Research Methods and Dissertation BA7GR12O

StudentIDNo21602906

Programme:MScShipping(MaritimeTechnology)

ModuleCodeandTitle:

BA7GR12O-ResearchMethodsandDissertation

A1Main sitAssignmentTitle:

ReductionofGreenhouseGasEmissionsbyShips:An EmpiricalComparative Investigation of the EU Monitoring, Reporting, and Verification System from 2018 to 2024

Moduleleader:Dr.KorinaSotirakopoulou

TableofContents

Abstract 4

1. Introduction 5
2. PreliminaryLiteratureReview 6
   • OverviewofGreenhouseGasEmissionsinMaritimeTransport 6

• HistoricalContextandCurrentTrends 6
• KeyContributorstoGHGEmissionsintheShippingIndustry 6
  • RegulatoryFrameworkandPolicies 6
• GlobalandRegionalRegulations(IMO,EUMRV) 6
• ImpactofRegulatoryMeasuresonEmissionReduction 6
• TechnologicalInnovationsforEmissionReduction 7
• EnergyEfficiencyTechnologies(e.g.,HullDesign,PropellerOptimization,AirLubrication)7
  • OperationalMeasuresforReducingEmissions 7
• SpeedOptimizationandSlowSteaming 7
• VoyagePlanningandRouteOptimization 7
• BestPracticesinMaintenanceandOperation 7
  • ImpactoftheEUMRVSystem 7
• ObjectivesandoperationalizationoftheMRVsystem 7
• ComparativeAnalysisofMRVDatafrom2018to2024 8
  • ChallengesandOpportunities 8
• DataCollectionandReportingChallenges 8
• OpportunitiesforImprovementandFutureResearchDirections 8
  • CaseStudiesandEmpiricalResearch 8
• ExamplesofSuccessfulEmissionReductionInitiatives 8
• EmpiricalStudiesontheEffectivenessofMRVandOtherRegulatoryMeasures 8

3. ResearchObjectivesandQuestions 9
   • Objectives 9

• EvaluatetheImpactoftheEUMRVSystemonReducingGHGEmissionsfromShips 9
• EmissionTrendsBeforeandAftertheImplementationoftheMRVSystem 9
• IdentifytheMostEffectiveMeasuresandPracticesinReducingMaritimeEmissions 9
• ProvidePolicyRecommendationsBasedonEmpiricalFindings 9
  • ResearchQuestions 9
• HastheImplementationoftheMRVSystemLedtoaSignificantReductioninGHG

EmissionsfromShips? 9

• WhatAretheTrendsinCO2EmissionsfromShipsBetween2018and2024? 10
• WhichMeasuresHaveBeenMostEffectiveinReducingEmissionsasperMRVData? 10
• WhatChallengesandOpportunitiesExistinFurtherReducingMaritimeEmissions? 10

4. ProposedMethodologyandAnalysis 11
   • ResearchDesign 11
   • DataCollection 11

• PrimaryDataSources 11
• SecondaryDataSources 11
  • DataCollectionProcedures 11
  • DataAnalysis 12
• QuantitativeAnalysisMethods 12
  • QualitativeAnalysisMethods 12
• ToolsandSoftwareforAnalysis 12
• StatisticalMethods 12
• Limitations 13
• MitigationStrategies 13

5. Conclusion 14
6. References 15

Abstract:

The research proposal calls for an empirical comparative short-term study of the EU MRV system from 2018 to 2024 that focuses on the effectiveness of the EU MRV system in reducing GHG emissions from ships. This study will adopt a mixed-method research design, which means combining the quantitative data with qualitative understanding. Data will be gathered: quantitative data predominantly from the MRV system (CO2 emissions, fuel consumption, operational data details etc.) The info here will be qualitative in nature and will be an aggregation of everything that I am able to scrounge up from industry reports, academic literature, regulatory documents etc. This combination will lead to a better understanding of how effective MRV system is and which emission reductions performed well.

