Estimate the shippingemission of carbon diox-ide (CO2) for inland-rivervessels in the port area ofRotterdam, the Nether-lands

Master Thesis in Supply Chain Management

Rotterdam School of ManagementErasmus University Rotterdam

Layba Minha Agha357148

Supervisors Erasmus University:Coach: dr. Jan van DalenCo-reader: prof. Rob Zuidwijk

Supervisors Company: TeqplayCoach: Léon Gommans

Date: 10-08-2016

Preface

This document was written by L.M. Agha who declares that she takes theresponsibility for the content of the entire document. She states that in thetext and work presented her, no sources other than those mentioned in thetext and bibliography have been used for this master thesis.

The copyright of the master thesis rests with the author. I am responsi-ble for its content. RSM is only responsible for the educational coachingand cannot be held liable for the content.

Acknowledgement

This thesis is written as part of the master Supply Chain Management atthe Rotterdam School of Management (RSM), Erasmus University. After,five inspiring and amazing years at the university this thesis was the lastchallenge to achieve my second MSc degree. A project in which all aca-demic skills acquired over the last five years were applied to a topic whichintrigued me as it aligns my strong interest in data and SmartPorts.

First of all, I would like to thank my thesis coordinators dr. Jan van Dalenand prof. Rob Zuidwijk. Dr. Van Dalen was very supportive, providedme with critical comments and helped me a lot during the data analysisstage. My thanks goes also to prof.Zuidwijk, who has a lot of expertise onthe topic SmartPorts which amongst others helped me to gain additionalinsight into current research conducted within this area.

Secondly, I would like to thank the co-founders of Teqplay, Léon Gommansand Richard van Klaveren, who were very supportive and helped me toachieve my goals. They gave me access to their knowledge, network anddata. I also appreciate their patience and availability for me, as we met onregularly basis to discuss my progress. Moreover, my appreciation goesto various experts working in the SmartPort area who have helped me orhave been connected to my thesis.

I sincerely hope that you will read this thesis with joy, and get enlightenedwith the insights of this thesis.

Layba Minha AghaAugust, 2016Rotterdam

Abstract

This study estimates the shipping emission of carbon dioxide for inland-river vessels within the Port of Rotterdam. As a result of the growingtransportation over the inland waterways and emission target of the Eu-ropean Union, port authorities, the government and individual shippinglines need to have a thorough understanding of the influence of vesselscharacteristics and shipping behaviour on CO2 emission. A, according tomany, highly relevant research domain due to amongst other the lack ofan accurate emission method, which asked for substantial academic andmanagerial attention. After an extensive theoretical and practical explo-ration on the shipping emission domain, this paper continues developingan method to estimate the shipping emission of carbon dioxide. It is an-alyzed if vessel characteristics, speed and acceleration influence the CO2emission for inland-river vessels within the port area of Rotterdam. Ev-idence is found for a significant difference between the aforementionedvariables and the shipping emission of carbon dioxide. The geographicaldistribution of CO2 emission within the port area shows the key emissionhot spots. The scientific evidences found in this paper have contributed ingaining more insights into the emission factors on the inland waterways.The findings have both academic and managerial implications.

Contents

List of Tables 5

List of Figures 6

1 Introduction 7

2 Research objective and research question 122.1 Research objective . . . . . . . . . . . . . . . . . . . . . . . . . 122.2 Research question . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.1 Sub-question . . . . . . . . . . . . . . . . . . . . . . . 142.3 Practical relevance . . . . . . . . . . . . . . . . . . . . . . . . 14

3 Theoretical background 173.1 Effects of shipping estimation . . . . . . . . . . . . . . . . . . 173.2 Bottom-up and top-down approach of shipping emission . . 183.3 Methods to estimate shipping emission . . . . . . . . . . . . 19

3.3.1 The use of AIS data . . . . . . . . . . . . . . . . . . . . 213.4 Measurement of concept . . . . . . . . . . . . . . . . . . . . . 24

3.4.1 Emission measurements in the literature . . . . . . . 243.4.2 Emission measurements in practice . . . . . . . . . . 27

4 Data and method 304.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.2 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.4 Calculation of CO2 emission . . . . . . . . . . . . . . . . . . . 39

4.4.1 Emission Factor . . . . . . . . . . . . . . . . . . . . . . 394.4.2 Fuel consumption . . . . . . . . . . . . . . . . . . . . 40

5 Analysis 435.1 Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . 435.2 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . 445.3 CO2 emission . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.3.1 Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465.3.2 Acceleration . . . . . . . . . . . . . . . . . . . . . . . . 475.3.3 Vessel characteristics and CO2 emission . . . . . . . . 50

5.4 Geographical distribution of the CO2 emission . . . . . . . . 56

6 Discussion 616.1 CO2 emission . . . . . . . . . . . . . . . . . . . . . . . . . . . 616.2 Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626.3 Acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

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6.4 Vessel characteristics . . . . . . . . . . . . . . . . . . . . . . . 636.4.1 CEMT class . . . . . . . . . . . . . . . . . . . . . . . . 636.4.2 Ship type . . . . . . . . . . . . . . . . . . . . . . . . . . 646.4.3 Role . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

7 Conclusion 687.1 Academic implications . . . . . . . . . . . . . . . . . . . . . . 697.2 Managerial implications . . . . . . . . . . . . . . . . . . . . . 707.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717.4 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . 73

References 75

A CEMT class 88

B Results ANOVA 89

C Interview transcript 93C.1 Interview Expertise- en Innovatie Centrum Binnenvaart (EICB) 93C.2 Interview ThyssenKrupp Veerhaven B.V. . . . . . . . . . . . 96C.3 Interview Port of Rotterdam . . . . . . . . . . . . . . . . . . . 103

D R codes 107

List of Tables

1 Shipping emission literature incorporating AIS Data . . . . . 232 Emission measurements in practice . . . . . . . . . . . . . . . 283 Variables in AIS dataset . . . . . . . . . . . . . . . . . . . . . 344 Variables in the additional dataset . . . . . . . . . . . . . . . 365 Container ships . . . . . . . . . . . . . . . . . . . . . . . . . . 456 Average emission per speed level . . . . . . . . . . . . . . . . 477 Speed reduction . . . . . . . . . . . . . . . . . . . . . . . . . . 478 Acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 Overview average values per CEMT class . . . . . . . . . . . 5110 Overview average values per ship type . . . . . . . . . . . . 5311 Significant results Tukey analysis: emission & role . . . . . . 5412 Overview average values per role . . . . . . . . . . . . . . . . 5513 Anova Output Inland Vessels - Emission & CEMT class . . . 8914 Anova Output Inland Vessels - Emission & ShipType . . . . 8915 Anova Output Inland Vessels - Emission & Role . . . . . . . 8916 Tukey analysis Inland Vessels - Emission & CEMT . . . . . . 9017 Tukey analysis Inland Vessels - Emission & ShipType . . . . 9118 Tukey analysis Inland Vessels - Emission & Role . . . . . . . 92

List of Figures

1 Total fuel consumption and CO2 emissions by ships . . . . . 252 Port area of Rotterdam . . . . . . . . . . . . . . . . . . . . . . 323 Nautical boat terms . . . . . . . . . . . . . . . . . . . . . . . . 374 Ship fuel consumption dependence on speed, adapted from

Notteboom et al., 2009 . . . . . . . . . . . . . . . . . . . . . . 415 Ship fuel consumption dependence on speed, adapted from

Notteboom et al., 2009 . . . . . . . . . . . . . . . . . . . . . . 416 CEMT class . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Ship type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 Role . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 Emission over time . . . . . . . . . . . . . . . . . . . . . . . . 4610 Relative percentage cemt class and emission . . . . . . . . . 5211 Relative percentage ship type and emission . . . . . . . . . . 5312 Relative percentage role and emission . . . . . . . . . . . . . 5513 Maasvlakte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5714 Botlek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5715 Hartel Canal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5716 Rotterdam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5717 Port area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5818 Total emission in the port area . . . . . . . . . . . . . . . . . . 5819 Average speed in the port area . . . . . . . . . . . . . . . . . 5920 Average acceleration in the port area . . . . . . . . . . . . . . 6021 CEMT class . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

1 Introduction

The total waterway network in the Netherlands covers approximately 5,046kilometre of which 4,800 kilometre (95%) are used for the shipment ofgoods. The Netherlands is the gateway to the European hinterland dueto its location of several important rivers such as the Rhine, the Scheldeand the Maas. In addition, numerous canals and lakes connect the ma-jor cities, which results in an interconnected network for the shipment ofgoods. Thus, waterways provide great potential for transportation. More-over, the Netherlands has the largest inland freight fleet in Western Eu-rope with 5,815 vessels out of 11,546 vessels. The inland waterways arethe second mode of transport after road, as 366,626 thousand tons of goodswere transported in the year 2015. This implies that inland shipping playsa major role in the Netherlands (Binnenvaart, 2015, EC, 2015a, IVR, 2013,Keuken et al., 2013)

In the year 2015, the transport on the inland waterways of the Netherlandsfaced a rising demand. Further growth is expected in the next years, asmore shippers will choose for water transport instead of road transport. Inparticular, 35% of all containers is expected to be transported by road, 20%by rail and 45% by barge containers in 2030 (PoR, 2015b, Rabobank, 2015).

Over the past decade, the fuel consumption and thus shipping emissionhave been substantially increased, it is expected to increase even furtherdue to the rising demand. Several agreements have been implemented inorder to prevent the pollution from ships. However, this is only the begin-ning as the shipping emissions are rapidly rising, implying the problem isfar being solved. Hence, the shipping emissions have been recognized asa growing international problem (Eyring et al., 2005). Given the increasein water transport, shipping emissions are likely to become an even largerenvironmental problem in the near future, if there are no governmentalcontrols (Deniz and Durmusoglu, 2008).

Due to the growing demand in inland transportation and pressure from thegovernment, port authorities and shipping lines, initiatives are currentlybeing developed in order to make inland shipping more sustainable. Inparticular, reducing the CO2 footprint. Shipping emission standards for ni-trogen (NOx) exists, but there are no standards for carbon dioxide (CO2) yetin the Netherlands. The reason is that the government and port authoritiesprioritized the development of shipping emission standards for NOx overCO2 (Van der Linden, 2016, Rabobank, 2015). The emission standards forNOx stimulated the development and use of environmental friendly fuelsuch as LNG or hybrid motors. The NOx and CO2 emission decreased dueto the use of LNG, however a drawback of LNG is Methane slip. Methane

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slip has an enormous impact on the greenhouse gas effect as the globalwarming effect is 25 times higher than that of CO2 (Schoemaker, 2016,WPCI, 2015). This implies that LNG is not an optimal solution and there isstill a lack in CO2 emission control measures (Van der Linden, 2016).

The government and port authorities are highly interested to develop ship-ping emission control measures which minimize the CO2 emission. Oneof the reasons is that the contribution of air emissions released by ves-sels in the port area is unknown and/or uncertain (Tichavska and Tovar,2015a). Another reason is the agreement of the European Union to reducethe emission of CO2 for both inland waterways and ocean shipping with20% in 2020 compared to 2005 (Energieakkoordster, 2014). Reducing ship-ping emission is required as emissions have a huge impact on air qualityon see and land. Territorial waters, inland seas and port areas are most af-fected by shipping emission which have adverse health and environmentaleffects (Deniz and Durmusoglu, 2008, Tichavska and Tovar, 2015a).

To meet these ambitious targets of the European Union for 2020 and be-yond, the reduction of CO2 emission from inland transportation is receiv-ing a lot of attention and it is expected to receive more in the coming years(Boer, 2011). Moreover, in the past years, international organizations triedto standardize the measurement of shipping emissions in order to ensureconsistency, but there is no single and reliable internationally agreed ship-ping emission method yet (Cefic, 2011).

Various studies have been published which developed shipping emissionmethods in order to reduce the effect of shipping emission on air quality foroceangoing vessels operating in different regions in the world. Nonethe-less, a few academic articles have been published in the area of shippingemission estimation of CO2 for inland waterways (Corbett and Fischbeck,1998; 2000, Liao et al., 2010, Sun et al., 2013). Most examined the shippingemission for oceangoing vessels and conclude that vessel characteristics,shipping behaviour, weather and water conditions influence the emission.

Oceangoing vessels are not representative for the shipping emission of ves-sels operating on the inland waterways as there is a difference in enginesize, fuel type, vessel type and operating mode. In particular, inland water-ways distinguish themselves from the open sea by the fixity of the currents,as a results inland-river vessels use a high-speed marine engine which hasdifferent performance characteristics (e.g. oil consumption), resulting indifferent emission levels compared to oceangoing vessels. In addition, thevessels operating on inland waterways burn distillate fuel, while ocean ves-sels burn residual fuel oil (Corbett and Robinson, 2001, Wang et al., 2007a).Therefore, using the emission estimates based on oceangoing vessels are

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not appropriate for inland-river vessels.

Still, most of the shipping emission estimates for inland waterways arebased on emission factors identified for oceangoing vessels, as those fac-tors for inland-river vessels do not exist yet (Corbett and Robinson, 2001).In particular, many studies have calculated the shipping emission by mul-tiplying the average emission factors by vessel activity or fuel consumptionestimates (Marmer et al., 2009). Various studies have tried to improve theshipping emission estimates by including actual vessel activities. How-ever, there is still a huge variance in the shipping emission estimates due toseveral reasons: the use of different methodologies, different activity dataand uncertainty in emission factors (Marmer et al., 2009). Another rea-son is the use of different assumptions. Many studies have examined theshipping emission for inland waterways adopting various assumptions interms of operational characteristics such as the average speed and engineload (Georgaki et al., 2002, Giannouli et al., 2006). Moreover, some studiesdid not have access to traffic data nor geographic data resulting in inac-curate shipping emission estimates as all areas within a country were at-tributed with the same emission value (Giannouli et al., 2006).

The lack of an accurate shipping emission estimation method is one of thereasons that Viana et al. (2014) state research in the area of shipping emis-sion for inland waterways would be highly relevant to the scientific andpolicy-maker communities on global and regional scale due to its variousimpacts on human health, climate and ecosystems (Viana et al., 2014). Re-search in this area is needed in order to get more insights in the wider arrayof pollution units characterizing inland ports and waterways (Dooms et al.,2013, Lee et al., 2014, Viana et al., 2014). Moreover, in order to reduce theshipping emission, the ability to quantify emissions and develop accurateemission inventories for port areas is highly desired (Tichavska and Tovar,2015a).

As the government and port authorities emphasize the importance of re-ducing the shipping emission, this is not the first priority for individualemitters, i.e. shipping lines, operating on inland-waterways as they em-phasize the cost efficiency of their operations(Boneschansker and Davidse,2016, Van der Linden, 2016). Therefore, port authorities are trying tostimulate them in reducing the shipping emission by providing incentives(Schoemaker, 2016). In order to reduce the shipping emission and meet theexpected regulations, it is required to raise awareness of the shipping emis-sion. In particular, individual emitters need to be made aware and account-able for their shipping emission. Moreover, as fuel consumption influencesthe shipping emission, individual emitters need to be confronted with theirshipping behaviour,i.e. shipping speed.

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Real vessel traffic information, such as Automatic Information Systems(AIS) can be used to derive at reliable individual shipping emission valuesof shipping lines. Hence, shipping emission inventories in the port area.One of the reasons is that the input parameters such as ship type, locationand size are very accurate. In addition, integrating AIS data avoids theneed for various assumptions and the estimations are more precise as theyare based on the most reliable information, i.e. the position, course andspeed of a vessel which are updated every two seconds (Tichavska and To-var, 2015a;b). Moreover, the use of AIS data allows emission predictionsof single ships with a high spatial and temporal resolution. In addition, itis possible to study the allocation of shipping emission according to shiptypes, flag states, vessel routes and the geographical distribution of emis-sions can also be presented. This has not been possible with existing meth-ods used in the literature (Eyring et al., 2010, Jalkanen et al., 2009; 2014,Wang et al., 2007b). Furthermore, previous literature neglected the effectsof changes in shipping speed and the true position of ships on shippingemissions, which can be analyzed using AIS data. It also eliminates theneed to computationally construct shipping routes.

The main limitation of AIS data are the inaccuracies of the Global Posi-tioning Systems (GPS) and the information on the exact location of AIStransponders onboard, which might result in inaccuracies of a few hun-dred meters (Jalkanen et al., 2009). Despite the advantages, the use of AISdata for shipping emission have previously been neglected in a lot of stud-ies (Eyring et al., 2010, Wang et al., 2007b). Nevertheless, there is an evidentneed for a shipping emission method that can accurately describe the ship-ping behaviour and the geographical distribution of emissions (Lauer et al.,2007, Richter et al., 2004).

The use of real time traffic information in this study enables a real-timetracking and geographic distribution of shipping emission. In addition, adirect feedback loop to the individual emitters can be constructed as theycan be directly made accountable for their shipping behaviour. Also, ship-ping lines can immediately change their behaviour beneficial to the desiredemission targets.

Consequently, the development of an accurate shipping emission methodfor inland waterways is desired in order to quantify the emission in the portarea, gain insights into the emission factors and develop emission controlmeasures. An emission method which incorporates AIS data is neglected inthe literature. Nevertheless it enables to derive reliable emission estimatedbased on shipping behaviour. Hence, a shipping emission method basedon AIS data is beneficial to compute accurate and vessel-specific shipping

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emission. Moreover, this method can be used to make individual emittersaccountable and aware of their emissions.

This study will contribute to the academic literature by developing amethod which uses AIS data to estimate the shipping emission of CO2 andcharacterizing the pollution units for the inland ports and waterways. Thisstudy will focus on the inland waterways of the Netherlands as water trans-port has huge economic potential and research is needed to ensure that theinland shipping fleet of the Netherlands becomes cleaner. This is requiredin order to meet the expected regulations regarding the shipping emissionof CO2 by developing emission measures which will minimize the CO2footprint. The shipping emission around the Port of Rotterdam, an inlandport, will be examined. Research in this area is required to meet the strin-gent emission standard of the Port of Rotterdam from 2025 onwards, as thePort has the ambition to be the most sustainable port in the world (PoR,2015b;c, Rabobank, 2015).

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2 Research objective and research question

2.1 Research objective

The aim of this thesis is to develop a method which can be used to esti-mate the shipping emission of carbon dioxide (CO2) on inland waterwaysin the Netherlands. The main objective is to analyze the relationship be-tween vessel characteristics and shipping activities on shipping emissionfor inland-river vessels by using AIS data.

2.2 Research question

Aforementioned, huge variance in the shipping emission estimates existdue to the use of the different methodologies, activity data and uncertain-ties in emission factors (Marmer et al., 2009). In addition, many studiesfocus on estimating the global shipping emission on aggregate level andpresent shipping emission estimates per tonne kilometre or tonne nauticalmile (Endresen et al., 2003, Eyring et al., 2010). Most of these estimatesare derived by combining the estimated fuel consumption with specificemission factors (Corbett et al., 1999, Corbett and Koehler, 2003, Endresenet al., 2003; 2007, Eyring et al., 2005). The accuracy of these emission esti-mates is limited by uncertainty in the reliability of the data used and theassumptions made in the calculations (Eyring et al., 2010, Walsh and Bows,2012). In particular, most of the emission estimates for inland waterwaysare based on the emission factors measured on oceangoing vessels as emis-sion factors for vessels operating on inland waterways do not exist yet (Cor-bett and Robinson, 2001).

Further, most shipping emission estimates are based on the bottom-upor top-down approach. The top-down approach estimates emission with-out respect to vessel location and by quantifying the fuel consumption bypower production which will afterwards be multiplied by the emissionfactors. There is a huge uncertainty in global estimates when using thismethod, implying a low accuracy (Eyring et al., 2010). The bottom-up ap-proach can be more precise compared to the top-down approach, howeverlarge scale bottom-up inventories are also uncertain, because they estimatethe engine workload, ship speed and the locations of the route in order todetermine the spatial distribution of shipping emission (Eyring et al., 2010).

Most of the existing methods have relied on simplified information (Dal-søren et al., 2007, Endresen et al., 2003; 2005). Aforementioned, the use ofAIS data as an input for emission models has a lot advantages (Jalkanenet al., 2009; 2012). Due to fact that AIS transmitted vessels regularly updatethe vessel characteristics, such as the true position, its course, speed and

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engine status, integrating this highly reliable information avoids the needfor operative assumptions of vessels. Hence, the use of AIS provides op-portunities for highly refined vessel movements. Therefore, this results inprecise estimations based on the most reliable vessel specific informationavailable. Moreover, AIS can be used to provide more accurate emissionassessments in specific regions (Diesch et al., 2013, Jalkanen et al., 2009;2014, Perez et al., 2009).

Following, some studies took the water and weather conditions into ac-count (Astito et al., 2014). The use of AIS, water and weather data is oneof the reasons that scientific research improved the geographical distribu-tion of shipping emission in the past years. Due to this improvement, theshipping emissions more accurately represent the world fleet traffic (Cor-bett and Koehler, 2003, Dalsøren et al., 2007, Endresen et al., 2003, Wanget al., 2007b). However, there are still many uncertainties in the estimatedshipping emission from greenhouse gases such as carbon dioxide (Eyringet al., 2010).

Consequently, the development of a reliable shipping emission estimationmethod is desired. A reliable method for shipping emission estimation ofcarbon dioxide is necessary in order to reduce the emission of greenhousegases, particularly CO2, as these have health and ecosystems consequences.This is one of the reasons, research in this area has been given a lot of at-tention in the past years and has been recognized as a growing problemby scientists and policymakers (Eyring et al., 2010). The development of areliable shipping emission method for carbon dioxide enables to developemission inventories, allocate and forecast the emission on routes in inlandwaterways networks (Corbett et al., 2007). Moreover, a reliable methodwill enable to geographically characterize the shipping emission which canbe used to assess the environmental impact on shipping routes (Deniz andDurmusoglu, 2008, Wang et al., 2007b). Information about the geograph-ical distribution of shipping emission is scare. Therefore, a geographicalrepresentation of shipping emission on inland-river vessels can be usedfor logistics planning and disaster response planning (Jalkanen et al., 2009,Wang et al., 2007b).

This leads us the following research question:

(RQ) What method can be used to estimate reliable emission volumes for inland-river vessels and present a high-resolution geographical distribution for carbondioxide (CO2) based on AIS data?

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2.2.1 Sub-question

Shipping emission factors are used in the estimates generated by the meth-ods. According to Corbett and Robinson (2001) emission factors for inland-river vessels do not exist yet (Corbett and Robinson, 2001). Althoughsome studies tried to identify these emission factors, there is huge uncer-tainty due to amongst others the small sample of vessels in the datasetsused (Corbett and Fischbeck, 2000). Most of these studies only includedfuel-consumption characteristics, while others estimated the emission forinland-river vessels based on the wide range in emission factors for ocean-going vessels. Both methods lead to inaccurate estimates. Therefore, it isimportant to study the effect different factors, such as category, ship type,weights and flag states on shipping emission in order to improve the choiceof the ship movements, ship characteristics (e.g. the ship size and type) andloading decisions. Hence, the shipping emission with a country’s or na-tion’s product chain can be optimized (Diesch et al., 2013, Walsh and Bows,2012). Moreover, by identifying and updating the factors, reliable ship-ping emission methods can be developed and the emission estimates foreach trip can be improved (Wang et al., 2007b). The development of a reli-able shipping emission estimation can be used to analyze and gain a betterunderstanding of the CO2 emission by inland-river vessels (Geerlings andVan Duin, 2011). Furthermore, it can be used to make individual emittersaware and accountable of their shipping behaviour and emission values.

This results in the following sub-question:

Which vessel characteristics influence the shipping emission in terms of CO2 forinland-river vessels on the inland waterways in the Netherlands?

2.3 Practical relevance

Currently, the measures to reduce the shipping emission are limited to af-ter treatment systems, since emissions are reported on the basis of enginecertificate and bunked delivery notes (Boer, 2011). This implies, environ-mental fuels such as LNG have been identified as potential measures toreduce the emissions. However, more research is needed in order to con-tribute to the required carbon reduction targets of the European Union andindustry sectors need to develop decarbonisation strategies for their logis-tics operations in the next couple of years (Cefic, 2011).

Several parties are interested in the research outcomes in this area. Amongthem are the government and port authorities. They are highly interestedin the development of an accurate shipping estimation method in order toanalyze the effect of shipping emission on the air quality for inland ports

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and waterways of the Netherlands. Based on the outcomes, measures canbe taken which will minimize the shipping emission and thus reduce theimpact on air quality on see and land (Van der Linden, 2016). This impliesthat the government and port authorities can be classified as primary stake-holders. A reliable and accurate shipping emission method is also essentialfor these primary stakeholders in order to meet the regulations. In addi-tion, the outcome of the shipping emission method might reveal insightfulinformation for strategic decision making to the port authorities. Compet-itive advantages might exist due to these strategic decisions (Dooms et al.,2013).

