The economic impact of low sulphur compliance on future fuel
cost and container freight rates: a case study of Shanghai-Lagos

Abstract
Title of Dissertation: The Economic Impact of Low Sulphur Compliance
on Future Fuel Cost and Container Freight Rates:
A Case Study of Shanghai-Lagos
Degree: Master of Science
The International Maritime Organization (IMO) has set 0.5% wt m/m limits on the
sulphur content of marine fuel oil used by ships to reduce the harmful effects and
environmental damage caused by sulphur dioxides emissions on human health and the
environment resulting from the combustion of marine fuels. The IMO 2020 sulphur
regulation will enter into effect from 1 January 2020. In less than six months, ships
will have to comply with one of the available options; scrubber retrofitting, using low
sulphur fuel oil (LSFO) or switching to LNG propulsion engines. This study examined
the potential economic impacts of the new IMO regulation on liner shipping
companies in Nigeria, with an emphasis on container freight rates and future fuel costs.
A mixed-method approach was used to modelling the relationship between bunker cost
and container freight rates, validated by a survey. The three scenarios considered
indicate the competitiveness and risk of various compliant options against uncertain
freight rates. The findings show shipowners in Nigeria will comply with LSFO and
Scrubbers with an expected 15-25% freight rates increase. MGO/LSFO is ranked best
compliance solution, and HFO/Scrubbers is the second-best alternative.
Liquefied Biogas and Methanol are the preferred future fuels because of their zero
sulphur emission and commercial prospect. Based on the research findings,
governments advised mitigating unforeseen transitional issues while shipowner
explores cost-cutting measures.
KEYWORDS: Air emission, MARPOL Annex VI, Economic impact, Marine fuel,
Container, Lagos, Freight rates
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Table of Contents
Declaration ……………………………………………………………………………………………………. ii
Acknowledgements ……………………………………………………………………………………….. iii
Abstract ……………………………………………………………………………………………………….. iv
Table of Contents …………………………………………………………………………………………… v
Table of Tables ……………………………………………………………………………………………. vii
List of Figures …………………………………………………………………………………………….. viii
List of Abbreviations …………………………………………………………………………………….. ix
1 Introduction ………………………………………………………………………………………………… 1
1.1 Background ……………………………………………………………………………………………… 1
1.2 Problem Statement ……………………………………………………………………………….. 3
1.3 Scope of the Study ……………………………………………………………………………….. 5
1.4 Significance of the Study ………………………………………………………………………. 5
1.5 Research Questions ………………………………………………………………………………. 6
1.6 Research Hypotheses ……………………………………………………………………………. 7
1.7 Methodology ……………………………………………………………………………………….. 7
1.8 Delimitations ……………………………………………………………………………………….. 8
1.9 Research Structure ……………………………………………………………………………….. 8
2 Literature Review ……………………………………………………………………………………… 10
3 Data and Research Methodology …………………………………………………………….. 17
3.1 Data Collection Method …………………………………………………………………….. 18
3.2 Econometrics Analysis ……………………………………………………………………… 18
3.2.1 Dependent Variable ……………………………………………………………………….. 19
3.2.2 Independent Variable ……………………………………………………………………… 20
3.3 Data ………………………………………………………………………………………………… 21
3.3 Model Estimation ……………………………………………………………………….. 23
3.4 Model Diagnostic Check …………………………………………………………….. 23
3.4.1 Residual Diagnostics …………………………………………………………………… 24
3.4.2 Stability Diagnostics (Ramsey-Reset Test) ……………………………………. 24
3.4.3 Autoregressive Moving Average (ARMA) ……………………………………. 25
4 Findings ……………………………………………………………………………………………….. 26
4.1 Descriptive Statistics …………………………………………………………………………… 26
4.2 Unit Root Test ……………………………………………………………………………………. 27
4.3 Correlation Result ………………………………………………………………………. 28
4.3 T-Test and Coefficient Restriction Test (F-Test) …………………………………….. 28
4.4 Cointegration Test ………………………………………………………………………. 29
4.5 Normality Test Results (Jarque-Bera) …………………………………………… 30
4.6 Model Results …………………………………………………………………………………… 31
5.0 Discussion/ Study Application …………………………………………………………….. 33
5.1 Freight Rates …………………………………………………………………………………….. 33
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5.1 Nigerian Maritime Industry …………………………………………………………………. 35
5.2 Lagos Port Complex ………………………………………………………………………….. 35
5.3 Cargo Throughput ……………………………………………………………………………… 37
5.4 Sulphur Regulation-Nigeria ………………………………………………………………… 39
5.5 Questionnaire Result …………………………………………………………………………… 40
5.6 Scenario Analysis ……………………………………………………………………………….. 48
5.6.1 TOPSIS Method ………………………………………………………………………………. 51
5.6.2 Sensitivity Analysis ………………………………………………………………………….. 53
6.0 Conclusions and Recommendations ……………………………………………………… 55
References …………………………………………………………………………………………………… 58
Appendices ………………………………………………………………………………………………….. 64
Appendix-A Time series data- 2009-2019 ………………………………………………… 64
Appendix-B Eviews Flowchart ………………………………………………………………. 66
Appendix-C Regressional Analysis ………………………………………………………….. 67
Appendix-D Questionnaire ……………………………………………………………………… 70
Appendix- E TOPSIS Analysis ………………………………………………………………….. 72
Appendix-F Dissertation Work plan …………………………………………………………… 73
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Table of Tables
Table 1 Dependent and Independent Variables ………………………………………………… 19
Table 2 Descriptive Statistics ………………………………………………………………………… 26
Table 3 Unit Root Test results ……………………………………………………………………….. 27
Table 4 Correlation Test. ………………………………………………………………………………. 28
Table 5 Cointegration Test result ……………………………………………………………………. 29
Table 6 Main Regression Results …………………………………………………………………… 31
Table 7 List of concessionaires in Nigeria ……………………………………………………….. 36
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List of Figures
Figure 1 Research Structure …………………………………………………………………………….. 9
Figure 2. Summary of SOx and NOx emission limits(Source, Author). ………………. 12
Figure 3 Methodology Flowchart (Source: Author). ………………………………………… 17
Figure 4 Shanghai to Lagos Freight Rates ……………………………………………………….. 21
Figure 5 Bunker 380CST ………………………………………………………………………………. 22
Figure 6 Normality Test Result ……………………………………………………………………… 31
Figure 7 Eviews Forecasting ………………………………………………………………………….. 32
Figure 8 Container Freight Rates (Source: adapted from Clarksons) ………………….. 33
Figure 9 Freight Rates and Bunker cost (Source: adapted from Clarksons) ………… 34
Figure 10 Lagos Port Complex (Source: adapted from NPA, 2019) ……………………. 36
Figure 11 Cargo Throughput (Source: Author) ……………………………………………….. 38
Figure 12 Container Port Traffic (Source: Author) …………………………………………… 39
Figure 13: Future fuel costs & Freight rates ……………………………………………………. 50
Figure 14 TOPSIS Results …………………………………………………………………………….. 52
Figure 15 Crystal ball simulation ……………………………………………………………………. 52
Figure 16. Sensitivity analysis ……………………………………………………………………….. 53
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List of Abbreviations
ADF Augmented Dickey-Fuller
AHP Analytic Hierarchy Process
AMSA Australian Maritime Safety Authority
ARMA Autoregressive moving average
BAF Bunker Adjustment Factor
CEO Chief Executive Officer
CO Carbon Dioxide
DMA Danish Maritime Authority
DWT Dead Weight Tonnes
EAP Environment Action Programme
EC European Commission
EEA European Economic Area
EEDI Energy Efficiency Design Index
EEDI Energy Efficiency Design Index
EEZ Exclusive Economic Zone
EGCS Exhaust Gas Cleaning System
EMSA European Maritime Safety Agency
EU European Union
EVA Economic Valuation of Air Pollution
GDP Gross Domestic Product
GHG Greenhouse Gas
HSFO Heavy Sulphur Fuel Oil
IAPP International Air Pollution Prevention Certificate
ICCT International Council on Clean Transportation
IMO International Maritime Organization
JB Jarque Bera
KPMG Klynveld Peat Marwick Goerdeler
KPSS Kwiatkowski–Phillips–Schmidt–Shin
LBG Liquefied Biogas
LNG Liquefied Natural Gas
LSFO Low Sulphur Fuel Oil
MCE Ministry of Climate and Environment
MDO Marine Diesel Oil
MEOH Methanol
MEPC Marine Environment Protection Committee
MPA Maritime Port Authority of Singapore
MSC Mediterranean Shipping Company
NIMASA Nigerian Maritime Administration and Safety Agency
NMA Norwegian Maritime Authority
NO Nitrogen Dioxide
NPA Nigerian Ports Authority
NPV Net Present Value
x
NT National Territory
PM Particular Matters
SECA Sulphur Emission Control Area
SIP Ship Implementation Plan
SO Sulphur Dioxide
SSA Singapore Shipping Association
TCIP Tincan Island Port
TEU Twenty-Foot Equivalent
UNCLOS United Nation Convention on Law of the Sea
UNCTAD United Nation Conference on Trade and Development
UNEP United Nation Environment Programme
UNFCC United Nation Framework Convention on Climate Change
USD United State Dollar
WHO World Health Organization
WMU World Maritime University
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1 Introduction
1.1 Background
Maritime transport is the pillar of the world economy, linking countries via trade. Over
90% of the global trade by volume, and 70% by value, is transported via maritime
transport. This shows that shipping is a derived demand from trade and has doubled
37 times (tonnage) over the last three decades. Today, the seaborne trade reached 12
billion tons in 2018 and is expected to increase substantially by 2020 (UNCTAD,
2018; Cullinane, 2012).
These figures show that the global economy relies heavily on the proper functioning
of the maritime industry to flourish due to their close relationship (Notteboom,
2011). Therefore, to satisfy maritime transport demand, ships bunker over 3.5 million
barrels of heavy fuel oil, to generate 90% of all sulphur emissions (Goldman Sachs,
2018). Thus, while bunker fuel accounts for just 7% of transport demand, the 15 largest
ships emit more SOxand NOx than the entire world’s cars combined (KPMG, 2019),
indicating small but high magnitude of impacts on human health and economy.
Given international shipping emissions are estimated at 2.1% of the global greenhouse
gas (GHG) emissions, 12% SOx emissions and 3% of global carbon emissions from
fuel combustion (Third IMO GHG study 2014; Corbett, Wang, & Winebrake, 2009;
Notteboom, 2011). These emissions according to the World Health Organisation
(WHO), resulting in over 400,000 premature deaths from lung cancer and
cardiovascular disease. Likewise, 14 million childhood asthma cases were recorded
annually linked to air pollution. The damaging health effect needs to be controlled, in
terms of both lives and cost. For instance, in Central Europe alone, over 437,550
million Euro was spent on health externalities (Ballini, Ölçer, Brandt, & Neumann,
2017).
Therefore, the sheer scale of international shipping emissions and resultant adverse
effects on air quality and human health has been questioned, so the International
Maritime Organization (IMO) needs to adopt deliberate and conscious efforts to
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reduce SOx emissions from shipping (Kalli, Repka, & Alhosalo, 2015). IMO took
MARPOL Annex VI, to reduce air emissions from ships, placing an initial 4.5%
sulphur content limit on marine fuel oil globally in 2005 (Cullinane & Bergqvist
2014). This led to the establishment of emission control Areas (ECA’s) in the North
and Baltic Seas, the North American seas and later in the US Caribbean. The IMO’s
sulphur regulations remain a state responsibility, but other regional communities took
a more stringent approach to their implementation.
For instance, in 2010 the European Union Commission (EC), placed initial1.5% to
1.0% and later 0.1% wt m/m sulphur content limits on all ships passing through
European ports in 2015 (Kalli et al., 2015). These regulations are covered in council
directives of 1999/32/EC, 2005/33/EC, and (EU) 2016/802 aimed at reducing air
pollutions within EU waters. Similar, in 2015, EU set out new sulphur regulation
2015/757 to monitor, report and verification of carbon dioxide emissions from
maritime transport (EMSA, 2019).
As shipping anthropogenic activities increase greenhouse gases, carbon dioxide and
methane to the atmosphere (Shepardson, Niyogi, Choi, & Charusombat, 2011), there
is need for a timely response to the intense environmental concerns, contributed to
improving global health and environment, particularly for coastal communities. In
October 2016, Marine Environment Protection Committee (MEPC) 70th session,
limits of 0.50% m/m global sulphur content in bunker fuel used by ships globally were
approved except designated emission control areas (IMO), effective from 1 January
2020, and an 87% reduction from the existing 3.5% limit.
Accordingly, extensive research has been conducted (Halff, Younes, & Boersma,
2019; Holmgren, Nikopoulou, Ramstedt, & Woxenius, 2014; Lindstad, Rehn, &
Eskeland, 2017; Notteboom, 2011) to examine the impact on refining, bulk and tanker
markets with little or no research conducted to investigate the impact on the container
shipping industry. This study aims to fill that gap by examining the economic impact
of the regulations on container shipping. As IMO, sulphur regulations may have likely
3
repercussions on container freight rates and fuel cost, uncertainty remains on what to
expect from the regulations from the perspective of operators and shippers alike.
1.2 Problem Statement
The increased anthropogenic shipping activities are raising environmental concerns
related to air emissions (Viana et al., 2014). These ships’ emissions take place within
400 km of land affecting local and coastal communities’ air quality and health. There
is no doubt that without shipping, the global exchange would not have been possible
since shipping is the most cost-efficient means of transporting bulk cargo (IMO). Over
80 per cent of the world trade is seaborne trade, and while including developing
countries, this per cent is 90 per cent (Munim & Schramm, 2017). Statistically, world
seaborne trade quadrupled to 11, 832 million tons in 2018 from 2,605 million tons in
1970 (UNCTAD, 2018).
During this period 1970 to 2018, the shipping industry had enjoyed a peak and
managed troughs including the most recent event of the global financial crisis that
affected many industries shipping included (Slack, 2010). These events had been with
the shipping industry for the last hundred years (Stopford, 2009). Shippers and carriers
seize the opportunity to predict shipping cycles and their adverse effects on freight
rates, to make appropriate decisions while saving cost.
This is especially the case with tankers and dry bulk carriers where volatility and
market cycle has been studied (Beenstock & Vergottis, 1989; Randers & Göluke,
2007; Scarsi, 2007), but this explained why hedging freight rates using forward freight
agreements (FFAs) had not been widely accepted in the industry (Alizadeh &
Nomikos, 2011;Kavussanos et al, 2015; Tsouknidis, 2016). Nevertheless, container
trade is quite different from other shipping segments of bulk and tanker vessels,
operating in structured services based on schedules. As a result, its freight rates are
estimated using the same supply and demand economics as the case of bulk shipping.
However, container freight rates have the cyclicality and volatility inherent in the
shipping sector.
Today, container trade reached nearly 8 times to 1.9 billion tonnes in 2018 from 238
million tons in 1990 (Clarkson, 2019) while its supply side represents 14% of the
4
global fleet and a market share of 17 per cent of the global seaborne trade in volume
terms and 50% in value terms (Coyle et al., 2017). Thus, this indicates strong growth
of containerised cargo within half a century, sustained by stable commodity prices,
globalisation and economies of scale.
However, the high level of vulnerability and cyclic pattern saw freight rates fall by
14% in 2009 (UNCTAD, 2009), which was caused by the dramatic reduction of 10.8%
of containerised cargoes. This prompted shipping companies to adopt cost saving
measures to managed the situation (Notteboom & Vernimmen, 2009). Before the
financial crisis of 2009, container ship bunkering costs represented 60% for an
intermediate ship operating cost because they maintain relative high speed of 21 knots
(Stopford, 2009). As a result, major liner shipping operators ordered bigger ships to
achieve energy efficiency and economies scale, and this expanded the fleet by 30%
(Clarksons, 2019). These vessels are large enough to cause crises, especially in the
anticipated recessionary scenario with little hope.
Furthermore, the mega-ships trend saw fleet growth increase substantially from 1960
to 2016 to reach 22,000 TEUS capacity (Ham et al., 2012). Overall, annual fleet growth
rates fell from 15.51% in August 1998 to 6.21% in 2018 (Clarkson, 2019). These large
ships have increased travel time and speed reduction while intermediate ships were
deployed to less traffic and low volume north-southbound routes.
Therefore, considering IMO sulphur regulations may be more complex and
challenging for intermediate ships of more than 15 years. Shippers would see sudden
increase in freight rates as demand tries to catch up with supply. Likewise, the current
low earnings due to weaker demand and overcapacity saw the freight rate fall 3 per
cent in 2014 as compared to 2013 (Alixpartners, 2015) and by 2017 the container
freight rates improved from awkward moments; in 2016, demand grew by 6.4%
against a supply of 3.8% with significant improvements recorded on the main trade
lane. The other north-south lanes in Africa also improved with Shanghai to Lagos
improved 49.9% in 2017 from a negative 18.5% in 2016.
5
These improvements noticed in the Shanghai to Lagos route might be upset by the
IMO sulphur regulations as container liners carrier/operators are expected to comply
with the new regulations toward IMO’s GHG strategy, setting aside additional cost for
scrubber retrofitting, using low sulphur fuel oil, or switching to Liquefied Natural Gas
(LNG). Given the fact that 1st January 2020 is fast approaching, the industry is
expecting weak demand on heavy sulphur fuel oil, resulting in a massive surplus of
over 2.6 mm bbl/d in 2020 (KPMG, 2019). Consequently, compliance with the IMO
sulphur regulation requires investment and various trade-off solutions, particularly on
fuel alternatives (Ölçer & Ballini, 2015). Given its variant ramifications by individual
companies, ship types or regions, the objective of this research is to examine the
economic implications of the regulation on container freight rates and future fuel cost.
1.3 Scope of the Study
The research focused on analysing the economic impacts of the 0.5% global sulphur
limit on container freight rates and future fuel costs. Abatement options, namely
scrubbers, liquefied natural gas and low sulphur fuel as well as other alternatives such
as methanol, biofuel cells and batteries were examined. However, more focus is geared
towards alternative energy sources that contribute positively to societal wellbeing and
the environment. Fuel prices depend on micro and macroeconomic factors; hence they
are not within the scope of this research. Fuel types have technical pros and cons that
fall outside the scope of this study. Therefore, the study is limited in scope to Shanghai
to Lagos container freight rates volatility and IMO 2020 sulphur regulation.
1.4 Significance of the Study
Given the anticipated magnitude of the impact of IMO’s Sulphur regulation, the
purpose of this research is as follows:
1. To facilitate the smooth implementation of IMO’s Sulphur regulation towards
the improvement of human health and the environment.
2. To provide far-reaching operational solutions on optimal compliance methods
with minimal disruption to container shipping.