The research goals are to assess the effectiveness of the MRV system on controlling GHGs, compare emission trends before and after the implementation of the MRV system, identify the technology and operation measures that work, and create policy recommendations from the results. The paper will discuss shortcomings of data quality, external disturbance, and technological diversity in the studies and provide some recommendations to mediate these issues.

The research is designed to (a) analyze MRV data; (b) develop a synthesis of qualitative insights from the field; and (c) demonstrate significant decreases in CO2 emissions following the implementation of MRV systems, identify best practices, and provide policy- and industry support evidence based recommendations. In the long term, this study aims to support international efforts to mitigate global policies by generating insights as to which policies can help to effectively reduce emissions from the shipping sector.

1.Introduction

With some 90% of the world's goods transported by sea (International Maritime Organization, 2020),themaritimeindustrycannotbeoverlookedinglobaltrade.Itis,however,amajorsourceof globalgreenhousegas(GHG)emissions,atalmost3%oftheworld-widefigure(IMO,2021).The European Union (EU) has introduced the Regulation of CO2 emissions from maritime transport through Monitoring, Reporting, and Verification (MRV) system in 2018 in order to tackle the environmental issues and global climate changes (European Commission, 2018). These measures areintended to improve transparency and accountability within the shipping sector, to support the progressive reduction of GHG emissions on a global level through the further development of a data collection system.

The MRV system requires all large ships using ports of the Union, irrespective of their flag, to monitor and report the verified amount of CO2 emitted and further information on their hourly consumption of fuel and/or electricity and transport work (corresponding to distance sailed). (EMSA,2021).Thiscomprehensivedatasetprovidesanopportunitytoevaluateshippingcompanyresponsestothesemeasuresbyassessinghowenergyefficiencymeasureshaveaffectedemissions and operational performance.

This research proposal aims to empirically compare the EU MRV system between 2018 and 2024 as a case of the international GHG emissions reduction by ships. This research aims to provide insights into trends, challenges and opportunities in maritime emission reduction; through the analysis of comprehensive data collected under the MRV system. These results will offer important lessons on how well the MRV system works and it will help the European Commission in taking future policy measures for the establishment of sustainable shipping practices.

2.PreliminaryLiteratureReview

2.1.OverviewofGreenhouseGasEmissionsinMaritimeTransport

2.1.1.HistoricalContextandCurrentTrends

The maritime sector is a long-established source of high global GHG emissions as it has traditionally been entirely dependent on fossil fuels. International greenhouse gas emissions from shipping have been increasing along with global trade despite the efforts of the International MaritimeOrganization(IMO)toregulatetheseemissionsthroughmechanismssuchastheEnergy Efficiency Design Index (EEDI) and the Ship Energy Efficiency Management Plan (SEEMP) (IMO, 2020). Recent trends are, however, pointing to a move towards decarbonisation, with the IMO calling for a minimum 50% reduction in GHG emissions by 2050 against 2008 levels (IMO, 2018).

2.1.2.KeyContributorstoGHGEmissionsintheShippingIndustry

Inthemarinesector,themajorityofGHGemissionsarecausedbyburningofheavyfueloil(HFO), marine diesel oil (MDO), and marine gas oil (MGO). The former is particularly relevant in the forestrysector,whereCO2isresponsiblefor98%ofemissions(Smithetal.Addedtothemixisa rangeof other contributing factors,namely operational inefficiencies andusing outdatedtechnology insomeofitsantiquatedvessels.Dealingwiththesechallengestakesamixtureofmethods,carried out through rules, technology, and operations (Corbett et al. 2020).

2.2.RegulatoryFrameworkandPolicies

2.2.1.GlobalandRegionalRegulations(IMO,EUMRV)

TheIMOhasenergyefficiencystandardsforships-e.g.theEEDIandSEEMPwhichdefinesthe rateofimprovementofenergyefficiencyforshipsandcoversplanning,contributingtodocumented &improvedeffortstodecarbonisemaritime(IMO,2018).In2018theEU'sMRVregulationmade shore-based reporting of CO2 obligatory on ships over 5,000 gross tonnage each year (European Commission, 2019). It not only brings greater transparency, but also valuable information for additional emission reduction measures, thereby ensures greater accountability and compliance in the sector (European Commission, 2020).

