Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3
George Adamantios Psarros
Energy Efficiency
Clauses in
Charter Party
Agreements
Legal and Economic Perspectives
and their Application to Ocean Grain
Transport
Springer Series on Naval Architecture, Marine
Engineering, Shipbuilding and Shipping
Volume 3
Series editor
Nikolas I. Xiros, University of New Orleans, New Orleans, LA, USA
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More information about this series at http://www.springer.com/series/10523
George Adamantios Psarros
Energy Efficiency Clauses
in Charter Party Agreements
Legal and Economic Perspectives and their
Application to Ocean Grain Transport
123
George Adamantios Psarros
DNV GL AS
Høvik
Norway
ISSN 2194-8445
ISSN 2194-8453 (electronic)
Springer Series on Naval Architecture, Marine Engineering, Shipbuilding and Shipping
ISBN 978-3-319-50264-9
ISBN 978-3-319-50265-6 (eBook)
DOI 10.1007/978-3-319-50265-6
Library of Congress Control Number: 2016959248
© Springer International Publishing Switzerland 2017
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Αφιεϱώνεται στους γονείς μου
Dedicated to my parents
Foreword
Traditionally, energy efficiency of ships is an area that has been subject to attention
from charterers and shipowners. Operational energy efficiency is the sum of so
many factors, pointing in different directions. How can anyone “reverse engineer”
an observed operational energy efficiency of a ship to understand if it was good or
bad? However, we should not forget that all parties share a common interest of
optimal performance of the ships they build, operate and charter.
These are fundamental aspects that the shipping industry struggles to get their
heads around. Is it possible to observe a ship’s operational performance and from
that determine if the Master did a good job? Is it possible from the observed
operational efficiency of a ship to determine if it is the right one to charter?
Ships’ performance has traditionally been referred to in charter contracts as
warranties of speed and consumption at some defined loading condition. Such
simplistic warranties have worked, probably because they are easy to understand
and because it is similarly easy to determine if they are breached. The question if
simple speed and consumption warranties are effective and are incentivizing a
desired behavior amongst the decision makers in the industry could seem of less
importance.
A ship’s operational performance is a reflection of so many different parameters.
One set of these are a ship’s design characteristics, such as propulsion train design
and hull design. Historically, the hull design of a ship was determined by the ship
yard designers, taking into account contract design values for speed and consumption at design draught. For some designs these could be complemented by a
similar set of ballast draught values.
Today, the International Maritime Organization have adopted mandatory design
efficiency requirements for the major ship types in the form of Energy Efficiency
Design Index (EEDI). The regulation basically specifies an additional loading
condition to which it attaches a maximum power/speed correlation. In theory, the
EEDI in combination with traditional newbuilding contract specifications should
cater for more energy efficient ships across their operating draught ranges.
vii
viii
Foreword
A ship’s designed energy efficiency is to some extent reflected in the warranted
speed and consumption values that traditionally are used in charter parties, but only
to some extent. Warranties reflect the sum of operational behavior, environmental
conditions expected to be encountered throughout the voyages and the designed
energy efficiency.
It is a traditional view that the charter parties of today makes it an implicit
obligation to always seek to execute the contracted voyages as efficient as possible.
It is also a traditional view that the legal parties to charter arrangements have
freedom to agree on any supplemental clauses, including clauses on how to share
benefits if so wished. However, it appears that most short term charter parties are
currently agreed without such additional provisions.
This book makes an effort to suggest ways to separate the designed energy
efficiency from the actual operational efficiency to allow shipowners and charterers
obtain transparency and henceforth share benefits between the contractual parties. It
suggests ways to quantify these benefits and makes legal analysis on how the
quantified benefits may be incorporated in charter party terms to obtain the desired
behavior.
I wish you good reading.
Lars Robert Pedersen
Deputy Secretary General, The Baltic and International Maritime
Council
Preface
The work reported in this monograph describes the author’s search of excellence
and attempt of harvesting knowledge within business, management, sales and
marketing related topics. Furthermore, it portrays the author’s orientation to explore
new areas of specialization in a more commercial setting, as well as to fulfil the
ambition of operating across a range of disciplines beyond own functional specialism. It is admitted that this quest has been challenging not only due to full time
job commitments, but also on how to cognitively realize the meaning of leadership.