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3. To add a new body of knowledge on the cost of sulphur 0.5m/m compliance
options
The findings provide valuable insights for individual shipping lines to make the best
investment decisions on investing in new technologies. Thus, the research objective
is to examine all endogenous factors that influence freight rates and to provide an
assessment for the potential future development of the industry on environmental
regulations. Therefore, a full understanding of the economic impact of IMO’s Sulphur
regulations can provide valuable insights for long-term investment towards
environmental sustainability.
1.5 Research Questions
This research builds on previous research gaps that exist due to intense pressure for
shipping to reduce sulphur emissions from ships. As a result, IMO set a limit of 0.5%
sulphur content on fuel used by ships empowered by MARPOL Annex VI, regulation
14 dealing with SOx. This IMO directive takes effect from 1st January 2020, and all
ships must comply with the provision accordingly.
There is no doubt that the IMO sulphur regulation comes as a sudden shock to the
shipping industry and is a massive constraint for shipping companies switching from
heavy sulphur residual fuel oil (HSFO) to a much lighter oil within a limited amount
of time. Consecutively, it creates uncertainty on demand and supply of compliant fuels
and selecting the best compliance methods with minimal logistic disruptions.
However, the shipping industry has endured many changes, each with its ramification
on financing and operational dynamics.
Nevertheless, environmental regulations will continue to top government agendas at
least for the next 50 years, and the sustainability mantra in the industry is shrinking
earnings significantly. The research is motivated to investigate the economic impact
of the 2020 global sulphur regulation while simulating scenarios for selecting best
solution ahead of IMO sulphur regulation. The effects on trade may be challenging,
7
depending on the region and shipping companies. In this regard, the research asked
three questions and a research hypothesis:
1. What is the relationship between container freight rates and bunker cost?
2. What is the expected impact of the IMO sulphur regulation on liner shipping
companies in Nigeria?
3. What are the likely scenarios for selecting best compliance option? In
anticipation for freight rates and future fuels increase.
1.6 Research Hypotheses
Hᶦ: fuel cost does not significantly influence container freight rates.
1.7 Methodology
This section presents how the research attempts to answer the research questions. The
author uses a mixed-methods study to address the whole research, as a convergent
view is central for both quantitative and qualitative data (Creswell & Creswell,
2017). Evaluating the economic impact of low sulphur compliance on fuel cost and
container freight rates, and answering the research questions, the study is carried out
as follows:
1. First, an economic metric (Eviews) is used to answer the first research
question: What is the relationship between container freight rates and bunker
cost? The time-series data were collected from Clarkson, Drewry, UN
Comtrade, International Energy Agency and other open-source databases to
test the relationship of independent variables against the dependent variables
to forecast the future accurately.
2. Second, the questionnaire was used to examine the second research question:
What is the expected impact of the IMO sulphur regulation on liner container
shipping operators in Nigeria? The questionnaire aimed at investigating the
perception of liner container shipping operators in Nigeria on IMO sulphur
regulations, to underpin the motive behind individual shipping companies’
investment appetites against a given environment. Finding different behaviour
8
traits is essential to understand likely compliance patterns that may influence
industry competition; hence, a small sample was used to determine if the
qualitative findings generalise to a larger sample.
4. Finally, Microsoft crystal ball was used to analyse the third research question:
What are the likely scenarios for selecting best compliance option? In
anticipation for freight rates and future fuels increase? Using the results of the
questionnaire and expert projection formed scenario for the IMO 2020 sulphur
compliance.
1.8 Delimitations
The research success is dependent on the future cost of renewables, i.e. methanol, and
fuel cells, amongst others; hence, the cost will be based on future projection. Besides,
data were obtained about the price of batteries based on the ferries in Europe where
this technology has been in place. Due to the limitation of this type of research in
Africa, many of the analyses relied on the qualitative data obtained from industry
professionals, academia and government about their understanding of the future
market requirements.
1.9 Research Structure
The study consist of six chapters, as shown (see Figure 1) and appendices as follows:
 Chapter 1 provides a detailed background of the chosen field of studies and
addresses the relevance of the research, including the research steps used.
 Chapter 2 critically reviews the relevant literature to the scope of the study
and includes the research on international air pollution from ships (MARPOL)
and other studies that relate to freight rates, fuel consumption and sulphur
emissions from global, regional and national levels. The literature review will
Help in the development of a conceptual framework that will balance against
the theoretical Assessment of the impact of the sulphur regulation on shipping
and the environment.
 Chapter 3 presents the methodology for data collection, selection of variables
for the regression and steps in the regression.
9
 Chapter 4 presents an analysis of findings and results
 Chapter 5 Case study
 Chapter 6 presents the summary, conclusion and recommendations, including
the limitations of the research and further study.
Figure 1 Research Structure
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2 Literature Review
This chapter aims to provide background on air emissions but is limited to sulphur
oxide emissions. It relies on books and scholarly journals such as Transportation
Research Part A, B C, D and E, Journal of cleaner production, Marine Policy,
Transport and Telecommunication, and Ocean engineering. However, the study
synthesises the various kinds of literature from books, peer-reviewed journals,
conference proceedings and other cross-references materials to establish a theoretical
construction that forms the foundation of the research.
Global shipping emissions are estimated at 2.2% and 2.1% of CO and GHG
emissions in 2012 (IMO, 2014), a relative reduction from 2.7% of the global emissions
of CO in 2007 (IMO, 2009). The intense shipping anthropogenic activities have
sparked concern because ship exhaust gases are the primary sources of emissions.
Nevertheless, shipping has shown to be the least polluting sector compared to other
modes of transport (UNCTAD, 2017).
Further, global assessment of international shipping emissions has been conducted
(Corbett & Koehler, 2003; Eyring, Köhler, Van Aardenne, & Lauer, 2005). Corbett
concludes that international shipping is a contributor to global air emissions specific
to the coastal community and port cities. Most commercial ships use low-quality
bunker fuel for combustion, and these fuels have high sulphur content that produces
air pollutants when emitted. The emissions are sources of greenhouse gases such as
CO2, methane, NOx, SOx and PM, with impacts on human health and climate (Becker,
1998).
These impacts have led researchers to study air pollution and ship
emissions extensively (Boutin, 2010; Corbett et al., 2007; Cullinane & Bergqvist,
2014; Goldsworthy, 2010). Scholars have made further studies to assess the cost of
externalities imposed on society and the environment. For instance, Brandt et al.
(2013a) examined health costs of air emissions from shipping within Europe for the
year 2000 – 2011 and estimated health costs by 2020 using Economic Valuation of Air
Pollution (EVA) model.
11
The results indicate that air pollution remains a huge health concern, and will cost
Europe about 537 billion Euro by the year 2020, while premature deaths are estimated
to reach around 450 000 in the year 2020. These figures show that air emissions from
ships are a significant health and environmental problems for many European cities
that must be addressed, using various emission reduction technologies (Ballini, Ölçer,
Brandt, & Neumann, 2017; Brandt, Silver, Christensen et al., 2013b).
Nevertheless, the cost of SOx, NOx and PM health externalities has been studied
(Ballini, Ölçer, Brandt, & Neumann, 2017; Brandt, Silver, Christensen et al., 2013a;
Corbett, Wang, Winebrake, & Green, 2007). In 2000, the health cost of shipping
emissions in Europe amounted to 58.4 billion Euro and was likely to increase to 64.1
billion Euro by 2020 (Brandt et al., 2013a, b). In addition, recognising the essence of
tackling this problem, Ballini et al., (2017) examined health-related economic
externalities of air pollution using hybrid-wind Helped ship propulsion and compared
two-emission reduction scenarios. The projections indicate that without deliberate,
concerted efforts by the international community, the emissions of SOx and NOx from
the maritime sector will continue to grow, surpassing all land-based sources,
particularly in Europe.
The International Maritime Organization took measures to curb the resultant effects of
shipping emissions on health and the environment by amending MARPOL to include
Annex VI, which aims to reduce air pollution from ships. Regulation 14 of MARPOL
Annex VI amendments sets limits of SOx content in heavy residual fuels used by ships.
Consequently, it established global and stringent regional limits in Sulphur Emission
Control Areas (SECAs). The ECA primarily tackled SOx and later extended to include
NOx.
The SECAs limits are implemented in stages, as illustrates in Figure 3 and has been
mostly successful in controlling the effect of shipping emissions, including SOx, NOx
and PM (Nikopoulou, 2017; IMO, 2008).
12
Figure 2. Summary of SOx and NOx emission limits(Source, Author).
Note; ¹ the maximum allowable concentration of sulphur as per cent by weight in fuel
oil used on board ships (mass/mass).
13
IEM: Internal Engine Modification.
Therefore, given heightening environmental concern, the International Maritime
Organization (IMO) set a more stringent environmental regulation at the 70th session
of the Marine Environment Protection Committee (MEPC), held on October 2016, to
limit sulphur content to 0.5% in marine fuel used by ships, effective from 1 January
2020.
This regulation is in line with IMO’s initial GHG strategy to reduce emissions from
international shipping that falls within a broader context of other existing united nation
(UN) instruments. The instrument include United Nations Law of the Seas
(UNCLOS), United Nations Environment Programme (UNEP), United Nations
Framework Convention on Climate Change (UNFCCC) and other relevant agreements
like Marrakesh Accords, Paris Accords and Kyoto protocol.
Furthermore, IMO initial strategy underscores the urgency to reduce greenhouse gas
(GHG) emissions from ships by targeting carbon intensity reduction through the
implementation of energy efficiency design index (EEDI) for new ships; as a result
carbon intensity of international shipping to decline by at least 40% by 2030, 70% by
2050, compared to 2008 (IMO, 2019). More so, GHG emissions from international
shipping also expected to decline 50% by 2050, setting a vision green shipping and
decarbonisation consistent with the Paris Agreement temperature goals.
Moreover, the cascading effect of the regulation would vary by region. For instance,
the Europe Union issued directives that aim to reduce sulphur oxide emissions from
maritime transport. For example, the Council Directives 1999/32/EC as amended
2005/33/EC set the first sulphur limits of 0.1% for marine fuels used by ships at berth
in EU ports, which took effect from 1 January 2010 and extended the scope of
Directive 1999/32/EC to include all marine liquid fuels used by ships operating within
community waters.
Most recently, the Council Directive (2012/33/EC) recognises the impact of using low
sulphur fuels, particularly in SECAs, which could result in an increase in fuel cost and
thus create less competitiveness of short sea shipping as compared to other transport
14
modes and other industries in the SECA member states. This suggests a possible modal
shift and concern for the investment cost of abatement methods.
Consequently, the EU took a more ambitious strategy in line with its 7th Environment
Action Programme (EAP), to guide Europe environment policy until 2020,
demonstrating European Union long-term vision to be a carbon-neutral economy by
2050 (EU, 2019). Further studies were conducted within the context of EU directives
(Schembari et al., 2012; Vestreng, Myhre, Fagerli, Reis, & Tarrasón, 2007). These
have indicated the preparedness of EU against the upcoming regulation, but that was
not the case for other regions like Africa. Nevertheless, relevant literature (i.e.
Animah, Addy-Lamptey, Korsah, & Sackey, 2018) has indicated that several African
states are contracting parties to MARPOL Annex VI, and aim at enhanced air quality
and environment against emissions from shipping activities. However, the African
Union has limited unified instruments to control the emissions from international
shipping within the region. While these studies focused on the environmental and
social effects of sulphur emission, little attention is directed to the economic cost of
sulphur reduction on future fuels and container freight rates.
Furthermore, even when all these commitments align with the IMO GHG strategy, the
existing compliance options remain insufficient in achieving IMO’s target for full
decarbonisation of the shipping industry, which forms the fulcrum to this study. Miola,
Ciuffo, Giovine and Marra (2010) conducted an analytical review of policy strategy
for regulating air emissions from ships and identified three available alternative
compliance options: Low-sulphur fuels, ultra-low sulphur fuels and alternative fuels
(biofuels, natural gas and hydrogen).
Schinas and Stefanakos (2014) observed several methods available for compliance,
such as liquefied natural gas (LNG) and methanol exist. However, Beecken et al.
(2014) found that abatement technologies are available, but these methods are limited
on costs of investments. Schinas and Butler (2016) evaluated the feasibility of
promoting the use of LNG as fuel for ships and proposed various commercial
incentives. While Brynolf, Fridell and Andersson (2014) conducted a life cycle
assessment of the impact of liquefied methane and methanol. Brynolf et al. (2014) also
15
believe that liquefied biogas and methanol have the potential to reduce the effect of
climate change from shipping beyond 2020.
Kim and Seo (2019) performed an empirical analysis using the Fuzzy AHP Method to
examine abatement options including LNG, scrubbers and low sulphur fuel considered
by Korean shipping companies. Kim and Seo (2019) results indicate that the
investment cost is the most influential decision making factor in selecting the best
compliance method. This study corroborates with previous research by Svindland,
(2018); Sys, (2009). Kim and Seo (2019) results indicate that Korean shipping
companies consider both operational and investment costs of compliance options.
Therefore, a cost-benefit analysis is necessary to determine the best compliance
options. Jiang, Kronbak and Christensen (2014) used net present value (NPV) to
compare marine gas oil and scrubbers. Jiang et al., (2014) found that marine gas oil
tends to have higher NPV compared to scrubbers, but Jiang et al. (2014) believe that
scrubbers are more beneficial to install on a new ship rather than an old ship with less
than four years lifespan. Antturi et al. (2016) calculated the abatement costs of low
sulphur fuel and scrubbers, excluding LNG to determine operators’ optimal decision.
Antturi et al. (2016) found that sulphur directives reduce emissions significantly within
Baltic Sea shipping.
Schinas and Stefanakos (2012) used stochastic linear programming model to assess an
optimal method of resource allocation to respond to new operational requirements in
light of uncertain demand. In contrast, Yang et al. (2012) examined available
compliance methods, excluding LNG. Therefore, Yang considers scrubbers
uncompetitive because of high capital cost. Hence, existing compliance are weight
against the level of uncertainty on demand and cost of future fuel, which may threatens
a possible modal shift to other land-based modes (Zis, Psaraftis, Panagakos, &
Kronbak, 2019). This situation may further be aggravated when investment cost is
internalised via the bunker adjustment factor leading to higher freight rates.
Liner shipping freight rates are relatively stable compared to other shipping sectors,
but cyclicality and seasonality affect characterised earning pattern in general
(Stopford, 2009). The seasonality effects could be detrimental to the survival of liners,
16
as observed from the fall of the Korean Shipping giant Hanjin Shipping, which
suffered from the collapse of freight rates, bad timing for ship investment and loss of
customers ahead of Christmas and New Year Holidays (Song, Seo, & Kwak, 2018).
In addition, the current mega-ship trends by liners combined with the rise of fuel cost
force liners to form alliances to ensure cost-saving options, including slow steaming,
consolidation, merger and acquisition (Lindstad, Asbjørnslett, & Strømman, 2011;
Psaraftis, Kontovas, & Kakalis, 2009; Schinas & Stefanakos, 2012; Ölçer & Ballini,
2015). The mega-ships are deployed to enjoy economies of scale and fuel efficiency
to stay competitive (Psaraftis et al., 2009). Therefore, the relevance of the IMO 0.5%
sulphur limits underscores the previous studies and highlights the limited depth of the
studies on container shipping ahead of 1 January 2020. Thus, there is a need for this
research to fill that gap by examining the underlying economic factors that interplay
between future costs and earnings of container shipping.
17
3 Data and Research Methodology
This chapter addresses the step used in this research to achieve its objective. Data
collection process and analysis are explained. The methodology adopted include the
following:
 Econometrics (Eviews) focused on answering the first research question
(Appendix-2): What is the relationship between container freight rates and
bunker cost?
 The questionnaire focused on assessing the second research question
(Appendix D): What is the expected impact of the IMO sulphur regulation on
liner container shipping operators in Nigeria?
 Microsoft crystal ball examined the third research question: What are the likely
scenarios for selecting best compliance option? In anticipation for freight rates
and future fuels increase.
Therefore, the chapter begins with an illustration of the whole methodology process
adopted in the research in line with the objectives discussed, as shown in (Figure 3).
Figure 3 Methodology Flowchart (Source: Author).
Theories (Previous Studies)
Estimation of Theoretical Model
Data Collection
Questionnaire Time series Sensitivity/Scenario/TOPSIS
Analysis
NO Is the Model
statistically
YES
Reformulate Model Use for analysis Interpret Model
18
The methodology involves a systematically way of solving the research problem
(Kothari, 2004). As indicated Figure 5 illustrates various methods, techniques, steps
that are generally adopted in the study in answering the research questions.
3.1 Data Collection Method
The study is an attempt to answer the research questions collected, both primary and
secondary data. The primary data were obtained directly from the following database:
1. Clarkson’s Research, a reputable industry most authoritative provider of data
and intelligence for global shipping. Clarkson’s shipping intelligence Network
has over 100,000 pages of data about shipping industry.
2. Methanol Institute: A global trade association with an active market assessment
about methanol. A member of the methanol market service Asia (MMSA).
3. United Nation Conference on Trade and Development Statistics- UNCTADstat
a reliable, comprehensive data centre, with a unique coverage for countries
maritime profile, maritime transport and economic indicators.
4. Questionnaire: well-structured Surveys was administered to the liner shipping
companies in Nigeria to aggregate their responses to the IMO 0.5% sulphur
limit.
However, the secondary data were sourced from online scholarly published articles
and IMO documents (IMODOCS).
3.2 Econometrics Analysis
The model estimation considers freight rates as dependent variable (Yt) and
independent explanatory variables are denoted as X1…k (see Table 1). The regression
model uses data from November 2009 to April 2019 with a monthly frequency of 114
observations as reported by Clarkson Shipping Intelligence, MI and UNCTAD. These
databases are reliable and consistent.
19
Table 1 Dependent and Independent Variables
VARIABLES ATTRIBUTES
Shanghai-Lagos Freight Rates Yt
Container Orderbook (%) X
Average age (year) X
Total container sales ($m) X
Clarkson Container Average Earnings ($/day) X
Container Newbuilding Prices ($m) X
Second-Hand Prices ($m) X
Demolition (, 000 TEU) X
Bunker Price 380cst Rotterdam X
MGO Singapore ($/Tonne) X
Fleet Development (million TEU) X
Oil Production (mbpd) X
Freight Rates Shanghai-Durban ($/TEU) X
Freight Rates Shanghai-Dubai ($/TEU) X
Freight Rates Shanghai-Melbourne ($/TEU) X
Freight Rates Shanghai- Santos ($/TEU) X
Freight Rates Shanghai-Europe ($/TEU) X
Freight Rates Shanghai- West Coast America ($/TEU) X
Freight Rates Shanghai- East Coast America ($/TEU) X
Freight Rates Shanghai- West Japan ($/TEU) X
Libor (%), X
Exchange rates (USD) X
Inflation rates (% yr/yr) X
Methanol (Asia Posted Contract X23
3.2.1 Dependent Variable
Freight rates play a central role indicating the current states of the shipping industry,
as it serves as the ultimate regulator of supply and demand in the shipping industry
(Stopford, 2009). The complex interaction of supply and demand establishes freight
rates through negotiation on cargo and capacity. Thus, proper Assessment of the freight
rates will depend on an analysis of demand and supply factors affecting container
freight rates. These factors can be tackled using supply side flexibility tactics such as
slow steaming and rerouting, scrapping older ships, smaller fleet deployment to other
geographic regions on the north-south and south-south trade lanes and replaced with
mega-ships. Nevertheless, liner container shipping companies introduce fuel
surcharges to upset fluctuating fuel prices in form of surcharges (booking fees, fuel
surcharges).