Several initiatives exist in Europe where seaport authorities, terminal oper-ations and shipping lines have invested in relationships with inland ports(Van der Horst and Van der Lugt, 2009, Rodrigue and Notteboom, 2009).These relationships are established to formulate strategies for ports, espe-cially to develop an economically efficient and environmental distributionof port traffic (Hall et al., 2011, Van der Horst and Van der Lugt, 2009).Consequently, the shipping emission estimates based on the developedmethod for inland river-vessels in the Netherlands might result in com-petitive strategic decisions when preparing for the new regulations. As aresult, these parties can be classified as an important stakeholder.

The reduction of emission in the port area is important for the Port ofRotterdam. The port stimulates sustainable fuel, green shipping and de-veloped the Environmental Ship Index (ESI) in collaboration with North-European ports a couple of years ago. The ESI indicates the environmentalperformance of vessels related to the emission of the air pollutants nitrogenoxide (NOx) and sulfur oxide (SOx). Vessels with a sufficient environmen-tal performance and thus low emission of NOx and SOx get an incentivefrom the Port or other port related organizations (PoR, 2014a, Schoemaker,2016). Next to that, the Port of Rotterdam will increase port dues for shipswhich do not meet the emission standards with 10% as of 2020. The rev-enue gained from these port dues will be used for air pollutants innovationprojects (Boer, 2011). This indicates the importance for the reduction ofemission for ship owners which can be classified as an important stake-holder as well. Therefore, the method which will be developed to estimatethe shipping of carbon dioxide can be used by shipping lines beneficial to ahigh level of preparedness for the new regulations (Helfre and Boot, 2013).

Moreover, several shippers have long-term contracts with ship-owners co-operatives. The ships in service are equipped with non-environmentalfriendly catalysts. In order to comply to the new regulations, ship ownersare willing to invest in new technologies which reduce the ship-emission.However, almost half of the contracts are negotiated on the spot markets,

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implying that ship-owners need to be ensured of the shipper’s interest forenvironmental friendly vessels (Boer, 2011). According to Van der Linden(2016) shippers are interested in environmental friendly vessels due to thestringent future regulations (Van der Linden, 2016). This implies that ship-owners can use the insight in the shipping emission factors characterizinghigh levels of carbon dioxide, gained from this study, in their developmentof new technologies which aims to reduce the shipping emission of carbondioxide.

Further, environmental friendly vessels and a low CO2 emission is of highimportance for shipping lines. One of the reasons is their competitive ad-vantage. In particular, (potential) customers are highly interested in theCO2 emission of shipping lines as they are responsible for the total CO2emission of their supply chain. Shipping lines are part of their supply chainand they prefer to use vessels which have a low emission value in order toreduce their costs (Boneschansker and Davidse, 2016). Consequently, ship-ping lines can use the insights of this study to calculate their shipping emis-sion and control it where possible. In addition, their customers can use theinsights in order to select shipping lines with a low CO2 emission beneficialto low operational costs(Boneschansker and Davidse, 2016).

The European Union has set a target to reduce the emission of greenhousegases (GHG), which includes CO2, by 20% in the year 2020 compared to2005. There are currently no regulations regarding the emissions of GHGpollutants. To achieve this target, the European Union supports the im-plementation of emission trading schemes in which companies can receiveor buy tradable emission allowances (EuropeanCommission, 2016, Helfreand Boot, 2013). Tradable emissions schemes will have a direct effect forthe ship owners and the shipping emission estimates of CO2 based on thedeveloped method will help to comply to these new regulations. Moreover,new opportunities might emerge from the insight gained when analysingthe shipping emission estimates based on the method.

Consequently, this study provides different stakeholders insight about theshipping emission estimation of carbon dioxide for the inland waterwaysnetwork of the Netherlands, which they can successfully incorporate tomeet future regulations.

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3 Theoretical background

In this section research undertaken in the area of this thesis will be evalu-ated, which allows to identify methodologies and variables having an in-fluence on the shipping emission. There are very few articles publishedwhich examine the specific area of shipping emission for inland waterways.However, there is more theory on the topic of shipping emission for ocean-going vessels. Since shipping estimates for inland-river vessels is related tothe estimates for oceangoing vessels, those studies are relevant in order toidentify methodologies and emission factors which enable to estimate theshipping emission of carbon dioxide for inland-river vessels.

3.1 Effects of shipping estimation

Ocean vessels are known as the most carbon-efficient mode for transport-ing goods compared with road, rail and air transport. Ocean vessels emiton average 10 gram CO2 per kilometre when carrying 1 ton of cargo, whilefor rail, road and air transport the emission is respectively 21 gram, 59 gramand 470 gram per kilometre (WorldShippingCouncil, 2016). As a result,ship transport is generally considered to be environmental friendly com-pared to other transportation modes. However, they still emit various airpollutants, for example CO2, NOx and SO2, which are increasing globally(Deniz and Durmusoglu, 2008, Lee et al., 2014). Moreover, ocean shippingis one of the major sources of NOx, SOx, PM and CO2 (Wang et al., 2009).These pollutants have a huge impact on the air quality which contributesto serious health and environmental effects (Deniz and Durmusoglu, 2008).In particular, carbon dioxide (CO2) is the major greenhouse gas which leadsto global warming (Wang et al., 2009).

Many studies have focused on the climate impacts of shipping emissions(Agrawal et al., 2008, Moldanová et al., 2009, Petzold et al., 2008). Thesestudies mostly analyze the environmental pollutants, such as CO, NOx,SO2 and CO2, which have a negative effect on ports and the port city areas,for example the depletion of the ozone layer and the production of acidrain (Lee et al., 2014).

Furthermore, several studies have been conducted to quantify the impact ofoperational measures of fuel consumption and CO2 emission in containershipping. In particular, speed reduction, berth scheduling and route re-engineering are quantified (Corbett et al., 2009, Fagerholt et al., 2010). Cor-bett et al. (2009) concluded that speed reductions would reduce the CO2emissions by up to 70% for a containership.

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3.2 Bottom-up and top-down approach of shipping emission

Two types of shipping emission estimation methods exist: the top-downapproach and the bottom-up approach. The top-down approach takes theutilization and analysis of marine fuel sales as a starting point, while thebottom-up approach is based on the analysis of actual shipping activitiesand combines data on shipping attributes (e.g. the engine type), ship move-ment and ship operations (Cullinane et al., 2015, Endresen et al., 2003).

The top-down approach allocates shipping emission using spatial proxies.However, the accuracy of this method is limited by the representativenessof the spatial proxy of the ship traffic and the accuracy of the total globalemission in which uncertainties exist (Corbett and Koehler, 2003, Wanget al., 2007b). The accuracy of the bottom-up approach, which takes intoaccount the actual shipping activities, ship operations and ship attributes,is higher as it provides a better representation of the actual shipping emis-sions (Cullinane et al., 2015). This is one of the reasons that the bottom-up approach has displaced the top-down approach and thus has become aproven and established methodology. The bottom-up approach is mostlyused for global, national or regional context (Cullinane et al., 2015).

Furthermore, the bottom-up approach is mostly used in combination withthe fleet activity-based method which uses data on ship movement, shipclasses, corresponding fuel consumption figures and emission factors.When using the fleet activity-based method, emission factors are often ex-pressed in mass of pollutant per unit of engine power or mass of pollutantper unit mass of fuel (Lonati et al., 2010).

The activity-based method is mostly based on aggregated activity data fordifferent ship sizes and types. Therefore, a fixed ship speed and a constantload factor are assumed for the same category of ships. Further, ship loadand empty containers repositioning policy are mostly not taken into ac-count, while it determines the empty ship movement sized and thus animportant factor in the shipping emission estimates. Moreover, sailingdirections and weather conditions may affect the vessel’s speed and fuelconsumption and thus shipping emission (Song and Xu, 2012, Wang et al.,2007b).

The fuel-based method is another approach to estimate the shipping emis-sion. This method uses fuel sales data in combination with fuel-relatedemission factors. In particular, the fuel-based emission factors are mul-tiplied by the estimated daily fuel-consumption, based on the fuel salesdata, in order to estimate the daily emission for each pollutants in kilo-gram (Corbett and Fischbeck, 2000). However, most studies use the fleet

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activity-based method, which uses data on ship movement, ship classes,corresponding fuel figures and emission factors, as this method is moreaccurate.

3.3 Methods to estimate shipping emission

Various methods exist to estimate the shipping emission. Many shippingemission estimates are based on dispersion of the greenhouse gases, chem-ical transport models, emission inventories and on board emissions (EEA,2013).

Applications of emission inventory methodologies have been developedfor ships in the port area in the past decade. An example is the researchof Kesgin and Vardar (2001) which used data on the following vessel char-acteristics: engine system, fuel type, cruising time and speed, to estimatethe following pollutants for passenger ships and transit vessels: NOx, COx,CO2 and PM. One of their contributions is that cargo vessels instead of pas-senger ships are the largest polluters in the Turkish Straits (Kesgin and Var-dar, 2001). The study of Kesgin and Vardar (2001) differentiates from thestudies mentioned so far, as they estimate the shipping emission in a spe-cific region. Several studies had a similar approach and aimed at estimatingshipping emission in different regions in the world instead of global ship-ping emission. For example, Onagawa (1995) estimated the SOx and NOxemissions in the Tokyo Bay area and the emission for the NE Atlantic re-gion is estimated by Carlton et al. (1995).

Various data is used to measure the shipping emission. For example, thestudy of Lucialli et al. (2007) used data related to the time spent by shipsin different operation modes, fuel consumption and gross tonnage in orderto estimate the emission (Lucialli et al., 2007). Following, Deniz and Dur-musoglu (2008) estimated the shipping emission by using the fuel type,engine type, fuel consumption, time of cruising and the operational mode.They obtained the cruising time from the ship speed and the length of thestraits (Deniz and Durmusoglu, 2008). Trozzi and Vaccaro (1998) also usedthe fuel type and engine type in their methodology to calculate the gasemissions from ships (Trozzi and Vaccaro, 1998).

The research of Hulskotte and van der Gon (2010) used more variables thanthe one mentioned so far. In particular, they included the vessel’s type, thecapacity of the vessel, year of build and the fuel consumption of differentstages of shipping transit: cruising in open sea, manoeuvring towards theharbour and while the ship is at the berth (Hulskotte and van der Gon,2010). Two of these different stages of shipping transit: manoeuvring andberthing, are also used in the research of Tzannatos (2010) who’s research

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also included data on engine type, engine load factor and fuel consumption(Tzannatos, 2010). Cullinane et al. (2015) adds that the time spent in dif-ferent operation modes has a significance influence on the ship emission.Moreover, Cullinane et al. (2015) conclude that the time a vessel is in theberth has a strong and direct relation in ports with little or no congestion(Cullinane et al., 2015). This implies an indirect relationship with the ship-ping emission. Further, Endresen et al. (2003) used a statistical approachfor the emission estimations based on the fuel consumption and emissionsfigures from the international ship fleet (Endresen et al., 2003).

However, other research than the ones discussed indicate the importanceto include other variables in the calculation of shipping emission. For ex-ample, a lot of studies agree on the relevance to include the loading and un-loading of a vessel as it is shown that this operation contributes to the emis-sion (Alastuey et al., 2007, Eyring et al., 2010, Lonati et al., 2010, Morenoet al., 2007). In particular, Alastuey et al. (2007) state that loading and un-loading may have significant impact on PM levels which negatively affectsthe human health (Alastuey et al., 2007).

Another way to estimate the shipping emission is using commodity flowassociated with major see routes. The accuracy of this method is low as it isassumed that there is a relationship between the volume of trade flow andemission, implying that the fleet activity method is used. This method isnot an accurate way as shipping emission is more related to ship character-istics such as the engine type (Wang et al., 2007b).

Next, most of the mentioned studies estimate the shipping emission onan aggregated level and made a lot of assumptions. For example, Cor-bett and Fischbeck (1997) and Corbett et al. (1999) constructed a globalrepresentative of shipping emission by assuming that the reported ship-ping fleet is representative to the world fleet, the distribution of the shipreporting frequencies represents the distribution of the ship traffic inten-sity and the emissions are proportional to the traffic intensity. Endresenet al. (2005) improved this global representative of shipping emission bygiving the reported frequencies a weight which was based on the ship size.Moreover, they assumed that the emission is proportionally distributed ac-cording to the ship size (Corbett and Fischbeck, 1997, Corbett et al., 1999,Endresen et al., 2005). Other studies made assumptions on the average en-gine load and the average number of annual operating hours (Miola andCiuffo, 2011).

Consequently, many studies used the bottom-up approach to calculate theshipping emission. This can be explained by the fact that this approachtakes several emission-related parameters into account, such as engine

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type, fuel type, engine load, and time spent in the port, resulting in a higheraccuracy than the top-down approach (Cullinane et al., 2015). Almost allstudies obtained these values from shipping databases.

Further, it can be concluded that most of the studies used a shipping emis-sion method which is based on marine exhaust emission test data whichreports fuel-based emission factors for the following pollutants: NOx, SOx,COx, VOC and PM. The emission factors are calculated based on the op-eration mode of the ships, the engine system and the type of fuel used(Kesgin and Vardar, 2001). In order to get an accurate shipping estimationthe following variables should be included: vessel type, capacity of vessel,fuel type, engine type, vessel load, ship navigating speed, time of cruising,time spent in the port, fuel consumption when during the different types ofshipping transit: cruising in open sea, maneuvering towards the harbourand while in berth, weather conditions and water level.

Nonetheless, it is quite remarkable that almost all studies in this area do notinclude the acceleration rate in their calculation, as it is generally knownthat acceleration influences the emission. In particular, the shipping emis-sion increases as a result of an increase in the acceleration rate (Bokare andMaurya, 2013). Larsson and Ericsson (2009) adds that understanding theeffects of different speed levels and acceleration profiles can help to im-prove the emission, hence decrease it.

However, there is still no consensus how drivers and pilots should acceler-ate beneficial to reduce the fuel consumption (Ding and Rakha, 2002, Eric-sson, 2001, Larsson and Ericsson, 2009). According to Austin et al. (1993)the correlation between speed or acceleration and emission is not straight-forward. Nevertheless, other studies have shown the hard acceleration re-sults in a higher emission, amongst other CO2 (Joumard et al., 1995, Hansenet al., 1995, Ross, 1994). The studies mentioned examined the emission forvehicles, however the effect of acceleration on fuel consumption for vesselsis importance (Corbett and Winebrake, 2008). Hence, it should be takeninto account when estimating the shipping emission.

3.3.1 The use of AIS data

The shipping emission and emission factors can also be derived by usingAutomatic Identification System (AIS) data. Even though AIS data has alot of advantages and can easily be collected as it is open source, very fewstudies used it. An overview of these studies, including their methods, isprovided in table 1.

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Table 1: Shipping emission literature incorporating AIS DataAuthor Title MethodAstito et al.,2014

Estimating Carbon Dioxide and Par-ticulate Matter Emissions from Shipsusing Automatic Identification SystemData

Estimated the shipping emission by taking intoaccount the ship navigation speed, mass den-sity of water, weather conditions, engine size,age and energy density of fuel

Diesch et al.,2013

Investigation of gaseous and partic-ulate emissions from various marinevessel types measured on the banks ofthe Elbe in Northern Germany

Used AIS data to separate vessels into seventypes. Vessel characteristics, e.g. speed and sizediffer for the types. Based on scatter plots itis concluded that fuel composition, engine typeship size and operation type of vessels stronglyinfluence the emission

Jalkanen et al.,2009

A modelling system for the exhaustemissions of marine traffic and its ap-plication in the Baltic Sea area.

Analyzed SOx and CO2 emission based on fuelconsumption. It is concluded that waves havean influence on fuel consumption. In addition,the ships were classified based on flag statedin order to analyze the difference in shippingemission. STEAM is also used.

Jalkanen et al.,2012

Extension of an assessment model ofship traffic exhaust emissions for par-ticulate matter and carbon monoxide.

Analyzed the shipping emission based on theresistance of vessels, the auxiliary power en-gine load, ship speed and fuel suplhur content.

Jalkanen et al.,2014

A comprehensive inventory of theship traffic exhaust emissions in theBaltic Sea from 2006 to 2009.

Used the developed emission modeling systemby (Jalkanen et al., 2009) to present a detailedemission inventory based on flag state, vesselsize, vessel type and fuel consumption.

Ng et al., 2013. Policy change driven by an AIS-assisted marine emission inventory inHong Kong and the Pearl River Delta.

Used AIS data to determine the main engineload factors through vessel speed and opera-tion mode characterization. The emission wascalculated based on these variables and theSTEAM model.

Song, 2014 Ship emissions inventory, social costand eco-efficiency in Shanghai Yang-shan port.

Emission estimates are based on a functionof the ship energy demand multiplied by theemission factor. The energy demand is relatedto a ship’s maximum continuous rated enginepower, engine load factor, speed, distance trav-elled and duration of operational activities.

Tichavska andTovar, 2015

Environmental cost and eco-efficiencyfrom vessel emissions in Las PalmasPort.

Used STEAM in order to estimate the shippingemission. Shipping activity data and technicalinformation provided by AIS data were incor-porated in the STEAM model.

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Tichavska andTovar, 2015

Port-city exhaust emission model: Anapplication to cruise and ferry opera-tions in Las Palmas Port.

Used STEAM in order to estimate the shippingemission. Shipping activity data and technicalinformation provided by AIS data were incor-porated in the STEAM model.

Yau, Lee, Cor-bett, Wang,Cheng, 2012

Estimation of exhaust emission fromocean-going vessels in Hong Kong.

The total emission is the sum of the emissionfrom the main and auxiliary engine and theemission of the boiler. Emission from the en-gines are estimated by multiplying the enginepower by engine load factor, ship activity andemission factor. The emission from the boileris a function of the fuel consumption rate, theship activity and emission factors.

Table 1: Shipping emission literature incorporating AIS Data

Based on table 1 it can be concluded that all the studies had a differentapproach. However, it is agreed that using AIS data for a certain periodallows for more accurate shipping emission estimates. It also improves thereliability of emissions and fuel consumptions estimations (Buhaug et al.,2009). AIS data can also be used to derive routes, the average speed andtravel times between ports (Miola and Ciuffo, 2011). However, there areseveral concerns regarding the use of AIS data. First, there might be noAIS signal in certain areas resulting in an overestimation of the travel dis-tance. Secondly, AIS data on the dynamic position of a ship cannot be usedfor the emissions around ports as unrealistic and low accuracy values arerecorded. As a result, data regarding the accelerations and decelerationare hidden which are responsible for the main fuel consumption and emis-sion in the port area. In addition, not all vessels have an AIS system onboard resulting in low accuracy emission estimations (Miola and Ciuffo,2011). Therefore, Miola and Ciuffo (2011) argue that AIS data should becombined with other data sources in order for accurate shipping emissionestimations. They advice to use AIS data to derive at the number of shipson a route, while other data sources such as port call and weather condi-tions can be used for fuel consumption calculations and emission estimates(Miola and Ciuffo, 2011). However, this opinion is not shared by other re-searchers as they estimated accurate shipping emission values within portareas and reliable fuel consumption values based on AIS. Moreover, theyargue that almost all (ocean) vessels are required to have an AIS system onboard (Diesch et al., 2013, Jalkanen et al., 2009; 2012; 2014). Nonetheless,Yau et al. (2012) included port calls in their analysis, in order to gain addi-tional insights.

A lot of the presented studies used the Ship Traffic Emission AssessmentModel (STEAM) or STEAM2 (version 2) beneficial to estimate the SOx NOx,

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PM and CO emission. STEAM includes detailed vessel technical informa-tion such as the main and auxiliary engine power, engine load, ship type,fuel type and engine type (Jalkanen et al., 2009). In addition, internationalregulations of IMO and the EU for marine fuels are included in the model.The model can provide fully dynamic ship emission inventories includingvessel operations and changes in routes (Jalkanen et al., 2009).

Most studies used STEAM in combination with AIS in order to increasethe accuracy of the shipping emission estimates and derive at detailed esti-mates on individual vessel level. In particular, AIS data such as speed, truelocation and operation mode are used in STEAM to determine the fuel con-sumption (Jalkanen et al., 2009; 2012; 2014, Tichavska and Tovar, 2015a;b).Consequently, the difference between STEAM and AIS is that STEAM in-cludes very detailed technical information about the vessels, while AIS in-cludes operational information.

3.4 Measurement of concept

In the previous subsections the different methodologies and variables usedto estimate the shipping emission are described. In this section the formu-las which can be used to calculate the shipping emission will be introduced.

3.4.1 Emission measurements in the literature

Efforts to estimate the shipping emission started in the 1990s, when pollu-tants, such as CO2 and SO2, were steadily rising. Due to this increase fuelconsumption faced an upward trend as well (Wang et al., 2009). This directrelation between the carbon dioxide emission and fuel consumption is sup-ported in the literature. In particular, Corbett and Koehler (2003) state thatthe amount of fuel used by vessels is directly related to the shipping emis-sion values (Corbett and Koehler, 2003). Therefore, many studies use thefuel consumption in order to calculate the shipping emission. For example,Eyring et al. (2005) used the bottom-up approach and calculated the emis-sion by multiplying the fuel consumption by the emission factor (Endresenet al., 2005).

The calculation of Eyring et al. (2005) is based on the methodology of Cor-bett and Koehler (2003), who obtained engine power and applied vesselactivity data to compute the fuel consumption (Corbett and Koehler, 2003).Eyring et al. (2010) add that the shipping emission depends more on en-gine type than on the fuel type (Deniz and Durmusoglu, 2008, Eyring et al.,2010). Next, the study of Corbett et al. (2009) argues that the CO2 emissionis a function of the carbon content of the fuel, energy density of the fueland the combustion efficiency. Most ships use marine diesel which con-

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verts nearly all carbon to carbon dioxide. Therefore, fuel consumption canbe used to calculate the emission of CO2 (Corbett et al., 2009).

Other studies elaborated on this method and included the berth time, sail-ing time and sailing speed (Song and Xu, 2012). However, this detailedapproach does not necessarily result in an accurate shipping estimation asthe authors conclude that accurate estimations can also be gathered whenselecting appropriate values for the shipping speed and load factor (Songand Xu, 2012).

The study of Corbett et al. (2009) examined measures which can reducethe CO2 emission for international shipping. One of those is speed reduc-tion as a speed reduction with 50% can decrease the CO2 emission up to70% (Corbett et al., 2009). This implies a direct relation between the speedof a vessel and the CO2 emission.

Consequently, the CO2 emission can be calculated by the amount of fuelconsumed, as a direct relation between the fuel consumption and CO2emission exits. This relation is displayed in figure 1, derived from the studyof Eyring et al. (2005).

Figure 1: Total fuel consumption and CO2 emissions by ships

Many studies use an activity-based model to calculate the emission of CO2in kilogram per trip. Corbett et al. (2009) estimated the CO2 emission forvessels on a trip as:

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CO2 = 3.17K

∑k=1

[MFk

(S1k

S0k

)3

+ AFk]dij

24 ∗ S1k(1)

MFk and AFk represent respectively the main engine daily fuel consump-tion and auxiliary engine daily fuel consumption in g/kwh. These volumesare determined by the power of the vessel, the fuel consumption rate ing/kwh and the engine load factor. S1k represents the operational speed,while the design sea-speed is represented by S0k. Both are indicated inunits of nautical miles per hour. dij represents the distance between twomiles in nautical miles. The multiplier 3.17 is obtained by multiplying thefuel’s carbon fraction defined at 86.4%, by the carbon to carbon dioxideconverting factor equal to 44

12 (Corbett et al., 2009).

MFk and AFk are assumed to be equal to 206 g/kWh and 221 g/kWh, re-spectively. This assumption is based on the study of Wang et al. (2009). Anaverage main engine load of 0.8 and an average auxiliary engine load of 0.5are also taken into account Corbett et al. (2009).

The formula developed by Corbett et al. (2009) is a very detailed method-ology for constructing fuel-based emission inventories. In addition, it isa reliable method as amongst others it uses the emission factors recom-mended by EMEP/CORINAIR (Psaraftis and Kontovas, 2009a). Moreover,this formula consists of several components which are validated in variousstudies. A drawback of this formula is the use of various assumptions, inparticular the main engine- and auxiliary daily fuel consumption, whichcan lead to incorrect shipping emission values (Eyring et al., 2010, Psaraftisand Kontovas, 2009a).