2.2.2.ImpactofRegulatoryMeasuresonEmissionReduction

The EEDI and SEEMP for example, have encouraged the development and operation of ships with improved energy-efficiency, saving 20% in fuel compared to an older vessel (imo, 2020). The provision of clear reporting under the EU MRV regulation allows policymakers to follow the performance and design focused interventions as opposed to a one-size-fits-all approach. And these regulatory measures have also led to the innovation of green technologies, thereby making maritime industry sustainable in nature (Smith et al, 2015).

2.2.3.TechnologicalInnovationsforEmissionReduction

AdvancesinFuelTechnology(e.g.,LNG,Hydrogen,Biofuels):LiquefiedNaturalGas(LNG)can significantly reduce emissions, such as CO2 by 20% and sulfur oxides (SOx) and nitrogen oxides (NOx)(Paxianetal.,2015)Andhydrogenfuel,usedinfuelcells,emitsonlywaterandisnowbeing researched for its ability to greatly decrease GHG emissions (DNV GL, 2020). Biofuels, when producedfromsustainablefeedstocks,canbeusedinexistingenginesandprovidesignificantGHG reduction but scaling and economic feasibility are still a concern (IRENA, 2019).

2.2.4.EnergyEfficiencyTechnologies(e.g.,HullDesign,PropellerOptimization,Air Lubrication)

The latest hull designs are designed for minimal water resistance, reducing fuel usage as much as 10% (Faber et al., 2011). Propelling efficiency is further advanced by enhanced propeller designs such as winglets. By enablingAir lubrication system that utilizes a layer of air bubbles along the hull, fuel savings of up to 10?n be achieved (ABS Publishing/2018/). Such technological advancements are essential to achieve the emission reduction goals set by industry.

2.3.OperationalMeasuresforReducingEmissions

2.3.1.SpeedOptimizationandSlowSteaming

Given that slow steaming, which reduces the speed of vessels, reduces both fuel consumption and emissions by large margins. A 10% reduction in speed can lead to a 19?crease of fuel consumption (Notteboom &Vernimmen, 2009).This is acommon practice, especially in times of economic downturn, as a means to save on costs and environmental damage.

2.3.2.VoyagePlanningandRouteOptimization

Weather routing software coupled with real-time data helps with advanced voyage planning with optimal routes and fuel & emissions savings. Good voyage planning can reduce fuel consumption upto10%(Wang&Meng,2012).Predictiveanalyticsandmachinelearningcanbeintegratedwith these measures to make them more effective as well.

2.3.3.BestPracticesinMaintenanceandOperation

Release the hull and the propeller on a regular basis, for example, to remove residual and polish, which can maintain the best hydrodynamic performance and reduce fuel consumption. Fuel consumption may increase up to 20% with biofouling (Schultz et al. 2011). Ensuring the energy efficiency of the ancillary systems and the energy management systems are also critical for improving the energy consumption.

2.4.ImpactoftheEUMRVSystem

2.4.1.ObjectivesandoperationalizationoftheMRVsystem

ThisonecomesfromtheEUMRVsystemwhichwasputinplacein2018toincreasetransparencyandaccountabilityofinternationalshipping.Itmakesshippingcompanieslandwithshipsabove5,000 gross tonnage to tally and report their CO2 outflows once a year with a specific end goal to back the EU atmosphere targets (European Commission, 2019). By including reductions from the maritime into the EU-wide policy framework, the European Maritime Safety Agency (2020) indicates the potential for the system to foster long-term sustainability practices.

2.4.2.ComparativeAnalysisofMRVDatafrom2018to2024

Over 2018 to 2024 emissions intensity is analysed for MRV data and as a result the impact is positive and a reduction. Such comparative analysis reveals advances in fuel saving practices and overallperformancepershiptypeandrouteresultingfromthetransparencyandaccountabilitythe MRV system made possible (EMSA 2021).