20
Moreover, shipping demand and supply are a function of the vessel expected freight
rates. Martin Stopford illustrated this relationship in 2007:
the price of freight today is great. because the ships, you’ll understand, are high
priced too, costing when new. far more than they used to if you’d know why
their price is high, consider this, berth costs are great because the trade, on
which freight’s paid grows faster than ships can be made only one thing left to
know. what it is that makes trade grow; the world needs its grain and ore;
sometimes less, but mostly more. when judging if the price is high what matters
most is…. when you buy (Stopford, 2009, p.135)
Consequently, freight rates became an extremely importance regulator of shipping
demand and supply. The relationship is characterised with frequent fluctuations and
seasonality to adjust market demand and supply. Thus, shipping investors perceived
inherent volatility as either an opportunity or a threat. In addition, the maritime
industry is a highly regulated industry with the (IMO), a UN agency playing critical
role to promote and regulate global shipping. IMO control air pollution from ships
under MARPOL annex VI adopted 0.5% sulphur limits on marine fuel used by ships
effective from 1 January 2020.
Therefore, with less than five months before the IMO 2020 sulphur regulations take
effect, shipping companies and cargo owners will face uncertain economic impacts of
the regulations, particularly on fuel cost and freight rates. Hence, the risk continues to
increase by expected demand and supply compliant fuel, which has remained
contentious. On this basis, the study modelled container freight rates against some
demand and supply factors to reduce the anticipated uncertainty and to forecast
potential market reality by 2020.
3.2.2 Independent Variable
The model assumes that the shipping market is influenced several factors, which
include GDP, commodity prices, average haul, random shock, transport costs, world
fleet, freight rates, ship scrapping, fleet productivity and shipbuilding production
(Stopford, 2009). The interaction of these variables would give more insight into the
mechanism of shipping freights. Thus, some economic indicators also key role in
21
shaping the pattern of trade such as inflation, interest rates and exchange rates.
Furthermore, Gross Domestic Product (GDP) and container trade provide a clear
reflection on the level of economic activity and corresponding trade pattern; both
computed yearly and the study utilises monthly frequency, hence, excluding both GDP
and container trade. Moreover, the model was estimated with the full sample
(November 2009-June 2019), to capture shipping cyclicality and similar effects of the
shipping crisis. The model was validated with primary data obtained from a
questionnaire administered to shipping companies within the scope of the research for
in-depth opinions and proficient conjectures.
3.3 Data
Preliminary data analysis carried out ensures quality assurance of date of entry and
visualisation for ambiguity, missing values and trends using a line graph. Figures 5, 6
show the basic features of the freight rates Shanghai to Lagos, bunker 380cst, average
age and second-hand prices data (Continuity, abnormality volatility) (see appendices
for the rest of the variables).
Figure 4 Shanghai to Lagos Freight Rates
Figure 4 shows how volatile the freight rates for Shanghai to Lagos have been from
2009 to 2019. High peak points were noticed in January 2010 at $2,575, and by
800
1,200
1,600
2,000
2,400
2,800
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Yt
22
February 2011, less than a year it crashed to $1,586. Since then freight rates have been
lower to reach the lowest points of $975-$1,012 from March-August 2016. This
justifies the insertion of two dummies within those periods respectively. A sharp rise
and decline were observed within two months May to July 2017 where rates peak at
$2,554 precisely same as in 2010 to fall to $1,315, and now the rates recovered to its
high peak of $2670 as at February 2019. These figures justify the selection of the rates
for the study to underpin factors causing this fluctuation.
100
200
300
400
500
600
700
800
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
X6
Figure 5 Bunker 380CST
The bunker costs represent key variables for estimating shipping freight rates, which
have direct relationship with oil prices. Figure 7 shows the bunker cost trend from
2009 until 2019. The figure indicates that the bunker cost has maintained a consistent
high peak of $500-$650 dollars per ton from 2011 to 2014. The oil prices fell sharply
in 2014 affecting the bunker cost to fall even much lower between $250-$450/ton from
2015 to 2019. The decline in bunker prices coincides with crude oil price monthly
average decline by 44 per cent between June 2014 and December 2014. By March
2015, the price fell even lower than expected to $44 per barrel (Javan & Vallejo, 2016).
This has led to the increase in volatility because of crude oil supply and demand price
determinant. Moreover, the IMO sulphur regulation may likely have adverse effect on
bunker cost even higher than expected historical prices.
23
3.3 Model Estimation
The model developed assumes that container freight rates might depend on several
factors. Therefore, estimation evaluates the relationship between the dependent
variable ‘Yt’ and other independent variables denoted as ݔଵ ݔଶ … ݔ .௞
઻ ൌ હ ൅ ઺₁܆₁ ൅ ઺₂܆₂ ൅ ઺₃܆₃ ൅ ઺₄܆₄ ൅ ⋯ ൅ ઺ ܆ૄ ൅
Where:
ߛ = Container freight rates Shanghai to Lagos
X = Independent Variables
Constant = ߙ
ߚ₁ = Coefficient
ߤ = Error term
The Eviews software is estimated using this formula DLOG_YT C DLOG_X1
DLOG_X2 DLOG_X3 DLOG_X4 DLOG_X5 DLOG_X6 LOG_X7 DLOG_X8
DLOG_X9 DLOG_X10 DLOG_X11 DLOG_X12 DLOG_X13 DLOG_X14
LOG_X15 DLOG_X16 DLOG_X17 DLOG_X18 LOG_X19 DLOG_X20
DLOG_X21 LOG_X22 DLOG_X23
The equation can also be written as DLOG_YT = C(1) + C(2)*DLOG_X1 +
C(3)*DLOG_X2 + C(4)*DLOG_X3 + C(5)*DLOG_X4 + C(6)*DLOG_X5 +
C(7)*DLOG_X6 + C(8)*LOG_X7 + C(9)*DLOG_X8 + C(10)*DLOG_X9 +
C(11)*DLOG_X10 + C(12)*DLOG_X11 + C(13)*DLOG_X12 + C(14)*DLOG_X13
+ C(15)*DLOG_X14 + C(16)*LOG_X15 + C(17)*DLOG_X16 + C(18)*DLOG_X17
+ C(19)*DLOG_X18 + C(20)*LOG_X19 + C(21)*DLOG_X20 + C(22)*DLOG_X21
+ C(23)*LOG_X22 + C(24)*DLOG_X23
3.4 Model Diagnostic Check
All the selected time series data need to undergo diagnostic checks to make a valid
forecast. First, the variables were transformed with natural logarithms to avoid
spurious regressions leading to invalid statistical assumptions for asymptotic analysis.
This transformation stabilises a non-stationary variance, ensures exponential trends in
the time series, and becomes linear (Cullinane, 2005). The time-series data on monthly
24
levels were checked for stationarity using the Augmented Dickey and Fuller (ADF,
1981), Phillips and Perron (PP, 1988) and Kwiatkowski-Phillips-Schmidt-Shin
(KPSS, 1992). The results indicate that seventeen variables become stationary on first
difference while six variables are stationary in levels. Then correlation was checked
and no correlation in the level and first difference data. This prevents inflating the ܴ²
and because if the ܴ² is inflated the ‘ݐ-ratios’ will not follow a ݐ-distribution and thus
hypothesis testing cannot be carried out (Cullinane, 2005). Then a cointegration test
was conducted to check the linear combination in level confirmed by Johansen (1991)
cointegration. It reveals no stochastic trend or long-run equilibrium relationships
between the endogenous variables.
3.4.1 Residual Diagnostics
Accordingly, the selected forecasting model was checked for normality, serial
correlation and heteroscedasticity tests. The Jarque and Bera (1980) normality test
shows asymptotic distributions and sample period with sensitive outliers because the
skewness and kurtosis in the JB test can be susceptible to extreme observation (Gel &
Gastwirth, 2008). Finally, JB test critical value p>0.05 indicates acceptance of H =
(null hypothesis) indicating the residuals are normally distributed after inserting two
dummies to satisfy the normality test. However, the JB test statistics is sometimes not
sufficiently accurate in small to medium range sample data. However, the results of
the Breusch (1978) and Godfrey (1978) residual diagnostics tests conducted confirmed
that the model is Homoscedasticity and no serial correlation. The analysis illustrates
the nonexistence of conditional heteroscedasticity and temporal dependency by past
errors that may affect the statistical inferences of the model (Godfrey & Tremayne,
2005). Hence, the null hypothesis is accepted in both situations, meaning
Homoskedacity Var(ų ) =ߪ² and no serial correlation for p>0.05 critical values.
3.4.2 Stability Diagnostics (Ramsey-Reset Test)
The study checked the existence of a linear relationship between dependent and
independent variables. The linearity of the model illustrates the straight-line
relationship between y and x variables (Chris, 2008) and the linear parameters
introduced by the Ramsey (1969) Reset Test. The study also considered the higher
25
order of the fitted square values (࢟ෝ ², ࢟ෝ ³ሻ in the regression, and was estimated using
࢚ෝ࢟₂ࢻ ൅ ૚ࢻ ൌ ࢚࢟ :formula the
²
࢚ෝ࢟₃ࢻ+
³
࣏ ൅ ᵢढᵢࢼ ∑+ ࢚ෝ࢟ ࢻ……………+
The ߩ െ value indicates that the test statistics are significant at >0.05.
3.4.3 Autoregressive Moving Average (ARMA)
The ARIMA model is formulated, which the combination of autoregressive (AR) and
moving average (MA). (1, O, 1) estimated using Classic Linear Series (CLS) at 5%
critical value.
DLOG_YT = -4.6981893457e-05 + 10.8278124846*DLOG_X16 +
0.112995942245*DLOG_X17 – 0.282855811563*DLOG_X6 –
0.367282625269*DLOG_X13 – 0.190498009714*DUMMY2016M08 +
0.286403424523*DUMMY2016M10 + [AR(1)=-
0.57219961288,MA(1)=0.778501834985,BACKCAST=2010M01,ESTSMPL=”2010
M01 2019M03″]
26
4 Findings
4.1 Descriptive Statistics
The study uses an independent variable and 22 others to estimate the model (see Table
2). The full sample from November 2009 to April 2019 has 114 observations.
Container freight rates, exchange rates and container sales have positive skewness and
high kurtosis. This clearly illustrates leptokurtic distribution, a departure from
normality. There is clear evidence of volatility clustering in spot freight rates. There
are high freight rates volatility mixed seasonality, which suggests the presence of
heteroscedasticity. The supply of ships within this lane does not balance with the
demand for containerised cargoes; the redeployment of feeder vessels to this lane
affects the rates elements and the demand for shipping products tend to increase during
the Christmas or New Year period. The sharp curve for these variables indicates their
level of fluctuations and associated risks. Nevertheless, most variables have normal
distribution, which confirmed normality distribution of Jarque-Bera.
Table 2 Descriptive Statistics
Variables Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque‐Be Probabilit Observati
YT 1858.074 1892.25 2670 974.6 416.1148 ‐0.11902 2.368663 2.162421 0.339185 114
X1 1.471533 1.48166 1.60181 1.34716 0.075667 0.025187 1.549539 10.00528 0.00672 114
X2 0.009382 0.00591 0.02892 0.00323 0.007422 1.394572 3.718627 39.40479 0 114
X3 93.63211 93.44 102.07 85.86 4.400602 ‐0.06665 1.862713 6.228152 0.04442 114
X4 222.3719 223.4012 267.4127 168.4684 28.65771 ‐0.20666 1.86911 6.886256 0.031965 114
X5 41684675 41865737 61909461 27132018 7796919 0.148158 2.62297 1.092285 0.57918 114
X6 468.2974 462.5 733.3 159.625 155.4767 ‐0.14246 1.852062 6.644977 0.036063 114
X7 708.1744 704.5833 1503.25 270.25 256.2626 0.759579 3.697464 13.27291 0.001312 114
X8 758.4719 741.2333 1608.5 264.25 295.4696 0.658805 3.327883 8.757107 0.012543 114
X9 931.3618 878.7 1675.5 325.4 302.6029 0.5455 3.177039 5.80272 0.054948 114
X10 1591.128 1555.125 3606.75 182 753.1945 0.142593 2.648048 0.974706 0.61425 114
X11 703.889 657.55 1027.9 291.25 209.2586 ‐0.03996 1.623793 9.026581 0.010962 114
X12 86.80044 87.75 97 76.5 4.834447 ‐0.23012 2.774149 1.248477 0.535669 114
X13 55.66667 60 80.5 24 14.30478 ‐0.51237 2.577197 5.837014 0.054014 114
X14 9752.465 8644.574 17411.09 5519.97 2801.212 0.87627 2.904562 14.63239 0.000665 114
X15 155.8234 129.3125 537.15 3.5 125.0037 1.181307 3.83289 29.80935 0 114
X16 10.64964 10.6452 11.14371 10.02954 0.335869 ‐0.147 1.853267 6.656784 0.035851 114
X17 1024.248 922.75 2075 223.5 409.3581 0.714694 2.927603 9.729852 0.007712 114
X18 1802.486 1803.5 2801.8 796.5 455.0605 0.024359 2.642922 0.616922 0.734577 114
X19 3042.499 3106.325 4991 1589.5 686.9114 0.083723 3.019035 0.134904 0.934773 114
X20 259.0782 231.75 378.8 81.25 80.01815 ‐0.43969 2.024845 8.190068 0.016655 114
X21 0.018857 0.01918 0.033 0.004 0.007268 ‐0.21903 2.405556 2.590005 0.273897 114
X22 25.44328 21.6725 101.221 0 20.74263 1.207573 4.442364 37.58837 0 114
X23 403.2456 420 590 255 76.47946 ‐0.04002 2.521889 1.116237 0.572285 114
27
4.2 Unit Root Test
The stationarity of a series can strongly influence its behaviour and properties. The
statistical tool used to test for stationarity in this study includes Augmented Dickey
and Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin
(KPSS) test. The results reject the null hypothesis of the stationarity of all variables
except Shanghai to Dubai, Durban and East Coast of America freight rates, Clarkson’s
average earnings, total container sales and demolition (see Table 3).
Table 3 Unit Root Test results
ADF[1] PP[2] KPSS[3]
Level 1st Diff 2nd Diff Level Ist Diff Level Ist Diff
Yt ‐2.229 ‐8.938 ‐3.541 ‐7.852
X1 ‐1.698 ‐14.409 ‐1.951 ‐14.96
X2 0.126 ‐4.995 1.384 ‐4.878
X3 ‐1.183 ‐11.519 ‐1.183 ‐11.526
X4 ‐1.622 ‐4.296 ‐1.59 ‐7.299
X5 ‐2.273 ‐9.316 ‐2.214 ‐9.715
X6 ‐1.385 ‐7.346 ‐1.187 ‐7.211
X7 ‐3.137 ‐3.126
X8 ‐3.21 ‐2.878 ‐8.707 0.637 0.067
X9 ‐8.0041 ‐2.428 ‐7.945
X10 ‐2.347 ‐9.052 ‐2.45 ‐8.883
X11 ‐1.363 ‐7.46 ‐1.204 ‐6.904
X12 ‐2.476 ‐4.772 ‐2.01 ‐4.806
X13 ‐1.178 ‐6.761 ‐1.353 ‐7.071
X14 ‐3.418 ‐2.591 ‐5.363 0.0672
X15 ‐8.57 ‐8.572
X16 ‐1.489 ‐2.528 ‐10.13 ‐0.975 ‐6.972 0.774 0.28
X17 ‐3.526 ‐2.38 ‐11.164 0.728 0.101
X18 ‐2.603 ‐9.184 ‐2.992 0.528 0.062
X19 ‐2.939 ‐2.685 ‐8.309 0.438
X20 ‐1.601 ‐7.262 ‐1.114 ‐7.234
X21 ‐1.881 ‐8.121 ‐1.848 ‐8.074
X22 ‐2.907 ‐4.112
X23 ‐6.848 ‐7.675
The ADF, PP and KPSS contain an intercept and no trend. The null hypothesis is that
the series is non-stationary. This hypothesis is rejected if the statistical results are
higher than the test critical values. The regression added lagged one 1(1).
The results in Table 3 indicates that lagged 1(0) of most variables are non-stationary,
while their lagged 1(1) first differences are stationary. This suggests that these
variables are integrated of order one 1(1). The exceptions are Shanghai to Dubai,
Durban and East Coast of America freight rates, Clarkson’s average earnings, total
container sales and demolition, which are stationary 1(0). Thus, it shows all other
28
variables have a unit root generated by a stochastic process, as shown in Table 3.
Therefore, the variables for the model estimation are a mixture of both 1(0) and 1(1)
process. Consequently, the combination of this two-variable cannot be used to make
statistical inference and thus, the study converts their stationarity of order 1(1) to deal
with the unit root so that statistical inference is made from the results.
4.3 Correlation Result
The variables correlation test was done using Microsoft Excel in 2016 (see Table 4).
This correlation table summarises a bivariate relationship, where two variables with
80 per cent mean they are highly correlated.
Table 4 Correlation Test.