Cubic relationAforementioned, some researchers estimated the shipping emission basedon the fuel consumption. Psaraftis and Kontovas (2009b) estimated the fuelconsumption by the ’cube law’ defined by Hughes (1996). The cube law im-plies that the fuel consumption of a vessel can be approximated by a cubicfunction of the speed (Hughes, 1996). In particular, the fuel consumptioncan be calculated as follow:

FC = α ∗ S3 (2)

FC is the ship fuel consumption, S is the speed in knots and α is a knownconstant. The constant is a function of the loading condition of the ship andof other vessel characteristics such as engine, age and geometry (Psaraftisand Kontovas, 2009b).

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The cubic relation can be used to assess the fuel consumption by differentspeeds. The cubic relation is often used to analyze the effect of speed re-duction as an emission control measure for the same ship. A disadvantageof this method is that the value of α is often assumed (Psaraftis and Konto-vas, 2009b;a). Moreover, various researchers argue that the relation betweenspeed and fuel consumption actually depends on the engine’s type and itsload (Faber et al., 2010). In addition, the cubic relation is not often used toestimate the shipping emission in the literature and preference is given tothe activity-based model defined by (Corbett et al., 2009).

3.4.2 Emission measurements in practice

Besides academic articles, shipping emission measurements are developedin various reports from (international) organizations. An overview of thesemeasurements is provided in table 2.

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Table 2: Emission measurements in practiceOrganization Method NotesConnekt 1 Estimated the CO2 emission by multiplying

the energy used by the energy factor. How-ever, as the majority of the freight transportis outsourced most shippers do not have di-rect access to fuel consumption data. There-fore, at least the fuel type and distance trav-elled of the vessel in tonne-kilometer arerequired for shipping emission estimated(Connekt, 2010).

The energy used depends on the length ofthe vessel, the load factor, the size and depthof the waterways and the speed. Further, al-most all vessels use EN590 fuel since 2010,which is characterized as ultra low sulphurdiesel, in the Netherlands (Connekt, 2010).

Cefic 2 Easiest and most accurate way to estimateCO2 emission is to multiply the fuel con-sumption by the fuel emission conversionfactor.

This formula can be justified by the fact thatthe carbon dioxide emission depends onlyon the fuel consumption. In particular, CO2is caused by the oxidation of the carbonpresent in the fuel. Therefore, the CO2 emis-sion completely depends on contents of car-bon in the fuel and the quantity of fuel used(Cefic, 2011).

EICB 3 Developed a mobile application, called Eco-naut, which calculates the CO2 emission.The CO2 emission equals to the amount otfuel used on a trip (Van der Linden, 2016).

The application determines the distancetravelled based on GPS, while shippers needto enter the load factor of the vessel and theamount of fuel in the tank at the beginningand end of the trip.

ThyssenKruppVeerhaven 4

Estimated shipping emission by taking intoaccount the fuel consumption, which isbased on the distance travelled and the en-ergy capacity of the engine. The fuel con-sumption is multiplied by the fuel con-version factor in order to derive at theCO2 emission (Boneschansker and Davidse,2016).

The load factor is also taken into ac-count when determining the fuel consump-tion. The CO2 emission conversion fac-tor is provided by amongst others TNO(Boneschansker and Davidse, 2016).

Table 2: Emission measurements in practice

As can be seen from the table, the ECIB calculates the emission based onGPS data. According to Van der Linden (2016) the CO2 emission calcu-lated by the application might not be accurate, because the emission value

1http://www.connekt.nl/home/2http://www.cefic.org/3http://www.eicb.nl/4http://www.thyssenkruppveerhaven.com/nl/

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depends on the input of shippers. In particular, they might not enter thecorrect values of fuel used. Moreover, he concludes that AIS data is a bettermethod to determine the distance travelled on a trip, as more informationis available, i.e. the vessel speed (Van der Linden, 2016).

Consequently, the shipping emission measures in practice are not that de-tailed compared with the literature, i.e. the formula of Corbett et al. (2009).This can be concluded by the fact that most shipping lines do not havedetailed information regarding the daily fuel consumption of their engine(Schoemaker, 2016). Therefore, the most accurate way of calculate shippingemission by the shippers themselves is the formula of Cefic (2011) which isbased on the activity-based method. This formula is as follows:

CO2 emission = fuel consumption x fuel emission conversion factor (3)

The CO2 emission is measured in tonnes, while the fuel consumption is inliters and the unit of measurement for the fuel emission conversion factoris in kilogram CO2 per liter fuel / 1.000.

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4 Data and method

In this section the method and data used for this paper will be described. Inparticular, the method used will be introduced, followed by a descriptionof the AIS data. Lastly, the formula used to calculate the shipping emissionwill be presented.

4.1 Methodology

This paper uses a combination of a design study and theory testingmethodology. Design study research is a methodology that intends toadd analysis and explanation, specification for interventions to transformpresent practices and improve the effectiveness of organizations (Denyeret al., 2008). Design studies are often used to design and implement solu-tions for a field problem. Following, Becher and Trowler (2001) state thatthis approach is useful for “how” and “what” research questions (Becherand Trowler, 2001). In addition, the research questions are being driven byan interest in field problems (Denyer et al., 2008).

The design study methodology is used in multiple disciplines. Design sci-ence research in the management discipline is solution-oriented and aimsto developed knowledge that can be used in designing solutions for fieldproblems (Van Aken, 2005). In particular, interventions can be designed inorder to solve managerial problems, systems can also be designed benefi-cial to solve construction problems. (Denyer et al., 2008).

As design-studies are solution-oriented, they use the results of amongstothers explanatory disciplines to develop knowledge which can be used byprofessionals in the field beneficial to design solutions for their problems(Van Aken, 2005). Explanatory science aims to develop knowledge to de-scribe, explain and possibly predict an object of interest (Van Aken, 2005).Most researchers in the management discipline use explanatory research todevelop theory (Van Aken, 2005).

Theory is often used as it allows researchers to understand, describe, ex-plain and predict outcomes of interest (Cook et al., 1979, DiMaggio, 1995,Kerlinger and Lee, 1999, Mohr, 1982). One way to make theoretical con-tributions is to build theory. Theory building is a methodology that sup-plements existing theory or introduces relationships and constructs whichserve as a foundation for new theory (Colquitt and Zapata-Phelan, 2007). Inparticular, previously unexploited relationships can be contribute to exist-ing theories and/or be a foundation for new theory (Colquitt and Zapata-Phelan, 2007).

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The theory building approach enables to exploit causal relationships be-tween shipping behaviour and CO2 emission. This insight can be used bypractitioners in the field in order to find a solution for the increasing ship-ping emission.

Furthermore, when using a design study research the research product isa technological rule instead of a causal model (Bunge, 1967). The techno-logical rule is ’a chunk of general knowledge, linking an intervention orartifact with desired outcomes or performance in a certain field of applica-tion’ (Van Aken, 2005). The core of the technological rule consists of a gen-eral solution to a type of field problem. In this research the technical ruleis a shipping emission method for CO2 on inland waterways networks inthe Netherlands in order to establish causality between shipping behaviourand shipping emission. This insight can be used to develop measured todecrease the emission of carbon dioxide and meet the regulations.

Design studies and theory building are the best suitable research designsfor the purpose of this study, since this research is driven by an interest togain insight into a field problem in order to reduce the shipping emission.This implies, that the theory building approach can be used in the designapproach beneficial to find a solution for the growing shipping emission.Furthermore, the proposed intervention consists of designing a methodwhich can estimate the shipping emission for inland-river vessels in theNetherlands. In particular, the method can be used to approximately de-scribe the behaviour (CO2 emission) of a socio-economic system (inlandwaterways in the transportation system).

4.2 Scope

The shipping emission of carbon dioxide for inland-river vessels in the portarea of Rotterdam is calculated. The geographic domain of this study is thePort Area of Rotterdam, which lies between latitude 51°54’ 15’ North andlongitude 4°26’ 32’ East. The port area is depicted in figure 2. The port areaof Rotterdam is the largest logistics and industrial hub in Europe. The portarea, including the industrial complex, has a length of 40 kilometers andcovers 12,426 hectares, water and land included (PoR, 2012). In addition,the Port of Rotterdam is the only port in Northwestern Europe, which of-fers access to ships with the deepest draughts (PoR, 2012).

The Port of Rotterdam was the world’s busiest port from 1962 until 2004(Tankcom, 2015, WSC, 2015). In 2011, the port was ranked as the world’seleventh-largest container port in terms of Twenty-foot Equivalent units(TEU) handled and it was the sixth-largest ports in terms of annual cargotonnage in 2012 (Moleveld, 2014). Further, the World Economic Forum con-

31

cluded that the Netherlands has risen to the first place in the world rakingfor ports in 2012 (PoR, 2012).

Furthermore, the Port of Rotterdam has different areas for the followingfive types of activities: container/breakbulk, liquid bulk, dry bulk, distri-bution, chemical industries/refineries/energy and other activities. More-over, the port has 10 container terminals, 30,000 ocean-going vessels and110,000 inland-river vessels visit the port every year (PoR, 2015a; 2013).

Figure 2: Port area of Rotterdam

4.3 Data

This research is in collaboration with Teqplay 5, which collects real-timedata related to ports, both ocean-going and inland-river vessels in order todevelop dashboards and applications which create value and make the lo-gistic chain more efficient. Automatic Identification System (AIS) data of atotal of 7 days (16-04-2016 till 23-04-2016) is used.

In 2002, the International Maritime Organization (IMO) required the useof Automatic Identification System (AIS) for vessels over 300 GT in orderto reduce the risk of collision between vessels and support maritime navi-gation. AIS is a tool for the short-range identification and tracking of ships,while at the same time it aims to improve the safety and efficiency of wa-ter transport. VHF radio transmissions are used and the typical maximumrange of an AIS station is usually from 50 to 90 kilometer (Doris, 2015, Jalka-nen et al., 2009). The AIS transmitted vessels track and regularly updatethe position, course and speed with a range from two seconds to six min-utes (Tichavska and Tovar, 2015a). In particular, vessels equipped with an

5http://teqplay.nl/

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AIS transponder send three types of information: static information (e.g.the ship number, name, length, beam and draught of a vessel), dynamicinformation (e.g. position, speed and course) and vogaye-related informa-tion (e.g. draught loaded, destination and estimated time of arrival) (Doris,2015, Tichavska and Tovar, 2015a).

Inland AIS technologies are used for the automatic identification of inland-river vessels. Inland AIS is a river Information Service (RIS) which auto-matically exchanges identification and nautical data between vessels andbetween vessels and shore locations (CCR, 2012, MarineCadastre, 2015).From January 1, 2016 all vessels operating on the inland waterways areobligated to have an AIS system on board in the Netherlands. In particu-lar, this obligation holds for all inland-river vessels of the Conférence Eu-ropéene des Ministres de Transport (CEMMT) class I or higher (BTB, 2015).

The AIS dataset retrieved from Teqplay consisted of the following vari-ables: ID, MMSI, Status, Speed over ground, Latitude, Longitude, Courseover ground, True Heading, Destination, Date last update, Time Last, Ex-pected time of arrival. A description of these variables is provided in table3.

For the purpose of this study the MMSI, Speed over ground, Latitude, Lon-gitude and Time Last were relevant. The MMSI enabled to identify thevessels and conduct analysis on individual ship level. The latitude andlongitude were used in order to get a geographical distribution of the CO2emission, while Time Last was used beneficial to get the time between twoobservations. The variable Speed over ground was used to determine thefuel consumption. Moreover, both Speed over ground and Time Last wereused to calculate the acceleration rate.

Table 3 on the next page gives an overview of all variables in the AIS datasetincluding their description.

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Table 3: Variables in AIS datasetVariable DescriptionID Unique identifier of the observation in order to uniquely

classify this information in the databaseMMSI Maritime Mobile Service Identity - nine-digit identification

code of a vesselStatus Navigation status of the vessel which is manually set by

the pilot. The status of a vessel is one of the following: con-strained by her draught, moored, aground, engaged in fish-ing, under way sailing, reserved for future amendment ofnavigational status for ships carrying IMO hazard or pollu-tant category C, power-driven vessel towing astern, power-driven vessel pushing ahead or towing alongside, reservedfor future use, AIS-Start and undefined

Speed over ground The speed of the vessel in knotsLatitude A geographic coordinate used to specify the north-south

position of a vessel on the earth’s surface. This angle rangesfrom 0°to 90°

Longitude A geographic coordinate used to specify the east-west po-sition of a vessel on the earth’s surface. This angle rangesfrom 0°to +180°eastward and -180°westward

Course over Ground The direction of the vesselTrue heading Position of the AIS transponder/transceiver. All vessels in

the dataset have a value of 511 (default value) which indi-cates that the position is not available.

Destination The destination of the vesselDate last update The date of the last updateTime last The time of the last updateExpected time of arrival Expected time of arrival at the destination of the vessel

Table 3: Variables in AIS dataset

The AIS dataset did not contain the length, width, beam and draught ofa vessel. The reason is that only minimum relevant information is storedby Teqplay. However, the CO2 emission might be dependent on the Con-férence Européenne des Ministres de Transport (CEMT) class, as vessels ina particular class might have a less or stronger engine (Boer, 2011, Cefic,2011, Schoemaker, 2016). Therefore, the vessels should be categorized ac-cording to this class in the dataset.

The CEMT class is a classification of vessels, which have been establishedin 1992. The inland waterways in Europe are divided according the CEMT

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class. The CEMT class are based on the dimension of standard vessels andpush barges and the maximum sizes of vessels are established per class.The waterways are classified based on the following criteria: the length,width and carrying capacity of the largest vessels or push barge which isallowed to use the waterways or for which the waterway is considered tobe appropriate. The CEMT class can be used by ship owners beneficial todetermine which waterways can be used for a specific type of vessel (Bin-nenvaart, 2015, EICB, 2015).

An overview of the CEMT class can be found in figure 21, appendix A.In particular, the maximum length, width, depth and carrying capacity pervessel and push barge type are provided.

As the length and width were not provided in the dataset, which are re-quired to classify the vessels according to the CEMT class an additionaldataset from Teqplays database was used. This dataset contained of thefollowing variables: Location.type, IMO number, Name, Ship Type, Callsign, Max Draught, Role, Stern Type, Bow Type, Distance to Bow, Distanceto Stern, Distance to Starboard and Distance to Port. In particular, thesevariables were provided for all MMSI codes in the AIS dataset. A descrip-tion of these variables is provided in table 4 on the next page.

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Table 4: Variables in the additional datasetVariable DescriptionLocation.type Type of the vessel. The vessels in the dataset are of the fol-

lowing type: pointIMO number A unique ship identification number for registered ship

owners and assigned to all ships by the IHS Maritime as itbecame mandatory in January 1996. This number consistsof seven digits. An important note is that only ocean-goingvessels have an IMO number

Name Name of the vesselShip type Vessels in the dataset have on the following types: tanker,

cargo, tug, other type, tanker hazardous category A, wingin ground, passenger, dredging underwater operation, porttender, high speed, pleasure craft, pilot vessel

Call sign A unique identification for the transmitter stationMax draught Max draught of the vesselRole Vessels in the dataset have one of the following roles:

bunker, cargo barge, tanker barge, tug, push barge, tender,swog, authorities, pilot, crane, waste, water, fender, boat-man, supply barge

Stern type Type of the back of the vesselBow type Type of the forward part of the vesselDistance to Bow Distance from the center of a vessel to the BowDistance to Stern Distance from the center of a vessel to the SternDistance to Starboard Distance from the center of a vessel to the StarboardDistance to Port Distance from the center of a vessel to the port

Table 4: Variables in the additional dataset

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For the purpose of this study the following variables out of the additionaldataset were used: IMO Number, Name, Ship Type, Role, Distance to Bow,Distance to Stern, Distance to Starboard, Distance to Port.

The Ship Type and role were used to analyze if these vessel characteris-tics influence the CO2 emission, thus if a causal relation exists. The Bow,Stern, Starboard and Port are nautical boating terms. The Bow is the for-ward part of the hull of a vessel, while stern refers to the back of a vessel.Port is the left-hand side of a vessel, while the right-hand side is indicatedby Starboard. All these four variables are illustrated in figure 3 beneficialto a better understanding.

Figure 3: Nautical boat terms

In order to classify the vessels according to the CEMT class, the length andwidth of the vessels are required. The length of a vessel is the distancefrom stern to bow, while the width is the distance between the port andstarboard. The variables distance to bow, distance to stern, distance to star-board and distance to port refer to the distance from the center of the vesselto one of these nautical terms. Therefore, the length and width of a vesselequals:

Length of a vessel = distance to Bow + distance to SternWidth of a vessel = distance to Starboard + Distance to Port (4)

The International Maritime Organization (IMO) number is used to identifythe inland-river vessels. An IMO number is a ship’s identification num-

37

ber introduced in 1987. It aims to enhance the maritime safety, preventpollution and facilitate and prevent maritime fraud. An IMO number isa permanent number for a ship, registered ship owners and managementcompanies, which would remain unchanged despite transfer of the ship toother flags. The IMO numbers became mandatory in January 1, 1996 and isshown on a ship’s certificate (IMO, 2014).

An IMO number consists of the three letters "IMO" followed by a seven-digit number, assigned to ocean-going vessels over 100 GT. This implies,that inland-river vessels and pleasure yachts do not have an IMO number(IMO, 2014). The difference between an IMO number and MMSI code isthat an IMO number is a permanent number assigned to ocean-going ves-sels, while a MMSI code is a nine-digit number sent in a digital form overa radio frequency in order to uniquely identify a vessel. In addition, allapplicable electronics on-board of a vessel, such as an AIS transponder andDSC radio, use the same MMSI code as it also used for communicationpurposes. Moreover, all vessels regardless the size have an MMSI code(BoatU.S., 2015, ShineMictro, 2008).

Aforementioned, only ocean-going vessels have an IMO code, hence ves-sels without an IMO code or an IMO code of 0 can be classified as inland-river vessels.

Furthermore, data about the following four container terminals: APM,Delta, Euromax and Rotterdam World Gateway (RWG) is used. These dataonly contained of the MMSI code of vessels that visited the terminal in thelast 5.5 months, as they were updated every 15 minutes during the times-pan of 5.5 months, which improves the reliability. The use of these dataallows to get the distribution of container vessels within the dataset andprovide a geographical distribution of the CO2 emission within these fourterminals.

Lastly, three structured interviews are conducted in order to get insightsinto the current practical developments and research in the area of this pa-per. In particular, one interview with ThyssenKrupp Veerhaven (a ship-ping line)6, Expertise- en Innovatiecentrum Binnenvaart7 and the Port ofRotterdam8 are conducted. During these interviews questions were askedregarding current practices in shipping emission, methods to estimate theCO2 emission and vessel characteristics which might influence the ship-ping emission. The three interviews were recorded, transcribed and sent

6http://www.thyssenkruppveerhaven.com/nl/7http://www.eicb.nl/8https://www.portofrotterdam.com/

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to the interviewer beneficial to avoid the interviewer bias. The interviewtranscripts can be found in Appendix C.

4.4 Calculation of CO2 emission

The shipping emission of carbon dioxide is calculated using the bottom-upapproach, as the estimates are based on shipping behaviour. Aforemen-tioned, the bottom-up approach has a higher accuracy as it takes shippingactivities into account, resulting in a better representation of the shippingemission. In addition, the fleet based activity-based method is used in com-bination with the bottom-up approach as shipping behaviour based on AISdata, ship classes, fuel consumption figures and emission factors are incor-porated beneficial to reliable estimates.

Aforementioned, the formula of Cefic (2011) is the most appropriate onefor shippers to calculate the CO2 emission. As the aim of this study is todevelop a method which can accurate estimate the CO2 emission in orderto make individual emitters, i.e. shipping lines, aware and accountable oftheir emission, the formula of Cefic (2011) will be used. Moreover, thisformula is the most appropriate one as technical details about the vessel,i.e. energy capacity of the engine, are not available in the dataset and theuse of assumption might negatively influence the accuracy of the shippingemission calculated. Consequently, the CO2 emission will be calculated asshown in formula 3.

4.4.1 Emission Factor

Cefic (2011) recommends a well-to wheel fuel emission conversion factorof 2.9 kg CO2 per liter, when using diesel oil. However, Stimular9 recom-mends a fuel conversion factor of 3.23 kilogram CO2 per liter for inland-river vessels using diesel oil in the Netherlands. This conversion factorwill be used in this paper, as all inland-river vessels use the EN590 type ofdiesel since 2010 in the Netherlands (Connekt, 2010). However, some ves-sels use both diesel and LNG (Boneschansker and Davidse, 2016). As thedataset does not provide any information about the type of fuel used, it isassumed that all vessels only use diesel oil.

The conversion factor of Stimular is appropriate for the aim of the paperas it is recommended in a range of papers, e.g. Boer (2011), Cefic (2011),Connekt (2010). This implies that the conversion factor of 3.23 kilogramCO2 per liter diesel oil is a reliable one, hence it will be used in this paper.

9http://www.stimular.nl/over_stimular

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4.4.2 Fuel consumption

Aforementioned, there is a relation between the fuel consumption and theCO2 emission. Hence, the shipping emission of carbon dioxide can be cal-culated by the amount of fuel consumed. Moreover, it is concluded that aship’s fuel consumption increases with the speed of a vessel. In particular,an increase in speed results in an increase of the fuel consumption, which isa function of both the main and auxiliary power, due to amongst other theresistance of water (IMO, 2016a, Jonkeren, 2009). Consequently, the fuelconsumption can be determined based on the speed of a vessel. Limitedstudies in the literature examine the fuel consumption based on speed, al-though it is generally known that fuel consumption depends non-linearlyon the sailing speed and ship payload (Psaraftis et al., 2016).

Aforesaid, some studies assumed the fuel consumption is a cubic functionof ship speed, however this is not a realistic approximation for slow or near-zero speeds as ships still consume some fuel, as a result of the auxiliaryengine which produces electricity (Psaraftis et al., 2016). Therefore, variousstudies calculated the daily fuel consumption of a ship as a function of thespeed and its payload. In particular, Notteboom and Vernimmen (2009) didextensive research to the relationship between fuel consumption and speed.They conclude that an increase in speed with a couple of knots already re-sult in a huge increase of fuel consumption (Notteboom and Vernimmen,2009). In addition, this relationship is analyzed for various types of vesselsand the effect of slow steaming is included as well. Figure 4 and 5 depictthe relation between the speed and fuel consumption (diesel oil) for differ-ent ship sizes, adapted from Notteboom and Vernimmen (2009).

These two graphs are used by various institutions, such as the IMO andthe European Sea Port Organization (ESPO), implying these are an accu-rate and reliable representation of the relation between speed and fuel con-sumption. Consequently, these figures are used for the calculation of theshipping emission of carbon dioxide in this paper. As the fuel consump-tion is given for diesel oil, the use of these graphs is an appropriate methodfor the aim of this paper (IMO, 2016a). The fuel consumption for inland-river vessels might be different than the one presented in the figures, sinceinland-river vessels have different engines power. However, due to the lackof figures between speed and fuel consumption for inland-river vessels, itis assumed that figure 4 and 5 are a correct representation of the fuel con-sumption corresponding with speed levels for inland-river vessels.

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Figure 4: Ship fuel consumption dependence on speed, adapted from Not-teboom et al., 2009

Figure 5: Ship fuel consumption dependence on speed, adapted from Not-teboom et al., 2009

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Figure 4 and 5 are used in order to get the fuel consumption based on thespeed of the vessels in the dataset. However, the fuel consumption are pro-vided in tonnes per day. In order to get the fuel consumption, the variableTime Last is used, which indicates the time of the last AIS update. Thedifference between the value of Time Last of a current observation and theTime Last of a previous observations are calculated in days per MMSI code.The outcome, the time difference in days, is then multiplied by the fuelconsumption (in tonne per day) based on the speed. This gives the fuelconsumption in tonne on the distance travelled between two successiveobservations. Consequently, the fuel consumption of a vessel is calculatedequals:

Fuel consumption (FC) =i

∑i=1

(ti − ti−1)FC[vi] (5)

The fuel consumption is measured in tonnes, while the time is day and thespeed in knots.

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5 Analysis

In this section the results from the analyses conducted with the gathereddata will be presented. To start with a brief description of the data prepa-ration, followed by some descriptive statistics, subsequently the relationbetween speed, acceleration and the shipping emission of carbon dioxidewill be discussed. Further, the potential influence of vessel characteristicson shipping emission will be addressed. This chapter ends with an analysisof the geographical distribution of the CO2 emission.

5.1 Data preparation

The AIS dataset used for a total of seven days consisted of 1,205,499 ob-servations from which 751,549 (50%) were inland vessels. The 751,549 ob-servations refer to 811 inland-river vessels. A subset of these inland-rivervessels was made for which the CO2 emission was estimated using R.

