferry ports of call were retrieved from Ferry-site, they were
then georeferenced10, and for each vessel the average position
of all combinations of legs among those ports was computed.
Although our procedure is quite simple, it is appropriate
for Ro-Pax vessels since their routes (unlike for example
cruise ship routes) are for reasonably short distances and tend
to be straight, though obviously depending on topological
constraints and meteo-marine conditions. However, our pro-
cedure poses an issue for longer-range routes and for convex
coastlines. Moreover, if a vessel operates between spatially-
distinct clusters of ports of call, with only occasional transfer
voyages between clusters, the representativeness of a mean
annual location computed this way is questionable.
2) Filtering: The augmented dataset was filtered to prune
entries (i.e., vessels) that included inconsistencies, that is:
f1) zero total CO2emissions - these entries do not add any
information to the current analysis;
f2) emissions from MS ports greater than total emissions -
this is an inconsistency in the data, as total emissions
include by definition those among MS;
f3) off range carbon emission factor CF>4- this cannot
be true for most common fuel types, Tab. IV;
f4) no ports of call available or mean annual vessel location
out of the EEA - this is not accepted as data in THETIS-
MRV all refer to Ro-Pax ships calling at EEA ports.
f5) just a single port of call retrieved or average leg distance
greater than 450 nmi (corresponding to 15 h navigation at
a speed of 30 kts) - this implies no reliable identification
of the place of operation.
Criteria f1)-f4) include 4.2% of the emissions from the Ro-
Pax population and are applied throughout. Instead, f5) is
applied for specific purposes only. The number of vessels and
emissions ascribed to each criterium is reported in Tab. II.
Finally, there were some gaps in the THETIS-MRV dataset:
g1) no monitoring method reported - this is a mandatory field
for EU-MRV and ships not reporting it are not compliant
with the regulation;
10via https://getlatlong.net/
288
g2) non null CO2emissions reported, but no total fuel con-
sumption value - both data are needed in order to infer
the fuel type (see Sect. II-B3).
TABLE II
OUTLIERS ACCORDING TO THE CRITERIA OF SECT. II-B2
criterium # vessels CO2emissions
[ton] %
f11 0.00 0.0
f23 60,242.61 0.4
f31 75,880.91 0.5
f414 448,148.27 3.0
f578 2,610,077.09 18.6
g131 1,258,810.47 9.0
g27 293,298.06 2.1
3) Derived quantities: Fuel type was inferred from the fuel
emission factor CF. The latter was obtained from THETIS-
MRV data as the ratio of total CO2emissions to total fuel
consumption. The closest value among the emission factors11
reported in Tab. IV was used for assigning the fuel type.
An effective velocity veff can be computed by taking the
ratio of the total CO2emissions (C) not at berth (Cb) to the
total time spent at sea Tsand dividing by the CO2emissions
per distance dC/dx:
veff =(CCb)
TsdC
dx (1)
In the EU-MRV revision proposal12, “hours underway” re-
places “time at sea”, as in the IMO-DCS. In fact, “hours
underway” provides a more accurate estimation of veff .
Ship energy efficiency is a crucial indicator for any regula-
tory framework, as absolute emissions should be compared
to distance sailed, capacity, transport work, or some other
normalization parameter. A brief account of the use of var-
ious indicators in the ongoing debate on the assessment of
energy efficiency is provided in [7]. IMO adopted the Energy
Efficiency Design Index (EEDI) for new ships in 2011 [8].
EU-MRV mandates report several fields that can be used to
assess energy efficiency. The emissions per distance sailed per
passenger is a form of Energy Efficiency Operational Indicator,
here called “EEOIpax”. Finally, the Energy Efficiency per
Service Hour (EESH) is given by the ratio of total emissions
Cby total time at sea Ts.