DLOG_YT DLOG_X1 DLOG_X2 DLOG_X3 DLOG_X4 DLOG_X5 DLOG_X6 LOG_X7 DLOG_X8 DLOG_X9 DLOG_X10DLOG_X11DLOG_X12DLOG_X13DLOG_X14 LOG_X15 DLOG_X16DLOG_X17DLOG_X18 LOG_X19 DLOG_X20DLOG_X21 LOG_X22 DLOG_X23
DLOG_YT 100%
DLOG_X1 ‐4% 100%
DLOG_X2 9% ‐8% 100%
DLOG_X3 5% 3% ‐6% 100%
DLOG_X4 ‐11% ‐9% ‐5% 18% 100%
DLOG_X5 17% 9% 2% ‐4% ‐16% 100%
DLOG_X6 2% 3% ‐8% ‐13% ‐14% ‐3% 100%
LOG_X7 9% ‐2% ‐23% ‐2% 28% ‐16% ‐11% 100%
DLOG_X8 ‐11% 19% ‐2% 3% ‐3% 0% 2% ‐5% 100%
DLOG_X9 19% ‐1% 1% 19% ‐15% 6% ‐8% 4% 35% 100%
DLOG_X10 ‐4% ‐1% ‐3% ‐10% ‐10% ‐16% 10% 4% 19% 10% 100%
DLOG_X11 ‐9% 10% ‐15% ‐15% ‐14% ‐3% 89% ‐11% ‐5% ‐13% 9% 100%
DLOG_X12 ‐8% 11% ‐11% 0% 23% 4% ‐3% ‐3% ‐10% ‐17% ‐11% 2% 100%
DLOG_X13 6% 9% ‐13% 1% 32% ‐5% ‐3% 15% 3% ‐9% ‐11% ‐5% 9% 100%
DLOG_X14 5% ‐2% 6% ‐15% 9% ‐21% 11% 25% ‐22% ‐3% 1% 9% 11% 17% 100%
LOG_X15 ‐3% 2% 9% ‐9% 8% 15% ‐4% 14% 0% ‐11% ‐19% ‐4% 6% 15% 16% 100%
DLOG_X16 11% 13% 2% 0% 13% ‐13% 24% 28% 5% ‐9% ‐3% 27% 10% 21% 23% 24% 100%
DLOG_X17 26% 1% ‐4% 8% ‐2% ‐13% ‐3% 7% 16% 6% 29% ‐3% ‐11% ‐4% ‐1% ‐1% ‐6% 100%
DLOG_X18 21% ‐4% 14% 12% 2% ‐25% ‐5% 6% 38% 40% 6% ‐21% ‐17% 1% 3% 5% 8% 33% 100%
LOG_X19 2% ‐10% ‐20% 8% 27% ‐5% ‐18% 64% ‐6% 7% ‐27% ‐20% 13% 4% 16% 4% ‐7% ‐16% 11% 100%
DLOG_X20 6% ‐1% 6% ‐19% ‐9% 9% 3% ‐15% 2% 4% 2% 4% ‐5% ‐9% ‐5% 2% ‐7% 16% 6% ‐28% 100%
DLOG_X21 ‐1% 10% ‐6% ‐2% ‐14% 16% ‐10% ‐16% ‐11% ‐8% ‐5% ‐1% 2% ‐8% ‐17% 4% 12% 1% 0% ‐22% ‐12% 100%
LOG_X22 4% ‐11% ‐6% ‐18% ‐40% ‐4% ‐7% 3% 4% 10% 18% ‐14% ‐16% ‐16% 5% ‐20% ‐33% 9% 2% ‐12% ‐2% ‐1% 100%
DLOG_X23 4% ‐3% ‐11% 2% ‐17% 10% 3% ‐8% ‐5% 0% 2% 5% 1% 2% ‐5% ‐3% 4% 14% ‐1% ‐4% ‐1% 18% ‐1% 100%
The results of Table 4 shows that heavy sulphur and Marine Gas Oil (MGO) are highly
correlated with 0.8 per cent limits hence MGO Singapore was removed.
4.3 T-Test and Coefficient Restriction Test (F-Test)
T-test is conducted using the formula:
DLOG_YT C DLOG_X1 DLOG_X2 DLOG_X3 DLOG_X4 DLOG_X5 DLOG_X6
LOG_X7 DLOG_X8 DLOG_X9 DLOG_X10 DLOG_X12 DLOG_X13 DLOG_X14
LOG_X15 DLOG_X16 DLOG_X17 DLOG_X18 LOG_X19 DLOG_X20
DLOG_X21 LOG_X22 DLOG_X23
29
The output result indicates the following coefficients:
DLOG_YT = -0.131449119035 – 0.0753013405885*DLOG_X1 +
0.0900477091006*DLOG_X2 – 0.143921996459*DLOG_X3 –
5.43280954358*DLOG_X4 + 0.817251248484*DLOG_X5 +
0.580932328055*DLOG_X6 + 0.00593021030334*LOG_X7 –
0.17550054479*DLOG_X8 + 0.151609426415*DLOG_X9 –
0.0408319822687*DLOG_X10 – 0.756525102216*DLOG_X11 –
0.0594374422933*DLOG_X12 + 0.210610986644*DLOG_X13 –
0.0375472044515*DLOG_X14 – 0.0147352134307*LOG_X15 +
9.56376255887*DLOG_X16 + 0.165683191242*DLOG_X17 +
0.0657937473293*DLOG_X18 + 0.0235855815211*LOG_X19 +
0.0102228730898*DLOG_X20 – 0.0337030986536*DLOG_X21 +
0.000365298837062*LOG_X22 – 0.0884499522199*DLOG_X23
The result shows that Bunker Cost, Second-hand Price, Container Average Age and
Shanghai to Europe Freight rates are significant and Wald Test-Coefficient restrictions
confirmed the removal of the variables:
c(2)=0, c(3)=0, c(4)=0, c(5)=0, c(7)=0, c(8)=0, c(9)=0, c(10)=0, c(11)=0, c(12)=0,
c(14)=0, c(15)=0, c(18)=0, c(19)=0, c(20)=0, c(21)=0, c(22)=0, c(23)=0.
The results indicate significant variables at 5 per cent critical values C(8), C(11),
C(18), C(23) and the variables were retain.
4.4 Cointegration Test
Following the result of the Unit root test, it reveals that the model has a combination
of both 1(0) and 1(1) process. This indicates the need to filter out the unit root in all
the series by testing a linear combination of all 1(1) process. Error correction terms for
all the significant variables of order one 1(1) first difference are tested (see Table 5).
Table 5 Cointegration Test result
30
DLOG_YT = C(1) + C(2)*DLOG_X16
c 0.1055
0.7771
No cointegration No cointegration No cointegration No cointegration
DLOG_YT = C(1) + C(2)*DLOG_X6 DLOG_YT = C(1) + C(2)*DLOG_X13 DLOG_YT = C(1) + C(2)*DLOG_X17
0.8223
0.4226
0.3003
0.0794
4.5958
0.1937 ߩ
The results of table 4 indicate that long-run equilibrium relation does not exist in the
series at the critical value >5% but the study considered 10% critical accept that normal
long-run relationship exists between dlog-Yt (Container Freight rates CSNSH-LOS
1(1) and dlog_x13. The model was re-estimated using this equation
DLOG_YT = C(1) + C(2)*DLOG_X13 + C(3)*DLOG_X16 + C(4)*DLOG_X17 +
C(5)*DLOG_X6 + C(6)*RESID01(-1).
The result indicates that the resid01(-1) is not significant and hence removed from the
estimation. Johansen (1991) cointegration test finds there is no cointegration among
them all the variables in the model except second-hand price, and hence rejected the
existence of cointegration. The second-hand price error correction term is inserted into
the model as a discounted series (-1), and the result indicates that the resid01 critical
value is >5% (β 0.122865; ρ 0.1729ሻ thus removed from the model.
4.5 Normality Test Results (Jarque-Bera)
The Jarque–Bera test confirms these results and accepts the normality hypothesis. The
JB is > 5% meets the requirement of the normality test and the kurtosis and skewness
both meet the requirement (see Figure 6).
0
4
8
12
16
20
-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15
Series: Residuals
Sample 2010M01 2019M05
Observations 113
Mean 8.60e-19
Median 2.78e-17
Maximum 0.140225
Minimum -0.141548
Std. Dev. 0.047743
Skewness -0.101097
Kurtosis 3.679896
Jarque-Bera 2.368957
Probability 0.305906
31
Figure 6 Normality Test Result
The normality test satisfies the assumptions that the variance of the residuals is
constant and finite and the linear independence of errors (Brooks, 2014).
Consequently, the models have no serial correlation and Homoscedasticity.
4.6 Model Results
The ARMA model (3, 0, 3) was formulated after all diagnostic test were conducted to
enable the researcher to use the model to make inferences (see Table 6).
Table 6 Main Regression Results
Type T-TEST ARMA BGSC-LM
Test
HT-BPG Ramsey
RESET
18.93234 14.85184 1.558847
0.16761
0.67212
0.21593
DLOG_X5 0.724213 0.384373
0.0072 0.1147
DLOG_X6 0.741906 0.935378
0.0035 0.0006
DLOG_X8 -0.17261 -0.21737
0.0009 0
DLOG_X9 0.197298 0.21953
0.0059 0.0014
DLOG_X11 0.95109
0.0014
-1.12173
0.0008
DLOG_X16 8.565502
0.0086
7.478541
0
DLOG_X17 0.153599
0.0001
0.181395
0
The results of the model in Table 6 indicate an adjusted R²of 73.7%, no serial
correlation and homoskedacity. The errors are normality distributed as shown in figure
9 and in all cases; the null hypotheses were rejected with ƿ value of less than 5% critical
value. Furthermore, the Jarque-Bera normality test (see Figure 6) and Ramsey Reset

1 ߩ probability of ˂5%. The independent variable ᆸ is significantly influencing Y dependent variable. 2
Greater than > 5% critical acceptance level of null hypothesis H that residuals of the errors are normally distributed estimated
in this equation: ߤܰ~ ሺ0, ߪଶሻ 3
Greater than > 5% to accept the null hypothesis H that there is no serial correlation, estimated Cov (ߤᵢ, ߤᵢሻ ൌ 0 at Lag 14
32
confirmed that the model is valid for the forecast and other statistical analysis (see
Figure 7).
-.8
-.6
-.4
-.2
.0
.2
.4
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
STATIC DYNAMIC DLog_Yt
Figure 7 Eviews Forecasting
In order to establish the accuracy of the model, Figure 6. Shows that static forecast is
more accurate for high-performance accuracy. The blue line represents the Statis
forecast in the figure indicated.
33
5.0 Discussion/ Study Application
5.1 Freight Rates
The container freight market has improved from weak earnings, reflecting a
challenging market environment in 2016 (UNCTAD, 2018). The global seaborne
container trade recorded 2.5% growth in 2019 and 2020 is projected to grow by 3.4%
in volume terms (TEUs) while fleet capacity grew by 2.3% and is expected to increase
to 3.1% by the end of 2019. As the IMO 2020 Sulphur regulation is set to commence
by 1 January 2020, this growth in container trade is expected to experience some
challenges. This regulation is in addition to the complex operating conditions of liner
container operators, struggling with delivery of mega container ships and
consolidation. Shanghai to Lagos freight rates have remained active and growing
steadily over the years as shown in Figure 8.
Figure 8 Container Freight Rates (Source: adapted from Clarksons)
As shown in Table 3.1, freight rates went up, although they remained volatile,
indicating positive market trends within the trade lane. The average Shanghai to Lagos
spot freight rates is $1,867.69 representing an increase of 6 per cent compared to -18.5
per cent in 2016. This shows that the freight rates are relatively good to carriers and
strained cargo owners. With the IMO 0.5 sulphur regulation taking effect on 1 January
0.00
500.00
1,000.00
1,500.00
2,000.00
2,500.00
3,000.00
Dec-2009
Ma
y-2010
Oct-2010
Mar-2011
Au
g-2011
Jan-2012
Jun-2012
Nov-2012
Apr-2013
Sep-2013
Feb-2014
Jul-2014
Dec-2014
Ma
y-2015
Oct-2015
Mar-2016
Au
g-2016
Jan-2017
Jun-2017
Nov-2017
Apr-2018
Sep-2018
Feb-2019
Jul-2019
Freight Rate $/TEU
34
2020, the industry is facing daunting challenges to address the lingering nontransparent bunker adjustment factor or surcharge. Therefore, the effects of these
changes to the cargo owners may be overwhelming. Looking at the impact of bunker
fuel cost on freight rates will provide some indication of the potential implications of
the new IMO 0.5 sulphur regulation to consumers. There is a relationship between
bunker costs and container freight rates Shanghai to Lagos (see Figure 8)
Figure 9 Freight Rates and Bunker cost (Source: adapted from Clarksons)
Figure 9 shows that 380cst bunker prices in Shanghai are closely related to container
freight rates Shanghai-Lagos. Therefore, an increase in bunker cost would lead to an
increase in container freight rates. Vivid Economics (2010) estimated an average
elasticity of 0.11 for container ships because of environmental compliance. However,
the IMO 0.5 sulphur limit is to be route-specific, influenced by other factors that
determine shipping rates and transport costs. These include distance, trade imbalances,
fuel prices and consumption, ship size, asset utilisation factor and port characteristics.
Therefore, while it is necessary to reduce greenhouse gas emissions in maritime
transport, it is equally important to appreciate the concern for vulnerable states such
as Nigeria that faces acute logistical challenges and high transport costs limiting the
growth of seaborne trade.
0.00
1,000.00
2,000.00
3,000.00
4,000.00
5,000.00
Dec-2009
May-2010
Oct-2010
Mar-2011
Aug-2011
Jan-2012
Jun-2012
Nov-2012
Apr-2013
Sep-2013
Feb-2014
Jul-2014
Dec-2014
May-2015
Oct-2015
Mar-2016
Aug-2016
Jan-2017
Jun-2017
Nov-2017
Apr-2018
Sep-2018
Feb-2019
Jul-2019
380cst Bunker Prices, Shanghai $/Tonne
SCFI Shanghai-W Africa (Lagos) Freight Rate $/TEU
380cst Bunker Prices, Singapore $/Tonne
380cst Bunker Prices, Rotterdam $/Tonne
35
5.1 Nigerian Maritime Industry
Nigeria being a lower-middle-income state with a population of 191 million people
and a GDP of 397 billion current USD is Africa’s biggest economy (UNCTAD, 2017).
The country is among the wealthy nations of the world due to the endowment of long
stretch of maritime domain of about 853km coastline, 37,934km.sq continental shelf
surface and an Exclusive Economic Zone (EEZ) covering 210,900km.sq. The Nigerian
seaports border the Atlantic Ocean in the Gulf of Guinea, with Lagos Port complex
being the most significant and oldest West African seaport. The volume of cargo
received at the nation’s seaport consists of 92% liquid bulk (Crude oil & gas) and 8%
others with containerized cargo valued at $ 25, 402 million USD including cars and
motor parts in 2016 (United Nations, 2016). This shows the importance of the maritime
sector to the Nigerian economy and the critical role of the industry towards the
reduction of spatial inequality and poverty eradication.
5.2 Lagos Port Complex
The Lagos Port complex is the oldest and largest premier port in Nigeria. It is located
in Lagos State, the nation’s commercial hub and megacity (see Figure 10). The port
complex began in the 19th century with four deep-water berths of 1,800ft in length and
later extended to 2,500 lengths of berth in 1948. Oil and gas account for 92% of the
nation’s export as of 2017 (UNCTADstat, 2019). This clearly shows the port plays
critical role in the Nigerian economy and contribute significantly to the social
development being the cheapest mode of transporting large bulk of cargoes from one
point to the other.
36
Figure 10 Lagos Port Complex (Source: adapted from NPA, 2019)
The Nigerian Ports Authority (NPA) manages the nation’s seaports, established by
NPA Act CAP N126 LFN, 2004 (formerly Port Act, CAP 155 laws of the Federation
of Nigeria), with the sole responsibility to control and manage the seaports.
In the quest to make the Nigerian economy competitive, the federal government of
Nigeria undertook port reform programmes and adopted the landlord port model. The
seaports were concessioned to 26 international and local private terminal operators for
a lifespan of 10 to 25 years lease agreement (see Table 7).
Table 7 List of concessionaires in Nigeria
Terminal operator Terminal Tenor Effective date
1. Apapa Bulk Terminal Limited Apapa Terminal A 25 3 April 2006
2. Apapa Bulk Terminal Limited Apapa Terminal B 25 3 April 2006
3. ENL Consortium Apapa Terminal C 10 3 April 2006
4. ENL Consortium Apapa Terminal D 10 3 April 2006
5. Greenview Dev. Nig. Ltd. Apapa Terminal E 25 3 April 2006
6. APM Terminals Limited Apapa Container Terminal 25 3 April 2006
7. Lilypond Container Depot Nigeria Ltd. Ijora Container Depot 10 3 April 2006
8. Josepdam Ports Services Limited TCIP Terminal A 10 10 May 2006
9. Tin Can Island Container Limited TCIP Terminal B 15 10 May 2006
10. Ports & Cargo Handling Services Ltd. TCIP Terminal C 10 10 May 2006
37
11. Five Star Logistics Limited TCIP RORO Terminal 15 10 May 2006
12. Port & Terminal Multiservices Limited TCIP Terminal E 25 18 August 2009
13. Ports & Terminal Operators Nig. Ltd. Port Harcourt Terminal A 15 23 June 2006
14. BUA Ports & Terminals Limited Port Harcourt Terminal B 25 23 June 2006
15. Intels Nigeria Limited Onne FOT A 25 21 June 2006
16. Brawal Oil Services Ltd. Onne FLT A 25 21 June 2006
17. Intels Nigeria Ltd. Onne FLT B 25 21 June 2006
18. Atlas Cement Co. Limited Jetty FOT Onne 25 21 June 2006
19. Intels Nigeria Limited Calabar New Terminal A 25 23 June 2006
20. Ecomarine Nig. Limited Calabar New Terminal B 10 1 August 2007
21. Addax Logistics Nigeria Limited Calabar Terminal C (Old Port) 25 26 May 2007
22. Intels Nigeria Limited Warri Old Port Terminal A 25 23 June 2006
23. Associated Maritime Services Limited Warri Old Port Terminal B 10 12 June 2007
24. Intels Nigeria Limited Warri New Port Terminal B 25 23 June 2006
25. Julius Berger Plc Warri New Port Terminal C 25 4 May 2007
26. Greenleigh Limited Koko Terminal 10 12 June 2007
(Source: Author)
The concessionaire led to a massive injection of capital to upgrade of the Lagos Port
to handled 3.9 million TEUs of containers, and there are 27 berths with 11 to13.5
meters depth. The average turnaround time of vessels is within 5.13 days and 55.76%
berth occupancy rate. Although the concessions programme is successful, balances
remain with higher charges by the terminal operators, which cannot be separated with
a freight rate. Most of the container terminal operators are the carriers like AP MollerMaersk, Denmark, and the world’s largest liner container carrier. The research
collected responses from the liner carriers operating in Nigeria to analyse the economic
impact of IMO 0.5% sulphur limits on carriers and shippers.
5.3 Cargo Throughput
The volume of cargo handled at the Nigerian ports reached about 70 in thousands of
deadweight tonnes (DWT) between 2007 to 2017 (see Figure 10). The cargo
throughput comprises liquid bulk, general cargo, and dry bulk and containerised
cargoes. The figures illustrate consistent growth in all sectors from the six seaports in
the country. Tincan Island Port and Lagos Port (TCIP) complex account for over 48%
38
of the total cargo throughput handled within a decade. Over the years, the total cargo
throughput growth is attributed to the strong economic growth, consumer appetite for
import and improved infrastructure (PWC, 2016).

Figure 11 Cargo Throughput (Source: Author)
Furthermore, liquid bulk accounts for 42 million tons, the highest share of the nation’s
seaborne trade followed by general cargo at 18 million tons while dry bulk accounted
for 10 million tonnes in 2017. Containerized cargo accounts for the smallest share but
has grown substantially to a peak of 1.6 million TEUs in 2017, and projected to
increase by an average of 6.1% annually (UNCTAD, 2019). Figure 12. Illustrates
containerised cargoes measured in a twenty-foot equivalent unit from the period of
2003 to 2017.