Prior to the analyses the missing values in the dataset were analyzed. TheAIS dataset did not have missing values, while the additional dataset didhave missing values for Distance to Bow, Distance to Stern, Distance toStarboard and Distance to Port. Aforementioned, the length and width arecalculated with these variables, required for the categorization according tothe CEMT class. Therefore, the missing values for these variables, equal to17,693 (2.35%) observations, were deleted. Furthermore, the values for theIMO codes were checked as it was detected that thirty vessels had more orless than seven numbers, which implies incorrect values. The IMO codesin questions were manually checked in order to examine the vessel type.Twenty-two vessels out of these were inland-river vessels.

Following, inland-river vessels which had one the following roles were re-moved from the dataset: tug, authorities, pilot, boatman and waste. Thereason is that tugs, pilot and boatman usually do not sail on inland wa-terways. In addition, authorities and waste are vessels owned by the portand/or government and not by individual shipping lines, hence the emis-sion of these is outside the scope of this paper. Moreover, vessels whichbelonged to the category leisure and ship type passenger were removedas well, as the emission of leisure ships and passenger ships is outside thescope of this research.

Next, the variables Time Last and Expected Time of Arrival were convertedfrom epoch time (amount of milliseconds from January 1 1970) to the fol-lowing format: year-month-day.

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5.2 Descriptive Statistics

Before presenting the results, three pie charts are presented which provideinformation on the sample characteristics. In particular, the distribution ofthe CEMT class, Ship Type and Roles are displayed below. As can be seen,the majority of the inland-river vessels are large Rhine vessel. In addition,57% have cargo as a ship type, while the role of 55% of the vessels is cargobarge.

Figure 6: CEMT class Figure 7: Ship type

Figure 8: Role

Further, the distribution of the flag states of the inland-river vessels is an-alyzed. The vessel’s flag can be identified by the Maritime IdentificationDigits (MID), which are the first three digits of the MMSI code. All thevessels in the dataset have a MID of 244 which represents the Netherlands,implying that for all these inland-river vessels their home country of basearea is the Netherlands (MaritimeTraffic, 2014). This implies that the differ-ence in emission between flag states could not be analyzed.

The distribution of inland-river vessels which have visited one of the fol-

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lowing terminals: RWG, APM, Delta and Euromax, over the last 5,5 monthsis presented in table 5. In particular, the table indicates the number of con-tainer ships within the dataset. It can be concluded that on average almosthalf of the vessels can be classified as container ships. This significant largeamount of container ships implies that the distribution of CO2 emissionaround these terminals need to be analyzed.

Terminal Number of vessels

RWG 152,222 (41.05%)APM 182,684 (49,26%)Delta 197,247 (53,19%)Euromax 177,013 (47,74%)

Table 5: Container ships

5.3 CO2 emission

The shipping emission of carbon dioxide was calculated for all observa-tions in the dataset. However, during the timespan of seven days, almostall vessels left the port area for quite some time, as was indicated by thevariable Time Last. In particular, a time difference of more than an hourindicated that a vessel left the port area or the vessel was in the terminalwaiting for the loading/unloading process (Schoemaker, 2016). Therefore,observations which had a time difference between two observations of anhour or more were not included in the calculations.

The CO2 emission ranges from 0.000 to 0.044 tonne per observation, andthe average time between two observations equals 1.1 minute. The averageshipping emission per vessel equals 0.012 tonne over a timespan of 7 days,an average time spent in the port area of 3.9 days and average distance trav-elled of 120 kilometer. Moreover, the CO2 emission for a total over sevendays equals 10.49 tonne. Large differences between the individual vesselswere observed, which will be discussed in the next section. In particular,the association between the following vessel characteristics: CEMT class,role and ship type, and shipping emission of carbon dioxide will be ana-lyzed.

The emission over time was analyzed based on figure9. It can be concludedthat on average the shipping emission does not fluctuate over the timespanof seven days, implying there is a constant pattern. In addition, a lot of ves-sels have an emission of nearly 0 tonne, implying those are in the terminalswaiting to be loaded or unloaded.

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Figure 9: Emission over time

5.3.1 Speed

The effect of speed on shipping emission is also analyzed. An overviewof the average emission per minute per speed level is provided in table6. It can be concluded that on average there is a significant relation be-tween emission and speed level, in particular a higher speed level resultsin a higher shipping emission. This can be explained by the fact that ahigher speed implies more fuel consumption, resulting in higher emissionlevels. However, the emission corresponding a speed level of 40.00 knotsis higher than the emission by a speed level of 50.00 knots. As only 17 ob-servations are recorded by a speed level of 40.00 knots and 10 by a speedof 50.00 knots, no clear explanation for this result can be found. Due to therelation between speed and emission, acceleration might also influence theshipping emission. This potential relationship will be analyzed in the nextsection.

Furthermore, the effect of speed reduction on emission is analyzed. Ta-ble 7 provides an overview of the average emission values correspondinga speed reduction of 10% and 20%. In addition, the percentage with whichthe CO2 emission is decreased is shown in brackets. It can be concludedthat a speed reduction of 10.00% and 20.00% drastically reduces the ship-ping emission on overall. This can be explained by the fact that less fuel isconsumed, hence less shipping emission of carbon dioxide.

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Speed (knots) Emission (tonne)

10.00 1.66e−0520.00 0.00130.00 0.00240.00 0.02050.00 0.01060.00 0.01170.00 0.01480.00 0.01590.00 0.021102.30 0.051

Table 6: Average emission per speed level

Speed (knots) Emission (tonne) 10% speed reduction (% decrease) 20% speed reduction (% decrease)

10.00 1.66e−09 1.05e−09 (36.75%) 3.95e−08 (>100 %)20.00 0.001 0.0007 (30.00%) 0.0005 (50.00%)

30.00 0.002 0.001 (50.00%) 0.0006 (70.00%)40.00 0.020 0.013 (35.00%) 0.0012 (95.00%)50.00 0.010 0.0073 (3.00%) 0.0023 (80.00%)60.00 0.011 0.007 (36.36%) 0.0021 (90.91%)70.00 0.014 0.012 (14.29%) 0.0023 (83.57%)80.00 0.015 0.011 (26.67%) 0.0024 (84.00%)90.00 0.021 0.017 (19.05%) 0.0112 (47.67%)102.30 0.051 0.033 (35.29%) 0.0151 (70.59%)

Table 7: Speed reduction

5.3.2 Acceleration

Acceleration refers to the process of speeding up and is calculated ac-cording to the following formula used in the literature by amongst others(Bokare and Maurya, 2013):

a =δvδt

=(vi − vi−1)

(ti − ti−1)(6)

Where, a is acceleration in (m/s2), v the speed in m/s and t the time in sec-onds.

The acceleration rate for every observation is calculated and in some cases

47

this rate is negative, this refers to the process of slowing down which iscalled deceleration. Table 8 provides an overview of the average emissionand speed values during an acceleration manoeuvre per minute. The speedis given in knots in order to be consistent with further analyses and drawconclusions.

It is seen from table 8 that there is a variation in the shipping emission withdifferent speed and acceleration rates. The lowest emission rate is obtainedwith an acceleration rate of -2.69 meter per second squared and speed rateof 1.25 knots, while an acceleration rate of 0.36 (m/s2), corresponding witha speed rate of 89.09 knots results in the highest emission equal to 0.018tonne. Moreover, it can be concluded that the effect of acceleration on emis-sion is more prominent at higher speed rates (above 0.14(m/s2)), howeverthe highest speed rate of 102.30 knots does not results in the highest ship-ping emission.

Based on the deceleration rates, it can be stated that on overall the high-est deceleration rates result in the lowest shipping emission. However, hereagain a mild deceleration rate, from -0.06(m/s2) till -0.65(m/s2), result in thehigher CO2 emission than aggressive deceleration rates, i.e. −1.15 (m/s2).Consequently, the lowest emission rates are obtained during the decelera-tion manoeuvre, implying a slower speed rate than during acceleration.

Furthermore, there is a relation between acceleration and speed. Vesselswith a higher speed have a higher acceleration rate. However, this doesnot hold for deceleration.

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Acceleration (m/s2) Emission (Tonne) Speed (Knots)

5.26 0.0001 102.304.69 0.0002 102.302.59 0.0003 102.301.36 0.0005 102.300.95 0.0007 102.300.85 0.0008 101.570.75 0.0009 101.530.66 0.0010 99.680.54 0.0013 95.890.44 0.0015 93.090.36 0.0018 89.090.23 0.0017 60.320.14 0.0009 32.650.06 0.0007 19.160.02 0.0008 9.270.007 0.0004 6.560.002 0.0001 4.040.001 0.0001 3.950 8.18e−06 3.18-0.001 2.65e−06 4.91-0.002 4.99e−06 4.93-0.007 1.67e−06 4.67-0.02 6.96e−06 3.15-0.06 1.32e−05 4.36-0.15 6.67e−05 5.76-0.23 2.09e−05 2.41-0.34 3.13e−05 3.00-0.45 2.91e−05 5.98-0.57 2.26e−05 2.66-0.65 3.10e−05 3.38-0.74 2.08e−06 2.88-0.85 1.36e−06 2.04-0.96 1.61e−06 4.32-1.15 7.77e−07 2.17-2.69 7.54e−08 1.25-4.62 4.11e−07 3.50

Table 8: Acceleration

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5.3.3 Vessel characteristics and CO2 emission

To analyze if a relation exists between vessel characteristics and CO2 emi-sison, a one-way Anova is conducted. As the vessel characteristics are cat-egorical variables, while the emission is measured at a continuous scale,the one-way Anova is an appropriate test to use. One-way Anova can onlybe used when the emission is normally distributed for each category ofthe three vessel characteristics (CEMT class, ship type and role). For largesamples (>200 observations) it can be assumed that the data is normallydistributed according the the Central Limit Theorem. Consequently, it isassumed that the emission levels for the CEMT classes, role and ship typesare normally distributed (Altman and Bland, 1995, Elliott and Woodward,2007, Field, 2009, Ghasemi et al., 2012, Lumley et al., 2002).

One-way Anova indicates if a statistically significant difference exists inthe CO2 emission for the different groups, if the p-value is smaller than thesignificance level of 5.00%. An important note is that the one-way Anovaanalysis cannot indicate which specific groups are statistically significant,as it only indicates that at least two groups were significant.

Therefore, if the outcome of the one-way Anova analysis indicates a statis-tically significance relation, Tukey’s HSD test is used as post hoc analysisto determine where the statistically significance (p < 5.00%) difference liesbetween the classes, i.e. which classes have a higher CO2 emission.

5.3.3.1 CEMT classThe outcome of the one-way Anova shows a statistically significant differ-ence between the CEMT classes and the shipping emission (F = 13.131, p << 2.2e−16) as can be seen in table 13, appendix B. Implying that the CEMTclasses, thus length and width of a vessel, does influence the shipping emis-sion of carbon dioxide.

The result of the Tukey HSD test indicates that there is a significant differ-ence in the shipping emission between the following classes: large Rhinevessel and Dortmund-Ems canal vessel (p = 0.036), Spits and Large Rhinevessel, (p = 0.00), Spits and Campine vessel (p = 0.00), Spits and RhineHerne canal vessel, (p = 0.00), push convoy of 2 barge and Spits (p =0.003) and push convoy of 6 barge and Spits (p = 0.010). These results canbe obtained from table 16, appendix B. In particular, vessels of the typeDortmund-Ems canal vessel have a higher shipping emission than largeRhine vessels. Further, Spits have a higher amount of CO2 emission com-pared with large Rhine classification vessels. The same is the case for thecomparison between Spits and Campine vessels, as Spits vessels have ahigher emission. In addition, the use of vessels classified as Spits results in

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a higher level of shipping emission than Rhine Herne canal vessels. Lastly,both push convoys of 2 barge and 6 barge have a lower shipping emissionthat Spits vessels. Consequently, it can be concluded that the use of Spitsvessels results in a higher shipping emission for carbon dioxide.

The conclusion that Spits vessels have the highest shipping emission ofcarbon dioxide is also supported from table 9, which gives an overviewof the average speed per minute, emission value during the timespan ofseven days and acceleration per minute per CEMT class. Spits vessels haveone of the highest speed levels and the highest acceleration rate. The factthat they have the highest shipping emission can be explained by their highspeed rate. Further, the lowest emission is obtained by push convoy of 4barges, while those have the second highest speed rate. However, this classof vessels has the lowest acceleration rate. This implies that both speed andacceleration have an influence on the shipping emission.

CEMT class Speed (knots) Emission (Tonne) Acceleration (m/s2)

Campine vessels 5.01 0.008 0.0025Dortmund-Ems 5.13 0.012 0.0012Large Rhine 5.31 0.011 0.0013Rhine Herne 5.46 0.010 0.0015Spits 5.32 0.039 0.0016Push convoy of 2 2.91 0.008 0.0012Push convoy of 4 5.92 0.003 0.0011Push convoy of 6 6.03 0.004 0.0012

Table 9: Overview average values per CEMT class

Furthermore, the comparison between the relative percentage of the ves-sels according to the CEMT class and the emission per class shows that onoverall the shipping emission of carbon dioxide are not equally distributedaccording to their relative proportion, as can be seen in figure 10. In partic-ular, large Rhine vessels represent 49.00% of all ships, while their emissioncorresponds with 38.79% of the total CO2 emission. Aforesaid, Spits ves-sels have the highest emission, while only 14% of the vessels belong to thisclass. Moreover, the shipping emission for both push convoys of 2 bargeand 4 barge have a lower emission percentage compared to their relativepercentage of the CEMT class.

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Figure 10: Relative percentage cemt class and emission

5.3.3.2 Ship typeThe one-way Anova analysis shows a statistically significant difference be-tween the ship type of a vessel and the shipping emission of carbon dioxide(F = 68.590, p < 2.2e−16), as can be seen from table 14, appendix B. This im-plies that the null hypothesis, which states that there is no difference in themeans (shipping emission) of the groups (ship types), is rejected.

The results of the Tukey HSD test indicate that there is significant differencebetween the shipping emission between the following ship types: Cargoand Tanker (p = 0.005), High speed and tanker (p = 0.000), High speed andCargo (p = 0.00), High speed and other type (p = 0.000) and High speed andDredging under water OPS (p = 0.001), as indicated in table 17, appendix B.In particular Tanker vessels have a higher shipping emission than Cargo.However, Tanker, Cargo, Other Type and Dredging under water OPS allhave a lower shipping emission compared with High speed vessels. Con-sequently, high speed vessels have the highest shipping emission. This isalso supported by table 10 as the emission for High speed vessels is equalto 0.861 tonne. In addition, high speed tankers also have the highest speedrate. Port tender vessels have the second highest speed rate and also thesecond highest emission and acceleration rate. The lowest shipping emis-sion is obtained by vessels with the following type: Dredging under water.Vessels belonging to this class have the lowest speed rate and also one ofthe lowest acceleration rates.

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Ship Type Speed (knots) Emission (Tonne) Acceleration (m/s2)

Cargo 5.26 0.011 0.0017Dredging Under Water 3.45 0.004 0.0013High speed 13.32 0.861 0.0251Tanker 5.32 0.010 0.0018Tanker Hazcat C 4.24 0.001 0.0011Other Type 4.75 0.013 0.0018Port tender 5.64 0.246 0.0027Wing in ground 5.34 0.025 0.0016

Table 10: Overview average values per ship type

Figure 11 shows the comparison between the relative percentage of thevessels according to their ship type and the shipping emission per shiptype. It can be concluded that the shipping emission of carbon dioxideare not equally distributed according to their relative proportion. In par-ticular, Cargo vessels represent 34.11% of all ships, while their emissioncorresponds with 48.72% of all ships. A similar pattern is found for thehigh speed vessels, as their emission is nine times their relative proportion.Moreover, the majority of the vessels is a tanker ship, while their total ship-ping emission only accounts for 33.61%.

Figure 11: Relative percentage ship type and emission

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5.3.3.3 RoleFrom the outcome of the one-way Anova in table 15, appendix B it can beconcluded that a statistically significant difference exists between the roleof a vessel and the CO2 emission (F = 23.543, p < 2.2e−16). Consequently,the role of a vessels has an influence on the shipping emission.

The significant differences of the Tukey HSD analysis are summarized in ta-ble 11 and the total results of the analysis is presented in table 18, appendixB. Based on the result, it can be stated that a vessel which role is tenderwill result in higher shipping emission compared to other roles. Moreover,cargo barges have a lower shipping emission compared with tanker bargeand swog (Ship with other goods). Vessels which role is swog have a higheremission compared with water and push barges roles, while push bargeperforms better, hence lower emission, compared with tanker barge.

Role A higher/lower emission Role B p-value

Tender higher Bunker 0.0000Tanker barge higher Cargo barge 0.0002Tender higher Cargo barge 0.0000Swog higher Cargo barge 0.0020Push barge lower Tanker barge 0.0250Tender higher Tanker barge 0.0000Tender higher Push barge 0.0000Swog higher Push barge 0.0010Swog lower Tender 0.0000Fender lower Tender 0.0010Supply barge lower Tender 0.0000Water lower Tender 0.0000Water lower Swog 0.0400

Table 11: Significant results Tukey analysis: emission & role

The results from the Tukey analysis is also supported by table 12, whichgives an overview of the average speed per minute, average shipping emis-sion of carbon dioxide over a timespan of seven days and acceleration rateper minute per role. Tender vessels have the highest speed level and ac-celeration rate. This explains the fact that they have the highest shippingemission. Vessels which role is "water" (water supply vessels, i.e. thosesupply water to inland vessels) have lower speed rates and acceleration lev-els compared to tenders, which explains their relative low shipping emis-sion. Further, the lowest shipping emission is obtained by push barges, asthey have one of the lowest speed and acceleration levels. Consequently,

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the variables: speed and acceleration influence the shipping emission ofcarbon dioxide.

Role Speed (knots) Emission (Tonne) Acceleration (m/s2)

Bunker 5.86 0.019 0.0020Cargo barge 5.21 0.010 0.0017Fender 5.07 0.009 0.0014Push barge 4.85 0.006 0.0016Supply barge 6.98 0.023 0.0020Swog 5.20 0.037 0.0021Tanker barge 5.30 0.012 0.0019Tender 6.27 0.233 0.0099Water 4.78 0.013 0.0017

Table 12: Overview average values per role

Figure 12: Relative percentage role and emission

A comparison between the relative percentage of the vessels according totheir role and shipping emission per role is shown in figure 12. As canbe seen, the CO2 emission is not equally distributed according to the rel-ative proportion. Aforementioned, tender vessels have the highest ship-ping emission, while they represent a small percentage of the total fleet.Tanker barges represent 29% of all ships, while their emission correspondswith 34.33% of the total fleet. A similar pattern is found for ships withother goods (swog), while for the remaining categories their total emission

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is lower than their relative percentage.

5.4 Geographical distribution of the CO2 emission

In this section the geographical distribution of the CO2 emission within theport area will be discussed. In order to present the geographical distribu-tion, a grid of the port area was used. In particular, the port area as shownin figure 2 was divided into small geographical areas of 500 by 500 meters.Afterwards, the total emission per geographical area was calculated.

First, the position of the ships was analyzed. Heatmaps of the port areaare made as can be seen in figure 13,14,15 and 16. The heatmaps indicatethe position of the ships and the colour indicate the density of the ships,i.e. red means a lot of ships, while blue means a few. As can be seenfrom the heatmaps, the position of most of the vessels is the Maasvlakte,where amongst other the APM terminal is located, the Botlek or the riverthe Nieuwe Maas.

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Figure 13: MaasvlakteFigure 14: Botlek

Figure 15: Hartel Canal Figure 16: Rotterdam

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The geographical distribution of the CO2 emission within the port area ispresented in figure 17, while figure 1610 shows the port area including rivernames beneficial to a better understanding.

Figure 17: Port area

Based on figure 18 it can be concluded that the shipping emission of carbondioxide is amongst other low in the Maasvlakte, although a lot of vesselsare located here. This can be explained by the fact that a lot of terminals,e.g. APM, RWG, Delta and the Euromax terminal are located here. In par-ticular, the ships might arrive or leave the terminals, implying they sail fora few minutes, resulting in low emission values.

Figure 18: Total emission in the port area

The emission is relative high at the river Nieuwe Waterweg, Caland canal,Hartel canal and part of the river Nieuwe Maas. This can be explainedby the fact that a lot of vessels are sailing there in order to arrive or leavethe terminals, hence the port area. Further, the highest shipping emission

10Retrieved from the Port of Rotterdam

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is obtained in the Botlek, Vulcaanhaven, Waalhaven, Maashaven and thelast part of river Nieuwe Maas. This is consistent with figure 14 as a lot ofvessels are located around the aforementioned locations and they have anaverage speed level of 9.5 knots, as can be seen in figure 19, which presentsthe average speed within the port area. Moreover, the average accelerationwithin the port area can be seen in figure 20.

Figure 19: Average speed in the port area

Based on figure 19, it can be concluded that the lowest speed levels are ob-tained in amongst others, Maasvlakte, Botlek and in Waalhaven. Aforesaid,this can be explained by the fact that a lot of terminals are located over here.Furthermore, an average speed of 9.5 knots is observed at the Hartel canaland Nieuwe Maas, where the shipping emission is relative high. Moreover,the highest speed levels equal to 9.5 knots (17.59 knots) is also observed onthese canals. This is consistent with the significant relation between speedand emission, as higher speed level negatively influence the shipping emis-sion of carbon dioxide, hence increase it.

A speed limit on the Nieuwe Maas at the level of Noordereiland over alength of 4 kilometer and at the Hartel canal between the junction withthe Oude Maas and Harmsenbridge over a length of 10 kilometer is imple-mented from October 1, 2014 onwards (PoR, 2014c). Based on figure 19 itcan be seen that the average speed at the Noordereiland (latitude 51.913297and longitude 4.496107) and at the Hartelcanal (latitude 51.902317 and lon-gitude 4.211784) equals approximately 7 knots. This implies that almost allvessels obtain the maximum speed allowed.

Although the speed level is high, the shipping emission is not at somepoints on the Hartel canal, one possible explanation is that the number ofvessels located over here is relative low as can be seen in figure 15.

One of the highest shipping emission values is observed in the Botlek. Inparticular, around the Botlek Tank terminal (latitude 51.87495, longitude

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4.28255). The corresponding speed level around this area equals 9.5 knots.Moreover, a lot of vessels are located in this area, as can be seen from figure14. Consequently, it can be stated that the shipping emission is higher inareas where more vessels are located.

Further, speed values of 7 knots are observed in Vondelingenplaat (latitude51.88666 and longitude 4.33085). The highest shipping emission rate is alsoobserved in this area and the density of vessels in this area is moderate (300vessels) as can be seen in figure 15.

Figure 20 shows the average acceleration values within the port area. Itcan be concluded that on overall the locations where acceleration valuesare relative high corresponds with the high emission spots. In particu-lar, at the river Nieuwe Waterweg, Caland Canel, Dintelhaven (latitude51.94895 and longitude 4.12438), Beneluxhaven (latitude 51.95070 and lon-gitude 4.12892), Botlek Tank terminal, Botlek and Waalhaven. One possibleexplanation is that a lot of vessels are located over there. In addition, thehighest acceleration rates can be observed at locations where there is a highspeed rate.

Figure 20: Average acceleration in the port area

Lastly, for a better representation of the contribution of shipping emissionof inland-river vessels within the port area, a geographical distribution ofCO2 emisison of oceangoing vessels would be relevant. However, as therewere only 4 oceangoing vessels in the dataset, a comparison could not bepresented and analyzed.

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6 Discussion

The aim of this paper is to develop a method which can be used to estimatereliable shipping emission of carbon dioxide on inland waterways in theNetherlands. One of the reasons for this study was to contribute to existingliterature on the emission for inland-river vessels, in particular to identifyemission factors. The scope of this research was whether or not specificvessel characteristics had an influence on the CO2 emission for inland-rivervessels. The results of this study are presented in the previous chapter andthis section will elaborate further on the findings.

6.1 CO2 emission

To obtain the accuracy of the estimated CO2 emission in the port of Rotter-dam, the estimated emission was compared with other studies. However,because of differences in method and scope of the inland-river emission re-search domain, comparing the shipping estimates was difficult. The prac-tical studies of Inlandlinks (2015) and Van Ommeren (2011) were selectedfor comparison, as their scope includes the Port of Rotterdam.