III. RES ULTS
This section presents the results of applying the methodol-
ogy outlined in Sect. II. As mentioned in Sect. II-B1, some
of the mean annual locations are weakly identified, and this
may occasionally (less than 8% of the ships) mean that they
are near the coast but actually on land.
11http://bit.ly/verifaviaCF
12http://bit.ly/COM2019-38
Fig. 1. CO2monitoring methods (cf. Tab. III). The dashed boxes correspond
to the domains of Tab. I.
A. CO2monitoring method
In accordance with the EU-MRV Regulation [2], for each
vessel one of the four CO2monitoring methods in Tab. III
must be selected. Method A corresponds to the top-down ap-
proach also used in the Third IMO GHG Study [9]. However, a
few Ro-Pax vessels in the THETIS-MRV dataset do not have a
value for the monitoring method (set g1of Sect. II-B2). Some
vessels also reported more than one method. Fig. 1 highlights
that method D (direct CO2measurement) was not used by
any Ro-Pax ships in 2018. Method C was primarily used in
the North West Shelf (NWS) and Baltic seas. However, there
are some “Cs” in the northern Tyrrhenian Sea, two “B&Cs”
in the Mediterranean, and several “As” in both the NWS and
the Baltic. No ferries were identified in the vicinity of Iceland,
though Iceland is part of the EEA.
B. Speed
In Fig. 2 the effective speed veff from Eq. 1 is compared
to the service speed vserv. While both are well correlated, on
average veff is about 5 kts lower than vserv. This might not
just correspond to a slow-steaming practice, but also to the
overestimation of travel time in veff due to use of Tsin Eq. 1.
We introduce the Froude number Fn=vserv /g·LOA
for relating speed to LOA13, which exhibits a large spread
in the ferry population (cf. Fig. 8). Furthermore, at least for
13g= 9.8m/s2is the gravity acceleration.
TABLE III
EU-MRV CO2MONITORING METHODS
name description
D Direct CO2emissions measurement
C Flow meters for applicable combustion processes
B Bunker fuel tank monitoring on-board
ABunker fuel delivery notes
and periodic stocktakes of fuel tanks
289
Fig. 2. Effective speed veff from Eq. 1 vs. service speed vserv, with marker
colors relating to the year of build. High-speed Ro-Pax are shown as crosses;
the 1:1 line is black and dashed.
mono-hulls, Fn>0.4corresponds to the transition to a semi-
displacing regime [10]. Fig. 2 shows that such “high-speed”
Ro-Pax vessels were all built around 2000 or later.
C. Energy efficiency
The mean annual CO2emissions per distance sailed per
passenger (EEOIpax) is georeferenced in Fig. 3. Some of the
most efficient vessels with respect to this parameter operate in
the British Isles, but also in the western Mediterranean and in
the Adriatic sea.
EEOIpax is a quite sensitive indicator as, in the THETIS-
MRV vessel population, it varies over five orders of magnitude.
This spread likely reflects not just the various ferry sizes, but
also propulsion features, age, and the various combinations of
vehicles and passenger services (number of cabins, vehicles,
entertainment facilities) in their payloads [11].
Fig. 4 shows how the EEOIpax relates to service speed, fuel
type and the ratio of the number of beds to passengers (this
ratio may exceed one, possibly because crew members are not
included in the passenger count). There is some evidence that
the logarithm of emission intensity increases with speed. The
figure also suggests that there are at least three subpopulations
(EEOIpax units are g CO2/(pax·nmi) and vserv kts):
i) 102<EEOIpax <104and vserv <30 : here most vessels
with nB/nP1are found;
ii) 103<EEOIpax <104and vserv >35 : these are the
high speed ferries;
iii) EEOIpax <101and vserv <25 : these vessels tend to
have nB/nP0.5.
Fuel type can be any, apart from in cluster ii) which consists
entirely of vessels using MDO as a bunker.
D. Age
Year of build is shown in Fig. 5. There is no clear divide
between northern and southern European sea vessels, with
Fig. 3. CO2emissions per passenger per distance sailed (EEOIpax).