0
20000000
40000000
60000000
80000000
100000000
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
CARGO THROUGHPUT FROM 2007-2017 (TONNES)
Lagos Port Complex Tincan Island Port Rivers Port
Onne Delta Calabar
Total Cargo Throughput
39

Figure 12 Container Port Traffic (Source: Author)
From 2005 to 2006, the Federal Government embarked on the concessions programme
when container traffic was unavailable within that period and which spilt over to 2007.
The linear trend line for containerised cargo indicates a positive gradient, representing
a reasonable estimation for continued growth. However, some issues remain with
increase in freight rates, higher administrative cost and poor customs practices. To
balance against complying with IMO 0.5% sulphur limits and maintain profitability
without adding untold hardship to the shippers, the researcher analysed the level of
preparedness of the liners to forecast future freight rates for imports from Shanghai to
Lagos.
5.4 Sulphur Regulation-Nigeria
Nigeria being IMO contracting party to the marine pollution conventions (SOLAS
1974 and MARPOL 73/78 as amended) is expected to set the pace for industry
compliance and provide practical guidance’s through her nodal agency, the Nigerian
Maritime Administration and Safety Agency (NIMASA). NIMASA is empowered by
its enabling acts, the Merchant Shipping Act 2007 and the NIMASA Act 2007, to
ensure implementation and enforcement of (IMO) Instruments adopted and ratified by
Nigeria. Thus, the IMO 0.5 sulphur limit is expected to be enforced globally. The
decision is timely to demonstrate the clear commitment by the IMO to reduce air
emissions from shipping in actualising the United Nations’ environmental obligations.
588478512610
7250087000
1232000
1510900
1723000
1580000
1700000
1400000 1437000
1656000
0
500000
1000000
1500000
2000000
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Container port traffic (TEU: 20 foot equivalent
units)
40
Under the new global sulphur limit, ships have to use fuel oil with a sulphur content
of no more than 0.50% against the current limit of 3.50% that has been in effect since
1st January 2012. Exemptions are provided for situations involving the safety of the
ship or saving life at sea or if a ship or its equipment is damaged.
Consequently, NIMASA is expected to commenced preparation with bunker suppliers
to ensure availability of compliant fuel for ships calling at its ports ahead of 1 January
2020. In addition, issue guidelines on how enforcement to the IMO sulphur regulation
would be conducted and survey emission zones within the coastal areas to prevent
discharge of washwaters. Ships need to use fuel oil that is inherently low enough in
sulphur to meet 0.5 sulphur limit requirements, install exhaust gas cleaning systems
(Scrubbers), or switch to engines that can use different fuels such as liquefied natural
gas, methanol, liquefied biogas and other fuels that contain low or zero sulphur. These
fuel options are acceptable by flag States as alternative means to meet the sulphur limit
requirement.
5.5 Questionnaire Result
Questionnaires were administered to liner shipping companies operating in Nigeria to
obtain practical information that can support the modelled quantitative data. Besides,
the results of this questionnaire can provide an overview of the actual state that exists
in the industry from the perspective of the operators. The study focused on economic
implications of the IMO 0.5 sulphur limit on liner shipping companies and shippers in
Nigeria. Eleven (11) questionnaires were distributed between July 22nd to August 16th
2019, and 3 responses to the surveys were received. Most respondents are chief
operating officers (CEOs) and some senior shipping executives with a minimum of 15
years’ experience in the shipping industry, indicating a higher level of reliability of the
investigation results. The study alluded that the lower responses were due to the
confidentiality that characterised the liner shipping, as shippers remain concerned by
lack of standardisation of fuel surcharge methodology, which could lead to fuel
surcharges, a competitive factor for liner shipping freight (IHSmarkit, 2019).
41
The results of Q1 indicates that Nigerian shipping companies are most concerned with
the scrubber retrofitting, and hence 67% preferred to use LSFO, followed by scrubber
technology with 33%. The results illustrate that the majority of the Nigerian shipping
companies view the use of LSFO in the short-term as the most advantageous.
The compliance pattern is consistent with the strategy adopted by the biggest liners,
such as 2M alliance Maersk and MSC, the world’s biggest liner container operators
with over 30% market share, will be using low sulphur fuels for most of their fleets.
Drewry (2018) conducted a survey that indicates 66% of the shipowner are willing to
use low sulphur fuels. However, the low sulphur fuel oil (LSFO) is expected to
increase significantly compared to a reduction of heavy sulphur fuel oil.
The LSFO option provides both old and new vessels a compliant fuel to meet the IMO
2020 sulphur regulation without any investment cost or evaluating shipyard capacity.
The expected fuel cost increase can be weight against scrubber installation and fuel
cost differential. It is worthy to note that an estimated additional cost of $50-$250 $/ton
is projected for low sulphur fuel oil by IMO 2020. Moreover, Clarksons (2019)
estimates the total scrubber count over ~4,000 vessels representing 11% of the world
fleet by tonnage capacity scrubber fitted by 2020, which shows scrubber is feasible to
meet with IMO sulphur regulation and before the end of the year an increase of 15%
will be achieved. This indicates that several other operators are complying with the
exhaust gas cleaning system (EGCS), an option against the projected 40%-60% LSFO.
42
Furthermore, non-compliance could lead to additional cost due to non-availability of
HFO or lack of effective monitoring. Overall, the cost would be huge if considered on
the total annual bunker cost; the risk of quality of the LSFO and engine failures made
scrubbers a competitive option. Consequently, the liner shipping companies in
Nigerian have made strategic decisions in acknowledging their vessel conditions, age,
trade routes and location, but in the mid to long-term, they are insufficient towards
cleaner shipping and healthy climate in line with IMO GHG ambitions.
The survey result of Q2 shows that the IMO sulphur limit would have a significant
impact on companies’ finances and operations. As mentioned briefly earlier in Q1 on
cost, Nigerian shipping companies expect a challenging business operating
environment, constrained by limited financing options for sulphur regulatory
compliance. The result of the survey is in line with the position of the top 10 liner
companies, estimating an additional cost of $1-2 billion annually. There is no iota of
doubt that costs are the crucial issues of concern to shipping companies, and in
appreciating their plight the government can render Helpance by providing subsidies
to reduce their costs and risks. In addition, Nigerian shipping companies need to adopt
new cost-cutting measures to stay competitive and improve profitability.
43
The majority of the respondents indicate their readiness to comply with IMO 2020
sulphur regulation. Consequently, the federal government of Nigeria through its nodal
agency, NIMASA needs to intensify efforts to address any unforeseen transitional
issues leading to smooth implementation of the IMO sulphur regulation in Nigeria.
However, besides the transitional issues, several global best practices had been
undertaken by Australia, Singapore, Norway and Denmark in this regard.
The Australian Maritime Safety Authority has held six roundtable discussions in
Adelaide, Canberra, Gore Bay and Sydney in 2019 to address issues and discuss
solutions with the industry in Australia (AMSA, 2019). Similarly, issues on compliant
options compatibility, crew training and familiarisations were laid to rest, while
AMSA is working on publishing the list of bunker suppliers and identify ports that
provide compliant fuels. In addition, on 1 February 2019, an advisory letter was sent
to ships detailing how the compliance checklist would be conducted (AMSA, 2019).
This put all stakeholders on their toes to mitigates financial and market conditions
ahead of IMO 2020 sulphur regulation compliance. However, other table issues
concerning the exhaust gas cleaning system (EGCS) discharge and Liquefied Natural
Gas (LNG) bunkering are still matters begging to be addressed by AMSA, State and
National territory (NT) authorities and the Great Barrier Reef marine park.
The Maritime Port Authority of Singapore in collaboration with Singapore Shipping
Association (SSA) issued technical guidelines on the 0.5% sulphur compliance
44
regulation, which contained Singapore’s implementation plan, port state control,
handling of non-compliant fuel and sample collections as well as enforcement (MPA,
2019). Likewise, licensed bunker dissertation writing Helpance suppliers were published on the MPA websites, and
industry engagement has intensified ahead of the date of effect on1 January 2020.
Conversely, the Norwegian Maritime Authority (NMA), the Ministry of Climate, and
the Environment (MCE) jointly surveyed to map out discharge and emission areas in
the Norwegian fjords for all types of ships (NMA, 2019). In Denmark, authorities
deployed sulphur detection drive to monitor compliance with the IMO 0.5% sulphur
regulations (DMA, 2019). This illustrates various coordinated efforts by IMO member
states towards achieving global compliance as failure to address the transitional issues
could complicate the compliance results in non-compliance.
The result of Q4 shows purposeful and focused direction for the shipping industry
toward green shipping in consonant with IMO’s ambition to achieve full
decarbonisation of international shipping. The options highlighted in Q1 are
insufficient to meet carbon nutrient, but liquefied biogas and methanol are future fuel
technology that if harnessed commercially would set in the industry in a new trajectory
as evidently made with ferries operating with methanol in Europe (Notteboom, 2011).
Therefore, future fuels such as LBG and methanol will Help ship owners to avoid
carbon tax offering both social and financial benefits. Thus, LBG and methanol offer
45
potential environmental and social benefits towards zero emissions and complying
with UN sustainable goals (Brynolf, Fridell, & Andersson, 2014; Srivastava, Ölçer,
& Ballini, 2018).
Q6 illustrates the challenges that shipowners would likely face when the regulation
takes effect. Higher bunker cost can be mitigated using bunker cost and vessel speed
reductions. According to a study by Yao, Ng and Lee (2012), liner-shipping companies
need to evaluate their optimal bunker fuel management strategy, which includes
optimal bunkering ports, bunkering amounts and ship speeds at each leg of the service
route, to minimise the total bunker fuel cost.
46
The results indicate that the payback period for scrubber retrofitting by most Nigerian
liner shipping companies is between the ranges from three to four years. Considering
the average age of the ships, operating within the trade lane is 10 years from a lifespan
of 27-30 years, the investment is profitable considering all other issues factored.
Q8 reveals the uncertainty of the freight rates, but shippers are certainly expecting
freight rates increase, yet how much increase expected is yet to be known.
The findings of Q9 provide the basis to establish a base scenario. The liner shippers
indicate a range from 10% to 30% and thus align with the result of the model
confirming the existence of a relationship between bunker cost and container freight
rate between Shanghai to Lagos.
47
The result of the survey by the Nigerian liner shipping companies shows that bunker
cost is the principal factor affecting container freight rates. The bunker cost represents
60% of the total voyage cost for an intermediate container ship (Stopford, 2009).
The result of Q11 supports the position that Nigerian shipping companies viewed that
IMO regulation is in place with few areas to be addressed by the authority, particularly
on enforcement by port and flag state officers and issuance of relevant certificates.
IMO has approved and adopted a comprehensive set of guidelines to support member
states towards consistent implementation of the 2020 sulphur regulation as contained
in MEPC.1/Circ.878 (9 November 2018). Subsequently, IMO approved additional
48
resolutions on covering the carriage ban, reporting on fuel oil quality and availability,
fuel non-availability, exhaust gas cleaning system, monitoring and programmes.
Therefore, the Nigerian Maritime Administration and Safety Agency (NIMASA)
needs to work with the shipping companies to ensure that relevant shipowners fulfil
all the requirement of the new regulation, with respect to the Ship Implementation Plan
(SIP), including risk assessment mitigation, fuel oil system modification, tank
cleaning, fuel oil capacity, and segregation facility. Overall, the responses of the
shipping companies indicate proper government guidance and consistent tools for
documentation and monitoring desirably provide that successful implementation.
The majority of the Nigerian shipping companies think that unexpected noncompliance attitude should be avoided to eliminate any attitude that affects industry
competition. This tendency has been a prevalent practice by shipping companies in the
past, which are relatively unprepared to respond to the sulphur regulation. However,
most shipping companies have decided to prepare ahead of the implementation date.
Therefore, the government must do everything within its power to ensure that the
industry achieves full and successful compliance with the regulation.
5.6 Scenario Analysis
This section discusses the expected economic impacts of 0.5% sulphur limit regulation
on container freight rates and future fuel prices. Indeed, there are plethora of issues to
resolve for choosing alternatives to comply with IMO 2020 sulphur regulation.
49
Therefore, the study made some assumptions to show the application of the developed
model in line with survey results.
 4738 TEU fully cellular container ship, diesel two-stroke, eco-ME engine by
Wartsila at a speed of 18 knots bunker 82 tonnes per day, Heavy fuel oil (IFO
380), horsepower 29,789 kW (Clarksons, 2019).
 The equivalent fuel HFO to MGO and MeOH estimated using the formula
୩୎/ୱሿୀሾ୩୛
௟௩.஗
kW = useful results Ɩv: Lower calorific value and η efficiency.
 Distance: 10254 nm at 18 speed from Shanghai, China to Apapa, Nigeria, 23
days (Sea-distances.org, 2019)
 Time series forecast of future freight rates and fuel prices for 67 months from
June 2019 to June 2024 is used for the scenario (Figure 16 & 17).
 Capex: 3.2 million and 14.3 million for Scrubber and Methanol-Stena
Germenica is used for the analysis (Andersson & Salazar, 2016).
 CO emission: 3.114 (HFO), 3.154(MGO), 3.204(MeOh) (IMO, 2014).
 Fuel cost:
In lights of the above assumptions, HFO is expected to decline by a negative 30 per
cent, and LSFO is expected to increase by 65 per cent. The alternatives fuels become
more attractive in the future due to uncertainties and complexities of the distillates and
residual oils. Shipowners/carries will incur substantial financial cost to comply with
IMO sulphur regulations, and regional differentiation will likely create more volatility
and challenges.
Consequently, TOPSIS method is used to help shipowners choose the best alternative
from decision-maker point of view to balance the interest of the parties involved in the
business, i.e. shipowner/operator, cargo owner and port managers.
50
-100 0 100 200 300 400 500 600 700 800
Historical price (Clarksons)
Predicted Price (Crystal Ball)
Difference
Future Fuels
MeOh MGO/LFO HFO+EGC
Figure 13: Future fuel costs & Freight rates
As shown in figure 16, MGO prices increase by 13.43%, HFO decreased by 10% while
MeOH peak by 8.96%. The predictions are based on the mean of the time series from
2009 to 2019 and as confirmed in the developed model. There is a positive relationship
between bunker costs and freight rates provided other explanatory variables remain
constant. Simple regression is estimated with each fuel time to see the exact associated
relationship, and it shows that HFO influences freight rates by 58% while LSFO and
Methanol has a negative relationship. However, because HFO and LSFO are highly
co-corrected it can be concluded that bunker cost increase may likely increase freight
rates by 58%.
However, the relationship between bunker prices and freight rates in liner shipping is
almost settled with the introduction of Bunker Adjustment Factor (BAF) in 1970
following oil crisis (Cariou P., & Wolf F., 2006). The underlying justification for the
BAF by liner was to serve as a bumper for the devastating effect of bunker prices
fluctuation. Notteboom (2010) noted that an increase in bunker cost in container
shipping might not lead to increase in freight rates in the short term as the BAF
compensated via surcharges paid to the carriers.
51
5.6.1 TOPSIS Method
As illustrated in the introduction section, selecting the best alternative amongst other
options is a great challenge for shipowners and decision-makers ahead of 1 January
2020. These alternatives are weight against competing attributes and in line with the
scope of this research the attributes emphasis the economic: fuel cost, freight rates,
cost of investment (CAPEX) and environment: consumption, CO (see appendix-5
TOPSIS Analysis). Hwang & Yoon (1981) developed the TOPSIS method based on
the intuitive principle that the chosen alternative should have shortest distance from
the positive ideal solution and the longest distance from the negative ideal solution.
The computational procedure of the TOP method involves two steps.
a) Normalised Rating: to transform various attribute dimensions into the nondimensional attributes, which allows comparison across the attributes.
b) Weighted Normalised Ratings: this is calculated by multiplying each row of
the normalised matrix with its associates’ weight w୨ (See appendix-5). Where
w୨ is the weight of the attribute?
c) Identify positive-ideal (A*) and negative-ideal (A⁻ ) solutions. These solutions
are defined in terms of weighted normalised values.
d) Calculate separation measures. The attributes are measured by the ndimensional Euclidean distance.
e) Calculate similarities to the positive-ideal solution. Relative closeness (or
Similarity – C୨*) of A୨ to A* is calculated.
f) Rank preference order. Choose an alternative with the maximum Ϲᵢ and the
alternative Aᵢ is closer to A* than A* as Ϲᵢ approaches 1.
There are three alternatives and five attributes, and the attributes include A : CO ,
A : Freight Rates, A : Fuel consumption A : CAPEX and A : Bunker Cost. The
researcher assumes the position of the policymaker and assigns associated weight
based on degree of importance (w , w , w , w , w ) = (0.2, 0.3, 0.1, 0.2, and 0.2).
Appendix-5 shows the WNR of each attribute in respect to the three alternatives.
52
Overall, figure 14. Shows the ranked alternatives following the preference order,
MGO/LSFO is the best alternative solution and second alternatives HFO/Scrubber is
the second-best solution illustrated in Figure 14 and 15. 0.472419956 0.844528613
0.456431906
0.266395149
0.47622528
0.257379571
2
1
3
HFO+EGC MGO/LFO MEOH
TOPSIS RESULT
Dj Normalized Dj Rank
Figure 14 TOPSIS RESULTS
The results show that MGO gained higher values desirables as the ideal solution 0.84
and normalised at 0.47
Figure 15 Crystal ball simulation
The overlay chart clearly shows the 3-D simulation of the three alternatives, MGO and
HFO are desirable to comply with the attributes above.
53
5.6.2 Sensitivity Analysis
Figure 16. Shows the sensitivity analysis conducted and it indicates that Capex is the
primary contending reasons influencing the selection.
Figure 16. Sensitivity analysis
From the sensitivity results in figure 15. Depicted CAPEX is critical for compliance.
Moreover, MGO prices are expected to soar by 65% yet considering it does not require
any investment but some technical and operating issues main unresolved (i.e. fuel
quality, availability, engine failures) and if adequate care is not provided it would
render the choice of LSFO uncompetitive as against HFO/Scrubber, which requires
huge investment in addition to operational cost. HFO prices will fall sharply, and it is
widely available, but post-2020 implementation may likely affect HFO availability in
some port across the globe, and local trading restrictions are increasing for open-loop
scrubbers. Assuming all these constraints are addressed in addition to off-hire period
and space compromised, HFO+Scrubber offer economic advantage in short to medium
terms. The sensitivity confirmed that HFO/scrubber retrofitting is competing option to
meet with IMO 2020 sulphur regulation.
Moreover, the result of the regression indicates Durban freight rates; Orderbook and
container sales have positive relationship with Shanghai to Lagos Freight rates. This
indicates areas where future study can be carried out to investigate extend of this
54
relationship and how it interplay with port competitiveness. A striking finding is the
relationship of the freight rates with second market and Orderbook. The more subSaharan Africa improve port operations, turnaround time and logistics efficiency it
will attract bigger ships which ultimately reduces transport costs and lower freight
rates.