The latter can be encapsulated through Siegel (2008), where the hunger of learning
and assimilating facts is a self-governing intellectual ability which is not implied to
be displayed or transferred. Nevertheless, the years to come will prove whether the
further commitment on such executive education would be rewarding and successful in search of alternative career opportunities, or simply would contribute to
adding another postgraduate degree on the shelf. In any case, the reader is cordially
tempted to experience the author’s knowledge expedition.
The remit of improving maritime industry’s energy efficiency has attracted
significant attention not only due to recent regulatory imperatives, but also inconsistencies between demand and supply as well as volatile market behavior.
A long-term flexible solution is obtained through nurturing the wider application of
innovative energy efficient technologies. This requires the existence of suitable
incentives that embrace adoption of new technological solutions and reduce their
performance uncertainty. A supporting mechanism to safeguard energy efficient
investments can be envisaged through the chartering process (undertaking
responsible for a vessel’s employment), its negotiation stages (orchestration of the
signatories’ commitment for a successful enterprise) and the charter party agreement (prevailing contract form). Undoubtedly, developing a solid grasp of the
regulatory background and gaining a practical insight into the legal, contractual,
commercial and economic principles underpinning energy efficiency is a crucial
aspect that the buyers and sellers of maritime transportation services need to be
aware of in order to reduce impacts on revenue.
Hence, the current book proposes a conceptual framework on how to include an
incentive mechanism for energy efficiency within the existing charter party
ix
x
Preface
contracts. An answer to this call is given by examining key concepts of case law
and the generated theoretical proposition is demonstrated for time and voyage
charter contracts (book’s qualitative section). Realizing that such development may
challenge the traditional business setting of vessel chartering, it is argued how the
interests of the contracting parties can be balanced with the use of game theory.
This scientific approach is able to solve information problems of strategic behavior.
Furthermore, it is believed to be well suited for modeling the chartering negotiations, where the fair investment sharing is perceived by astute price determination.
A working example applied to the ocean grain transport is offered to clearly
enhance the reader’s understanding and advance an analytical approach during
contract negotiation (book’s quantitative section). To the author’s best knowledge,
game theoretic applications on such topic have not been exposed to extensive
scientific research and the current volume is aimed at filling this gap. Additionally,
it is hoped that the contained ideas will trigger additional interest for continued
work and will facilitate communication between industry practitioners, as well as
set a comprehensive foundation for modernizing the chartering process radically.
The views discussed herein are treated with adequate legal as well as mathematical scholarship and the reader is not required to possess sophisticated knowledge for their accumulation. This fascinating book is intended to be attractive to
academics (educators, tutors, researchers, scientists) and professionals (shipowners
and operators, charterers, cargo owners, shipbrokers, lawyers and insurers, commercial and investment bankers, commodity and energy traders, institutional
investors, market analysts and consultants, private equity firms, industry associations) engaged in the maritime industry who wish to sharpen their insights on how
to obtain sustainable ocean freight service pricing. It is a valuable asset for enabling
the reader to interact across multidisciplinary roles and providing the skills to
bargain from a position of strength. The author is confident that the reader will
attain a competitive edge and be able to navigate profitably within uncertain markets and an ever demanding shipping environment.
Elements of the book’s qualitative section have been prepared as part of the
author’s Master of Business Administration dissertation and he would like to
express his gratitude to Dr. Lakshmi Narasimhan Vedanthachari, Module Leader
Business Transformation Project, Middlesex University, Business School, for
accepting his suggested topic. The author is pleased to acknowledge his supervisor
Dr. Katerina Konsta, Lloyd’s Maritime Academy, for her valuable comments and
constructive feedback on earlier versions of the dissertation. The author’s ambition
to quantitatively support the presented ideas of the dissertation equipped him with
the desire to extend the work and materialize the current manuscript. Hence, the
author is indebted to the editorial team of Springer Series on Naval Architecture,
Marine Engineering, Shipbuilding and Shipping, Prof. Nikolaos I. Xiros and
Dr. Leontina Di Cecco for their favorable willingness to welcome the book proposal as well as the meaningful guidance during the publication stage. Further,
sincere thanks need to be given to Mr. Amer Badawi, Columbia Grain Inc.,
Ms. Susan Burns, Bunge Limited, Ms. Marion Danneboom, BayWa AG, Ms. Lori
Haugh, Cargill Inc., Mr. Jean-Luc Renaudeau, Groupe Sica Atlantique and
Preface
xi
Ms. Megan Sweet, The Vancouver Fraser Port Authority for prominently dealing
with the author’s illustration inquiries, sharing the impressive as well as captivating
images and granting permission to use the available material from their plentiful
media resources. The author is grateful to Mr. Yoji Ito, Japan Ship Exporters’
Association, for providing the requested publication and Prof. Koichiro Tezuka,
Nihon University, College of Economics for making available his conference paper.