The average shipping emission of carbon dioxide for an inland-river vesselequals 0.012 tonne, not taking into account the differences between vesselcharacteristics. A comparison of the presented results with those of In-landlinks (2015) indicate that in general the predicted shipping emissionbased on AIS data in this paper differ 33% of the average CO2 emissionfor inland-river vessels reported by Inlandlinks (2015), as a value of 0.018tonne is reported. In particular, the CO2 emission calculated in this pa-per is 33% lower. Larger differences, up to 70% can be found due to dif-ferent datasets and approaches used. For example, the scope of Van Om-meren (2011) was Rotterdam instead of only the port area and the emissionis calculated based on the energy used. Inlandlinks (2015) also includedthe energy used in their STEAM model and the emissions are calculatedon basis of different variables, such as mode of transport, type of fuel, en-gine type and route. One possible explanation for the difference betweenthe emission calculated in this paper and the ones from practical studiesis the use of detailed technical information, which positively influencesthe accuracy of the shipping emission estimates. In particular, the use ofAIS data incorporated in the STEAM model, results in accurate emissionestimates, as a difference of 8.00% to 14.00% are observed compared tothe estimates conducted by the European Monitoring and Evaluation Pro-gramme11(Jalkanen et al., 2012).

11http://www.eea.europa.eu/themes/air/links/institutions/emep-european-monitoring-and-evaluation-programme

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6.2 Speed

A relation between speed and shipping emission of carbon dioxide is ob-served, as higher speed levels results in higher emission levels. This is con-sistent with the literature as it is concluded that a vessel’s speed influencesthe fuel consumption, hence shipping emission (Corbett et al., 2009, Songand Xu, 2012, Wang et al., 2007b). In particular, figure 18 and19 illustratethat locations with a high speed level, have a corresponding high emissionrate.

Furthermore, reducing the speed with 10.00% and 20.00% drastically re-duces the CO2 emission. This is in line with the study of Corbett et al.(2009) who states that speed reductions would reduce the CO2 emission.Psaraftis and Kontovas (2009a) adds that reducing a vessel’s speed by 10%,decreases the emission by 10-15%. The corresponding emission values bya speed reduction of 10.00% are on overall reduced by more than 15.00%,except by a speed level of 50.00 knots. No clear explanation could be found,as only 10 observations were recorded for a speed level of 50 knots. More-over, the emission reductions across inland-river vessels can be up to 90%when the speed is halved, this is consistent with the study of Corbett et al.(2009). Consequently, speed reductions can be used to mitigate the effectof CO2 emission. However, a drawback is the substantial losses in revenue(Psaraftis and Kontovas, 2009a). The costs of speed reductions were notanalyzed as this is outside the scope of this paper.

6.3 Acceleration

Based on the results, it can be concluded that the shipping emission of car-bon dioxide varies with variation in speed and acceleration. This is in linewith other research, e.g. the study of Unal et al. (2004) and Wang et al.(2007b). Further, the effect of acceleration on CO2 emission is greater onlower speeds than at higher speed levels. This is consistent with the studyof Wang et al. (2011). However, El-Shawarby et al. (2005) state that mildacceleration, 40.00% of the maximum acceleration rate results in the high-est emission rates, compared with normal and aggressive acceleration, re-spectively 60.00% and 100.00% of the maximum rates. The results of thispaper do not support this, as the highest emission rate corresponds with6.82% of the maximum acceleration rate. Therefore, in this study the high-est emission rates are obtained from 6.82% till 14.25% of the maximum rate.Nonetheless, mild acceleration indeed results in higher emission rates com-pared with normal and aggressive acceleration. This can be explained bythe fact that mild and normal acceleration take some time in order to reachthe desired speed level, implying the shipping emission is higher due tothe large sailing time required. Contrary, aggressive acceleration results in

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a higher fuel consumption, as can be seen by the speed level, for a shortperiod of time as the desired speed will be reached earlier (El-Shawarbyet al., 2005).

Pidgeon and Dobie (1991) and Ross (1994) state that emission levels willbe affected by variability in shipping behaviour, which are caused by dif-ferences in the amongst other speed and acceleration. This is also the causein this paper, as the shipping emission is influence by both the speed andacceleration. Consequently, the shipping behaviour does influence the CO2emission.

When analyzing the deceleration rates, it is concluded that there is no rela-tion between deceleration and speed. In particular, the highest decelerationrates does not employ higher speed rates nor does the lowest decelerationrates imply lower speed rates. This is supported by Maurya and Bokare(2012) as it is stated that various researchers report that there is no clearassociation between speed and deceleration rates. Moreover, Bennett andDunn (1995) concludes that deceleration manoeuvre occurs irrespective ofthe speed rate. This is supported by Wang et al. (2007b) stating that there isno clear relationship between the average and maximum deceleration ratesand corresponding speed rates.

6.4 Vessel characteristics

6.4.1 CEMT class

The associated dependencies in the emission factors were evaluated. Withinthe available variables from the AIS dataset and the additional dataset, itcan be stated that the CEMT class, thus the length and width of a vessel,influence the shipping emission of carbon dioxide. This is supported fromthe literature as it agreed that vessel class, do influence the shipping emis-sion (Diesch et al., 2013, Jalkanen et al., 2009, Walsh and Bows, 2012). Dueto the lack of extensive research on the effect of CEMT class on shippingemission for inland-river vessels, the results of this paper will be comparedwith practical studies. The same holds for the next two sections: ship typeand role.

Spits vessels can be characterized as "high CO2 emitters". Contrary, pushconvoys of 6 barge are "low CO2-emitters". High CO2 emitters show a dif-ferent behaviour, they exhibit lower speed levels than low emitters, buthave the highest acceleration rate. However, the acceleration rate of highemitters equal to 0.016 m/s2 belongs to the mild acceleration class, whichresults in the highest emission rates (El-Shawarby et al., 2005). A mild ac-celeration rate implies an increase in speed, hence more fuel is used result-

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ing that a higher carbon concentration is combusted into carbon dioxide.As a result, a high carbon dioxide concentration implies more pollution ina particular surface area.

Schilperoord (2004), consultant by Royal Haskoning B.V.12, did not findthe same results. In particular, according to Schilperoord (2004) CampineCanal vessels have the highest emission, spits the second and Rhine Hernethe third highest emission rates. The lowest emission rates are obtainedwhen using the push convoys, which is consistent with the results of thispaper. Schilperoord (2004) estimated the shipping emission based on datafrom 2000, which consists of the average lifetime of engines and informa-tion regarding technical developments conducted by surveys. One possi-ble explanation for the different results is that the technical improvementson engines from 2009 onwards leads to different emission estimates (Boer,2011, Cefic, 2011).

Nonetheless, the study conducted by (Van Ommeren, 2011) concludes thatSpits vessels have the highest fuel consumption rate, hence shipping emis-sion. Campine canal vessels have the second highest and push convoysthe lowest CO2 emission. These results do on overall support the resultsconducted from this paper. These results are consistent with the researchconducted by Binh and Tuan (2016). This implies that the results in thispaper are consistent with other literature, with the exception of CampineCanal vessels.

6.4.2 Ship type

Based on the analysis it can be concluded that there is a relationship be-tween the ship type and the shipping emission of carbon dioxide. This issimilar to the findings of Diesch et al. (2013), Jalkanen et al. (2009), Walshand Bows (2012).

High speed vessels are classified as "high CO2 emitters", while Tanker Haz-cat A vessels are "low CO2 emitters". Here again, the high emitters showa different behaviour. In particular, they have the highest speed and ac-celeration rates. This is consistent with the findings of Chan (2015) as heconcludes that high speed vessels have the highest fuel consumption, andas a result the highest shipping emission. Moreover, the results for shipswith the following types "cargo" and "other type" are also consistent as theyhave one of the lowest shipping emission (Chan, 2015).

Further, port tenders supply and support port operations (MarineTraffic,

12https://www.royalhaskoningdhv.com/

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2014). The shipping emission of port tenders it not determined. However,it is concluded that the use of port tenders significantly impact the shippingemission, hence increase it (EC, 2014). This is consistent with the results ofthis paper, as port tenders have the second highest emission rates, imply-ing the use of tenders results in an increase of the CO2 emission.

The CO2 emission for dredgers (ship type: dredging under water) is usu-ally high as its purpose is the transport of materials and excavation. Thislast process requires a large significant amount of the vessel’s energy con-sumption (IADC, 2014). One possible explanation for the low shippingemission rate for this type of vessel in the port area is that the excavationprocess had not taken place between 16 and 24 april 2014 (PoR, 2016). Fur-thermore, the shipping emission estimated for Wing In Ground vessels isconsistent with previous research. Wing In Ground vessels belongs to boththe shipping industry and aviation industry. In general the aviation in-dustry accounts of 2.0% of the global CO2 emission, while the shippingemissions for 2.5% (Atag, 2016, IMO, 2015). Wing in Ground vessels areable to operate at substantially higher speed levels than high-speed vessels,however WIG vessels reduce its drag to a minimum. As a result less fuelis consumed, implying lower CO2 shipping emission rates. (IMO, 2016b,WigCraft, 2012). This is supported by the results of this paper, as WIG ves-sels have a lower emission rate than high-speed vessels.

Moreover, the shipping emission of tanker vessels is not consistent withthe literature. One possible explanation is the technical improvementsfor inland tankers due to the CCNR4 emissions standards from January 12016 onwards (Tankershipping, 2015). In particular, a lot of shippingyards,Chemgas group, Damen and Ecotankers are investing a lot in technologi-cal developments which aim to decrease the shipping emission for inlandtankers, as those represent one of the highest percentage of the total vesselson inland waterways (PoR, 2014b, EC, 2015c). However, as data regardingthe technical details of inland vessels were not available from the dataset,further research into this matter should be conducted.

6.4.3 Role

The results indicate that the role of a vessels influences the CO2 emission.This is supported by the studies of (Chan, 2015, Jalkanen et al., 2009). A lotof studies do not take into account the role of a vessel in their calculation,despite having a significant relation.

Vessels with the following role: tender can be classified as "high emitters",while fenders are "low CO2 emitters". A difference in the behaviour be-tween high and low CO2 emitters is observed, as high emitters have higher

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speed and acceleration rates. Tenders are small vessels which transportsuppliers or people from or to another ship Airzone (2015). According toAirzone (2015), on overall tenders have a high shipping emission. This issupported by the results of this paper as tenders have the highest ship-ping emission for carbon dioxide. According to Airzone (2015) the use ofberthing facilities will decrease the tender emissions with 90.00%. As thedataset did not contain information regarding berthing facilities, future re-search in this area will provide more insight in the emission during differ-ent operation modes of tender vessels.

Furthermore, the lowest emission is obtained by fender vessels, which aremarine equipment used to prevent that ships collide against each other oragainst docks (MarineInsight, 2016). The emission of fender vessels is un-known, implying the accuracy of the value conducted from this researchcannot be compared. The same holds for water supply vessels and shipswith other goods, as most literature analyzed the difference in ship classand type instead of role (Chan, 2015).

Supply barges are characterized as high energy consumers, which resultsin a high shipping emission of carbon dioxide (Becker, 2016, Boer, 2011).As a result, a lot of initiatives are being developed which aim to reduce theemission, for example by using LNG (Becker, 2016). This implies that thehigh emission rate for supply barges can be explained by the fact that theyconsume a lot of energy, resulting in higher amounts of fuel consumption,hence CO2 emission (EC, 2015b). Following, the emission of tanker bargesis relative high as they emit a lot during the degassing process (Buck et al.,2013). The estimated emission in this paper for tanker barge is one the low-est. One possible explanation for this inconsistency is that the emissionduring the degassing process is not taken into account, hence further re-search in this area beneficial to gain additional insights.

Moreover, the shipping emission for push barge and cargo barge are rela-tive small compared to the other roles. This is consistent with the literatureas it is stated that the emission of inland barge transportation is more en-vironmental friendly compared to other vessels. In addition, barges have alower shipping CO2 emission compared to other modes of transportation,such as rail and by truck (AmericanWaterways, 2009, Schilperoord, 2004).

Lastly, no clear emission estimation for inland bunker vessels exists in theliterature. However, various shipping lines, i.e. Vinotra (2016) and orga-nizations sponsored by the European Union, i.e. Green4Sea (2016) takeinitiatives beneficial to reduce the CO2 emission of bunkers. This impliesthat the emission of bunker vessels is relative high, which is consistent withthe results of this study as inland bunkers have one of the highest shipping

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emission.

Consequently, on overall the results conducted from this paper are in linewith existing literature.

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7 Conclusion

Based on all the previous, this final section will combine theory, the find-ings, and discussions to give an answer to the proposed research question.

The aim of this study was to develop a method which can be used toestimate the shipping emission of carbon dioxide for inland-river vesselsbased on AIS data and to identify emission factors, i.e. vessel characteris-tics. The use of AIS data facilitates a mapping of the ship traffic, includingthe detailed location, speed, acceleration and emission of each inland-rivervessels in the Port of Rotterdam. In addition to the estimations and ge-ographical distribution of the shipping emission, it is possible to classifythe inland-river vessels according to various characteristics, i.e. length andwidth.

This study is one of the ones to estimate the shipping emission for inland-river vessels based on AIS data in the Netherlands. It is demonstrated thatAIS data should be used as it provides high-resolution vessel movementsand speed information of vessels, which avoids the needs for various as-sumptions. Moreover, a geographical distribution of carbon dioxide can bepresented based on the position of the vessels.

Furthermore, this study has shown that an AIS-data activity based method-ology can be used to derive at emission inventories for inland-river vessels.The main contribution of this study to literature is the application of a ship-ping emission method based on AIS data. In addition, shipping emissionare presented according to CEMT class, ship type and role. This is the firststudy using AIS data to estimate the CO2 emission for inland-river vessels.

However, the results obtained are not accurate and the largest uncertain-ties of the method used to estimate the shipping emission of carbon dioxideprobably arise from the use of a simple dataset, which did not contain vari-ables such as engine load, engine type and fuel type. The lack of this infor-mation explains the inaccuracy of 33% for the estimated shipping emission.Consequently, it can be stated that the use of AIS data is not sufficient toestimate reliable emission volumes, although it has various advantages, i.e.assumptions about speed are avoided and a geographical distribution forcarbon dioxide can be presented based on position of the vessels. Nonethe-less, vessel specific information needs to be incorporated beneficial to de-rive at reliable estimates. In particular, the fuel consumption need to beestimated not only based on the speed level, but also on the ships load, en-gine type and fuel type.

(Corbett and Fischbeck, 2000) concluded that shipping emission factors for

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inland-river vessels did not exist and should be identified beneficial to re-liable estimates. The results of this paper have confirmed that vessel char-acteristics influence the shipping emission of carbon dioxide. These vesselcharacteristics are: length and width (CEMT class), ship type and role. Inaddition, the speed of a vessel and its acceleration also influence the CO2emission. Furthermore, different categories of the vessel characteristics re-sults in a higher shipping emission. However, uncertainties in classificationof the vessels according to these characteristics exist, due to the lack of ves-sel specific information as aforesaid.

These findings have various implications for academics and practitionerswhich will be discussed in the next sections.

7.1 Academic implications

The shipping emission of carbon dioxide is an extensively discussed topicin the academic literature. However, methods to estimate the CO2 ship-ping emission for inland waterways is barely discussed, especially usingAIS data. This paper is one of the first, that provides a method to estimatethe shipping emission of carbon dioxide based on AIS data.

Scientist need an accurate shipping emission estimation method for car-bon dioxide, not one limited by uncertainties in emission factors, aggre-gated international fuel consumption data and a lot of assumptions used.The method developed in this paper can be used to calculate the shippingemission based on the most reliable vessel information available, withoutthe need for various assumptions.

Moreover, the shipping emission method developed provides emission fac-tors for inland-river vessels and enable scientists to determine the vessel-specific inventories. Furthermore, the shipping emission calculated in thispaper can be used as an input for studies of the regional transportationof pollutants in the Netherlands, and its environmental and health effects.In particular, the results can be used to evaluate the effect of various mea-sures which aims to reduce the CO2 emission. Following, the geographicaldistribution of the CO2 emission can be used as an input for atmosphericdispersion modeling for carbon dioxide in the port area of Rotterdam.

One of the main contributions is that it is demonstrated that the emissiondetermined by the method used vary by inland-river vessel class, ship typeand role. Hence, this method not only estimates the CO2 emission, but alsoprovides an example about the use of AIS data for academics and emissioninventory practitioners. As a result, this study contributes to the shippingemission literature in general as it is shown how value can be delivered

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using AIS data. In particular, it is shown how shipping emission can becalculated based on reliable open source data, which avoids the need forvarious assumption such as ship speed and positively influences the accu-racy of the estimates.

Further, the shipping emission for carbon dioxide method based on AISdata has provided a foundation for academics to calculate the emission andengage with different parties beneficial to develop emission reduction mea-sures. In addition, it is illustrated that scientific research is a critical startingpoint from which regulations can be illustrated. Key findings of this paperare the CO2 emission for inland-river vessels and its location within thePort of Rotterdam.

Lastly, this study provides insights into the effect of acceleration on emis-sion for inland-river vessels. This can be used as an input for studies de-veloping a relationship between the ship’s speed, acceleration, decelerationand CO2 emission. In particular, those which aim to get an idealized valueof speed, acceleration and deceleration rate taking the shipping emissioninto account. Furthermore, the insights can be used an input for studiesanalyzing the effect of emission control measures on inland waterways.

7.2 Managerial implications

This paper encourages the use of AIS data in order to derive at shippingemission estimates for carbon dioxide. In addition, various incentives andmeasures can be designed according to the geographical distribution of theCO2 emission and the polluting profiles for different types of vessels. Thiscan be used by port authorities and the government to develop policies, forexample speed limits in order to mitigate an increase of shipping emissionand its impact on global warming and human health. The results can alsobe used to promote green shipping.

Moreover, the method developed can be used to identify emission hot spotswithin the port area or on inland waterways, in order to evaluate the im-pact of CO2 emission and examine rerouting possibilities beneficial to alower emission. Furthermore, the geographic location of the CO2 emissionwill allow further assessment on the port layout, improvements of the portstructure and reallocation decisions to quays surrounded by the lowest rateof affected inhabitants (Tichavska and Tovar, 2015a).

Furthermore, there are many factors which influence the shipping emis-sion (Diesch et al., 2013). Nonetheless, there are only a few measurementsof these factors used in practice. This paper is valuable for the investiga-tion of the influence of shipping emission of carbon dioxide in an emission

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controlled area, i.e. an area with speed limit. Moreover, the work presentedhere can be used for comparing the emission factors with practical research,as data on CO2 emission for inland river-vessels is limited.

Another main implication is that it provides various stakeholders insightthe CO2 emission within the port area. These insights can be used by thecargo owners, the shipping and port industry, as they have a strong interestin measures which aim to reduce the environmental impact on their globaloperations (Ng et al., 2013). Furthermore, based on the geographical dis-tribution of the vessels measures, e.g. speed limits, can be developed onlocations near to the emission heat spots. In addition, the health effects canbe analyzed as most of the emission heat spots are located near to residen-tial areas.

Moreover, individual emitters can calculate their shipping emission usingAIS data. Individual emitters get insights and made into their shippingbehaviour. At the same they can be made accountable for their behaviour,for example the effect of their acceleration and speed rate on rivers nearto residential areas. In addition, they can improve the choice of their shipmovements and ship characteristics, e.g. the choice of a vessel type whichhas a low CO2 emission.

Lastly, the insights can be used by various institutions, i.e. EICB13, as theyoffer courses to shipping lines which aim to provide insights into the ship-ping behaviour and associated fuel consumption. In particular, the effectof speed levels and acceleration rates on shipping emission can be incorpo-rated in their course.

7.3 Limitations

As already depicted on in the methodology sections, several assumptionsare used in this paper. In addition, this study has several limitations whichwill be discussed in this section.

To start with, the formula used to calculate the shipping emission assumesthat all vessels only use diesel oil. Moreover, due to the lack of extensive re-search in the shipping emission area for inland-river vessels, it is assumedthat figure 4 and 5 are a correct representation of the fuel consumption cor-responding with speed levels for the vessels in the dataset. Accurate infor-mation regarding the fuel type used per vessels and correct figures betweenthe speed-fuel consumption relation for inland waterways would have pos-

13http://www.eicb.nl/

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itively influences the shipping emission estimations of carbon dioxide.

Secondly, a simple AIS dataset was used which did not provide technicalinformation about the vessels, e.g. engine system and energy-efficiency ofthe vessels. In addition, the dataset did not contain the time spent in theport by ships in different operating modes, e.g. maneuvering towards har-bour and in berth. This implies that the relation between time spent in theport and emission could not be analyzed. Moreover, the ships load was nottaken into account as this data could not be provided. Incorporating thisinformation would have been beneficial for the accuracy of the estimations.In addition, more insight into the differences in shipping emission betweendifferent classes, ship types and roles would have been obtained.

It is assumed that the emission levels for the CEMT classes, role and shipsare normally distributed according to the Central Limit Theorem. How-ever, when analyzing the bar charts, it was concluded that these were notexactly equally distributed. This might have influenced the results and amore equally distribution of these variables would have been beneficial.

Further, all the inland-river vessels had the Netherlands as their homecountry, implying differences in emission between flag states could not an-alyzed. A dataset consisting of various flag states would have been morebeneficial as the relation between flag state and shipping emission could beexamined.

Moreover, the dataset used consisted of four oceangoing vessels, implyingthe contribution of the CO2 emission for inland-river vessels could not becompared with the total shipping emission within the port area. Therefore,a dataset consisting of an equal distribution of oceangoing vessels and ves-sels operating on the inland waterways would have been more beneficialas the conclusions could be drawn based on a better representation of thecontribution of CO2 emission of inland-river vessels within the port area.

Aforementioned, an AIS dataset with limited variables was used. This im-plies that the presented emission factors reflect the emission factors for av-erage inland-river vessels. As a result, the estimated emission cannot beused for a detailed study which changes the fleet composition used in thispaper. Lastly, the shipping emission is calculated based on AIS data forseven days. The use of a small dataset limits the generalizability of theresults and a bigger dataset, i.e. data of half a year, would have been bene-ficial in order to test its validity.

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7.4 Future research

In this section suggestions for future research continuing on some of theabove mentioned limitations will be made.

First of all, researchers should continue with analyzing the shipping emis-sion of carbon dioxide for inland-river vessels and especially on inlandwaterways. Another interesting direction would be to further analyze theemission of NOx, SO2 and PM for inland-river vessels within the port areaand on inland waterways. In addition, it should be examined if the asso-ciations found in this paper remain the same for other port areas, i.e. thePort of Amsterdam.

Secondly, researchers should continue using AIS data to estimate the ship-ping emission, as this avoids the needs for various assumptions, i.e. thespeed of a vessel. However, vessel specific information, e.g. engine loadand fuel type should be included in order to derive at accurate estima-tions. Another interesting direction would be to include water and weatherconditions, as literature concludes that its use improves the geographicaldistribution of the shipping emission (Astito et al., 2014). Moreover, vari-ous researchers conclude that the time spent by ships in different operationmodes in the port area has a significance influence on the emission (Cul-linane et al., 2015, Lucialli et al., 2007, Tzannatos, 2010). The effect of thisvariable on the CO2 emission should also be examined to gain more in-sights into the variables influencing the shipping emission of carbon diox-ide.

Furthermore, various researchers conclude that the loading and unloadingoperation of a vessel contribute to the shipping emission (Alastuey et al.,2007, Eyring et al., 2005, Lonati et al., 2010, Moreno et al., 2007). The ship-ping emission corresponding these operations should be analyzed benefi-cial to draw better conclusions about the ship activities which significantlyinfluence the CO2 emission, hence negatively affect the environmental andhuman health.

Following, more research into the associations of vessel characteristics onthe shipping emission for inland-river vessels should be conducted. In par-ticular, variables such as flag state, ship weights and ship load should betaken into account beneficial to gain more insights into the variables influ-encing the shipping emission. In addition, a better geographical distribu-tion of the shipping emission can be drawn.

Lastly, it should be examined if the characteristics which results in lowershipping emission of carbon dioxide also hold for other areas than the Port

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of Rotterdam. In addition, it should be analyzed if the same results holdwhen using a bigger dataset beneficial to analyze the CO2 emission on ayearly basis.

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A CEMT class

Figure 21 gives an overview of the CEMT class. In particular, the length,width, depth and maximum load per class are displayed.