Ultimately, the regression confirmed the acceptance of the hypothesis that there is a
positive relationship between freight rates and bunker cost but the finding recognises
the ambiguity of bunker surcharge introduces by the liners. Therefore conclude that
freight rates increases would depends on the level of transparency and accountability
of the liner companies with respect to their investment and future fuel costs.
55
6.0 Conclusions and Recommendations
The objective of this research was to examine the economic impact of the IMO sulphur
regulation on container freight rates and future fuel cost. As mentioned earlier, there
has been increasing concern regarding air emissions, resulting combustion of residual
fuel oils used by ships and harmful effects of sulphur oxides emitted by ships, which
worsen public health and the environment. The sulphur emissions are a clear result of
premature deaths from lung cancer and cardiovascular diseases, and asthmatic
conditions among children as well as causing acid rain and increased carbon dioxide
(CO ) levels and other forms of air pollution.
Therefore, since CO levels and GHG has increased substantially, resulting damaging
effects to the coastal communities. It has become necessary for the International
Maritime Organisation to drastically lower the sulphur content in marine fuels from
existing level, to improve air quality and human health.
Thus, in order to answer the three research questions, a mixed-method approach was
adopted. The first question on finding the relationship between container freight rates
and bunker cost was answered using econometrics technics while survey questionnaire
answered the second question on the economic impact of IMO sulphur regulation on
liner shipping companies in Nigeria. The final question was answered using TOPSIS
and sensitivity technics to evaluating a scenario to determine the responses of liner
shipping companies on their preferred compliance options.
The modelled variables confirmed existence of positive relationship between bunker
cost and freight rates by a coefficient of 0.589152 and a probability of 0.0015. Hence,
increase in bunker cost would have a significant influence on container freight rates
provided other explanatory variables remain constant. This was confirmed by the
findings of the survey where shipowners, operators indicates that freight rates may
increase by about 25 per cent. The survey also results show that a majority of the
shipowners will comply using low sulphur compliant fuels or installing scrubbers to
meet the regulation requirements. In short, to medium term, low sulphur fuels and
scrubbers are preferred, while LSFO is a viable alternative to meet the requirements
of the regulation by 1 January 2020, which does not require any investment, yet it is
56
expected that demand and supply would undoubtedly increase in price. Furthermore,
shipowners using LSFO might face challenges bothering on fuel quality, availability
at specific ports and even engine failure.
These limitations of LSFO made scrubber retrofitting a competitive option although it
needs an upfront investment of $2-5 million depending ship type in addition to huge
Capex to continue burning HSFO, which will be cheaper than LSFO in 2020. The
result of the study indicates three years or more payback period of scrubber
installation. Nevertheless, shipowners believe that both options preferred in the short
term are not attractive from a broader perspective. Considering all these options
selected by the operators/carriers will result in high operational costs. Liner shipping
operators in Nigeria indicated a great need and interest to explore alternative solutions
such as liquefied biogas (LBG) and methanol in the future, as they are inherently low
in sulphur and has more prospect for greenhouse gas benefits.
The scenario indicates that MGO/LSFO ranked best compliance solution having gain
normalized positive ideal of 0.47 against HFO/Scrubbers second-best with 0.27 while
methanol compete with 0.25 positive ideal base on five attributes. The study concluded
that although in medium to long term basis Methanol is the worst compliance option
considerable interest in lowering CO , NOx and SOx made Methanol most potential
alternative in the future because of its low prices, zero SOx and can be stored easily.
Overall, the study highlighted the complexities and enormity of the IMO sulphur
regulation, which reveal practical hurdle and ambiguities creating market uncertainty.
In light of the preceding, the following recommendations were proposed:
 The Federal Government, through its responsible agency, NIMASA should
collaborate with the Federal Ministry of Environment to survey emission
discharge areas within the nation’s ports.
 The Nigerian Maritime Administration and Safety Agency should publish the
list of bunker suppliers on its websites and technical guidelines detailing the
IMO sulphur regulation compliance checklist.
 The Agency should hold stakeholders’ roundtable discussions with a view to
resolved critical transitional issues and ways to facilitate seamless compliance.
57
 The Federal Government should impose strict penalties to shipping companies
for non-compliance and illegal discharge within Nigerian territorial waters.
 The government should pursue long-term commitment and comprehensive
enforcement, to consider continuous monitoring and build competency on new
technologies.
 The Federal Government should create a new vision through financial support
for a PhD in this respect and joint research with competent institutions. In
addition, a Green shipping award and certification should be used as an
incentive to drive investment in the environment.
Regardless of the contribution of this research over existing research, it also has some
limits. Future research will find merits a year or two after the implementation to
capture the state of the market. The accuracy of the model could be improved if GDP,
container trade, fleet productivity and Shanghai bunkers were used because the
interaction of these variables would give more insight into the freight rates volatility.
Now, the researcher believes that future studies should consider these factors and
multi-facet perspective to make a comprehensive analysis on social, economic and
environmental impact of the sulphur regulation, which will lead to more accurate and
reliable analyses. In addition, the possibility of examining the entire West African subregion, within the context of the continental free trade agreement, container integration
and the environment should explore. As outlined in the current paper, forecasting is
still in demand, so future research may also combine the ARIMA Model and GARCH,
which are suitable for strategic decision making of the liner shipping companies as
well as policymakers and other market players respectfully.
5
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6
Appendices
Appendix-A Time series data- 2009-2019
6
Date YT X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23
Dec-2009 2,247.50 1.60 0.01 85.90 168.47 61,909,461 463.00 638.25 1,490.00 1,495.33 2,429.00 607.00 87.25 48.00 5,520 45.93 10.55 1,446.25 1,360.75 2,350.25 298.00 0.01 39.43 300
Jan-2010 2,456.00 1.58 0.00 85.86 169.01 60,302,219 484.20 672.67 1,598.33 1,588.33 2,509.33 632.50 86.50 48.75 5,525 56.25 10.56 1,873.00 1,800.00 2,860.33 307.00 0.02 34.14 330
Feb-2010 2,575.00 1.56 0.00 86.21 169.58 58,629,482 460.38 704.50 1,608.50 1,675.50 2,568.50 620.00 86.00 48.75 5,569 135.50 10.57 2,075.00 2,101.50 3,144.50 325.00 0.02 25.01 350
Mar-2010 2,444.50 1.54 0.00 86.84 170.36 57,487,763 462.25 707.75 1,497.00 1,667.00 2,549.50 651.88 86.00 48.75 6,330 122.67 10.61 2,058.25 2,082.50 3,109.25 309.75 0.02 14.05 350
Apr-2010 2,388.25 1.53 0.00 86.70 170.95 56,245,004 481.30 724.25 1,406.25 1,649.25 2,514.25 707.00 86.25 48.75 6,675 278.17 10.63 1,888.00 2,069.25 3,130.25 324.50 0.02 7.21 365
May-2010 2,389.33 1.52 0.00 86.78 172.43 54,650,738 456.38 974.00 1,268.67 1,628.00 2,350.00 658.00 87.00 51.00 7,877 180.10 10.68 1,848.00 2,540.67 3,654.33 322.33 0.02 30.72 345
Jun-2010 2,505.25 1.48 0.01 86.40 174.21 51,732,113 440.00 1,277.75 1,083.50 1,593.75 2,211.50 641.25 87.00 53.00 10,345 115.80 10.70 1,870.25 2,741.50 3,841.00 323.75 0.02 13.89 310
Jul-2010 2,503.20 1.47 0.01 86.35 175.45 50,117,897 438.80 1,372.20 908.80 1,635.80 2,233.20 635.50 88.00 57.75 12,901 492.42 10.72 1,895.20 2,801.80 4,006.80 320.00 0.02 9.37 310
Aug-2010 2,417.75 1.51 0.01 87.14 177.04 48,377,092 447.50 1,190.50 817.00 1,511.00 2,301.00 652.50 92.50 60.75 13,638 291.87 10.75 1,854.00 2,749.00 4,134.00 315.00 0.02 7.47 310
Sep-2010 2,206.33 1.52 0.01 86.98 180.00 46,636,141 440.25 983.67 1,034.33 1,347.00 2,181.00 646.13 96.00 65.00 14,481 26.65 10.79 1,740.00 2,565.00 4,016.33 308.00 0.02 7.95 310
Oct-2010 2,024.67 1.52 0.00 87.30 181.15 45,485,814 466.20 819.33 1,015.33 1,237.33 1,999.67 692.00 96.50 65.00 14,904 239.85 10.82 1,549.67 2,294.67 3,686.33 308.00 0.02 1.90 325
Nov-2010 1,945.25 1.57 0.00 88.22 182.41 46,360,157 488.50 846.00 1,095.75 1,183.50 1,850.25 725.00 96.50 73.00 13,612 216.20 10.84 1,460.50 2,073.75 3,317.00 309.50 0.02 4.43 345
Dec-2010 1,809.00 1.56 0.00 88.33 183.31 45,421,046 503.90 786.60 937.20 1,056.20 1,560.00 765.00 97.00 73.00 11,850 135.30 10.85 1,354.20 1,880.00 3,088.00 311.40 0.02 1.56 445
Jan-2011 1,761.00 1.53 0.00 88.10 183.42 45,970,836 541.00 825.25 871.75 1,035.75 1,448.25 810.00 95.00 73.00 12,577 159.50 10.87 1,340.00 1,970.25 3,192.75 312.00 0.02 9.01 460
Feb-2011 1,678.67 1.55 0.00 89.04 184.06 45,827,190 617.63 788.33 826.67 980.00 1,375.67 881.63 93.00 73.50 14,857 134.75 10.89 1,250.33 1,839.33 3,063.67 312.00 0.02 2.87 460
Mar-2011 1,585.50 1.56 0.00 88.94 185.37 45,632,570 640.13 726.75 746.75 869.00 1,193.00 971.88 93.00 75.00 15,799 499.80 10.93 1,057.75 1,654.00 2,864.50 331.75 0.02 6.30 420
Apr-2011 1,652.80 1.58 0.00 87.79 185.73 48,079,038 666.90 789.80 772.60 857.40 1,102.60 1,019.50 93.00 75.00 17,411 386.11 10.96 959.40 1,660.80 2,984.20 378.80 0.03 3.11 420
May-2011 1,777.50 1.60 0.00 87.14 186.92 48,885,677 639.50 980.00 804.00 869.50 1,139.25 960.00 93.00 75.50 17,177 229.60 11.00 900.50 1,825.75 3,194.75 370.50 0.03 6.11 395
Jun-2011 1,908.75 1.60 0.00 86.86 189.32 48,719,711 656.13 1,030.50 715.75 852.25 1,354.75 956.25 94.00 78.00 17,067 114.70 11.01 857.00 1,687.75 3,070.75 336.75 0.03 0.00 395
Jul-2011 2,048.40 1.60 0.00 88.07 191.36 48,786,405 665.90 997.80 680.60 944.60 1,764.20 957.50 94.50 78.50 16,540 33.40 11.05 810.60 1,625.00 3,102.80 333.60 0.03 5.23 420
Aug-2011 2,185.00 1.60 0.00 88.36 192.40 53,849,283 651.75 942.75 815.00 1,158.75 2,134.75 946.13 94.50 80.50 14,116 114.50 11.06 830.50 1,669.25 3,228.50 332.50 0.03 10.04 420
Sep-2011 2,158.60 1.60 0.01 89.00 193.45 53,072,349 659.70 840.40 757.60 1,163.20 1,958.00 949.00 94.00 80.50 12,809 174.30 11.10 787.00 1,631.40 3,198.00 333.40 0.03 2.89 470
Oct-2011 2,106.33 1.58 0.01 87.84 194.33 53,464,300 667.25 756.67 729.00 1,106.67 1,654.33 928.13 94.00 79.25 11,560 382.55 11.08 674.33 1,491.67 2,930.67 333.00 0.03 0.00 470
Nov-2011 2,039.25 1.57 0.01 88.80 195.01 52,797,914 686.25 737.50 800.25 1,062.00 1,416.00 958.75 93.00 77.25 8,487 232.00 11.10 559.25 1,469.00 2,671.00 332.50 0.03 6.01 470
Dec-2011 1,990.20 1.56 0.01 89.95 195.61 53,139,223 671.20 637.00 745.80 995.40 1,254.80 951.50 92.50 76.50 8,216 32.70 11.10 545.20 1,475.80 2,600.40 333.60 0.03 8.83 470
Jan-2012 2,010.33 1.55 0.01 89.62 196.31 52,639,685 732.00 645.33 851.00 1,060.33 1,546.00 976.25 92.50 69.00 7,466 18.70 11.13 733.33 1,820.00 2,954.33 335.33 0.03 26.53 470
Feb-2012 2,014.00 1.53 0.01 90.74 197.07 51,723,196 732.75 627.50 755.50 1,017.50 1,429.75 990.00 92.00 65.00 7,108 111.20 11.12 745.25 1,803.75 2,942.00 319.50 0.03 14.99 440
Mar-2012 2,085.80 1.55 0.01 90.82 197.89 50,568,553 733.30 1,172.80 888.20 1,011.00 1,440.00 1,027.90 90.50 61.00 7,139 150.60 11.13 1,442.00 1,911.20 3,075.20 345.00 0.03 19.18 440
Apr-2012 2,282.25 1.54 0.01 90.70 198.73 49,003,902 721.75 1,503.25 1,062.25 1,133.25 1,519.50 1,001.75 88.25 61.00 7,839 105.90 11.14 1,777.50 2,285.50 3,459.25 351.00 0.03 32.84 440
May-2012 2,259.50 1.54 0.01 91.19 200.55 46,720,200 672.25 1,503.25 938.50 1,124.00 1,586.75 956.25 87.00 61.00 8,186 152.33 11.14 1,801.25 2,367.00 3,522.75 355.00 0.03 34.07 440
Jun-2012 2,228.80 1.53 0.01 91.10 201.57 45,182,100 592.60 1,323.80 828.80 1,104.20 1,846.40 865.00 84.50 61.00 8,309 189.55 11.13 1,664.80 2,594.20 3,722.20 350.80 0.02 27.49 480
Jul-2012 2,193.50 1.51 0.01 90.55 203.02 43,463,629 614.38 934.75 853.50 1,064.25 1,972.25 905.00 82.50 63.50 8,079 96.54 11.13 1,742.50 2,448.50 3,609.75 342.75 0.02 23.21 465
Aug-2012 2,113.40 1.51 0.01 91.27 204.34 42,349,866 657.70 1,032.60 820.20 1,003.00 1,916.60 959.00 80.00 63.50 7,918 25.50 11.12 1,517.00 2,637.80 3,926.00 343.80 0.02 20.58 440
Sep-2012 2,070.50 1.51 0.01 90.89 204.74 43,217,708 663.00 857.25 973.50 1,039.25 1,925.75 978.13 77.50 63.50 7,860 33.63 11.14 1,208.00 2,664.50 3,773.75 348.25 0.02 21.40 425
Oct-2012 1,986.67 1.54 0.01 90.43 205.25 42,534,611 637.63 733.00 1,143.00 1,046.67 1,950.00 962.50 77.00 63.50 7,785 49.84 11.11 1,167.33 2,526.00 3,473.67 347.00 0.02 22.34 425
Nov-2012 1,966.20 1.54 0.01 91.50 205.97 42,164,544 605.00 784.60 1,075.00 1,023.20 2,073.20 937.50 77.00 63.50 7,570 38.90 11.12 1,241.20 2,215.40 3,237.80 349.80 0.02 29.16 435
Dec-2012 1,891.00 1.53 0.01 91.55 206.18 41,785,872 606.38 656.00 912.00 939.25 2,043.25 940.00 77.00 63.50 7,221 58.75 11.10 1,201.00 2,168.00 3,300.50 348.00 0.02 43.10 435
Jan-2013 1,956.25 1.54 0.01 91.14 206.71 41,059,185 634.13 713.25 967.50 923.25 2,157.50 945.00 76.50 63.50 7,162 23.10 11.07 1,340.75 2,394.75 3,552.50 348.50 0.02 46.41 435