An honor-bound appreciation should be expressed to Mr. Lars Robert Pedersen,
The Baltic and International Maritime Council, who despite his tight schedule,
accepted the invitation and provided the priceless foreword, which would have not
been feasible without Dr. Martin Stopford’s lambent help, Clarkson Research
Services Limited. The e-mail correspondence of Mr. Randy Cartmill, Terminal 5
Portland OR, Ms. Alexandra Morgano, Groupe Sica Atlantique and Mrs. Elizabeth
Ahlefeldt-Laurvig-Lehn, The Baltic and International Maritime Council is greatly
recognized. The indispensable morale support and encouragement from the
author’s parents is deeply appreciated.
The opinions and beliefs communicated in this book are those of the author and
should not be interpreted to reflect the views of his current employer DNV GL AS,
any governmental agency, industry association, as well as the acknowledged
individuals with their referred organizations.
Sandvika, Norway
August 2016
George Adamantios Psarros
Contents
Part I
Prologue
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2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Design Energy Efficiency Improvements . . . . . . . . . . . . . . . .
2.2.1 Design Energy Efficiency Specification . . . . . . . . . . . .
2.3 Types of Charter Party Agreements . . . . . . . . . . . . . . . . . . . .
2.4 Barriers on Implementing the Design Energy Efficiency
Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3 Methodology . . . . . . . . . . . . . . . .
3.1 Preamble . . . . . . . . . . . . . . . .
3.2 Research Design . . . . . . . . . .
3.2.1 Research Method . . .
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1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Design Energy Efficiency Contracting in Charter Parties . . . .
1.3 Outline of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1 Aim and Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.2 Research Design and Methodology . . . . . . . . . . . . . . .
1.3.3 Skeleton of the Book . . . . . . . . . . . . . . . . . . . . . . . . .
Part II
Qualitative Part
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Contents
4 Analysis and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Initial Coding: Themes, Properties and Dimensions . . . . . . . . . . . .
4.2.1 Themes: Time and Voyage Charters . . . . . . . . . . . . . . . . . .
4.2.2 Properties: Rights and Responsibilities (Obligations)
of Shippers or Charterers and Carriers or Ship-Owners . . . .
4.2.3 Dimensions: Breaking Down Groupings Dealing
with the Vessel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Theory Building: Balancing the Interests for Design Energy
Efficiency Improvements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Focused Coding: Content Analysis of Charter Party Contracts . . . .
4.4.1 Information Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.2 Content Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Revisiting the Theory from the Initial Coding . . . . . . . . . . . . . . . .
Part III
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Quantitative Part
5 Chartering Negotiations for Energy Efficiency . . . . . . . . . . . . . .
5.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Theoretical Applications of Games Within Chartering . . . . . .
5.3 Game Theory Model for Sharing Scheme Determination . . . .
5.3.1 Principal—Agent Part . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.2 Bargaining Part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4 Energy Efficiency Investment Appraisal . . . . . . . . . . . . . . . . .
5.5 Conceptualizing the Generated Knowledge . . . . . . . . . . . . . . .
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6 Application—Ocean Grain Transportation . . . . . . . . . . . . . . . . .
6.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Trading Grains by Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3 Dealing with the Inherent Uncertainties . . . . . . . . . . . . . . . . .
6.3.1 Parameter Uncertainties . . . . . . . . . . . . . . . . . . . . . . . .
6.3.2 Uncertainty Propagation . . . . . . . . . . . . . . . . . . . . . . .
6.3.3 Parameter Significance of Choice . . . . . . . . . . . . . . . .
6.4 Energy Efficiency Sharing Scheme for Handymax Vessel . . .
6.4.1 Time Charter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.4.2 Voyage Charter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.4.3 Time Charter Trip . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.5 Energy Efficiency Sharing Scheme for Panamax Vessel . . . . .
6.5.1 Time Charter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.5.2 Voyage Charter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.5.3 Time Charter Trip . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.6 Commenting Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Contents
Part IV
xv
Epilogue
7 Conclusions, Limitations and Recommendations. . . .
7.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3 Limitations and Suggestions for Further Research
7.4 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . .
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Appendix A: Generic Bulk Carrier Designs . . . . . . . . . . . . . . . . . . . . . . . 111
Appendix B: Parametric Distribution Fitting to Observed Data . . . . . . . 113
Appendix C: Monte Carlo Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Appendix D: Parameter Change Interval . . . . . . . . . . . . . . . . . . . . . . . . . 117
Appendix E: Assigned Parametric Distributions . . . . . . . . . . . . . . . . . . . . 119
Appendix F: Simulated Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Appendix G: Milestones of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Abbreviations
B
BIMCO
CAPEX
CAPM
CIHEAM
COGSA
CRSL
ECA(s)
EEDI
EEISS
GHG
IACS
IEA
IEEC
IMO
JSEA
LIBOR
L
MARPOL
MEPC
NPV
OECD
OPEX
PTC
RFR
SEEMP
T
Vessel’s Length
Baltic and International Maritime Council
Capital Expenditure
Capital Asset Pricing Model
Centre International de Hautes Études Agronomiques
Méditerranéennes
Carriage of Goods by Sea Act
Clarkson Research Services Limited
Emission Control Area(s)
Energy Efficiency Design Index
Energy Efficiency Investment Sharing Scheme
Green House Gases
International Association of Classification Societies
International Energy Agency
International Energy Efficiency Certificate
International Maritime Organization
Japan Ship Exporters’ Association
London Inter Bank Offered Rate
Vessel’s Length
International Convention for the Prevention of Pollution from Ships
Marine Environment Protection Committee
Net Present Value
Organisation for Economic Co-operation and Development
Operating Expenditure
Parametric Technology Corporation
Required Freight Rate
Ship Energy Efficiency Management Plan
Vessel’s Draught
xvii
xviii
UNCTAD
VC
WACC
WESTAC
Abbreviations
United Nations Conference on Trade and Development
Voyage Cost
Weighted Average Cost of Capital
Western Transportation Advisory Council
List of Figures
Figure 3.1
Figure 3.2
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 6.1
Figure 6.2
Simple schematic of the adopted sequential exploratory
research design. Note the inductive element of theory
creation from data and the iteration in the second stage
for revisiting the theory. Source Author . . . . . . . . . . . . . . .
Simple schematic conceptualizing the principal stages
of data collection, analysis and comparison.
Note that the reciprocal arrows indicate the comparison
element (triangulation) between initial and focused
coding. Source Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Simple schematic illustrating the contract mechanism
design for the EEISS. Source Author . . . . . . . . . . . . . . . . .
Simple diagram showing the game tree for the principal
(B)—agent (S) problem of moral hazard with hidden
action between a charterer/shipper B and a
ship-owner/carrier S. Source Adapted by Author
from Rasmusen (2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Simple diagram showing the game tree for the principal
(B)—agent (S) problem of adverse selection with
screening between a charterer/shipper B and a
ship-owner/carrier S. Source Adapted by Author
from Rasmusen (2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Simple diagram showing the game tree for bargaining
between a charterer/shipper B and a ship-owner/carrier
S with alternating offers and asymmetric impatience.
Source Adapted by Author from Bierman
and Fernandez (1998) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Time development of seaborne grain trades.
Source Author’s plot using data from CRSL (2016)
and World Bank (2016) . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The structure of ocean grain trades. Source Author’s plot
using data from CRSL (2016) . . . . . . . . . . . . . . . . . . . . . . .
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28
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52
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53
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55
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58
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66
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xix
xx
Figure 6.3
Figure 6.4
Figure 6.5
Figure 6.6
Figure 6.7
Figure 6.8
Figure 6.9
Figure 6.10
Figure 6.11
Figure 6.12
Figure 6.13
Figure 6.14
Figure 6.15
Figure 6.16
Figure 6.17
Figure 6.18
Figure 6.19
Figure 6.20
List of Figures
Elements of a generic grain logistics network.
Source Author’s drawing based on Abis et al. (2014)
and WESTAC (1998) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Loading and storage facilities at a grain export terminal
with a bulk carrier berthed. Photo credit Courtesy
of Bunge Limited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cargo hold being loaded and filled with grain
by a loading spout. Photo credit Courtesy of Cargill Inc. . .
Grain being discharged from a bulk carrier’s cargo hold.
Photo credit Courtesy of BayWa AG . . . . . . . . . . . . . . . . .