2.5.ChallengesandOpportunities

2.5.1.DataCollectionandReportingChallenges

Problems:1) Inaccurate data quality, different monitoring standards among countries cause inconsequent and unreliable figures (Psaraftis & Kontovas, 2013). Complying with these regulations places a substantial burden on companies, particularly smaller ones.

2.5.2.OpportunitiesforImprovementandFutureResearchDirections

Tangible opportunities here include: implementing common data gathering methodologies, provisionofdigitalreportingtoolsforautomaticdatatransfer,andextendingMRVtotheselection ofotherpollutants,e.g.SOxandNOx(Johnsonetal.2020).Furtherstudiesshouldinvestigatethe long-termeffectsofMRVregulationsandstudynewtechnologiesthatcouldbeusedforimproved transparency and traceability purposes.

2.6.CaseStudiesandEmpiricalResearch

2.6.1.ExamplesofSuccessfulEmissionReductionInitiatives

ProminentexamplesincludetheslowsteamingofMaerskLinethatledtoa12%reductioninCO2 emissions between 2007 and 2012 (Cariou, 2011) and the Green Ship of the Future project that introduced measures, such as energy efficient hull designs and air lubrication systems, to save up to10%offuel(Winnesetal.,2015).Nerol'sLNGpoweredferrieshavereducedCO2emissionsby 20% compared to traditional fuels (Paxian et al. 2015).

2.6.2.EmpiricalStudiesontheEffectivenessofMRVandOtherRegulatoryMeasures

ItisdemonstratedthroughempiricalstudiesthattheMRVsystemhasbeenbeneficialforemission reductionsimprovingtransparencyandaccountability.Forexample,Johnsonetal.ShiandNomura (2020) identify a systematic decrease in CO2 emissions per nautical mile by ships covered under theMRVsystem.IncreasedattentionisalsobeinggivenbystudiestotheeffectivenessoftheIMO' EEDIandSEEMPinimprovingfuelefficiencyandemissions(Psaraftis&Kontovas,2013;ICCT, 2020).

3.ResearchObjectivesandQuestions

3.1.Objectives

3.1.1.EvaluatetheImpactoftheEUMRVSystemonReducingGHGEmissionsfrom Ships

Appraise the Effectiveness of the MRV System in BringingAbout Emission Reductions: Look at MRV data to see whether the system has delivered tangible emission savings (European Commission, 2019.)

QuantifyIndividualMRVEmissionsChanges:CompareemissionsdatabeforeandafterMRVIaw implementation to segregate changes associated with the Regulations (Johnson et aI., 2020).

3.1.2.EmissionTrendsBeforeandAftertheImplementationoftheMRVSystem

AnalyzeEmissionData fromPreandPostMRVPhases:Observetrendsbetween2015-2017(pre- MRV) and 2018-2024 (post-MRV) for emission data (Psaraftis & Kontovas, 2013).

Historyofemissionchanges:Identifyadvancesandseverity,timerelation,seasonallydependence, inter-type differences, extraneous factors (Winnes et al.

3.1.3.IdentifytheMostEffectiveMeasuresandPracticesinReducingMaritime Emissions

Identifying What Has Worked Best in Terms of Technological and Operational Measures: Relate the implementation of measures on GHG savings (Paxian et al, 2015).

Investigate the Impact of Ship Types and Routes in In-Service Emission Reductions: Determine how ship size and operational routs play a role in making the measures effective (Cariou, 2011)

3.1.4.ProvidePolicyRecommendationsBasedonEmpiricalFindings

Policy Recommendations that can be implemented to improve bettering the emission reduction efforts: submit suggested policies using data analysis that are actionable to drive good act (ICCT, 2020 ).

Suggesting enhancement to the MRV System and other regulatory frameworks : Recommend standardreporting procedures, morecontrol and new technologies so thathighertransparency can be achieved (European Maritime Safety Agency, 2020).