Feb-2013 1,981.33 1.53 0.00 90.41 206.73 40,653,765 655.25 705.33 979.67 908.67 2,037.33 981.25 76.50 62.00 7,329 37.70 11.06 1,272.00 2,428.00 3,584.33 349.00 0.02 46.54 435
Mar-2013 1,942.80 1.53 0.00 91.04 207.49 41,201,959 636.90 804.40 1,004.80 852.40 1,804.80 939.50 76.50 62.00 7,432 63.46 11.05 1,184.00 2,193.20 3,343.20 348.00 0.02 32.30 435
Apr-2013 1,893.25 1.50 0.00 90.93 207.68 40,727,148 617.38 967.25 1,058.75 800.00 1,550.25 893.75 76.50 63.00 7,370 91.08 11.03 925.75 2,200.25 3,345.00 348.00 0.02 33.22 450
May-2013 1,881.60 1.50 0.00 91.45 209.36 39,212,984 604.10 914.40 928.80 768.20 1,215.20 866.00 77.50 63.00 7,717 59.95 11.04 686.80 2,035.40 3,192.80 343.40 0.01 46.07 450
Jun-2013 1,891.25 1.50 0.00 91.39 210.50 39,172,705 600.75 775.75 685.00 759.25 1,044.25 883.88 79.00 63.00 7,930 47.42 11.01 753.50 1,947.25 3,119.50 340.25 0.02 47.88 450
Jul-2013 1,959.25 1.51 0.00 91.24 211.65 40,407,429 595.50 655.00 571.25 751.25 901.75 935.00 79.75 64.50 8,151 159.87 10.95 1,309.00 2,023.75 3,313.50 338.75 0.02 33.43 450
Aug-2013 2,004.40 1.50 0.00 92.32 213.04 40,072,845 601.20 943.60 453.80 747.40 1,014.80 933.30 81.00 67.00 8,202 51.04 10.93 1,338.40 1,971.60 3,421.20 330.40 0.02 22.18 450
Sep-2013 1,953.75 1.51 0.00 92.18 214.18 42,182,461 605.38 818.25 578.75 764.25 828.50 931.88 82.50 67.00 8,297 157.15 10.93 919.75 1,908.25 3,316.75 328.00 0.02 46.54 450
Oct-2013 1,901.33 1.52 0.00 91.40 214.57 45,478,094 618.38 543.33 820.00 765.00 688.67 931.50 84.00 67.00 8,393 216.50 10.91 668.67 1,747.67 3,154.33 330.67 0.02 24.05 470
Nov-2013 1,872.60 1.54 0.00 91.77 216.05 44,674,648 610.60 765.80 1,009.40 854.60 1,652.80 931.00 84.50 67.00 8,414 95.20 10.86 1,206.80 1,788.80 3,103.40 323.40 0.01 20.39 490
Dec-2013 1,882.25 1.53 0.00 91.97 216.48 45,651,965 611.63 640.75 763.75 770.00 1,662.25 955.13 85.00 67.00 8,114 390.96 10.87 1,402.50 1,758.50 3,034.75 326.00 0.02 44.39 520
Jan-2014 1,942.20 1.52 0.00 92.16 216.77 45,214,090 612.60 572.00 875.40 758.20 1,457.00 929.40 85.50 67.00 8,104 24.30 10.86 1,659.40 2,002.00 3,327.40 334.80 0.02 47.18 550
Feb-2014 1,894.00 1.54 0.00 92.13 216.52 46,431,002 617.88 487.00 728.25 732.00 1,071.75 932.75 87.50 67.00 8,223 109.52 10.80 1,322.25 1,986.50 3,334.75 334.75 0.02 54.98 590
Mar-2014 1,839.00 1.54 0.00 92.94 217.25 48,027,715 606.13 557.75 642.25 705.50 796.00 926.00 88.00 67.00 8,319 193.50 10.80 983.25 1,851.00 3,261.00 335.00 0.01 84.74 590
Apr-2014 1,890.80 1.55 0.00 91.62 216.83 46,601,162 589.75 962.00 704.60 682.80 908.60 923.88 88.00 67.00 8,327 133.65 10.81 1,172.80 1,887.60 3,305.40 333.40 0.02 30.63 590
May-2014 1,941.75 1.55 0.00 92.63 218.66 45,624,802 594.60 1,195.00 522.50 668.00 651.00 921.70 88.50 67.00 8,642 60.55 10.79 1,257.75 1,920.50 3,364.00 333.50 0.02 28.55 480
Jun-2014 1,868.50 1.37 0.00 92.50 220.51 44,664,889 601.63 899.50 485.50 651.00 996.50 907.88 88.50 67.00 8,556 236.95 10.78 1,152.75 1,763.00 3,264.75 333.25 0.02 53.38 460
Jul-2014 1,855.25 1.54 0.00 93.40 220.94 44,318,283 595.75 930.75 626.00 643.75 1,276.25 894.13 88.50 67.00 8,590 139.50 10.77 1,287.75 1,803.25 3,588.75 298.25 0.02 21.07 430
Aug-2014 1,833.40 1.54 0.00 93.05 222.43 42,577,974 594.20 951.40 721.00 692.20 1,506.20 896.50 88.50 67.00 8,620 53.90 10.76 1,236.60 2,143.60 4,211.40 196.00 0.02 22.97 410
Sep-2014 1,797.75 1.53 0.00 93.48 224.37 40,465,086 574.75 693.50 618.25 795.00 1,003.50 859.50 88.00 65.00 8,652 284.70 10.76 978.50 2,128.75 4,385.75 171.00 0.02 15.58 410
Oct-2014 1,770.25 1.50 0.00 94.29 224.98 41,484,744 497.10 747.00 772.75 957.00 1,172.75 763.20 88.00 65.00 8,650 176.82 10.73 863.00 2,031.75 4,089.50 157.50 0.02 19.11 420
Nov-2014 1,736.25 1.49 0.00 94.66 225.66 42,661,768 465.75 963.75 802.75 985.25 1,447.00 715.00 88.50 62.00 8,544 107.70 10.72 914.25 2,020.00 4,109.50 141.50 0.02 5.20 435
Dec-2014 1,683.60 1.47 0.00 94.01 226.64 41,662,543 366.00 881.40 635.80 843.00 951.80 610.13 89.00 60.00 8,712 245.35 10.70 1,107.20 2,101.80 4,398.80 144.20 0.02 16.48 435
Jan-2015 1,688.25 1.46 0.00 94.62 227.67 40,495,415 289.50 743.25 717.25 864.25 1,006.75 518.00 89.00 60.00 8,759 17.90 10.66 1,098.00 2,015.25 4,672.00 161.75 0.01 19.95 415
Feb-2015 1,674.00 1.43 0.00 94.13 228.84 39,928,935 354.13 732.33 690.67 864.00 758.00 561.25 89.00 60.00 9,212 507.60 10.62 999.33 2,172.00 4,991.00 191.33 0.01 21.95 355
Mar-2015 1,598.00 1.41 0.00 94.78 230.11 40,878,245 332.63 624.50 557.25 801.00 617.50 553.13 89.00 60.00 9,491 177.77 10.62 682.50 1,801.25 4,469.75 130.00 0.01 12.18 315
Apr-2015 1,518.40 1.38 0.00 96.02 230.83 40,061,201 330.00 615.40 528.60 673.20 566.00 542.50 89.00 60.00 10,287 162.90 10.61 413.60 1,713.80 3,808.00 86.60 0.01 23.06 330
May-2015 1,436.50 1.36 0.00 96.45 232.63 39,936,956 375.70 617.00 431.50 630.25 599.00 583.00 89.00 60.00 12,762 61.40 10.56 576.25 1,526.00 3,220.50 84.25 0.00 15.11 365
Jun-2015 1,294.50 1.41 0.00 96.10 233.71 40,742,533 361.50 507.50 305.75 607.00 359.25 578.75 89.00 60.00 13,732 223.30 10.55 320.00 1,321.25 2,972.50 81.25 0.01 5.25 390
Jul-2015 1,226.60 1.40 0.00 96.92 235.19 39,760,674 308.50 436.00 370.20 607.00 350.20 516.60 89.00 60.00 13,412 465.80 10.49 721.00 1,321.20 2,797.20 81.60 0.01 9.56 390
Aug-2015 1,233.50 1.40 0.00 97.08 237.16 40,668,526 249.75 563.75 329.50 626.00 307.00 432.75 89.00 60.00 12,709 537.15 10.44 633.25 1,581.75 2,930.75 112.25 0.01 9.64 375
Sep-2015 1,569.75 1.40 0.01 97.04 238.82 43,777,792 239.38 368.00 351.50 741.50 235.50 447.50 89.00 60.00 11,794 138.60 10.42 530.00 1,396.75 2,521.75 136.25 0.01 3.61 360
Oct-2015 1,486.25 1.41 0.01 96.65 240.44 43,114,477 239.70 386.00 535.00 762.50 223.00 442.00 89.00 60.00 11,045 88.68 10.37 427.75 1,275.75 2,276.50 135.00 0.00 16.81 315
Nov-2015 1,281.75 1.41 0.01 96.98 241.87 45,196,410 227.75 354.50 562.50 647.25 257.50 430.00 89.00 60.00 8,885 88.93 10.35 483.00 992.25 1,809.00 126.25 0.01 6.93 305
Dec-2015 1,379.20 1.38 0.01 97.46 243.36 43,876,845 190.63 357.40 459.40 493.20 182.00 372.38 89.00 56.00 7,897 45.50 10.34 668.20 951.80 1,717.60 122.60 0.01 24.73 305
Jan-2016 1,395.00 1.41 0.01 97.15 244.24 44,395,936 164.00 357.75 617.75 526.00 193.25 305.60 89.00 56.00 7,565 66.10 10.31 671.50 1,420.00 2,471.25 125.25 0.01 48.39 305
Feb-2016 1,267.00 1.38 0.01 96.62 244.66 44,018,685 159.63 270.25 461.00 481.00 291.50 291.25 89.00 54.00 7,488 50.70 10.24 372.25 1,196.00 2,216.00 137.75 0.01 24.15 275
Mar-2016 1,044.25 1.40 0.01 96.50 245.16 42,956,556 178.00 279.75 353.00 386.25 469.75 333.75 89.00 50.00 7,351 3.50 10.21 223.50 800.75 1,706.00 153.75 0.01 31.98 255
Apr-2016 974.60 1.39 0.01 96.09 245.68 41,945,602 195.10 537.40 393.60 325.40 689.00 356.90 88.50 48.00 7,276 85.30 10.20 380.80 824.60 1,692.00 198.20 0.01 54.15 255
May-2016 1,218.50 1.41 0.01 96.09 246.06 40,481,843 224.00 411.25 401.25 331.75 1,296.25 426.25 87.50 48.00 7,232 24.13 10.21 622.25 805.50 1,617.75 199.00 0.01 42.63 265
Jun-2016 990.00 1.41 0.01 95.38 245.93 40,042,146 240.13 352.75 321.50 334.50 2,148.25 451.25 86.25 46.00 7,170 53.00 10.17 659.50 796.50 1,589.50 192.00 0.01 65.49 265
Jul-2016 1,172.60 1.41 0.01 96.08 246.60 38,757,428 245.50 344.80 395.80 381.20 2,545.40 429.00 84.50 46.00 7,216 403.41 10.14 950.40 1,282.80 1,817.00 190.40 0.01 49.86 275
Aug-2016 1,167.50 1.39 0.01 97.45 246.74 38,507,476 238.75 375.40 514.20 880.80 2,084.20 405.63 82.50 43.00 7,195 16.30 10.10 864.20 1,729.80 2,436.80 201.60 0.01 30.65 275
Sep-2016 1,179.20 1.40 0.01 96.77 247.39 38,160,500 257.90 531.33 855.67 907.00 2,392.33 417.10 82.50 38.00 7,146 97.60 10.12 824.67 1,955.00 2,654.67 205.00 0.01 64.87 275
Oct-2016 1,011.67 1.40 0.01 97.26 246.77 38,190,213 282.13 555.00 941.25 886.75 2,666.50 464.75 83.00 26.00 7,122 428.75 10.11 836.25 1,753.50 2,650.00 219.25 0.01 78.69 275
Nov-2016 1,209.00 1.38 0.01 98.11 246.91 37,052,254 278.75 515.80 794.80 876.60 2,187.20 451.25 83.00 26.00 6,883 28.55 10.06 1,039.80 1,582.00 2,618.80 217.20 0.01 63.36 285
Dec-2016 1,545.00 1.37 0.01 98.83 246.44 36,683,864 329.30 631.00 808.25 1,019.25 2,408.00 485.90 83.00 26.00 6,738 66.26 10.05 1,073.75 2,141.50 3,502.75 210.00 0.02 88.69 310
Jan-2017 1,622.50 1.35 0.01 97.87 246.26 35,834,498 340.50 586.00 618.00 882.25 1,863.75 490.63 83.00 26.00 6,727 85.89 10.05 928.75 1,869.25 3,341.00 204.50 0.02 59.88 350
Feb-2017 1,422.25 1.35 0.01 96.34 245.97 36,478,244 328.38 625.60 449.20 806.80 1,808.00 499.00 83.00 26.00 6,808 54.28 10.03 841.40 1,405.00 2,750.60 211.80 0.02 101.22 430
Mar-2017 1,569.40 1.36 0.01 97.05 245.12 36,202,696 306.90 847.00 435.25 1,035.00 2,540.50 485.50 83.00 24.00 7,099 136.30 10.06 900.75 1,416.00 2,432.00 217.25 0.03 67.66 430
Apr-2017 1,986.75 1.35 0.01 96.43 244.94 35,574,495 315.00 735.75 407.25 1,148.25 3,116.75 489.38 83.00 24.00 8,648 367.55 10.10 964.50 1,390.25 2,381.50 218.50 0.02 38.49 500
May-2017 2,223.25 1.36 0.01 96.11 245.76 34,350,228 310.38 925.20 348.40 1,259.80 3,569.00 465.63 83.00 26.00 9,733 322.93 10.13 929.00 1,261.20 2,222.20 216.80 0.02 34.63 390
Jun-2017 2,493.40 1.37 0.01 96.95 246.52 33,370,278 297.20 756.25 404.50 1,412.50 3,606.75 438.00 83.00 26.00 9,739 184.60 10.19 947.50 1,377.75 2,369.00 217.00 0.02 26.93 360
Jul-2017 2,553.75 1.38 0.01 97.70 247.13 32,331,360 307.25 582.00 500.00 1,326.50 2,876.50 449.88 83.00 30.00 9,107 144.20 10.24 926.75 1,625.50 2,573.00 218.00 0.02 9.64 320
Aug-2017 1,950.75 1.40 0.01 98.46 248.12 31,492,001 313.25 408.40 713.60 1,272.80 2,094.20 471.25 83.00 33.00 9,218 232.93 10.22 783.00 1,490.40 2,175.20 218.00 0.02 18.43 320
Sep-2017 1,315.00 1.41 0.01 97.62 249.31 30,025,329 335.90 518.67 1,057.33 1,241.00 2,685.33 501.00 83.00 35.00 9,670 293.25 10.26 703.67 1,415.00 1,877.00 213.33 0.02 25.21 320
Oct-2017 1,312.00 1.42 0.01 97.65 249.74 29,243,880 343.00 453.50 1,296.00 1,302.25 2,756.75 517.75 83.00 44.00 9,944 315.85 10.28 722.50 1,326.25 1,872.00 215.00 0.02 35.29 350
Nov-2017 1,426.00 1.41 0.02 98.00 251.31 31,763,470 373.38 362.80 1,184.20 1,200.40 2,912.80 547.88 83.00 46.00 9,967 161.02 10.29 789.00 1,227.60 2,012.40 214.20 0.02 14.07 400
Dec-2017 1,339.40 1.41 0.02 98.53 252.05 31,030,798 373.50 521.50 1,334.25 1,237.00 2,503.00 560.30 83.00 46.00 8,907 327.25 10.32 895.75 1,468.00 2,646.50 211.00 0.02 13.00 400
Jan-2018 1,446.50 1.42 0.02 97.58 252.76 31,496,821 392.13 583.50 1,304.00 1,300.00 2,561.00 594.25 83.00 46.00 9,349 113.55 10.34 911.75 1,483.50 2,773.25 215.75 0.02 14.04 430
Feb-2018 1,792.75 1.43 0.02 98.08 253.27 31,504,225 375.00 390.00 972.60 1,111.20 2,074.00 584.63 83.50 46.00 10,148 105.44 10.37 739.20 1,096.60 2,129.20 215.00 0.02 17.08 470
Mar-2018 1,446.40 1.45 0.02 98.12 255.75 30,301,484 367.20 383.50 848.00 922.50 1,925.25 586.20 84.00 48.00 11,213 224.65 10.41 640.00 1,208.75 2,226.50 221.25 0.02 6.65 480
Apr-2018 1,387.75 1.45 0.02 98.52 256.22 30,700,843 398.75 458.50 830.25 802.75 1,927.00 634.50 85.50 48.00 12,760 243.30 10.46 804.25 1,360.25 2,349.75 228.00 0.02 0.86 460
May-2018 1,875.00 1.45 0.02 98.36 257.28 30,544,110 441.75 527.60 732.80 796.40 1,875.40 671.00 86.50 48.00 13,404 128.80 10.50 865.40 1,361.60 2,351.60 226.60 0.02 1.16 460
Jun-2018 2,206.80 1.43 0.03 98.70 258.79 31,251,404 448.10 411.50 736.00 585.75 1,673.00 664.40 86.50 48.00 13,798 271.34 10.53 888.00 1,683.25 2,707.25 226.25 0.03 6.74 460
Jul-2018 2,045.75 1.41 0.02 99.66 260.84 29,212,694 462.75 379.00 671.40 547.40 1,642.80 655.00 86.50 48.00 13,732 129.83 10.54 940.60 2,130.40 3,266.40 225.60 0.03 1.70 490
Aug-2018 1,955.20 1.40 0.03 100.34 261.94 29,106,959 459.70 375.25 545.25 740.50 1,164.25 669.80 87.50 45.00 13,183 77.80 10.52 801.75 2,339.00 3,462.75 228.50 0.03 2.25 490
Sep-2018 1,791.25 1.40 0.03 101.03 262.76 28,384,144 471.50 427.67 736.67 942.67 1,049.00 706.13 89.50 43.00 12,517 15.80 10.54 739.67 2,559.00 3,300.67 222.33 0.03 5.22 480
Oct-2018 2,103.67 1.40 0.03 100.91 263.98 27,132,018 504.75 520.40 676.80 884.40 1,132.60 737.38 90.00 43.00 12,073 21.68 10.52 745.00 2,429.40 3,604.40 226.60 0.03 3.39 480
Nov-2018 2,517.40 1.39 0.03 101.68 264.70 31,063,747 478.60 579.50 581.25 856.75 762.25 657.10 88.50 43.00 11,607 161.70 10.50 868.50 1,902.75 2,946.25 226.00 0.03 34.06 495
Dec-2018 2,410.25 1.38 0.03 102.07 265.34 29,990,066 385.63 779.25 560.25 851.25 1,456.50 532.25 89.00 42.00 11,682 52.49 10.48 976.25 1,995.25 3,120.75 226.50 0.03 16.72 510
Jan-2019 2,553.00 1.39 0.03 101.54 265.46 30,590,826 385.13 704.67 500.67 818.00 1,319.00 548.63 89.00 42.00 11,260 178.00 10.44 899.00 1,849.00 2,953.00 224.33 0.02 23.25 430
Feb-2019 2,533.67 1.39 0.03 100.10 265.67 30,397,829 423.13 621.00 376.80 703.60 1,467.20 595.63 89.00 40.00 11,273 323.30 10.47 719.80 1,456.00 2,489.60 227.40 0.02 15.86 370
Mar-2019 2,656.80 1.39 0.03 99.64 266.89 29,773,290 431.50 712.00 326.25 641.75 1,305.25 617.10 89.00 40.00 11,793 34.60 10.49 663.25 1,584.00 2,655.25 231.50 0.02 11.76 345
Apr-2019 2,670.00 1.39 0.03 99.49 266.97 29,595,153 436.25 665.75 273.00 637.00 997.75 625.63 89.50 40.00 12,494 42.16 10.53 753.50 1,386.75 2,597.25 232.00 0.02 41.95 360
May-2019 2,574.00 1.39 0.03 99.60 267.41 30,517,054 415.10 809.50 264.25 748.75 1,791.00 620.60 89.50 40.00 12,791 152.35 10.54 735.25 1,489.25 2,539.75 231.00 0.03 26.07 370
6
Appendix-B Eviews Flowchart
Yes
No
Define the
variables Unit root test
Stationar
y?
Correlation
test
Convert to
return/remove
variable
Is there
correlatio
n?
Remove the
variable
T-test
Significan
t variable?
F-test
Cointegration
Cointegation
? Insert ect
ARMA
JarqueBera Test
Normality Insert dummy
variable
WG &
LM tests
Heterosce
dasticity?
Serial
Correlat
ion?
No
Yes
Yes
Yes
No
No
No
Yes
White correction
No
Yes
Serial
Correlation NW test Yes
No
Ramsey
Reset
Significant
variable? Linearity?