Handymax bulk carrier berthed alongside a grain export
terminal and being loaded. Photo credit Courtesy
of Columbia Grain Inc. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Panamax bulk carrier berthed alongside a grain export
terminal and being loaded. Photo credit Courtesy
of Groupe Sica Atlantique . . . . . . . . . . . . . . . . . . . . . . . . . .
Handymax period rates cumulative probability curves.
Source Plotted by Author . . . . . . . . . . . . . . . . . . . . . . . . . .
Handymax voyage rates cumulative probability curves.
Source Plotted by Author . . . . . . . . . . . . . . . . . . . . . . . . . .
Handymax voyage rates cumulative probability curves
(sensitivity analysis). Source Plotted by Author. . . . . . . . . .
Handymax technology investment payback period
cumulative probability curves for voyage contracts.
Source Plotted by Author . . . . . . . . . . . . . . . . . . . . . . . . . .
Handymax time charter trip rates cumulative probability
curves. Source Plotted by Author . . . . . . . . . . . . . . . . . . . .
Handymax technology investment payback period
cumulative probability curves for time charter
trip contracts. Source Plotted by Author . . . . . . . . . . . . . . .
Panamax voyage rates cumulative probability curves.
Source Plotted by Author . . . . . . . . . . . . . . . . . . . . . . . . . .
Panamax voyage rates cumulative probability curves
(sensitivity analysis). Source Plotted by Author. . . . . . . . . .
Panamax technology investment payback period
cumulative probability curves for voyage contracts.
Source Plotted by Author . . . . . . . . . . . . . . . . . . . . . . . . . .
Panamax time charter trip rates cumulative probability
curves. Source Plotted by Author . . . . . . . . . . . . . . . . . . . .
Panamax time charter trip rates cumulative probability
curves (sensitivity analysis). Source Plotted by Author . . . .
Panamax technology investment payback period
cumulative probability curves for time charter
trip contracts. Source Plotted by Author . . . . . . . . . . . . . . .
..
68
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69
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70
..
70
..
71
..
72
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76
..
79
..
82
..
83
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84
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86
..
91
..
93
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94
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96
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97
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98
List of Figures
Figure A.1
Figure A.2
Figure A.3
Figure F.1
Figure F.2
Figure F.3
Figure F.4
Figure F.5
Figure F.6
xxi
Typical cargo hold configuration for a single skin bulk
carrier. Note Drawing not in scale. Source Drawn
by author and based on IACS (2007) . . . . . . . . . . . . . . . . .
Generic views of a typical single skin handymax bulk
carrier (50,000–65,000 DWT). Average dimensions (m):
L × B × T: 200 × 32.26 × 12.9. Note Drawing not
in scale. Source Drawn by author and based on
JSEA (2016) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Generic views of a typical single skin panamax bulk
carrier (70,000–85,000 DWT) Average dimensions (m):
L × B × T: 227 × 32.26 × 14.1. Note Drawing not
in scale. Source Drawn by author and based on
JSEA (2016) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histogram of effort level for handymax period contract
(10,000 repetitions, average = 0.090, standard deviation =
0.242). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Histogram of agent’s reservation price for handymax
period contract (10,000 repetitions, average = 12,193,
standard deviation = 3,852). Source Author’s plot using
data from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histogram of depreciation for handymax vessel (10,000
repetitions, average = 0.064, standard deviation = 0.021).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Histogram of handymax period surplus fraction for corn
and basic technology upgrade (10,000 repetitions,
principal: average = 0.785, standard deviation = 0.0595,
agent: average = 0.215, standard deviation = 0.0602).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Histogram of handymax equilibrium period contract
for corn and basic technology upgrade
(10,000 repetitions, average = 11,434, standard
deviation = 2,684). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histogram of handymax period surplus fraction for corn
and advanced technology upgrade (10,000 repetitions,
principal: average = 0.785, standard deviation = 0.0588,
agent: average = 0.215, standard deviation = 0.0594).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
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xxii
Figure F.7
Figure F.8
Figure F.9
Figure F.10
Figure F.11
Figure F.12
Figure F.13
List of Figures
Histogram of handymax equilibrium period contract
for corn and advanced technology upgrade (10,000
repetitions, average = 11,448, standard deviation = 2,670).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Histogram of handymax period surplus fraction
for wheat and basic technology upgrade
(10,000 repetitions, principal: average = 0.787,
standard deviation = 0.058, agent: average = 0.213,
standard deviation = 0.0584). Source Author’s plot
using data from CRSL (2016a) and the excel add-in
by Barreto and Howland (2006) . . . . . . . . . . . . . . . . . . . . .