3.2.ResearchQuestions

• Hasthe Implementation of the MRV System Led to a Significant Reduction in GHG Emissions from Ships?
• Look at reductions since the MRV System was introduced in 2018: Analyse MRV data from 2018 2024 to search for major reductions (European Commission, 2019).
• Establish the impact of the MRV system using statistical methods such as the difference-in- differences (DiD) analysis, including comparing emissions data to the MRV system (Johnson et al., 2020).

3.2.2.WhatAretheTrendsinCO2EmissionsfromShipsBetween2018and2024?

Annual Emission Level Analysis to Visualize Temporal Trends Year-to-Year changes in CO2 emissions are also another critical metadata to be identified by analyzing annual emission data (Winnes et al. 2015).

Check Month-On-Month For Consistent Trends Or Abnormalities, Search For Abnormalities Suggesting Contamination By External Factors,AndAbnormal Emissions (Psaraftis & Kontovas, 2013).

3.2.3.WhichMeasuresHaveBeenMostEffectiveinReducingEmissionsasperMRV Data?

ExamineTechnologicalandOperationalMeasureswhichLeadtoLargestEmissionCuts:Digitalise effective approaches such as slow steaming and substitute fuels (Paxian et al, 2015).

Evaluatethe efficacyofindividualstrategies(e.g.,slowsteaming,alternativefuels):Performance comparison of ships employing these strategies with those not employing these strategies(Cariou, 2011).

3.2.4.WhatChallengesandOpportunitiesExistinFurtherReducingMaritimeEmissions?

Identify the Main Challenges Hindering Further Emission Reductions: Examine technical, economic, and regulatory barriers (ICCT 2020)

Innovate in New Technologies and Practices Strengthening Emission Reductions: Exploit opportunitiesfornew technologiesorpracticesto enhanceemissionreductions,e.g.digitalisation, advanced propulsion technologies (European Maritime Safety Agency, 2020).

4.ProposedMethodologyandAnalysis

4.1.ResearchDesign

The study is a mixed-method approach, with quantitative analysis informed by qualitative data points. The former employs data from 2018 through 2024 including CO2 emissions, fuel consumption, and operational variables while the latter uses industry reports, academic literature, and policy documents to provide context for its findings (Creswell & Plano Clark, 2018).

4.2.DataCollection

4.2.1.PrimaryDataSources

For this research, the main data will be collected from the EU Monitoring, Reporting and Verification (MRV) system. This dataset consists of high-quality data on CO 2 emissions, fuel consumption and other operational data from historic 5,000+ GT ships registered within the EU portsbetween2018to2024.ItcontainsvariableslikeCO2emissions,totalfuelconsumption,time spent at sea, technical efficiency etc. (European Commission, 2019b). This information enables a detailed analysis of emission trends, and the performance of the MRV system.

4.2.2.SecondaryDataSources

Industryreports,academicarticles,andregulatorydocumentswillprovidethesourceforsecondary data. Major sources of data are publications from the International Maritime Organization (IMO), the European Commission, the European Maritime Safety Agency (EMSA), and peer-reviewed journals on maritime transportation and environmental issues. They will provide more context so that primary data is supported and everybody can assure well-rounded understanding of what was investigated (IMO, 2020; EMSA, 2021).

4.3.DataCollectionProcedures

To perform the analysis, the data will be exported from the Thetis MRV system in Excel/SPSS format and uploaded in the relevant statistical tool. The data itself will also be meticulously cleaned and preprocessed in order to clean up any inaccuracies or inconsistencies in order to make the data ready for analysis (Wickham, 2016).

4.4.DataAnalysis

4.4.1.QuantitativeAnalysisMethods

Descriptive Analysis:Groupofmethodsforquantitativelysummarizingandcalculatingdata (Field, 2013).