TABLE IV
EMI SSI ON FAC TOR S (CO2BY FU EL MA SS )USE D IN T HIS W ORK
full name CF(g/g)
MDO Marine Diesel Oil 3.206
LFO Light Fuel Oil 3.151
HFO Heavy Fuel Oil 3.114
LNG Liquefied Natural Gas 2.750
some of the oldest Ro-Pax in the northern Tyrrhenian and in
the Adriatic sea, but also in the Baltic.
The upper histogram of Fig. 6 shows the distribution of
the year of build. Given that the oldest ferries in service
in 2018 were nearly 50 years old, the statistics indicate
an expansive age pyramid, with a median age of about 20
years. Furthermore, the average vessel size (lower histogram
in Fig. 6) shows that larger ferries are now being built.
Fig. 4. EEOIpax vs. service speed with marker color given by number of
beds per passenger and marker type given by fuel type (cf. Tab. IV).
290
The height of the bars in Fig. 6 represents the total emissions
and mirrors the distribution of the number of vessels built per
year. The average EEOIpax is used to colour this histogram.
Given the great spread in values, a linear EEOIpax averaging
would be dominated by the largest values. In order to mitigate
this, the following averaging is applied:
EEOIpax = exp log(EEOIpax)(2)
Some of the highest emissions come from newer vessels (built
in 2012).
E. Sea state
The International Maritime Organization (IMO) recom-
mends avoiding “rough seas and head currents”. This is among
the ten measures of the Ship Energy Efficiency Management
Plan [12]. Therefore sea state (waves) can be relevant when
considering carbon intensity [13]. Sea state can greatly change
during a year, as the timescale of its variability depends
on meteorological events. In wave spectra, wind waves can
be differentiated from swell14. Fig. 7 reports the wind-wave
component alone and shows an annual average Hs1m in
most domains, with higher values in western European seas,
and lower values in the Adriatic and in the Aegean Sea.
The mean annual EESH (Fig. 8) shows a weak dependence
on Hsand this only for the smaller vessels (Pearson’s R =
0.57 for LOA <120 m). More time-resolved data (daily, at
least) should highlight a clearer dependence of EEOI on sea
state and even sea currents [13]. Fig. 8 shows that EESH
increases with LOA, with larger vessels producing higher
unit emissions per time. The emission intensity of high-speed
ferries is comparable to that of much larger ferries.
F. Fuel types
Fuel type can be inferred for most THETIS-MRV Ro-
Pax vessels, but the g2)set of Sect. II-B2. Fig. 9 shows
14http://bit.ly/ECMWFwaves
Fig. 5. Ro-Pax vessels’ year of build.
CO2
Fig. 6. Total emissions by year of build of the vessels, with bar color given
by EEOIpax from Eq. 2. The number and cumulative distribution of vessels
built in each year are given in the upper histogram and the average LOA in
the lower one (both as grey columns).
Fig. 7. 2018 mean annual significant wave height Hsfrom the sea state
model outputs of Tab. I interpolated at the representative vessel locations. f5
criterium applied.
their distribution in the various European sea domains. HFO
is dominant in both the Mediterranean and the NWS. LFO
dominates in the Baltic. LNG is used for NWS and Baltic
ferries only. MDO is relevant in the NWS and in the Baltic
and, to a lesser extent, in the Mediterranean too. Fig. 9 also
highlights that the Mediterranean alone accounts for nearly as
many Ro-Pax emissions (6.9 Mton CO2) as all other European
seas combined (7.1 Mton CO2).
291
Fig. 8. Energy efficiency per service hour (EESH) vs. mean annual Hs, with
marker color given by vessel LOA. Crosses represent high-speed ferries.