Histogram of handymax equilibrium period contract
for wheat and basic technology upgrade
(10,000 repetitions, average = 11,408, standard
deviation = 2,667). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histogram of handymax period surplus fraction for wheat
and advanced technology upgrade (10,000 repetitions,
principal: average = 0.786, standard deviation = 0.0596,
agent: average = 0.214, standard deviation = 0.059).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Histogram of handymax equilibrium period contract for
wheat and advanced technology upgrade
(10,000 repetitions, average = 11,446, standard
deviation = 2,665). Source Author’s plot using
data from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histogram of capital cost (10,000 repetitions, average =
10.88, standard deviation = 0.545). Source Author’s
plot using data from CRSL (2016a), OECD (2016)
and the excel add-in by Barreto and Howland (2006) . . . . .
Histogram of handymax minimum RFR for period
contract and basic technology upgrade
(10,000 repetitions, average = 11,936, standard
deviation = 1,423). Source Author’s plot using data
from CRSL (2016a, b) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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List of Figures
Figure F.14 Histogram of handymax minimum RFR for period
contract and advanced technology upgrade (10,000
repetitions, average = 12,693, standard deviation = 1,434).
Source Author’s plot using data from CRSL (2016a, b)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.15 Histogram of environmental stewardship for handymax
voyage contract and corn as cargo (10,000 repetitions,
basic technology upgrade and low attribute: average =
0.0013, standard deviation = 0.0033, basic technology
upgrade and high attribute: average = 0.0037, standard
deviation = 0.0097, advanced technology upgrade
and high attribute: average = 0.0033, standard deviation =
0.0113). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.16 Histogram of environmental stewardship for handymax
voyage contract and wheat as cargo (10,000 repetitions,
basic technology upgrade and low attribute: average =
0.0018, standard deviation = 0.0047, basic technology
upgrade and high attribute: average = 0.0024, standard
deviation = 0.0081, advanced technology upgrade
and low attribute: average = 0.0019, standard deviation =
0.46, advanced technology upgrade and high attribute:
average = 0.0027, standard deviation = 0.0087).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.17 Histogram of agent’s reservation price for handymax
corn voyage contract and basic technology upgrade
(10,000 repetitions, average = 38.65, standard deviation =
20.11). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.18 Histogram of agent’s reservation price for handymax
corn voyage contract and advanced technology upgrade
(10,000 repetitions, average = 38.61, standard deviation =
20.12). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.19 Histogram of agent’s reservation price for handymax
wheat voyage contract and basic technology upgrade
(10,000 repetitions, Average = 38.43, standard deviation =
19.85). Source Author’s plot using data from CRSL
(2016a) and the Excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xxiii
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. . 128
xxiv
List of Figures
Figure F.20 Histogram of agent’s reservation price for handymax wheat
voyage contract and advanced technology upgrade
(10,000 repetitions, average = 38.70, standard deviation =
20.36). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.21 Histogram of handymax voyage surplus fraction
for corn and basic technology upgrade (10,000 repetitions,
principal: average = 0.786, standard deviation = 0.0587,
agent: average = 0.214, standard deviation = 0.0586).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.22 Histogram of handymax equilibrium voyage contract
for corn and basic technology upgrade (10,000 repetitions,
average = 38.42, standard deviation = 20.22).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.23 Histogram of handymax voyage surplus fraction for corn
and advanced technology upgrade (10,000 repetitions,
principal: average = 0.787, standard deviation = 0.0582,
agent: average = 0.213, standard deviation = 0.059).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.24 Histogram of handymax equilibrium voyage contract
for corn and advanced technology upgrade (10,000
repetitions, average = 38.48, standard deviation = 20.59).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.25 Histogram of handymax voyage surplus fraction
for wheat and basic technology upgrade
(10,000 repetitions, principal: average = 0.787, standard
deviation = 0.0588, agent: average = 0.213, standard
deviation = 0.0593). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.26 Histogram of handymax equilibrium voyage contract
for wheat and basic technology upgrade
(10,000 repetitions, average = 38.41, standard
deviation = 20.99). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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List of Figures
Figure F.27 Histogram of handymax voyage surplus fraction
for wheat and advanced technology upgrade
(10,000 repetitions, principal: average = 0.786,
standard deviation = 0.0592, agent: average = 0.214,
standard deviation = 0.0589). Source Author’s plot
using data from CRSL (2016a) and the excel add-in
by Barreto and Howland (2006) . . . . . . . . . . . . . . . . . . . . .