Figure 21: CEMT class

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B Results ANOVA

Table 13: Anova Output Inland Vessels - Emission & CEMT class

Df Sum Sq Mean Sq F value Pr(>F)

CEMT 7 0.000039 5.5548e−06 13.131 < 2.2e−16 ∗ ∗∗Residuals 370, 622 0.156783 4.2300e−07

Note: Signif. codes: 0 ‘***’; 0.001 ‘**’; 0.01 ‘*’; 0.05 ‘.’; 0.1 ‘ ’

Table 14: Anova Output Inland Vessels - Emission & ShipType

Df Sum Sq Mean Sq F value Pr(>F)

shipType 10 0.00029 2.8969e−05 68.59 < 2.2e−16 ∗ ∗∗Residuals 370, 619 0.15653 4.2240e−07

Note: Signif. codes: 0 ‘***’; 0.001 ‘**’; 0.01 ‘*’; 0.05 ‘.’; 0.1 ‘ ’

Table 15: Anova Output Inland Vessels - Emission & Role

Df Sum Sq Mean Sq F value Pr(>F)

role 8 0.00008 9.9569e−06 23.543 < 2.2e−16 ∗ ∗∗Residuals 370, 621 0.15674 4.2290e−07

Note: Signif. codes: 0 ‘***’; 0.001 ‘**’; 0.01 ‘*’; 0.05 ‘.’; 0.1 ‘ ’

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Table 16: Tukey analysis Inland Vessels - Emission & CEMT

diff lwr upr p adj

LARGE RHINE-DORTMUND-EMS -0.00001 -0.00003 -0.00000 0.036CAMPINE -DORTMUND-EMS -0.00001 -0.00002 0.00001 0.796RHINE HERNE -DORTMUND-EMS -0.00001 -0.00003 0.00000 0.376SPITS-DORTMUND-EMS 0.00001 -0.00000 0.00003 0.057PUSH CONVOY OF 2 -DORTMUND-EMS -0.00003 -0.0001 0.00001 0.197PUSH CONVOY OF 4-DORTMUND-EMS -0.00003 -0.0001 0.00005 0.940PUSH CONVOY OF 6-DORTMUND-EMS -0.00003 -0.0001 0.00001 0.300CAMPINE -LARGE RHINE 0.00001 -0.00000 0.00002 0.716RHINE HERNE -LARGE RHINE 0.00000 -0.00001 0.00001 0.966SPITS-LARGE RHINE 0.00003 0.00002 0.00004 0.000PUSH CONVOY OF 2 -LARGE RHINE -0.00002 -0.00005 0.00002 0.859PUSH CONVOY OF 4-LARGE RHINE -0.00002 -0.0001 0.0001 0.998PUSH CONVOY OF 6-LARGE RHINE -0.00002 -0.0001 0.00002 0.903RHINE HERNE -CAMPINE -0.00000 -0.00002 0.00001 0.998SPITS-CAMPINE 0.00002 0.00001 0.00004 0.000PUSH CONVOY OF 2 -CAMPINE -0.00002 -0.0001 0.00001 0.574PUSH CONVOY OF 4-CAMPINE -0.00002 -0.0001 0.0001 0.989PUSH CONVOY OF 6-CAMPINE -0.00002 -0.0001 0.00002 0.676SPITS-RHINE HERNE 0.00003 0.00001 0.00004 0.000PUSH CONVOY OF 2 -RHINE HERNE -0.00002 -0.0001 0.00002 0.716PUSH CONVOY OF 4-RHINE HERNE -0.00002 -0.0001 0.0001 0.995PUSH CONVOY OF 6-RHINE HERNE -0.00002 -0.0001 0.00002 0.794PUSH CONVOY OF 2 -SPITS -0.00004 -0.0001 -0.00001 0.003PUSH CONVOY OF 4-SPITS -0.00004 -0.0001 0.00003 0.649PUSH CONVOY OF 6-SPITS -0.00004 -0.0001 -0.00001 0.010PUSH CONVOY OF 4-PUSH CONVOY OF 2 -0.00000 -0.0001 0.0001 1.000PUSH CONVOY OF 6-PUSH CONVOY OF 2 -0.00000 -0.0001 0.00005 1.000PUSH CONVOY OF 6-PUSH CONVOY OF 4 0.00000 -0.0001 0.0001 1.000

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Table 17: Tukey analysis Inland Vessels - Emission & ShipType

diff lwr upr p adj

CARGO-TANKER -0.00003 -0.00005 -0.00000 0.005OTHERTYPE-TANKER -0.00003 -0.0001 0.00002 0.585WINGINGROUND-TANKER 0.00003 -0.0001 0.0001 0.999TANKERHAZCATC-TANKER -0.0001 -0.0005 0.0003 1.000DREDGINGUNDERWATER-TANKER -0.0001 -0.0002 0.0001 0.852PORTTENDER-TANKER 0.0001 -0.0001 0.0003 0.555HIGHSPEED-TANKER 0.0002 0.0001 0.0003 0.000OTHERTYPE-CARGO -0.00000 -0.00004 0.00004 1.000WINGINGROUND-CARGO 0.0001 -0.0001 0.0002 0.892TANKERHAZCATC-CARGO -0.00004 -0.0005 0.0004 1.000DREDGINGUNDERWATER-CARGO -0.00004 -0.0002 0.0001 0.994PORTTENDER-CARGO 0.0001 -0.00003 0.0003 0.227HIGHSPEED-CARGO 0.0002 0.0001 0.0003 0.000WINGINGROUND-OTHERTYPE 0.0001 -0.0001 0.0002 0.898TANKERHAZCATC-OTHERTYPE -0.00004 -0.0005 0.0004 1.000DREDGINGUNDERWATER-OTHERTYPE -0.00004 -0.0002 0.0001 0.997PORTTENDER-OTHERTYPE 0.0001 -0.00003 0.0003 0.238HIGHSPEED-OTHERTYPE 0.0002 0.0001 0.0003 0.000TANKERHAZCATC-WINGINGROUND -0.0001 -0.001 0.0003 1.000DREDGINGUNDERWATER-WINGINGROUND -0.0001 -0.0003 0.0001 0.760PORTTENDER-WINGINGROUND 0.0001 -0.0001 0.0003 0.974HIGHSPEED-WINGINGROUND 0.0001 -0.00002 0.0003 0.150DREDGINGUNDERWATER-TANKERHAZCATC -0.00000 -0.0004 0.0004 1.000PORTTENDER-TANKERHAZCATC 0.0002 -0.0003 0.001 0.974HIGHSPEED-TANKERHAZCATC 0.0002 -0.0002 0.001 0.820PORTTENDER-DREDGINGUNDERWATER 0.0002 -0.00003 0.0004 0.189HIGHSPEED-DREDGINGUNDERWATEROPS 0.0002 0.0001 0.0004 0.001HIGHSPEED-PORTTENDER 0.0001 -0.0001 0.0002 0.998

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Table 18: Tukey analysis Inland Vessels - Emission & Role

diff lwr upr p adj

CARGOBARGE-BUNKER -0.00000 -0.00002 0.00001 1.000TANKERBARGE-BUNKER 0.00001 -0.00001 0.00003 0.746PUSHBARGE-BUNKER -0.00002 -0.00005 0.00001 0.586TENDER-BUNKER 0.0001 0.0001 0.0001 0.000SWOG-BUNKER 0.00002 -0.00000 0.00005 0.062FENDER-BUNKER -0.00002 -0.0001 0.0001 0.998SUPPLYBARGE-BUNKER -0.00000 -0.00004 0.00003 1.000WATER-BUNKER -0.00001 -0.00004 0.00002 0.996TANKERBARGE-CARGOBARGE 0.00001 0.00000 0.00002 0.000PUSHBARGE-CARGOBARGE -0.00002 -0.00004 0.00001 0.567TENDER-CARGOBARGE 0.0001 0.0001 0.0001 0.000SWOG-CARGOBARGE 0.00003 0.00001 0.00005 0.002FENDER-CARGOBARGE -0.00001 -0.0001 0.0001 0.999SUPPLYBARGE-CARGOBARGE -0.00000 -0.00004 0.00003 1.000WATER-CARGOBARGE -0.00001 -0.00003 0.00002 0.999PUSHBARGE-TANKERBARGE -0.00003 -0.0001 -0.00000 0.025TENDER-TANKERBARGE 0.0001 0.00005 0.0001 0.000SWOG-TANKERBARGE 0.00002 -0.00001 0.00004 0.341FENDER-TANKERBARGE -0.00002 -0.0001 0.00004 0.961SUPPLYBARGE-TANKERBARGE -0.00001 -0.00005 0.00002 0.971WATER-TANKERBARGE -0.00002 -0.00004 0.00001 0.499TENDER-PUSHBARGE 0.0001 0.0001 0.0001 0.000SWOG-PUSHBARGE 0.00004 0.00001 0.0001 0.001FENDER-PUSHBARGE 0.00000 -0.0001 0.0001 1.000SUPPLYBARGE-PUSHBARGE 0.00001 -0.00003 0.0001 0.985WATER-PUSHBARGE 0.00001 -0.00002 0.00004 0.992SWOG-TENDER -0.0001 -0.0001 -0.00002 0.000FENDER-TENDER -0.0001 -0.0002 -0.00002 0.001SUPPLYBARGE-TENDER -0.0001 -0.0001 -0.00004 0.000WATER-TENDER -0.0001 -0.0001 -0.0001 0.000FENDER-SWOG -0.00004 -0.0001 0.00003 0.657SUPPLYBARGE-SWOG -0.00003 -0.0001 0.00001 0.422WATER-SWOG -0.00003 -0.0001 -0.00000 0.040SUPPLYBARGE-FENDER 0.00001 -0.0001 0.0001 1.000WATER-FENDER 0.00001 -0.0001 0.0001 1.000WATER-SUPPLYBARGE -0.00000 -0.00005 0.00004 1.000

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C Interview transcript

C.1 Interview Expertise- en Innovatie Centrum Binnenvaart (EICB)

Interviewer: Layba Minha AghaInterviewee: Erwin van der Linden, project manager Expertise- en Inno-vatie Centrum Binnenvaart (EICB)Date: 02/18/2016

Interviewer Momenteel wordt er veel onderzoek verricht naar de CO2-uitstoot van schepen. De overheid, jullie en schippers zijn bezig om maa-tregelen te ontwikkelen om de CO2-uitstoot terug te dringen en de targetsvan de European Union te halen (de uitstoot van GHG met 20% te ver-minderen). Kunt u mij iets vertellen over de recente ontwikkelingen op ditgebied?

Interviewee Ja, goede vraag. Momenteel zijn er veel normen omtrent deemissie. Vooral met betrekking tot de luchtkwaliteit. Stikstof (NOx) heeftdirect effect op de luchtkwaliteit en veel van deze normen zijn gericht opde NOx terug te dringen.

Tot een paar jaar geleden was CO2 minder belangrijk. Pas sinds de laat-ste jaren zijn er maatregelen opgesteld om de CO2-uitstoot terug te dringen.

Interviewer Kunt u mij meer vertellen over de bestaande normen?

Interviewee. De normen stellen de maximum NOx in kilogram per uurvast. Om dit te bewerkstelligen zijn er motoren ontwikkeld die milieu-vriendelijk zijn en systemen die stikstof kunnen verminderen. De CCR2regelgeving is een regeling van de European Union die gericht is op dezemilieuvriendelijke motoren en systemen.

Een voorbeeld is LNG, een milieuvriendelijke brandstof dat 20% aan CO2-uitstoot bespaard.

Interviewer Meten jullie de CO2-uitstoot van schepen en zo ja, hoe?

Interviewee Ja, wij hebben een app ontwikkeld: Econaut en aan de handvan de informatie die schippers invoeren wordt de CO2-uitstoot op een tra-ject berekend.

De CO2-uistoot is gelijk aan de brandstofrekening. Als je kijkt vanaf bedrijf-

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sniveau dan heeft de brandstofrekening invloed op de operationele kosten.Het is dus ook in het belang van schippers dan de CO2-uitstoot wordtteruggedrongen.

Interviewer Kunt u mij vertellen hoe de app werkt?

Interviewee Ja, natuurlijk. Per traject moeten schippers de brandstofopgeven. Dus, ze geven de hoeveelheid brandstof die aan het begin vande trip in de tank aanwezig is en de hoeveelheid aan het eind van de trip.Als je begin en eind van elkaar aftrekt dan heb je de hoeveelheid brandstofdie verbruikt is. De hoeveelheid brandstof wordt dan vermenigvuldigdmet een emissiefactor en zo heb je de CO2-uitstoot.

Een belangrijk punt is dat de betrouwbaarheid van de app afhangt vande data van de schippers. Als die data betrouwbaar is, dan is verkregenCO2-uitstoot ook betrouwbaar. Natuurlijk kan de data van de schippersniet geverifieerd worden, dus je neemt aan dat die data betrouwbaar is.

Daarnaast is het gebruik van de app ook een incentive voor schippers. In-dien ze een schoon ship hebben, dus met een lage uitstoot, dan kunnen zein aanmerking komen voor de green-award. Hiermee krijgen schippers ko-rting op het havengeld.

Interviewer Van welke factoren hangt de CO2-uitstoot volgens u af?

Interviewee Sowieso is er een verschil in de CO2-uitstoot wanneer ergevaren wordt voor eigen rekening of voor een opdrachtgever. Wanneerje voor eigen rekening vaart dan heeft het brandstofverbruik een directerelatie met de operationele kosten. Ook heeft de snelheid een grote in-vloed op de CO2-uitstoot. Je zou kunnen zeggen dat er een directe relatieis tussen de CO2-uitstoot en de snelheid. Daarnaast heeft het gewicht (delading) ook invloed op de afstand die gevaren is (in kilometers) en dus opde CO2-uitstoot. Ten slotte, de leegvaart heeft ook invloed en moet ookworden meegenomen in de berekening.

Je moet wel realiseren dat de CO2-uitstoot berekend met onze app afhanke-lijk is van de motormanagement gegevens. Ook maakt het gebruik vanGPS, door middel van GPS wordt een schip gevolgd. Het gebruik van GPSis een omslachtige methode. Daarentegen is AIS wel een betere optie om deCO2-uitstoot te bereken, omdat alle binnenvaart schepen verplicht zijn omAIS te gebruiken. AIS bevat naast een GPS deel ook een dynamisch deel,waarin gegevens van het schip opgeslagen worden. Er zijn dus meer mo-gelijkheden als je AIS data gebruikt, bijvoorbeeld zo kan je de CO2-uitstootper type schip berekenen.

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Interviewer Kunt u mij meer vertellen over de huidige normen omtrent deCO2-uitstoot?

Interviewee De meest gebruikte regelgeving is de wiel te wiel regel. Dithoudt 2.63 CO2-uitstoot per vestoten liter in, hierin is de uitstoot van deleegvaart ook meegenomen. Een andere norm is de EUR stage 5. Dit iseen toekomstige norm die de maximale uitstoot in gram aangeeft. Echter,dit is wel meer gebasseerd op stikstof en fijnstof. Zoals al eerder vermeldis de regelgeving omtrent CO2 redelijk nieuw en werd er eerst geen aan-dacht aan geschonken. Men was meer gericht op de uitstoot van NOx danCO2. Hierdoor is er nog geen maximale waarde voor de CO2-uitstoot on-twikkeled op regionaal niveau. Wel wordt er constant gezocht naar CO2besparende maatregelen. Hierbij valt te denken aan een zuinige motor.

Interviewer Als ik het goed begrijp is het onderzoek naar de uitstoot vanNOx al ver ontwikkeld, dit in tegenstelling tot CO2?

Interviewee Dat klopt. Daarom is een onderzoek in het gebied van CO2-uitstoot erg interessant, omdat er veel onwetendheid is. De preciezeuitstoot per schip is niet bekend. De overheid, havens en andere be-langhebbenden zijn druk op zoek naar maatregelen om de CO2-uitstoot tereduceren. Echter, de daadwerkelijker uitstoot, ook per schip, is onbekend.Dit zal eerst onderzocht moeten worden, voordat maatregelen ontwikkeldkunnen worden.

Inzicht in de CO2-uitstoot per schip is ook van toegevoegde waarde, zodatschippers verantwoordelijk kunnen worden gesteld en zij ook voortvarendkunnen varen.

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C.2 Interview ThyssenKrupp Veerhaven B.V.

Interviewer: Layba Minha Agha and Cindy P. Zwaan, Cindy’s question arenot covered in this transcript, except for introducing questionsInterviewee: Bé Boneschansker, Senior Manager Nautical & Technical Ser-vices and Jos Davidse, Senior Manager Shipping DepartmentDate: 05/23/2016

Interviewee B. Boneschansker Korte introductie: Wij kunnen je nietvertellen over de uitstoot van de hele binnenvaart vloot. Wij transportereneen behoorlijke hoeveelheid per jaar. Wij transporteren meer dan alle an-dere partijen. Natuurlijk gebruiken wij veel brandstof en is de vracht vanonze emissie best wel hoog. Wij willen dat graag relateren aan onze vervo-ersprestatie, dus wij proberen iedereen mee te krijgen om de emissie uit tedrukken in gram of gram per kilometer, zodat je jezelf dan kan vergelijkenmet andere vervoersbedrijven en methoden, zoals vervoer over de markt.Per 2020 is er een nieuwe wet, de Europese commissie heeft besloten datper 2020 nieuwe motoren, de non-road engines, moeten gaan voldoen aangelukkig Emission Regulation Part 4 in plaats van de EUR 6. EmissionRegulation Part 4 norm is voor vermogen tot 3000 miligram al behoorlijkdichtgedraaid met betrekking tot NOx.

Bij ons is een duwboord 40*15 meter. Die motoren hebben aardig watvermogen bij elkaar staat er 40.000 kilowatt. Als je er omheen allerleikatalysatoren gaat bouwen dan is dat lastig omdat je alleen kunt kiezentot hele grote diameters. Dus vanaf 2020, nieuwe motoren. Voor alsnogzijn er geen overgangstermijnen voor bestaande motoren. Ik denk ook nietdat die er komt. Ik ben natuurlijk wel voor de reductie van de emissie,maar het is natuurlijk aan mij hoe wij dat hier gaan doen. Wij houden onzeemissie waarden heel nauwgezet bij, wij weten precies hoeveel brandstofwij gebruiken en hoeveel gram wij transporteren. Je hebt dan al twee een-heden waarmee je kan rekenen.

Interviewer L. Agha Dus jullie bepalen de uitstoot aan de hand van de ho-eveelheid brandstof dat verbruikt is?

Interviewee B. Boneschansker Er zijn natuurlijk heel veel methoden omje emissie te bepalen. Het beste is, om continue te meten en continue hetasvermogen te meten. Dan heb je het uitgedrukt in gram per kilowatt uur.Dat vind ik persoonlijk een hele lastige. Wat wij doen, wij hebben onzeemissie laten meten door SGS, een organisatie die heel veel op emissiege-bied onderzoeken. Deze waardes vergelijken wij met de waardes die de

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motorenfabrikant aan ons geeft. Als een motor op een proefrit gaat danmeten ze (SGS) bij 100%, 75%, 50% en 25% de emissie. Deze hebben daneen bepaalde versterkingswaarde; bij 75% telt de waarde die daar gemetenis mee voor 0.5% bij 100% voor 0.2% mee en bij 50% en 25% tellen ze voor0.15% mee, samen heb je 100%. Wij kijken dan of deze waardes in de buurtliggen met de waardes die de fabrikant heeft opgegeven. Deze waardeszet ik in een tabel. Ik weet precies hoeveel brandstof wij gebruikt hebben,hoeveel wij getransporteerd hebben per schip en ik weet het aantal kilome-ters van een trip. Hiermee kan ik zeer eenvoudig de emissie in grammenof tonkilometer bepalen.

Een eenheid die wij intern gebruiken voor onze transportprestatie is litersgasolie per ton getransporteerd. Dus als wij goederen van A naar B ge-transporteerd hebben dan weten wij hoeveel gasolie wij per ton gebruikthebben.

Interviewer C. Zwaan Kunt u het proces uitleggen van een schip dat drogebulk (ijzererts, kolen) gaat vervoeren met name het process vanuit Neder-land naar Duitsland?

Interviewee J. Davidse De hoogovens produceren staal, daarvoor hebbenze grondstoffen nodig zoals ijzererts en kooks. Kooks maken wij ook zelfvan kookskolen en wij hebben eindblaaskolen. Dit zijn eigenlijk meer kolendie worden gebruikt voor het verwarmingsproces. IJzer maak je echt ineen hoogoven. Dat is echt een potje waar allerlei soorten ertsen en kooksin gaan voor de methologische werking. Dat potje wordt dan verwarmd.Over het algemeen alleen met gas, maar bij ons ook met kolen dus echt alsenergiedrager.

Die grondstoffen worden dus vanuit de hele wereld ingekocht. Dan wordter gepland hoeveel staal ze gaan produceren. Bij ons produceren zeongeveer 30.000 ton staal per dag. Daar heb je ongeveer 60 tot 70.000 tongrondstoffen per dag voor nodig. Nou we hebben daar een voorraad vaneen paar honderd ton, dus voor een paar dagen productie en wij proberendie voorraad op peil te houden. Wat direct in productie gaat, proberenwij iedere dag toe te varen. Ongeveer 95% gaat naar onze eigen termi-nal hier in de Europoort, maar we hebben ook terminals in Antwerpen,in Amsterdam en Gent. Daar komen ook allemaal zeeschepen aan metbepaalde kwaliteiten die we dan door moeten varen. Wij noemen dat JITtoevoer. Iedere dag 20-30 bestellingen van 2800 ton met 4-5 verschillendekwaliteiten uit verschillende havens. Die geven ze dan bij ons aan en wijmoeten de laadvensters plannen. Het meeste bij onze eigen terminal maarook wel bij andere terminals, moeten we proberen een ladenstamp te kri-jgen, dat we de eenheden op tijd kunnen laden en dan op tijd toevaren

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naar Duisburg. Daar worden die bakken gelost en die worden weer buitengelegd, dan komt er weer een boot en die neemt de lege bakken weermee. Die weet dan ook weer van die bakken moeten naar die terminal,die bakken moeten naar die laadterminal omdat daar weer gepland is ombepaalde kwaliteiten te laden.

Emissie percentage in de berekening bevat ook de de lege terugreis.

Interviewer C. Zwaan Gebruiken jullie realtime data? Bijv. AIS of BICS.

Interviewee B. Boneschansker Nou AIS is gewoon verplicht. Dus als iknee had gezegd had je kunnen zeggen dat ik strafbaar ben en BICS wordtgewoon bij elke reis ingevuld door de kapitein.

Interviewee J. Davidse AIS is positie bepaling he. Dus daardoor kan jegewoon zien wat voor schepen er op je afkomen enzo.

Interviewer C. Zwaan Jullie gebruiken het dus niet om te communicerenmet de schippers?

Interviewee J. Davidse Nee. Nou wel als je iemand nodig hebt want doorAIS staat er natuurlijk een naam bij. Dus als je hem nodig zou hebben dankan je gericht roepen zeg maar. In plaats van dat je roept ’het is een schipboven de bocht van Wurd’ kan je nu zeggen het is schip X.

Interviewer C. Zwaan Zijn alle schepen waar jullie mee varen eigendomvan ThyssenKrupp Veerhaven B.V.?

Interviewee B. Boneschansker 100%. Nou wij hebben 7 strekkenboten.Dat zijn boten die naar Duisburg varen, duwboten. Ongeveer 78 eigenbakken en dan nog 2 duwboten die huren we in met nog 35 duwbakkendie huren we ook in.

Interviewee J. Davidse Dus zeg maar 70% is eigendom en de rest is gechar-tered.

Interviewer L. Agha Hoe kan de CO2-uitstoot teruggedrongen wordenvolgens u?

Interviewee B. Boneschansker Dat kun je eigenlijk alleen maar doen dooreen andere brandstofsoort te gebruiken die misschien schoner is. Voor-namelijk het heeft meer te maken met je vaarsnelheid, de vorm van je schipen de weerstand.

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Interviewee J. Davidse Het is natuurlijk wel zo dat er veel projecten zijn inRotterdam zoals Kotech, die veel los varen en die hebben elektrische mo-toren (hybride). Als je los varen dan gebruiken je de hybride of elektrischebakken en als ze vol vermogen hebben dan schakelen ze die motoren in.Voor ons geldt dat niet.

Interviewee B. Boneschansker Het is natuurlijk interessant om een stukjeop accu te varen, maar de accu moet ook gevuld worden. Dat rende-mentslijn om het vermogen te krijgen is natuurlijk heel erg slecht.

Daarnaast een overgroot deel van onze kapiteins en stuurlieden hebbende cursus voortvarend besparen gevolgd, door energiezuinig te varen kande uitstoot ook worden teruggedrongen.

Interviewee J. Davidse Ja, energie zuinig varen enzo.

Interviewee B. Boneschansker Wij geven elk jaar onze kapiteins en be-manning een getal. Wij willen niet dat de omlooptijd langer wordt, zemoeten dus slimmer varen. Dus dat ze een bepaalde score halen, waar-van wij zeggen daar moet je onder zitten.