Fig. 9. Total Ro-Pax emissions in the various European sea basins; the
fractions due to the various fuel types are highlighted. The total emissions
in 2018 were 14.0 Mton CO2. Colorbar’s N/A is due to the g2set, the N/A
column to the f4set.
IV. CONCLUSIONS
This paper reports the first ever publication of ship-
distinctive CO2annual emission data (EU-MRV) from Ro-Pax
vessels in the EEA.
Some noisy geographic patterns were found and can be
summarized as: multiple CO2monitoring methods used in
northern European seas, newer vessels in western European
seas, weak dependence on mean annual sea state, and half of
the EEA emissions stemming from the Mediterranean Sea. The
latter fact is in line with findings from a bottom-up inventory
[14].
Although our georeferencing procedure provides a rough
attribution of emissions to specific sea basins and a visualiza-
tion of continental patterns, it could be improved by integrating
more detailed tracking data, such as the ports of calls of each
voyage or the full AIS data.
On the other hand, the non-spatial information shows that
the energy efficiency is quite diverse within the Ro-Pax pop-
ulation, reflecting service speed, fuel type, size, and, possibly,
type of payload. High-speed ferries stand out when it comes to
the relationship between energy efficiency and speed or size.
Dataset augmentation was key for both the spatial and
non-spatial part of this analysis. Due to the annual temporal
aggregation, it was not possible to clearly assess if sea state
affects the energy efficiency. Also, a more refined clustering
may help refine the dependence of the energy efficiency on the
other vessel parameters considered here. This would enable
comparisons among similar vessels, assessing their potential
for decarbonization, and facilitate a credibility test of the
reported data. As mentioned in the preamble of [2] this would
simplify the EU-MRV verification process.
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292
... This led to the first-ever publication of a ship-distinctive database of emissions (2019). For ferries, an assessment of these data was done in [5]. The Regulation entered an amendment procedure that, following a proposal approved by the European Parliament (EP) in September 2020 2 , will probably make its ambition bigger. ...
... The Adriatic Sea is routinely crossed by several ferry lanes joining ports in Italy with ports in Croatia, Montenegro, and Albania. Also, according to the MRV dataset of 2018, despite the fact that the number of Ro-Pax ships (transporting both vehicles and passengers) was only about 3% of all ships reporting calls in the EEA, the quota of their CO 2 emissions was an over-proportional 10% [5]. ...
... Furthermore, such savings are demonstrated for a Ro-Pax vessel. This is remarkable, as CO 2 emissions from this ship type are over-proportional with respect to their abundance in the fraction of the EEA fleet due to report per EU-MRV [5]. Also, present-day energy efficiency of ferries may not always be competitive with land-based alternatives, as pointed out in [7]. ...
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... According to the European Maritime Safety Agency (EMSA), companies should submit their emission report through the THETIS-MRV web platform [2]. This database was used to analyse CO 2 emissions in 2018 from Ro-Pax vessels calling at European ports [3]. International Maritime Organization (IMO) followed this example with the IMO Data Collection System module which requires ships larger than 5000 GT to report and record fuel oil consumption [4]. ...
Article
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Increasingly stringent environmental requirements for marine engines imposed by the International Maritime Organisation and the European Union require that marine engines have the lowest possible emissions of greenhouse and harmful exhaust gases into the atmosphere. In this research, exhaust gas emissions were measured on three Ro-Pax vessels sailing in the Adriatic Sea. Testo 350 Maritime exhaust gas analyser was used for monitoring the dry exhaust gas concentrations of CO2 and O2 in percentage, concentrations of CO and NOx in ppm and exhaust gas temperature in °C after the turbocharger at different engine loads. In order to compare and validate measured values, exhaust gas measurement data were also obtained from a Wartsila-Transas simulator model of a similar Ro-Pax vessel during the joint operation of the engine room and navigational simulators. All analysed main engines on three vessels had complete combustion processes in the cylinders with small differences which should be further investigated. Comparison of on board measured parameters with simulated parameters showed that significant fuel oil reduction per voyage could be accomplished by voyage and/or engine operation optimization procedures. Results of this analysis could be used for creating additional emission database and data-driven models for further analysis and improved estimation of exhaust gasses under various marine engine conditions. Additionally, the results could be useful to all interested parties in reducing the fuel oil consumption and emissions of greenhouse and harmful exhaust gases from vessels into the atmosphere.