Figure F.28 Histogram of handymax equilibrium voyage contract
for wheat and advanced technology upgrade
(10,000 repetitions, average = 38.52, standard deviation =
20.81). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.29 Histogram of handymax annual grain loadings for voyage
contracts (10,000 repetitions, Min = 1, Max = 3).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.30 Histogram of handymax spot duration (voyage and time
charter trip contracts) (10,000 repetitions, average = 48,
standard deviation = 24). Source Author’s plot using
data from CRSL (2015) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.31 Histogram of handymax voyage costs (10,000 repetitions,
basic technology upgrade: average = 1.159, standard
deviation = 0.94, advanced technology upgrade:
average = 1.094, standard deviation = 0.87).
Source Author’s plot using data from CRSL
(2016a, b) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.32 Histogram of handymax voyage RFR for basic technology
upgrade (10,000 repetitions, average = 36.51, standard
deviation = 5.26). Source Author’s plot using data
from CRSL (2016a, b) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.33 Histogram of handymax voyage RFR for advanced
technology upgrade (10,000 repetitions, average = 35.68,
standard deviation = 5.02). Source Author’s plot using
data from CRSL (2016a, b) and the excel add-in
by Barreto and Howland (2006) . . . . . . . . . . . . . . . . . . . . .
Figure F.34 Histogram of handymax annual grain loadings for time
charter trip contracts (10,000 repetitions, Min = 1,
Max = 10). Source Author’s plot using data from
CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xxv
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xxvi
List of Figures
Figure F.35 Histogram of effort level for handymax time charter
trip contract (10,000 repetitions, average = 0.128,
standard deviation = 0.266). Source Author’s plot
using data from CRSL (2016a) and the excel add-in
by Barreto and Howland (2006) . . . . . . . . . . . . . . . . . . . . .
Figure F.36 Histogram of agent’s reservation price for handymax
time charter trip contract (10,000 repetitions, average =
15,478, standard deviation = 8,510). Source Author’s plot
using data from CRSL (2016a) and the excel add-in
by Barreto and Howland (2006) . . . . . . . . . . . . . . . . . . . . .
Figure F.37 Histogram of handymax equilibrium time charter trip
contract for corn and basic technology upgrade (10,000
repetitions, average = 14,040, standard deviation = 6,829).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.38 Histogram of handymax equilibrium time charter trip
contract for corn and advanced technology upgrade
(10,000 repetitions, average = 14,083, standard
deviation = 6,925). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.39 Histogram of handymax equilibrium time charter trip
contract for wheat and basic technology upgrade
(10,000 repetitions, average = 14,211, standard
deviation = 6,854). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.40 Histogram of handymax equilibrium time charter trip
contract for wheat and advanced technology upgrade
(10,000 repetitions, average = 14,114, standard
deviation = 6,904). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.41 Histogram of handymax RFR for time charter trip
contract and basic technology upgrade (10,000 repetitions,
average = 11,534, standard deviation = 4,358).
Source Author’s plot using data from CRSL (2016a, b)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.42 Histogram of handymax RFR for time charter trip
contract and advanced technology upgrade
(10,000 repetitions, average = 10,615, standard
deviation = 3,162). Source Author’s plot using data
from CRSL (2016a, b) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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. . 135
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List of Figures
Figure F.43 Histogram of effort level for panamax period contract
(10,000 repetitions, average = 0.096, standard
deviation = 0.231). Source Author’s plot using
data from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.44 Histogram of agent’s reservation price for panamax
period contract (10,000 repetitions, average = 10,541,
standard deviation = 3,813). Source Author’s plot using
data from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.45 Histogram of panamax period surplus fraction for corn
and basic technology upgrade (10,000 repetitions,
principal: average = 0.833, standard deviation = 0.0352,
agent: average = 0.167, standard deviation = 0.0354).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.46 Histogram of panamax equilibrium period contract
for corn and basic technology upgrade (10,000 repetitions,
average = 9,819, standard deviation = 2,798).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.47 Histogram of panamax period surplus fraction for corn
and advanced technology upgrade (10,000 repetitions,
principal: average = 0.832, standard deviation = 0.0350,
agent: average = 0.168, standard deviation = 0.0355).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.48 Histogram of panamax equilibrium period contract
for corn and advanced technology upgrade (10,000
repetitions, average = 9,807, standard deviation = 2,812).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.49 Histogram of panamax period surplus fraction for wheat
and basic technology upgrade (10,000 repetitions,
principal: average = 0.833, standard deviation = 0.0350,
agent: average = 0.167, standard deviation = 0.0353).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.50 Histogram of panamax equilibrium period contract
for wheat and basic technology upgrade
(10,000 repetitions, average = 9,803, standard
deviation = 2,812). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xxvii
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xxviii
List of Figures
Figure F.51 Histogram of panamax period surplus fraction for wheat
and advanced technology upgrade (10,000 repetitions,
principal: average = 0.834, standard deviation = 0.0350,
agent: average = 0.166, standard deviation = 0.0353).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.52 Histogram of panamax equilibrium period contract
for wheat and advanced technology upgrade (10,000
repetitions, average = 9,826, standard deviation = 2,790).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.53 Histogram of panamax minimum RFR for period
contract and basic technology upgrade (10,000 repetitions,
average = 14,803, standard deviation = 3,406).