Remove the
variable(s)
Accurate
forecast? Forecast
Yes
No
No OLS fitted
No Yes
Yes
6
Appendix-C Regressional Analysis
Dependent Variable: DLOG_YT
Method: Least Squares
Date: 09/17/19 Time: 12:20
Sample (adjusted): 2010M01 2019M05
Included observations: 111 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
C -0.131449 0.430884 -0.305069 0.7610
DLOG_X1 -0.075301 0.492883 -0.152777 0.8789
DLOG_X2 0.090048 0.133181 0.676129 0.5008
DLOG_X3 -0.143922 1.526521 -0.094281 0.9251
DLOG_X4 -5.432810 3.735440 -1.454396 0.1494
DLOG_X5 0.817251 0.323796 2.523969 0.0134
DLOG_X6 0.580932 0.303276 1.915524 0.0587
LOG_X7 0.005930 0.046819 0.126662 0.8995
DLOG_X8 -0.175501 0.061990 -2.831109 0.0058
DLOG_X9 0.151609 0.086564 1.751423 0.0834
DLOG_X10 -0.040832 0.046446 -0.879129 0.3818
DLOG_X11 -0.756525 0.366008 -2.066961 0.0417
DLOG_X12 -0.059437 0.840766 -0.070694 0.9438
DLOG_X13 0.210611 0.178858 1.177531 0.2422
DLOG_X14 -0.037547 0.136065 -0.275950 0.7832
LOG_X15 -0.014735 0.010667 -1.381389 0.1707
DLOG_X16 9.563763 4.806355 1.989816 0.0498
DLOG_X17 0.165683 0.048074 3.446413 0.0009
DLOG_X18 0.065794 0.098039 0.671100 0.5039
LOG_X19 0.023586 0.074601 0.316157 0.7526
DLOG_X20 0.010223 0.114589 0.089213 0.9291
DLOG_X21 -0.033703 0.065350 -0.515735 0.6073
LOG_X22 0.000365 0.011863 0.030793 0.9755
DLOG_X23 -0.088450 0.129404 -0.683516 0.4961
R-squared 0.351417 Mean dependent var 0.000801
Adjusted R-squared 0.179953 S.D. dependent var 0.101828
S.E. of regression 0.092212 Akaike info criterion -1.740640
Sum squared resid 0.739767 Schwarz criterion -1.154796
Log likelihood 120.6055 Hannan-Quinn criter. -1.502981
F-statistic 2.049506 Durbin-Watson stat 2.164638
Prob(F-statistic) 0.009194
Dependent Variable: DLOG_YT
Method: Least Squares
Date: 09/17/19 Time: 12:51
Sample (adjusted): 2010M01 2019M05
Included observations: 113 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
C 0.006216 0.008429 0.737484 0.4625
DLOG_X5 0.724213 0.264077 2.742434 0.0072
DLOG_X6 0.741906 0.248525 2.985236 0.0035
DLOG_X8 -0.172612 0.050501 -3.417993 0.0009
DLOG_X9 0.197298 0.070243 2.808782 0.0059
DLOG_X11 -0.951093 0.290635 -3.272471 0.0014
DLOG_X16 8.565502 3.199048 2.677516 0.0086
DLOG_X17 0.153599 0.037467 4.099621 0.0001
R-squared 0.298998 Mean dependent var 0.001200
Adjusted R-squared 0.252265 S.D. dependent var 0.101162
S.E. of regression 0.087476 Akaike info criterion -1.966732
Sum squared resid 0.803471 Schwarz criterion -1.773643
Log likelihood 119.1204 Hannan-Quinn criter. -1.888379
F-statistic 6.397943 Durbin-Watson stat 2.168493
Prob(F-statistic) 0.000003
6
Dependent Variable: DLOG_YT
Method: ARMA Conditional Least Squares (Gauss-Newton / Marquardt
steps)
Date: 09/17/19 Time: 13:13
Sample (adjusted): 2010M04 2019M05
Included observations: 110 after adjustments
Failure to improve likelihood (non-zero gradients) after 27 iterations
Coefficient covariance computed using outer product of gradients
MA Backcast: 2010M01 2010M03
Variable Coefficient Std. Error t-Statistic Prob.
C 0.006207 0.002309 2.688472 0.0085
DLOG_X5 0.384373 0.241447 1.591954 0.1147
DLOG_X6 0.935378 0.262349 3.565403 0.0006
DLOG_X8 -0.217370 0.050184 -4.331484 0.0000
DLOG_X9 0.219530 0.066618 3.295370 0.0014
DLOG_X11 -1.121727 0.322511 -3.478110 0.0008
DLOG_X16 7.478541 1.608567 4.649195 0.0000
DLOG_X17 0.181395 0.037818 4.796473 0.0000
AR(1) -0.871515 0.116587 -7.475249 0.0000
AR(2) 0.328330 0.120859 2.716642 0.0078
AR(3) 0.547857 0.110881 4.940955 0.0000
MA(1) 0.702048 0.086398 8.125702 0.0000
MA(2) -0.773063 0.100204 -7.714911 0.0000
MA(3) -0.928323 0.088212 -10.52377 0.0000
R-squared 0.425722 Mean dependent var 0.000469
Adjusted R-squared 0.347955 S.D. dependent var 0.101976
S.E. of regression 0.082345 Akaike info criterion -2.037390
Sum squared resid 0.650944 Schwarz criterion -1.693692
Log likelihood 126.0564 Hannan-Quinn criter. -1.897984
F-statistic 5.474339 Durbin-Watson stat 2.108386
Prob(F-statistic) 0.000000
Inverted AR Roots .70 -.79-.40i -.79+.40i
Inverted MA Roots 1.00 -.85+.45i -.85-.45i
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 1.150074 Prob. F(14,80) 0.3297
Obs*R-squared 18.93234 Prob. Chi-Square(14) 0.1676
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 09/17/19 Time: 22:10
Sample: 2010M01 2019M05
Included observations: 113
Presample missing value lagged residuals set to zero.
Variable Coefficient Std. Error t-Statistic Prob.
C 0.001066 0.005329 0.200117 0.8419
DLOG_X5 -0.071492 0.176371 -0.405351 0.6863
DLOG_X6 -0.010522 0.197493 -0.053277 0.9576
DLOG_X8 -0.011927 0.035922 -0.332017 0.7407
DLOG_X9 0.029915 0.049639 0.602655 0.5484
DLOG_X11 0.046704 0.224354 0.208173 0.8356
DLOG_X16 0.422120 2.148315 0.196489 0.8447
DLOG_X17 -0.009331 0.025768 -0.362112 0.7182
DUMMY_2017M09 -0.030276 0.064063 -0.472599 0.6378
DUMMY_2015M09 0.008593 0.059969 0.143288 0.8864
DUMMY_2016M05 -0.005681 0.062439 -0.090985 0.9277
DUMMY_2018M02 0.036539 0.061487 0.594254 0.5540
DUMMY_2018M03 -0.064265 0.061545 -1.044189 0.2995
DUMMY_2016M06 0.006221 0.059720 0.104170 0.9173
DUMMY_2018M10 -0.085823 0.062898 -1.364488 0.1762
DUMMY_2016M12 -0.039241 0.063244 -0.620459 0.5367
DUMMY_2018M05 -0.001509 0.061845 -0.024402 0.9806
DUMMY_2016M11 0.009012 0.060724 0.148416 0.8824
DUMMY_2017M04 0.012313 0.059173 0.208077 0.8357
RESID(-1) -0.197304 0.130209 -1.515283 0.1336
RESID(-2) 0.069638 0.122743 0.567347 0.5721
RESID(-3) -0.183742 0.122849 -1.495672 0.1387
RESID(-4) 0.100910 0.122960 0.820671 0.4143
RESID(-5) 0.142154 0.125869 1.129375 0.2621
RESID(-6) -0.091101 0.134969 -0.674979 0.5016
RESID(-7) -0.045907 0.134991 -0.340072 0.7347
RESID(-8) 0.017535 0.125263 0.139986 0.8890
RESID(-9) 0.142772 0.139062 1.026682 0.3077
RESID(-10) -0.057990 0.129328 -0.448390 0.6551
RESID(-11) 0.042931 0.131636 0.326137 0.7452
RESID(-12) 0.308153 0.148654 2.072959 0.0414
RESID(-13) -0.228683 0.151882 -1.505656 0.1361
RESID(-14) -0.120341 0.163840 -0.734504 0.4648
R-squared 0.167543 Mean dependent var 8.60E-19
Adjusted R-squared -0.165440 S.D. dependent var 0.047743
S.E. of regression 0.051541 Akaike info criterion -2.854155
Sum squared resid 0.212521 Schwarz criterion -2.057661
Log likelihood 194.2597 Hannan-Quinn criter. -2.530946
F-statistic 0.503157 Durbin-Watson stat 1.903724
Prob(F-statistic) 0.984039
6
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.790230 Prob. F(18,94) 0.7066
Obs*R-squared 14.85184 Prob. Chi-Square(18) 0.6721
Scaled explained SS 13.77105 Prob. Chi-Square(18) 0.7439
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 09/17/19 Time: 22:13
Sample: 2010M01 2019M05
Included observations: 113
Variable Coefficient Std. Error t-Statistic Prob.
C 0.002510 0.000382 6.565385 0.0000
DLOG_X5 -0.002894 0.012012 -0.240906 0.8102
DLOG_X6 -0.000104 0.011825 -0.008814 0.9930
DLOG_X8 0.003855 0.002318 1.662785 0.0997
DLOG_X9 -0.006855 0.003150 -2.175987 0.0321
DLOG_X11 0.001625 0.013720 0.118461 0.9060
DLOG_X16 -0.103558 0.148070 -0.699381 0.4860
DLOG_X17 0.002715 0.001685 1.611508 0.1104
DUMMY_2017M09 -0.003744 0.003991 -0.938248 0.3505
DUMMY_2015M09 -0.001169 0.004085 -0.286132 0.7754
DUMMY_2016M05 -0.004115 0.004101 -1.003644 0.3181
DUMMY_2018M02 -0.001637 0.003886 -0.421371 0.6744
DUMMY_2018M03 -0.002568 0.003903 -0.657921 0.5122
DUMMY_2016M06 -0.002267 0.003911 -0.579530 0.5636
DUMMY_2018M10 -0.003010 0.003874 -0.776935 0.4391
DUMMY_2016M12 -0.001887 0.004025 -0.468985 0.6402
DUMMY_2018M05 -0.001976 0.003908 -0.505573 0.6143
DUMMY_2016M11 -0.003034 0.003911 -0.775743 0.4398
DUMMY_2017M04 -0.001408 0.003875 -0.363474 0.7171
R-squared 0.131432 Mean dependent var 0.002259
Adjusted R-squared -0.034889 S.D. dependent var 0.003715
S.E. of regression 0.003779 Akaike info criterion -8.166429
Sum squared resid 0.001343 Schwarz criterion -7.707842
Log likelihood 480.4033 Hannan-Quinn criter. -7.980339
F-statistic 0.790230 Durbin-Watson stat 1.658882
Prob(F-statistic) 0.706600
Ramsey RESET Test
Equation: UNTITLED
Specification: DLOG_YT C DLOG_X5 DLOG_X6 DLOG_X8 DLOG_X9
DLOG_X11 DLOG_X16 DLOG_X17 DUMMY_2017M09
DUMMY_2015M09 DUMMY_2016M05 DUMMY_2018M02
DUMMY_2018M03 DUMMY_2016M06 DUMMY_2018M10
DUMMY_2016M12 DUMMY_2018M05 DUMMY_2016M11
DUMMY_2017M04
Omitted Variables: Powers of fitted values from 2 to 3
Value df Probability
F-statistic 1.558847 (2, 92) 0.2159
Likelihood ratio 3.765886 2 0.1521
F-test summary:
Sum of Sq. df Mean Squares
Test SSR 0.008368 2 0.004184
Restricted SSR 0.255294 94 0.002716
Unrestricted SSR 0.246926 92 0.002684
LR test summary:
Value
Restricted LogL 183.8991
Unrestricted LogL 185.7821
Unrestricted Test Equation:
Dependent Variable: DLOG_YT
Method: Least Squares
Date: 09/17/19 Time: 22:15
Sample: 2010M01 2019M05
Included observations: 113
Variable Coefficient Std. Error t-Statistic Prob.
C -0.002728 0.006070 -0.449435 0.6542
DLOG_X5 0.361864 0.198709 1.821078 0.0718
DLOG_X6 0.589152 0.179845 3.275898 0.0015
DLOG_X8 -0.074883 0.033581 -2.229917 0.0282
DLOG_X9 0.082845 0.044351 1.867956 0.0650
DLOG_X11 -0.877851 0.226797 -3.870643 0.0002
DLOG_X16 8.026452 2.270285 3.535438 0.0006
DLOG_X17 0.087735 0.026773 3.276968 0.0015
DUMMY_2017M09 0.894320 0.719936 1.242222 0.2173
DUMMY_2015M09 0.090328 0.201026 0.449336 0.6542
DUMMY_2016M05 0.107668 0.173453 0.620735 0.5363
DUMMY_2018M02 0.081730 0.147873 0.552707 0.5818
DUMMY_2018M03 0.021353 0.131303 0.162622 0.8712
DUMMY_2016M06 0.015659 0.121208 0.129188 0.8975
DUMMY_2018M10 0.136604 0.085972 1.588932 0.1155
DUMMY_2016M12 -0.001280 0.205642 -0.006227 0.9950
DUMMY_2018M05 -0.167766 0.357857 -0.468809 0.6403
DUMMY_2016M11 0.103101 0.098958 1.041859 0.3002
DUMMY_2017M04 -0.002227 0.183874 -0.012114 0.9904
FITTED^2 -0.862469 1.316021 -0.655361 0.5139
FITTED^3 18.12385 11.87203 1.526601 0.1303
R-squared 0.784565 Mean dependent var 0.001200
Adjusted R-squared 0.737732 S.D. dependent var 0.101162
S.E. of regression 0.051807 Akaike info criterion -2.916497
Sum squared resid 0.246926 Schwarz criterion -2.409637
Log likelihood 185.7821 Hannan-Quinn criter. -2.710819
F-statistic 16.75218 Durbin-Watson stat 2.292783
Prob(F-statistic) 0.000000
7
Appendix-D Questionnaire
The questionnaire is designed to obtain data to answer thesis questions on the economic
impact of low sulphur compliance on future fuel cost and container freight rates: Case
study of Shanghai-Lagos. This thesis research is in partial fulfilment of a MSc. Maritime
Affairs (Shipping Management and Logistics) and your responses will be confidential
and anonymous. The questionnaire is structured in three (3) parts covering sulphur
emission regulation, freight rates and enforcement.
IMO 0.5% SULPHUR EMISSION
The International Maritime Organization (IMO) has set a limit of 0.5% on fuel oil sulphur
content used by ships to be effective by 1st January 2020
Q1. Given the effective date for the 0.5% global sulphur cap, what would be your company
compliance option(s)? *
1. Scrubbers /HSFO
2. Marine Gas Oil/ Liquefied Natural Gas
3. Marine Diesel Oil
4. Low Sulphur Fuel Oil
Q2. What is/are your major reason(s) for choosing the above compliance option(s)? *
 Cost
 LSFO availability
 LSFO Quality
 Vessel Condition
 Timeframe
 Others (i.e. Logistics)
Q3. Are your vessels technically and commercially ready to comply with the 0.5% sulphur
regulation by 1st January 2020? *
 Yes
 No
Q4. What alternative fuel would your company explore in the future towards healthy living and a
better environment? *
1. Hydrogen fuel Cell
2. Methanol LBG
3. Batteries
CONTAINER FREIGHT RATES
The 0.5% compliance presents a new paradigm shift to shipping companies faced with
depressing earnings, overcapacity and consolidation. These challenges would likely
create severe disruption and market distortion to box trade in some regions, such assubSaharan Africa, where demand and supply of compliant fuel may inflate prices
Q.5 In light of the above statement, what effect do these challenges have on shipping
lines?
1. Higher bunker cost
2. Financial Position (Investment)
3. Technical Issues
4. Legal obstacles
Q6. Considering your cost of compliance, what will be your expected payback period?
1. 1-year
7
2. 2-years
3. 3- or more
Q.7 In relation to Q7 above, will the cost translate into increased freight rates? *
 Yes
 No
Q8. How much increase in container freight rates is expected? *
1. 10%
2. 15%
3. 20%
4. 25% or more
Q9. Are there any other demand/supply factors influencing the changes in container freight
rates?Select one or more options *
 Number of demolitions
 Ship size
 Number of ships
 New building prices
 Second-hand prices
 Scrap value
 Exchange rates
 Interest rates
 Bunker cost
 Oil production
ENFORCEMENT
Consistent and effective global enforcement standard of the 0.5% sulphur limit is critical
for commercial consideration and to achieve the environmental benefits as envisioned by
regulation 14 of MARPOL Annex VI.
Q 10. Considering the above statement, what enforcement tool may pose challenges once the
regulation takes effect? *
1. Standard format for reporting procedures
2. Relevant certificate from authorities(i.e. Bunker Delivery Note, IAPP)
3. Port and coastal state oficer inspection
4. Transitional Issues
Q11. Would the above challenges hamper industry competition by not complying? *
 Yes
 No
7
Appendix- E TOPSIS Analysis
Attributes A A A A A
HFO+EGC 3.114 2544.17 82 6702807581 725859.9
MGO/LFO 3.154 2544.17 78.5 887506527.9 1309552.15
MeOh 3.204 2544.17 292 2182709672 2790651.3
Denominator 5.469033553 4406.631703 313.2894029 7104894005 3166943.97
Attributes A A A A A
HFO+EGC 0.56938762 0.577350269 0.261738824 0.943407119 0.229198845
MGO/LFO 0.576701527 0.577350269 0.250567045 0.124914816 0.413506574
MeOh 0.58584391 0.577350269 0.93204557 0.307212137 0.881181141
Attributes A A A A A
HFO+EGC 0.113877524 0.173205081 0.026173882 0.188681424 0.045839769
MGO/LFO 0.115340305 0.173205081 0.025056705 0.024982963 0.082701315
MeOh 0.117168782 0.173205081 0.093204557 0.061442427 0.176236228
PI 0.117168782 0.173205081 0.025056705 0.024982963 0.045839769
NI 0.113877524 0.173205081 0.093204557 0.188681424 0.176236228
dj+PI dj- NI
HFO+EGC 0.163735355 0.146616329
MGO/LFO 0.036906868 0.200480013
MeOh 0.151580536 0.127281556
Dj Normalized DRank
HFO+EGC 0.472419956 0.266395149 2
MGO/LFO 0.844528613 0.47622528 1
MeOh 0.456431906 0.257379571 3
Weighted Normalized Decision Matrix
Normalized Decision Matrix
Weighted 0.2 0.3 0.1 0.2 0.2 1
Cost(1)
Benefit (0) 00111
7
Appendix-F Dissertation Work plan & Mind Map
Dissertation Matrix Mapping
CHAPTER
1
CHAPTER
2
CHAPTER
3
CHAPTER
4
CHAPTER
5
CHAPTER
6
FINAL
DRAFT
19/06/2019
22/07/2019
16/08/2019
02/09/2019
10/09/2019
16/09/2019
24/09/2019
24
16
19
11
8
6
7
GANT CHART FOR DISSERTATION WORK PLAN
Start Date Days to Complete

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