Book
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This book contains extended abstracts of research results presented at the 15th Baška GNSS Conference: Technologies, Techniques and Applications Across PNT and The 2nd Workshop on Smart Blue and Green Maritime Technologies. The conference was held in Baška, island of Krk, in the period 8 - 13 May 2022. We would like to thank all the authors for their efforts and time, and excellent presentations of their work.
Article
To reduce CO 2 emissions from shipping activities to, from, and within the European Union (EU) area, a system of monitoring, reporting, and verification (MRV) of CO 2 emissions from ships are implemented in 2015 by the EU. Although the MRV records in 2018 and 2019 have been published, there are scarce studies on the MRV system especially from a quantitative perspective, which restrains the potential of the MRV. To bridge this gap, this paper first analyzes and compares MRV records in 2018 and 2019, and then develops machine learning models for annual average fuel consumption prediction for each ship type combining ship features from an external database. The performance of the prediction models is accurate, with the mean absolute percentage error (MAPE) on the test set no more than 12% and the average R-squared of all the models at 0.78. Based on the analysis and prediction results, model meanings, implications, and extensions are thoroughly discussed. This study is a pioneer to analyze the emission reports in the MRV system from a quantitative perspective. It also develops the first fuel consumption prediction models from a macro perspective using the MRV data. It can contribute to the promotion of green shipping strategies.
Article
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Aiming at reducing CO2 emissions from shipping at the EU level, a system for monitoring, reporting, and verification (MRV) of CO2 emissions of ships was introduced in 2015 with the so-called ‘MRV Regulation’. Its stated objective was to produce accurate information on the CO2 emissions of large ships using EU ports and to incentivize energy efficiency improvements by making this information publicly available. On 1 July 2019, the European Commission published the relevant data for 10,880 ships that called at EU ports within 2018. This milestone marked the completion of the first annual cycle of the regulation’s implementation, enabling an early assessment of its effectiveness. To investigate the value of the published data, information was collected on all voyages performed within 2018 by a fleet of 1041 dry bulk carriers operated by a leading Danish shipping company. The MRV indicators were then recalculated on a global basis. The results indicate that the geographic coverage restrictions of the MRV Regulation introduce a significant bias, thus prohibiting their intended use. Nevertheless, the MRV Regulation has played a role in prompting the IMO to adopt its Data Collection System that monitors ship carbon emissions albeit on a global basis.
Article
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The latest development of the ship-routing model published in Mannarini et al. (2016a) is VISIR-1.b, which is presented here. The new version of the model targets large ocean-going vessels by considering both ocean surface gravity waves and currents. To effectively analyse currents in a graph-search method, new equations are derived and validated against an analytical benchmark. A case study in the Atlantic Ocean is presented, focussing on a route from the Chesapeake Bay to the Mediterranean Sea and vice versa. Ocean analysis fields from data-assimilative models (for both ocean state and hydrodynamics) are used. The impact of waves and currents on transatlantic crossings is assessed through mapping of the spatial variability in the tracks, an analysis of their kinematics, and their impact on the Energy Efficiency Operational Indicator (EEOI) of the International Maritime Organization. Sailing with or against the main ocean current is distinguished. The seasonal dependence of the EEOI savings is evaluated, and greater savings with a higher intra-monthly variability during winter crossings are indicated in the case study. The total monthly mean savings are between 2 % and 12 %, while the contribution of ocean currents is between 1 % and 4 %. Several other ocean routes are also considered, providing a pan-Atlantic scenario assessment of the potential gains in energy efficiency from optimal tracks, linking them to regional meteo-oceanographic features.