TrendAnalysis:Howdoemissionsandfuelconsumptionchangefromyeartoyear.(Wooldridge, 2015). Few examples below,

• YearonyearTotalfuelconsumptionestimates
• YearonyearCruisingSpeedoverGround

Comparative:Apply statistical tests (e.g., paired t-tests), to study emissions levels before/afterMRV took place (Field, 2013). Few examples below,

• CO2emissionsbyFlagstates
• CO2emissionsbyVesselType
• VesselTypewiseSOGComparision

RegressionAnalysis:Amethod of investigating relationships among variables for the purpose of identifying the factors which influence the dependent variable (Gujarati & Porter, 2009). Few examples below,

• EfficientyvsFuelTypeRelation
• VesselTypevsAvgConsumtion Relation
• VesselSizevsCO2Emission

4.4.2.QualitativeAnalysisMethods

Thisisbasedonthefactthatcontentanalysiswillenablesecondarydataandsourceanalysestobe conducted in order to frame the quantitative finding (Krippendorff, 2018).

4.5.ToolsandSoftwareforAnalysis

ThisdatawillbeprocessedwithMicrosoftExcelandPython(quantitativeanalysis)andLLMwith NLP basedAI tool or NVivo (qualitative content analysis) (Wickham, 2016; NVivo, 2020) then data will be visualised through a Bussiness Inteligents tool (Microsoft Power BI).

4.6.StatisticalMethods

DescriptiveStatistics:Mean,median,modeandmeasuresofvariabilityStandarddeviationetcare typical statistics for descriptive analysis of data (Field, 2013).

Inferential Statistics:UseANOVAto compare emissions data across different periods and ship types (Wooldridge, 2015).

Regression Analysis:Create multiple regression models to identify determinants that affect emission reductions (Gujarati & Porter, 2009).

Trend Analysis: One of the methods consists in plotting annual CO2 emissions and fuel consumption data, in order to graphically illustrate trends and to indicate abrupt possible changes (Panneerselvam 2014).

CorrelationAnalysis:An Investigation of the coefficients of Pearson correlation was conducted between variables(Cohen, 2013).

4.7.Limitations

Data Qualityand ConsistencyVariationsin data collection and reporting practices can result in discrepancies, undermining the reliability of the data (European Commission, 2019).

ExternalInfluencesaroundstudies(i.e.economicconditions,fuelpricesetc)canalsocauseresults to be misleading (Psaraftis & Kontovas 2013).

Technological and operational variability- Current operational practices and technological advancements widely differ between vessels and will therefore make analysis more challenging (Johnson 2020).

Data Availability:Theavailabilityofgranulardataonindividualtypesofemissionreduction measures can be difficult and therefore a limitation to conducting analysis (EMSA, 2021).

4.8.MitigationStrategies

DataValidation:Cross-checkMRVdatawithothersourcestoensureaccuracy.

RobustStatisticalMethods:Usesensitivityanalysestocontrolforexternalfactors.

StandardizationEfforts:Encourage standardized data reporting practices (Creswell & Plano Clark, 2018).

5.Conclusion

Finally, this research proposal intends to provide an overall assessment of the effectiveness of the EU MRVsystem in improving greenhouse gas emissions reduction from shipping.The study will integrate quantitative data analysis and qualitative insights to come up with a comprehensive understanding ofhow the system works, using amixed-methods approach.Theprincipal datawill come from the MRVsystem and will besubject to detailed statistical analysis to detect trends and the effects of the regulatory measures, while also revealing the most suited emission reduction strategies. These results are expected to illustrate the large CO 2 reductions following the MRV system implementation, and reveal best MRV practices in the shipping sector as well (European Commission, 2019; Johnson et al., 2020).

Secondly, we will bring up some of the issues and limitations facing by the CT and CT imaging dataset in term of data quality, the impact of other external factors, technological variation and recommendations to minimize those pitfalls. These results will provide additional useful information for both policy makers and industry stakeholders and researchers, as they can deliver evidence-based recommendations for strengthening the measures put in place to reduce emissions and for further regulatory improvements (Psaraftis & Kontovas, 2013; EMSA, 2021). Overall, this research is aimed at contributing to the worldwide endeavors in combating climate change by improving the knowledge about the tactics that lower the impacts of maritime emissions.

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