Interviewer L. Agha Wordt er vanuit de overheid of de havens druk opjullie uitgeoefend om de CO2-uitstoot terug te dringen, bijvoorbeeld dathavens een incentive geven voor milieuvriendelijke duwboten? Waaromwel/niet?

Interviewee B. Boneschansker. Niet rechtstreeks, maar het is natuurlijkalgemeen bekend dat wij in 2030 er voor zorgen dat alle schepen onder denorm van de CCR 2 zitten. Nou is CCR2 niet echt een maat voor de CO2,maar meer voor NOx.

Voor nieuwe motoren vanaf 2003 geldt de CCR1 norm. Vanaf juli 2007de CCR2 norm. De emissies hiervan zijn voornamelijk NOx gerelateerd.Dus motoren fabrikanten kijken hoe ze de NOx naar beneden krijgen. Veelfabrikanten hebben ervoor gekozen om het inspootmoment van een dieselmotor te verlagen. Hij onsteekt dus ook later, hierdoor wordt de temper-atuur lager. In de warmwortel temperatuur wordt NOx verbouwd, als jedan de temperatuur naar beneden werkt dan is de NOx uitstoot lager, maarer wordt meer brandstof verbruikt. De machine is dus niet meer optimaal.

Interviewee J. Davidse Nieuwe machines stoten minder uit, maar ge-bruiken meer brandstof en hierdoor zijn ze minder optimaal. Samen stotenze dus nog meer uit.

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Interviewer L. Agha Wordt er geen incentive afgegeven als je een milieu-vriendelijke motor hebt?

Interviewee B. Boneschansker. Vier van onze schepen hebben een greenawards, zij voldoen aan allerlei eisen die gesteld worden. Zij leveren daneen voordeel op in de kosten van het havengeld. Voor ons als Vera is hetmeer imago dan echt een kostenbesparend.

Interviewer L. Agha Wat vindt u een goede manier om de CO2-uitstoot temeten (hoeveelheid brandstof verbruikt, afstand die gevaren is etc)?

Interviewee B. Boneschansker Van elk schip weet ik van elk jaar wat zeverbruikt hebben in gasolie. Dan bereken ik het vermogen uit de brand-stof, dus dat is de liters maal de stookwaarde van de olie maal 40% (asvermogen). De CO2-uitstoot rekenen ik uit met het aantal liters dat ik ver-bruik en ik weet dat er 2.6 kilogram CO2-uitstaat per liter. Dus heb ik mijnvracht aan CO2. De NOx meten wij en vergelijken wij met de waardes vande motorfabrikant. Waarvan ik het vermogen ken, kan ik precies uitreke-nen hoeveel NOx ze hebben geproduceerd. Precies hetzelfde doe ik voorPM10. Wij gebruiken heel veel brandstof, dus wij hebben best een flinkevracht aan emissie, maar als je dat uitrekend per kilometer dan zie je datwij rond dat de 18 gram CO2 per ton-kilometer zitten. Bij fijnstof zitten wijrond de 2.5 miligram per ton-kilomter.

Interviewer L. Agha U vergelijkt uw waardes met de waardes die de fab-rikant aan u doorgeeft, komt dat overeen?

Interviewee B. Boneschansker. Ja, CCR1 was veel slechter dan watiedereen verwachtte.

Interviewer L. Agha Gebruiken jullie milieuvriendelijke brandstof, zoalsLNG, en milieuvriendelijke motoren? Zo ja, waarom?

Interviewee B. Boneschansker Nee, je moet niet vergeten dat LNG op ditmoment niet interessant is vind ik. A) LNG is duurder dan de anderebrandstof en door de nieuwe emissie eisen in de toekomst. Als je 100procent LNG gebruikt dan duurt het een halve dag voordat de motor isopgestart. Snel nou, 100 procent LNG dan geeft hij een emissie van NOxop zijn best 1.8-2 gram per kilowatt uur. Dit betekent dat als ik dit doe datik alsnog niet op de gewenste waarden kan komen. Voor ons een schip opLNG is een dure investering, vooral omdat ik ook allerlei technieken eraanvast moet hangen om op de gewenste emissie-waarde te komen. Dit geldtniet alleen voor ons, maar voor iedereen. Wat ik zeggen wil is dat door denieuwe eisen LNG eigenlijk een beetje dood is. Je bent veel goedkoper uit

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als je diesel gebruikt en er een katalysator achter zet. Natuurlijk is LNGqua emissies fantastisch. Je hebt ook GTL (Gas to Liquid) en dat is ookveel beter voor het milieu. Echter, er zijn twee punten waarom wij dit nietgebruiken. Ten eerste, als je zoveel vervoerd zoals wij en zoveel brandstofgebruikt als wij dan is dan een hele grote kostenpost. Wij zijn dan eigen-lijk beter om onszelf uit de markt te prijzen, dan zegt de concurrentie datkan ik veel goedkoper. Het is wel schoon, maar er hangt een kostenplaatjeaan. Grote bedrijven, grote firma’s kijken natuurlijk hoe ze kunnen groeien,maar bovenaan staan de kosten. Imago is wel heel erg belangrijk.

Er zijn ook bedrijven die vragen mag ik even je CO2-uitstoot zien. De redenis dat zij straks over de emissie van hun gehele keten moeten betalen, daarhoren wij ook bij. Dus zij zeggen, ik wil natuurlijk wel zo schoon mogelijk,maar ook zo goedkoop mogelijk.

Interviewer L. Agha Van welke factoren hangt de CO2-uitstoot volgens uaf? Bijvoorbeeld, het type engine, de brandstof die gebruikt is, de ladings-graad etc.

Interviewee B. Boneschansker De maatvoering van een schip en de vormvan een schip hebben zeker invloed op het brandstofverbuik en het brand-stofverbuik heeft invloed op de emissie van CO2. Het vermogen van eenschip is gelijk aan de verdringing (gewicht van de bakken en boten plus delading) van het schip tot de macht twee derde maal de snelheid in knots totde macht drie gedeeld door een constante. De constante hangt af van devorm van een schip, het vermogen van een schip etc. Het vermogen aan deas is niet het vermogen dat je moet toevoeren.

Interviewer L. Agha Vindt u dat de CO2-uitstoot afhankelijk is van het typeduwboot? Bijvoorbeeld de lengte en breedte van de boot?

Interviewer B. Boneschansker Ja, de lengte en breedte hebben zeker in-vloed op de uitstoot.

Interviewer L. Agha Is er volgens u een verschil in de berekening van deCO2-uitstoot in de binnenvaart en op zee?

Interviewee B. Boneschansker Als ik hetzelfde brandstof zou gebruikendan zou ik het op precies dezelfde manier uitrekenen.

Interviewee J. Davidse Soms zijn er ook emissie controlled area.

Interviewee B. Boneschansker Als je van zee komt dan moet je al voordatje aankomt in de haven over stappen op een andere brandstof om uber-

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haupt binnen te mogen varen. Nieuwe schepen of nieuwe motoren om aande richtlijnen te voldoen. De normen vertellen je waar je onder moet zitten,maar ze vertellen ze niet hoe je dat moet doen.

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C.3 Interview Port of Rotterdam

Interviewer: Layba Minha AghaInterviewee: Jarl Schoemaker, Senior Advisor Policy & Planning, Environ-mental ManagementDate: 05/30/2016

Interviewer Kunt u mij een introductie geven van uw functie bij hetHavenbedrijf?

Interviewee Ik heb deze functie overgenomen sinds februari dit jaar en ikben verantwoordelijk voor het milieubeleid binnen het Havenbedrijf.

Interviewer Momenteel wordt er veel onderzoek verricht naar de CO2-uitstoot van schepen. De overheid, jullie en schippers zijn bezig om maa-tregelen te ontwikkelen om de CO2-uitstoot terug te dringen en de targetsvan de European Union te halen (de uitstoot van GHG met 20% te ver-minderen). Kunt u mij iets vertellen over de recente ontwikkelingen op ditgebied?

Interviewee Momenteel implementeren wij de CCR regeling met be-trekking tot de uitstoot van schepen. Daarnaast hebben wij als Havenbedrijfde "green award", dit is een incentive voor schippers om milieuvriendelijkeboten te gebruiken. Ook gebruiken sommige schippers elektrische boten enom het gebruik hiervan te promoten financieren wij een deel van de kosten.

Wat betreft de emissie binnen het havengebied, in het verleden richtten wijons meer op de uitstoot van NOx, omdat dit een grote lokale invloed heeftvoor omringende bewoners. Echter, de laatste jaren heeft CO2 veel aan-dacht gehad op mondiaal niveau en wij experimenteren met het gebruikvan LNG om te analyseren of dit leidt tot een lagere CO2-uitstoot.

Interviewer Wordt er vanuit de port druk op jullie uitgeoefend op bijvoor-beeld schippers om de CO2-uitstoot terug te dringen, bijvoorbeeld doorincentive te geven voor milieuvriendelijke schepen? Waarom wel/niet?

Interviewee Ja, onder andere door middel van de green-award. Met degreen-award belonen wij schippers die voldoen aan de toekomstige eisen.

Interviewer Meten jullie de CO2-uitstoot van schepen in de haven?

Interviewee Wij modelleren zelf geen modellen om de CO2-uitstoot temeten, omdat het niet aan ons is om de CO2-uitstoot structureel te meten.

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Wel gebruiken wij indicatoren van onder andere TNO. Hierbij kijken wijwelke typen schepen er zich in het havengebied bevinden en gebruiken wijde indicatoren van TNO om grofweg een indicatie te krijgen van de CO2-uitstoot.

Interviewer Wat vindt u een goede manier om de CO2-uitstoot te meten(hoeveelheid brandstof verbruikt, afstand die gevaren is etc)?

Interviewee De betrouwbaarheid van de berekende CO2-uitstoot hangt afvan de kwaliteit van de emissiefactoren die gebruikt zijn. Zoals al verteld,gebruiken wij de emissiefactoren ontwikkeld door TNO. Het gebruik vandeze emissiefactoren is een zeer goede en betrouwbare manier om de CO2-uitstoot te meten. Deze factoren houden ook rekening met de hoeveelheidbrandstof dat verbruikt is, het type brandstof, de afstand die gevaren is, hettype engine etc.

Interviewer Van welke factoren hangt de CO2-uitstoot volgens u af? Bi-jvoorbeeld, het type engine, de brandstof die gebruikt is, de ladingsgraadetc.

Interviewee Het hangt inderdaad af van het type engine, de brandstof diegebruikt is en de beladingsgraad. De CO2-uitstoot is voor een significantgroot deel afhankelijk van het brandstofverbruik. In het bijzonder, er is eensignificante relatie tussen het brandstofverbruik en de CO2-uitstoot.De CO2-uitstoot hangt ook af van het type moter en met name hoe je demotor inzet. Hier valt te denken aan de hoeveelheid weerstand etc.

Interviewer Vindt u dat de CO2-uitstoot afhankelijk is van het type schip?Bijvoorbeeld de lengte en breedte van de boot?

Interviewee Ja, de uitstoot is afhankelijk van het type schip. Het type schipheeft invloed op het type energieverbruik, wat ook afhankelijk is van delengte en breedte van een schip. Daarnaast is de CO2-uitstoot ook afhanke-lijk van de weerstand en het vermogen van een schip.

Interviewer Vindt u dat LNG een goede manier is om de CO2-uitstootterug te dringen?

Interviewee Een groot risico van LNG is methaanslip, dit is de uitstoot vanonverbrande methaan, er lekt dus als het ware methaan weg. Methaanslipwordt veroorzaakt door onvolledige verbranding van het gas. Methaanslipheeft 60-80 keer meer impact op het greenhouse gases effect, omdat het eenveel sterker broeikasgas is dan CO2 Kortom, de global warming intensiteitis veel sterker bij LNG dan bij andere brandstoffen. Het gebruik van LNG

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vermindert de CO2-uitstoot wel, maar is slechter voor het mileu door demethaanslip.

Interviewer Hoe kan de CO2-uitstoot teruggedrongen worden volgens u?

Interviewee De CO2-uitstoot kan door de volgende manier worden terugge-drongen: optimaal ontwerp van een schip en motor, logistieke oplossing(minder varen), schip wat minder brandstof verbuikt per container, alter-natieve brandstof, personeel trainen hoe ze het beste kunnen varen (milieu-vriendelijk varen).

Interviewer Is er volgens u een verschil in de berekening van de CO2-uitstoot in de binnenvaart en op zee?

Interviewee Zeeschepen zijn hele andere typen schepen, het gedrag is heelanders met het gevolg dat de emissie van zeeschepen lastiger te bepalen isdan de emissie van de binnenvaart. Zeeschepen hebben een andere weer-stand en ze hebben andere vaarroutes. Echter, binnen het havengebied kanwel dezelfde methode gebruikt worden om de CO2-uitstoot te bepalen eneen indicatie te krijgen van de uitstoot.

Interviewer Is er verder nog iets wat u mij kunt vertellen wat mogelijk rel-evant is voor mijn onderzoek?

Interviewee De CO2-uitstoot voor de binnenvaart is afhankelijk van deCEMT klassen en de emissiefactoren zijn vaak gerelateerd naar de CEMTklassen. Wellicht is het van belang om de binnenvaart schepen te classi-ficieren naar de CEMT klassen. Voor zeeschepen kan het van belang zijnom de schepen te classificieren naar de lengte. Ten slotte, de brandstof, typemotor en leeftijd van een schip kunnen invloed hebben op de CO2-uitstoot.

Ten slotte, als ik het goed begrijp bevat jouw dataset alleen de port areavan Rotterdam. Je moet er wel rekening mee houden dan schepen dit ge-bied verlaten en je daardoor geen records voor deze schepen hebt, totdatze zich weer binnen het havengebied bevinden. Hier moet je wel rekeningmee houden in je analyses en deze eventueel eruit filteren. Daarnaast moetje er ook rekening mee houden dat schepen geladen/gelost worden in determinals, gedurende dit process is er vaak geen AIS observatie.

Interviewer Bedankt, hoe lang duurt het proces van laden en lossenongeveer en wanneer kan ik aannemen dat de schepen het havengebiedhebben verlaten.

Interviewer Ik zou zeggen als je meer dan een half uur geen observatie

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hebt, dan kan je aannemen dat ze of in de terminal zijn of zich buiten hetgebied bevinden. Ik kan dit niet met zekerheid zeggen, maar dit nam ikaltijd aan tijdens mij vroegere rollen.

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D R codes

Please find the R codes used for this thesis below.

#-------------------------------------------------------# Program to convert json data to rda#-------------------------------------------------------install.packages("jsonlite")library(jsonlite)

#-------------------------------------------------------# Dataset Port of Rotterdam#-------------------------------------------------------dfPort <- stream_in(gzfile("C:/Users/KingLui/Documents/Thesis/

Thesis data/gz/rtm-port-co2_7days.json.gz"))

save(dfPort, file= "C:/Users/KingLui/Documents/Thesis/Thesis data/rda/Port-nl-co2.rda")

#-------------------------------------------------------# Load data#-------------------------------------------------------load("C:/Users/KingLui/Documents/Thesis/Thesis data/

rda/Port-nl-co2.rda")#-------------------------------------------------------# Convert Epoch time#-------------------------------------------------------dfPort$timeLast <- as.POSIXct(as.numeric(dfPort$timeLastUpdate$

’$numberLong’)/1000,origin="1970-01-01",tz="Europe/Amsterdam")

dfPort$timeETA <- as.POSIXct(as.numeric(dfPort$eta$’$numberLong’)/1000,

origin="1970-01-01",tz="Europe/Amsterdam")

dfPort$timeLastUpdate$’numberlong’ <- NULLdfPort$eta$’numberlong’<- NULLdfPort$timeLastUpdate <- NULLdfPort$eta <- NULL

#-------------------------------------------------------#Make a plot of 1 vessel (relation between timeLast#and SpeedOverGround)#-------------------------------------------------------library(ggplot2)ggplot(data=vessel1,aes(x=timeLast,y=speedOverGround))+geom_point

()+ geom_line()

#-------------------------------------------------------# Make a dataframe of the unique values in order to clean the data#-------------------------------------------------------uniqueMMSI <- data.frame(unique(dfPort$mmsi))

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#-------------------------------------------------------#Save the mmsi codes as a numeric data#-------------------------------------------------------mmsi <- as.numeric(levels(uniqueMMSI$"unique.dfPort.mmsi."))

#-------------------------------------------------------#Get additional variables from Teqplay database#-------------------------------------------------------#Define urlurlBase <- "http://backenddev.teqplay.nl/ship/"

urlmmsiteq <- paste0(urlBase,as.vector(mmsi),sep="")

url <- URLencode(urlmmsiteq) #Takes the first element of urlMmsistr(url)

#Turn off scientific notationoptions(scipen = 9999999)

#Create a temporary filetmp <- tempfile()

#Librarylibrary(jsonlite)library(plyr)

#Make an empty list to collect resultsoutput <- list()

#-------------------------------------------------------#Loop for MMSI codes - collects the variables based on mmsi#-------------------------------------------------------for (i in 1:length(mmsi)){

message(mmsi[i])mydata <- fromJSON(paste0(urlBase,mmsi[i]))

mydata$location$coordinates <- NULLmydata$locationStern$coordinates <- NULLmydata$locationBow$coordinates <- NULL

output[[i]] <- as.data.frame(mydata)}

#Save the outputdsMMSI <- do.call("rbind.fill", output)

save(dsMMSI, file = "MMSI.Rda")

#-------------------------------------------------------#Drop variables#-------------------------------------------------------dsMMSI$location.latitude <- NULL

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dsMMSI$location.longitude <- NULLdsMMSI$courseOverGround <- NULLdsMMSI$timeLastUpdate <- NULLdsMMSI$speedOverGround <- NULLdsMMSI$dimensions.draught <- NULLdsMMSI$dimensions.width <- NULLdsMMSI$dimensions.length <- NULLdsMMSI$dimensions.height <- NULLdsMMSI$trueHeading <- NULLdsMMSI$destination <- NULLdsMMSI$status <- NULLdsMMSI$locationStern.latitude <- NULLdsMMSI$locationStern.longitude <- NULLdsMMSI$locationBow.latitude <- NULLdsMMSI$locationBow.longitude <- NULL

#-------------------------------------------------------#Add a new variable - length and width of a vessel#-------------------------------------------------------dsMMSI$length <- c(dsMMSI$positionOfTransponder.distanceToBow

+ dsMMSI$positionOfTransponder.distanceToStern)dsMMSI$width <- c(dsMMSI$positionOfTransponder.distanceToStarboard

+ dsMMSI$positionOfTransponder.distanceToPort)

#-------------------------------------------------------#Remove variables#-------------------------------------------------------dsMMSI$positionOfTransponder.distanceToBow <- NULLdsMMSI$positionOfTransponder.distanceToStern <- NULLdsMMSI$positionOfTransponder.distanceToPort <- NULLdsMMSI$positionOfTransponder.distanceToStarboard <- NULL

#-------------------------------------------------------#Merge two datasets#-------------------------------------------------------myData <- merge(dfPort,dsMMSI, by="mmsi")

#-------------------------------------------------------# Cleaning the additional dataset - APM1#-------------------------------------------------------#APM1APM <- read.table("C:/Users/KingLui/Documents/Thesis/

Thesis data/gz/apm1.txt",fill=TRUE)APM$V2 <- NULLAPM$V3 <- NULLAPM$V4 <- NULL

#Remove all weird charactersAPM <- as.data.frame(APM[!(APM$V1=="areaName"),])colnames(APM)[1] <- "mmsi"APM <- as.data.frame(APM[!(APM$mmsi=="mmsiOfShipInside"),])colnames(APM)[1] <- "mmsi"APM <- as.data.frame(APM[!(APM$mmsi=="timestamp"),])colnames(APM)[1] <- "mmsi"

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APM <- as.data.frame(APM[!(APM$mmsi=="}"),])colnames(APM)[1] <- "mmsi"APM <- as.data.frame(APM[!(APM$mmsi=="{"),])colnames(APM)[1] <- "mmsi"APM <- as.data.frame(APM[!(APM$mmsi=="},"),])colnames(APM)[1] <- "mmsi"APM <- as.data.frame(APM[!(APM$mmsi=="["),])colnames(APM)[1] <- "mmsi"APM <- as.data.frame(APM[!(APM$mmsi=="[,"),])colnames(APM)[1] <- "mmsi"APM <- as.data.frame(APM[!(APM$mmsi=="],"),])colnames(APM)[1] <- "mmsi"APM <- as.data.frame(APM[!(APM$mmsi=="_id"),])colnames(APM)[1] <- "mmsi"

##Repeat the above section (clearning the additonal dataset)##for APM2, Euromax1, RWG1 and Delta dataset.

#-------------------------------------------------------#Connect the dataset with the other datasets#in order to analyse if its a container ship#-------------------------------------------------------#TRUE = container ship#FALSE = not a container ship

myData$APM <- myData$mmsi %in% APM$mmsimyData$APM2 <- myData$mmsi %in% APM2$mmsimyData$Delta1 <- myData$mmsi %in% delta1$mmsimyData$Euromax1 <- myData$mmsi %in% euromax1$mmsimyData$Rwg1 <- myData$mmsi %in% rwg1$mmsi

length(myData$container)unique(myData$container)

#-------------------------------------------------------# Filter the inland vessels with a IMO code of 0 as only sea going# vessels have an IMO code, thus a IMO code of 0 indicates# an inland-river vessel#-------------------------------------------------------InlandVessel <- (subset(myData,myData$imoNumber==0))

#-------------------------------------------------------#Check the unique IMO --> first make it a numeric variable#-------------------------------------------------------myData$imoNumber <- as.numeric(as.character(myData$imoNumber))

uniqueIMO <- data.frame(unique(myData$imoNumber))

#Check the unique IMO codes, an IMO code exists of 7 numbers,#however some IMO codes in the dataset exists of less/more# than 7 numbers. These IMO codes are manually checked.