Presentation
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Presentation given on June 13th, 2019 at TransNav2019, http://transnav2019.am.gdynia.pl/, and awarded with the "best presentation award". Related paper is: http://www.transnav.eu/Article_Preliminary_Inter-comparison_of_,49,874.html
Article
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Optimal ship tracks computed via the VISIR model are compared to tracks recorded by the Automatic Identification System (AIS). The evaluation regards 43 tracks in the Southern Atlantic Ocean, sailed during 2016-2017 by different bulk carriers. In this exercise, VISIR is fed by wave analysis fields from the Copernicus Marine Environment Monitoring Service (CMEMS). In order to reproduce vessel speed loss in waves, a new methodology is developed, where kinematic information from AIS is fusioned with wave information from CMEMS. Resulting VISIR tracks are analyzed along with AIS tracks in terms of their topological features and duration. The tracks exhibit quite diverse topological shapes, including orthodromic, loxodromic, and other paths with complex and dynamic diversions. The distribution of AIS to VISIR track durations is analyzed in terms of several parameters, such as the AIS to VISIR track length and their Fréchet distance. Model features of VISIR affecting the results are discussed and future developments suggested by the results are outlined.
Article
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Emissions originating from ship traffic in European sea areas were modelled using the Ship Traffic Emission Assessment Model (STEAM), which uses Automatic Identification System data to describe ship traffic activity. We have estimated the emissions from ship traffic in the whole of Europe in 2011. We report the emission totals, the seasonal variation, the geographical distribution of emissions, and their disaggregation between various ship types and flag states. The total ship emissions of CO2, NOx, SOx, CO, and PM2.5 in Europe for year 2011 were estimated to be 121, 3.0, 1.2, 0.2, and 0.2 million tons, respectively. The emissions of CO2 from the Baltic Sea were evaluated to be more than a half (55 %) of the emissions of the North Sea shipping; the combined contribution of these two sea regions was almost as high (88 %) as the total emissions from ships in the Mediterranean. As expected, the shipping emissions of SOx were significantly lower in the SOx Emission Control Areas, compared with the corresponding values in the Mediterranean. Shipping in the Mediterranean Sea is responsible for 40 and 49 % of the European ship emitted CO2 and SOx emissions, respectively. In particular, this study reported significantly smaller emissions of NOx, SOx, and CO for shipping in the Mediterranean than the EMEP inventory; however, the reported PM2.5 emissions were in a fairly good agreement with the corresponding values reported by EMEP. The vessels registered to all EU member states are responsible for 55 % of the total CO2 emitted by ships in the study area. The vessels under the flags of convenience were responsible for 25 % of the total CO2 emissions.
Article
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Recent advances in the hydrodynamic design of fast monohulls are discussed. Their merit is compared to the current state of development of the other advanced hull forms.
Anthropogenic and Natural Radiative Forcing, Climate Change 2013: The Physical Science Basis
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  • B Mendoza
G. Myhre, D. Shindell, F. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J. Lamarque, D. Lee, B. Mendoza et al., "Anthropogenic and Natural Radiative Forcing, Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 659-740," 2013.
MEPC.1/Circ.681 Interim guidelines on the method of calculation of the Energy Efficiency Design Index for new ships
IMO, "MEPC.1/Circ.681 Interim guidelines on the method of calculation of the Energy Efficiency Design Index for new ships," International Maritime Organization, London, UK, Tech. Rep., 2009.
EE-WG 2/INF.4 Information on a study describing a method for using the energy efficiency operational indicator (EEOI) in Ro-Ro passenger ships
  • Imo
IMO, "EE-WG 2/INF.4 Information on a study describing a method for using the energy efficiency operational indicator (EEOI) in Ro-Ro passenger ships," International Maritime Organization, Tech. Rep., 2011.