SpecialIMO <- subset(myData,(myData$imoNumber==205371190))SpecialIMO2 <- subset(myData,(myData$imoNumber==22577))

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SpecialIMO3 <- subset(myData,(myData$imoNumber==133120))SpecialIMO4 <- subset(myData,(myData$imoNumber==671744))SpecialIMO5 <- subset(myData,(myData$imoNumber==1224))SpecialIMO6 <- subset(myData,(myData$imoNumber==24965))SpecialIMO7 <- subset(myData,(myData$imoNumber==20933))SpecialIMO8 <- subset(myData,(myData$imoNumber==458752))SpecialIMO9 <- subset(myData,(myData$imoNumber==92677871))SpecialIMO10 <- subset(myData,(myData$imoNumber==50))SpecialIMO11 <- subset(myData,(myData$imoNumber==507510784))SpecialIMO12 <- subset(myData,(myData$imoNumber==62))SpecialIMO13 <- subset(myData,(myData$imoNumber==16777216))SpecialIMO14 <- subset(myData,(myData$imoNumber==205085300))SpecialIMO15 <- subset(myData,(myData$imoNumber==335))SpecialIMO16 <- subset(myData,(myData$imoNumber==147456))SpecialIMO17 <- subset(myData,(myData$imoNumber==256))SpecialIMO18 <- subset(myData,(myData$imoNumber==278528))SpecialIMO19 <- subset(myData,(myData$imoNumber==883529))SpecialIMO20 <- subset(myData,(myData$imoNumber==524288))SpecialIMO21 <- subset(myData,(myData$imoNumber==536870912))SpecialIMO22 <- subset(myData,(myData$imoNumber==311))SpecialIMO23 <- subset(myData,(myData$imoNumber==871469056))SpecialIMO24 <- subset(myData,(myData$imoNumber==438))SpecialIMO25 <- subset(myData,(myData$imoNumber==924404452))SpecialIMO26 <- subset(myData,(myData$imoNumber==16384))SpecialIMO27 <- subset(myData,(myData$imoNumber==809520128))SpecialIMO28 <- subset(myData,(myData$imoNumber==546004))SpecialIMO29 <- subset(myData,(myData$imoNumber==540672))SpecialIMO30 <- subset(myData,(myData$imoNumber==608))

#The following IMO codes are actually inland-river vessels:#IMO, IMO2, IMO3, IMO4, IMO5, IMO6, IMO7, IMO8, IMO9, IMO14,#IMO15, IMO17, IMO18, IMO19, IMO21, IMO23, IMO24, IMO25,# IMO26, IMO27, IMO28, IMO29

InlandVessels <- rbind(InlandVessel,SpecialIMO)InlandVessels1 <- rbind(InlandVessels,SpecialIMO2)InlandVessels2 <- rbind(InlandVessels1,SpecialIMO3)InlandVessels3 <- rbind(InlandVessels2,SpecialIMO4)InlandVessels4 <- rbind(InlandVessels3,SpecialIMO5)InlandVessels5 <- rbind(InlandVessels4,SpecialIMO6)InlandVessels6 <- rbind(InlandVessels5,SpecialIMO7)InlandVessels7 <- rbind(InlandVessels6,SpecialIMO8)InlandVessels8 <- rbind(InlandVessels7,SpecialIMO9)InlandVessels9 <- rbind(InlandVessels8,SpecialIMO14)InlandVessels10 <- rbind(InlandVessels9,SpecialIMO15)InlandVessels11 <- rbind(InlandVessels10,SpecialIMO17)InlandVessels12 <- rbind(InlandVessels11,SpecialIMO18)InlandVessels13 <- rbind(InlandVessels12,SpecialIMO19)InlandVessels14 <- rbind(InlandVessels13,SpecialIMO21)InlandVessels15 <- rbind(InlandVessels14,SpecialIMO23)InlandVessels16 <- rbind(InlandVessels15,SpecialIMO24)InlandVessels17 <- rbind(InlandVessels16,SpecialIMO25)InlandVessels18 <- rbind(InlandVessels17,SpecialIMO26)InlandVessels19 <- rbind(InlandVessels18,SpecialIMO27)

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InlandVessels20 <- rbind(InlandVessels19,SpecialIMO28)InlandVessels21 <- rbind(InlandVessels20,SpecialIMO29)

#-------------------------------------------------------#Remove rows --> exclude the following boats:#tugboats, WASTE, authorities, pilot,leisure and boatman#-------------------------------------------------------InlandVessel21 <- InlandVessels21[which(InlandVessels21$role

!="TUG"),]InlandVessel21 <- InlandVessel21[which(InlandVessel21$role

!="AUTHORITIES"),]InlandVessel21 <- InlandVessel21[which(InlandVessel21$role

!="PILOT"),]InlandVessel21 <- InlandVessel21[which(InlandVessel21$role

!="BOATMAN"),]InlandVessel21 <- InlandVessel21[which(InlandVessel21$role

!="WASTE"),]InlandVessel21 <- InlandVessel21[which(InlandVessel21$category

!="LEISURE"),]InlandVessel21 <- InlandVessel21[which(InlandVessel21$category

!="PASSENGER"),]

#-------------------------------------------------------#Apply CEMT klassen#-------------------------------------------------------#For the Cemt klassen it is required to distinguish#between push barge and other vessels.PushBarge <- subset(InlandVessel21, role == "PUSHBARGE")

#Remove push barge from datasetInlandVessel21 <- InlandVessel21[which(InlandVessel21$role

!="PUSHBARGE"),]

#Remove Inland vessels without a length and widthInlandVessel21 <- InlandVessel21[which(InlandVessel21$width !=0),]InlandVessel21 <- InlandVessel21[which(InlandVessel21$length !=0)

,]

#Check maximum and minimum values#Widthmax(InlandVessel21$width,na.rm=TRUE)min(InlandVessel21$width,na.rm=TRUE)

#lengthmax(InlandVessel21$length,na.rm=TRUE)min(InlandVessel21$length,na.rm=TRUE)

#make a new variableInlandVessel21$opp <- c(InlandVessel21$length*InlandVessel21$width

)

#librarylibrary(DescTools)

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#SpitsSpitsG <- InlandVessel21[which(InlandVessel21$opp %[]% c(30,299))

,]SpitsG$CEMT <- "SPITS"

#KempenaarKempenaarG <- InlandVessel21[which(InlandVessel21$opp %[]% c

(300,535)),]KempenaarG$CEMT <- "KEMPENAAR"

#Dortmund EemsDortmundEemsG <- InlandVessel21[which(InlandVessel21$opp %[]% c

(536,720)),]DortmundEemsG$CEMT <- "DORTMUND EEMS"

#Rijn-herne kanaalschipRijnHerneG <- InlandVessel21[which(InlandVessel21$opp %[]% c

(721,1044)),]RijnHerneG$CEMT <- "RIJN-HERNE"

#Groot RijnschipGrootRijnschipG <- InlandVessel21[which(InlandVessel21$opp %[]% c

(1045,6825)),]GrootRijnschipG$CEMT <- "GROOT RIJNSCHIP"

#Apply cemt klasse to push barges#make a new variablePushBarge$opp<- c(PushBarge$length * PushBarge$width)

#VbVbG <- PushBarge[which(PushBarge$opp %[]% c(137,2220)),]VbG$CEMT <- "Vb"

#VlaVla <- PushBarge[which(PushBarge$opp %[]% c(2221,2508)),]Vla$CEMT <- "Vla"

#VlbVlb <- PushBarge[which(PushBarge$opp %[]% c(2509,4446)),]Vlb $CEMT <- "Vlb"

#VlcVlc <- PushBarge[which(PushBarge$opp %[]% c(4447,6210)),]Vlc$CEMT <- "Vlc"

#Merge datasets into one final datasetFinalIV <- rbind(Spits,DortmundEems,GrootRijnschip1,Kempenaar,

RijnHerne,VBG,VLB,VLC)

#-------------------------------------------------------#Pie charts - descriptive statististics#-------------------------------------------------------#Make a pie chart of the distribution of the CEMT classslices <- c (25390, 181928, 41944, 60860, 53676,3536,655,2786)

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lbls <- c("Dortmund-Ems Canal vessel","Large Rhine vessel","Campine vessel","Rhine Herne canal vessel","Spits","Push convoy for 2 barge", "Push convoy for 4 barge","Push convoy for 6 barge")pct <- round(slices/sum(slices)*100)lbls <- paste(lbls, pct) # add percents to labelslbls <- paste(lbls,"%",sep="") # ad % to labelspie(slices,labels = lbls, col=rainbow(length(lbls)),

main="CEMT class")

#Make a pie chart of the distribution of Rolesslices <- c (17991,203268,108915,6974,11824,10559,989,3335,6967)lbls <- c("Bunker","Cargo barge","Tanker barge","Push barge",

"Tender","Swog","Fender","Supply barge","Water")pct <- round(slices/sum(slices)*100)lbls <- paste(lbls, pct) # add percents to labelslbls <- paste(lbls,"%",sep="") # ad % to labelspie(slices,labels = lbls, col=rainbow(length(lbls)),

main="Roles")

#Make a pie chart of the distribution of Ship Typesslices <- c (122150,207282,2758,207,1334,2051,3786,22392)lbls <- c("Tanker","Cargo","Wing in Ground","Tanker Hazcat A",

"Port Tender","Dredging under water OPS","High speed","Other Type")

pct <- round(slices/sum(slices)*100)lbls <- paste(lbls, pct) # add percents to labelslbls <- paste(lbls,"%",sep="") # ad % to labelspie(slices,labels = lbls, col=rainbow(length(lbls)),

main="Ship Type")

#-------------------------------------------------------#Get number of container ships - descriptive statistics#-------------------------------------------------------table(IV$Rwg1)table(IV$APM)table(IV$APM2)table(IV$Delta1)table(IV$Euromax)

#-------------------------------------------------------#Calculate emission - Inland Vessels#-------------------------------------------------------## Make repairs to the dataFinalIV$latitude <- FinalIV$location$latitudeFinalIV$longitude <- FinalIV$location$longitudeFinalIV$location <- NULL # remove, no longer needed

##Calculate time difference between two observations grouped bymmsi

FinalIV$tdiff <- unlist(tapply(FinalIV$timeLast,INDEX=FinalIV$mmsi,

FUN = function(x) c(0,’units<-’(diff(x),"days"))))

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##Import the fuel consumption corresponding to speed# using the clipboard functiondf = read.table("clipboard",header=TRUE)

##Use the vlookup function to match the speed of a vessel##to corresponding fuel consumption the fuel consumption##were gathered from two graphs, whose values were converted##into excel tables.FinalIV$FC <- df$FC[match(FinalIV$speedOverGround.x,df$Speed)]

##Calculate the total fuel consumptionFinalIV$TFC <- c(FinalIV$FC * FinalIV$tdiff)

##Calculate CO2 emissionconversion <- 0.00323 #fuel conversion factorFinalIV$emission <- c(FinalIV$TFC*conversion)

#-------------------------------------------------------#Acceleration - calculate acceleration#-------------------------------------------------------##Calculate speed difference between two observations grouped by

mmsiFinalIV$Speeddiff <- unlist(tapply(FinalIV$speedOverGround.x,

INDEX=FinalIV$mmsi, FUN = function(x) c(NA,diff(x))))

#Convert the speed to m/sconversionKnotsToms <- 0.514444444 #conversion factor knots to m/s

FinalIV$Speeddiff2 <- c(FinalIV$Speeddiff*conversionKnotsToms)

#Convert the time difference from days to secondsFinalIV$tdiff2 <- (FinalIV$tdiff*86400)

#Calculate acceleraitonFinalIV$Acceleration <- c(FinalIV$Speeddiff2/FinalIV$tdiff2)#-------------------------------------------------------# Plot emission against time#-------------------------------------------------------#ships who’s difference between two observations equals#to one hour of more were removed from the datasetIV1 <- subset(FinalIV,FinalIV$tdiff2 > 3600)

IV2 <- subset(FinalIV,FinalIV$tdiff2 < 3600) #dataset used

#Get maximum and minimum values for emissionmax(IV2$emission,na.rm=TRUE)min(IV2$emission,na.rm=TRUE)

library(ggplot2)ggplot(data=IV2,aes(x=timeLast,y=emission))+geom_point()ggsave("EmissionTimeG2.pdf")

#-------------------------------------------------------

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#Acceleration part 2#-------------------------------------------------------#Analyze the relation between emission and accelerationEmAc <-aggregate(emission ~ Acceleration, data=IV2,

FUN=function(IV2) c(mean=mean(IV2)))

#Anlyze the relation between speed and accelerationAcSP <-aggregate(speedOverGround.x ~ Acceleration, data=IV2,

FUN=function(IV2) c(mean=mean(IV2)))

##The filter options were used in R, afterwards both datasets were#exported to Excel, in which a table was made, i.e. corresponding

emission#by an acceleration rate

library(xlsx)write.xlsx(EmAc,

"C:/Users/KingLui/Documents/Thesis/Thesis data/rda/EmAcFINAL2.xlsx")

write.xlsx(AcSP,"C:/Users/KingLui/Documents/Thesis/Thesis data/rda/

AcSPFINAL2.xlsx")

#-------------------------------------------------------#Get the MDI code out of the MMSI code#-------------------------------------------------------IV2$MDI <- substr(IV$mmsi,0,3)

table(IV2$MDI) #ALL VESSELS ARE DUTCH VESSELS,#NO DIFFERENCES BETWEEN FLAG STATES CAN BE ANALYZED

#-------------------------------------------------------# Distance travelled per vessel#-------------------------------------------------------#install.packages("geosphere", dependencies = TRUE)library(geosphere)library(sp)

#install.packages("plyr", dependencies = TRUE)library(plyr)

# Change weird column namecolnames(IV2)[which(colnames(IV2) == "_id")] <- "ID"colnames(IV2)[which(colnames(IV2) == "speedOverGround.y")

<- "AverageSpeed"

#-------------------------------------------------------# Determine lagged values of longitude and latitude for each mmsi#-------------------------------------------------------# Sort the data by mmsi and by timeLast (which is hopefully

correct)IV2 <- IV2[order(IV2$mmsi, IV2$timeLast),]

# Determine lagged values of the longitude and latitude.

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myLag <- function(x, k = 1) c(rep(NA, k), x[1:(length(x)-k)])

# Add lagged longitude and latitude to the data frame.dfNew <- subset(IV2, select = c(mmsi, timeLast, longitude,

latitude))

dfNew <- ddply(dfNew, .(mmsi), transform,longitude.Prev = myLag(longitude, 1),latitude.Prev = myLag(latitude, 1)

)

#-------------------------------------------------------# Determine the Haversine distance between two# subsequent positions (in meters)#-------------------------------------------------------dfNew$distHaver <- distHaversine(p1 = dfNew[, 3:4], p2 = dfNew[,

5:6])

#-------------------------------------------------------# Determine the total distance traveled per mmsi (in meters)#-------------------------------------------------------dfTravel <- ddply(dfNew, .(mmsi), summarize, travelDistance =

sum(distHaver, na.rm = TRUE))

# Remove intermediate resultsremove(dfNew) # To save memory (It has no more use)

IV3 <- merge(IV2,dfTravel,by="mmsi")

#-------------------------------------------------------# Make subset in order to calculate average emission, average

speed,# average acceleration for CEMT class, ship type and role#-------------------------------------------------------#------------------Cemt classSP <- subset(IV2,IV2$CEMT=="SPITS")Spits <- aggregate(emission~mmsi,data=SP, FUN=function(SP)c(sum=sum(SP)))

Spits1 <- aggregate(tdiff2~mmsi,data=SP, FUN=function(SP)c(sum=sum(SP)))

Spits2 <- aggregate(speedOverGround.x~mmsi,data=SP, FUN=function(SP)

c(mean=mean(SP)))Spits3 <- aggregate(Acceleration2~mmsi,data=SP, FUN=function(SP)c(mean=mean(SP)))

##The above codes are repeated for all the cemt classes,##ship types and roles

#-------------------------------------------------------#Bar charts: emission according to relative percentagee#-----------------------------------------------------------#CEMT Classinstall.packages("plotly")

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library(plotly)library(ggplot2)

p <- plot_ly(x= c("Large Rhine Vessel","Rhine-Herne","Spits",

"Campine canal vessel","Dortmund-Ems vessel","Push convoy of 2 barge","Push convoy of 4 barge","Push convoy of 6 barge"),

y = c(49,16,14,11,7,1,0,1),type = "bar")

p

p2 <- add_trace(p,x = c("Large Rhine Vessel","Rhine-Herne","Spits",

"Campine canal vessel","Dortmund-Ems vessel","Push convoy of 2 barge","Push convoy of 4 barge","Push convoy of 6 barge"),

y = c(38.79,14.87,25.83,11.2,8.77,0.23,0.038,0.17),type = "bar")

p2

---#ShipTypep <- plot_ly(x= c("Tanker","Cargo","Other Type","High Speed",

"Dredging under water OPS","Wing in Ground","Tanker Hazcat A","Port Tender"),y = c(57,34,6,1,1,1,0,0),type = "bar")

p

p2 <- add_trace(p,x = c("Tanker","Cargo","Other Type","High Speed",

"Dredging under water OPS","Wing in Ground","Tanker Hazcat A","Port Tender"),y = c(33.6,48.7,4.87,9.11,0.13,1.04,0.021,2.5),type = "bar")

p2

---#Rolep <- plot_ly(x= c("Cargo Barge","Tanker Barge","Bunker",

"Push Barge","SWOG","Tender","Water","Supply barge","Fender"),

y = c(55,29,5,3,3,2,2,1,0),type = "bar")

p

p2 <- add_trace(p,

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x = c("Cargo Barge","Tanker Barge","Bunker","Push Barge","SWOG","Tender","Water","Supply barge","Fender"),

y = c(42.66,34.33,4.15,0.43,4.85,11.02,1.10,0.644,0.08),type = "bar")

p2

#-------------------------------------------------------#Anova model - Inland vessels#-------------------------------------------------------##Relation emission and CEMT classesmod <- lm(emission ~ CEMT, data = IV2)AnoIV1 <- anova(mod)

AnoIV1summary(AnoIV1)

#--Post hoc test: TukeyTukey <- TukeyHSD(aov(emission ~ as.factor(CEMT),IV2))

##Relation emission and rolemod1 = lm(emission ~ role, data = IV2)AnoIV2 <- anova(mod1)

AnoIV2summary(AnoIV2)

#--Post hoc test: TukeyTukey1 <- TukeyHSD(aov(emission ~ as.factor(role),IV2))

##Relation emission and ShipTypemod2 = lm(emission ~ shipType, data = IV2)AnoIV3 <- anova(mod2)

AnoIV3summary(AnoIV3)

#--Post hoc test: TukeyTukey2 <- TukeyHSD(aov(emission ~ as.factor(shipType),IV))

#-------------------------------------------------------#Stargazer in order to get the ouput into Latex#-------------------------------------------------------library(stargazer)stargazer((AnoIV1),title="Anova Output Inland Vessels -

Emission & CEMT class",summary = FALSE)

stargazer((AnoIV2),title="Anova Output Inland Vessels -Emission & Role", summary = FALSE)

stargazer((AnoIV3),title="Anova Output Inland Vessels -Emission & ShipType", summary = FALSE)

stargazer(head(Tukey1),title="Tukey analysis Inland Vessels -

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Emission & Role", summary = FALSE)

stargazer(head(Tukey2),title="Tukey analysis Inland Vessels -Emission & ShipType", summary = FALSE)

stargazer(head(Tukey),title="Tukey analysis Inland Vessels -Emission & CEMT",summary = FALSE)

#-------------------------------------------------------#heatmap - in order to indicate where the vessels are located#-------------------------------------------------------df = data.frame(IV2$longitude,IV2$latitude,IV2$emission)colnames(df)[1] <- "longitude"colnames(df)[2] <- "latitude"colnames(df)[3] <- "emission"

#librarylibrary(ggmap)library(ggplot2)install.packages("RColorBrewer")library(RColorBrewer)

# Get a map of Port of Rotterdam - 1port <- get_map(location = c(lon = 4.15080,lat = 51.94323),

zoom = 12)p <- ggmap(port)p

pdensity <- p + stat_density2d(data= df,aes(x = longitude, y = latitude),size = 0.3)

pdensity <-pdensity +stat_density2d(data = df,

aes(x = longitude,y = latitude,fill = ..level..,alpha = ..level..),size = 0.01,bins = 16, geom = "polygon") +

scale_fill_gradient(low = "green", high = "red") +scale_alpha(range = c(0, 0.3), guide = FALSE)

pdensity <- pdensity +scale_fill_gradientn(colours=rev(brewer.pal(7, "Spectral")))

pdensity

ggsave("Heatmap1.pdf")

#Different values for zoom wer used

#-------------------------------------------------------

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# Polygons to map the emission# A data file with the polygons (small boxes of 500 by 500 meter)# were retrieved from Teqplay, in order to plot the emission by# polygon and thus analyse where the emission rate is the highest.# Teqplay provided the data in a KML-file, those have been

converted# to SHP-files(shapfiles) using the FME software#-------------------------------------------------------library(rgdal)library(ggplot2)# setwd("< directory with shapefiles >")map <- readOGR(dsn=".", layer="Placemark_polygon", p4s=NULL)map <- spTransform(map, CRS("+proj=longlat +datum=WGS84"))grd_map <- gridlines(map, ndiscr=100)summary(grd_map)

#Plot the polygonsnPolys <- sapply(map@polygons, function(x)length(x@Polygons))region <- map[which(nPolys==max(nPolys)),] # another plot1plot(region, col="lightgreen")

#Plot the polygons with longitude and latituderegion.df <- fortify(region)ggplot(region.df, aes(x=long,y=lat,group=group))+geom_polygon(fill="lightgreen")+geom_path(colour="grey50")+coord_fixed()

#Link polygons with the dataset. In order to do so, we need to#indicate that longitude and latitude in the dataset (IV2)# are coordinatesIV2.xy <- IV2[c("longitude","latitude")]coordinates(IV2.xy) <- ~longitude + latitude

#-------------------------------------------------------# Matching coordinates IV2 with polygon 1-747# Coordinates are in IV2 (datasset)# Polygons are map#-------------------------------------------------------# Tell R that ship coordinates in dfCombinedIJssel are in the same# lat/lon reference system as the polygon data in map. This has

been# checked before (if not, the whole exercise may be futile)proj4string(IV2.xy) <- proj4string(map)

#-------------------------------------------------------# the following two actions are kind of redundant.# considering that the polygon in the shapefile is the same# polygon used to collect AIS data.#-------------------------------------------------------

# Combine is.na() with over() to do check if a ship (coordinate)is

# in any of the polygons in map

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inside.Port <- !is.na(over(IV2.xy, as(map, "SpatialPolygons")))

#-------------------------------------------------------# Again use the same instruction to identify the identifying

number of# the polygon. This time polygons are specified as a

SpatialPolygons-# DataFrame object, to determine which polygon contains the shipmap$Placema_ID <- getSpPPolygonsIDSlots(map)poly.Port <- over(IV2.xy, map)$Placema_ID

# Store the id in the data frameIV2$polyPort <- poly.Port

#-------------------------------------------------------# Determine the total emission per polygon#-------------------------------------------------------#Calculate the total emission by polygon (polygon number)EmissionPoly <- aggregate(emission ~ polyPort, data=IV2,

FUN=function(IV2) c(sum=sum(IV2)))

#Make a small subset, of the big dataset (IV) consisting#of the latitude, longitude and polygon numberPolygon <- IV2[c("longitude","latitude","polyPort")]Polygon <- na.omit(Polygon)

#Merge two EmissionPoly and Polygon dataframe by polygon number.#This gives the total emission per polygon numberFinalEmission <- merge(EmissionPoly,Polygon,by="polyPort")

# Make a plot of the polygonsdfRegion <- fortify(map) # Best plot so farp <- ggplot(dfRegion, aes(x = long,y = lat, group = group)) +geom_polygon(fill="lightgreen")+geom_path(colour="grey50")+coord_fixed()

p

#Spatial meaping the speed (choropleth map)

install.packages("maptools")library(maptools)library(rgdal)library(ggplot2)library(plyr)library(RColorBrewer)

data <- data.frame(id=rownames(map@data),NAME=map@data$"Placema_ID", stringsAsFactors=F)

data <- merge(data,FinalEmission,by.x="NAME",by.y="polyPort",all.x=T)

subsetmap.df <- fortify(map)subsetmap.df <- join(subsetmap.df,data, by="id")

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ggplot(subsetmap.df, aes(x=long, y=lat, group=group))+geom_polygon(aes(fill=emission))+geom_path(colour="grey50")+scale_fill_gradientn("Emission",

colours=rev(brewer.pal(8,"Spectral")),trans="log",breaks=c(0,3,6,9,12))+

coord_fixed() + labs(x="Longitude", y="Latitude",title="CO2 emission in the port area")

##Repeat the above codes for average speed and acceleration##per polygon

#-------------------------------------------------------# Ocean going vessels - the emission of ocean going vessels will# also be calculated in order to plot them on top of the polygons

and# get the percentage of emission of inland-river vessels in the# port of Rotterdam#-------------------------------------------------------##Order the data by mmsi and timeLastmyData <- myData[order(myData$mmsi, myData$timeLast),]

##Calculate time difference between two observations grouped bymmsi

myData$tdiff <- unlist(tapply(myData$timeLast,INDEX=myData$mmsi,FUN = function(x) c(0,’units<-’(diff(x),"days"

))))

##Make a subset of the Ocean going vessels (Ocean going vesselshave an IMO code)

OceanVessel <- (subset(myData,myData$imoNumber>0))

##Remove the inland vessels - IMO codes which are actually inlandvessels,

# those are identified above)OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=205371190)

,]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=22577),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=133120),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=671744),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=1224),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=24965),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=20933),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=458752),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=92677871)

,]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=205085300)

,]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=335),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=256),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=278528),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=883529),]

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OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=536870912),]

OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=871469056),]

OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=438),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=924404452)

,]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=16384),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=809520128)

,]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=546004),]OceanVessel <- OceanVessel[which(OceanVessel$imoNumber!=540672),]

##Connect with terminal dataOceanVessel$APM <- OceanVessel$mmsi %in% APM$mmsiOceanVessel$APM2 <- OceanVessel$mmsi %in% APM2$mmsiOceanVessel$Delta1 <- OceanVessel$mmsi %in% delta1$mmsiOceanVessel$Euromax1 <- OceanVessel$mmsi %in% euromax1$mmsiOceanVessel$Rwg1 <- OceanVessel$mmsi %in% rwg1$mmsi

##Remove vessels with the following role: Tug, Authorities,# Polot, Boartman and Waste.# Also remove vessels with the following category: LeisureOceanVessel <- OceanVessel[which(OceanVessel$role !="TUG"),]OceanVessel <- OceanVessel[which(OceanVessel$role !="AUTHORITIES")

,]OceanVessel <- OceanVessel[which(OceanVessel$role !="PILOT"),]OceanVessel <- OceanVessel[which(OceanVessel$role !="BOATMAN"),]OceanVessel <- OceanVessel[which(OceanVessel$role !="WASTE"),]OceanVessel <- OceanVessel[which(OceanVessel$category !="LEISURE")

,]OceanVessel <- OceanVessel[which(OceanVessel$category !="PASSENGER

"),]

#Only 5 vessels in the dataset were OceanVessels,#therefore no additional analyses were conducted

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