Source Author’s plot using data from CRSL (2016a, b)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.54 Histogram of panamax minimum RFR for period
contract and advanced technology upgrade (10,000
repetitions, average = 16,529, standard deviation = 3,821).
Source Author’s plot using data from CRSL (2016a, b)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.55 Histogram of environmental stewardship for panamax
voyage contract and corn as cargo (10,000 repetitions,
basic technology upgrade and low attribute: average =
0.0027, standard deviation = 0.0049, basic technology
upgrade and high attribute: average = 0.0020, standard
deviation = 0.0031, advanced technology upgrade
and high attribute: average = 0.0021, standard
deviation = 0.0053). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.56 Histogram of environmental stewardship for panamax
voyage contract and wheat as cargo (10,000 repetitions,
basic technology upgrade and low attribute: average =
0.0010, standard deviation = 0.0020, basic technology
upgrade and high attribute: average = 0.0012, standard
deviation = 0.0024, advanced technology upgrade
and high attribute: average = 0.0021, standard
deviation = 0.0030). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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List of Figures
Figure F.57 Histogram of agent’s reservation price for panamax
corn voyage contract and basic technology upgrade
(10,000 repetitions, average = 34.32, standard
deviation = 16.44). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.58 Histogram of agent’s reservation price for panamax
corn voyage contract and advanced technology upgrade
(10,000 repetitions, average = 34.33, standard deviation =
16.87). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.59 Histogram of agent’s reservation price for panamax
wheat voyage contract and basic technology upgrade
(10,000 repetitions, average = 34.52, standard deviation =
16.63). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.60 Histogram of agent’s reservation price for panamax
wheat voyage contract and advanced technology upgrade
(10,000 repetitions, average = 34.61, standard deviation =
16.71). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.61 Histogram of panamax voyage surplus fraction for corn
and basic technology upgrade (10,000 repetitions,
principal: average = 0.832, standard deviation = 0.0352,
agent: average = 0.168, standard deviation = 0.0351).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.62 Histogram of panamax equilibrium voyage contract
for corn and basic technology upgrade (10,000 repetitions,
average = 34.30, standard deviation = 16.58).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.63 Histogram of panamax voyage surplus fraction for corn
and advanced technology upgrade (10,000 repetitions,
principal: average = 0.831, standard deviation = 0.0351,
agent: average = 0.169, standard deviation = 0.0353).
Source Author’s plot using data from CRSL (2016a)
and the Excel add-in by Barreto and Howland (2006) . . . . .
xxix
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. . 142
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xxx
List of Figures
Figure F.64 Histogram of panamax equilibrium voyage contract
for corn and advanced technology upgrade (10,000
repetitions, average = 34.32, standard deviation = 16.85).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.65 Histogram of panamax voyage surplus fraction
for wheat and basic technology upgrade (10,000 repetitions,
principal: average = 0.833, standard deviation = 0.0349,
agent: average = 0.167, standard deviation = 0.0351).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.66 Histogram of panamax equilibrium voyage contract for
wheat and basic technology upgrade (10,000 repetitions,
average = 34.31, standard deviation = 16.58).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.67 Histogram of panamax voyage surplus fraction
for wheat and advanced technology upgrade (10,000
repetitions, principal: average = 0.833, standard
deviation = 0.0352, agent: average = 0.167, standard
deviation = 0.035). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.68 Histogram of panamax equilibrium voyage contract
for wheat and advanced technology upgrade
(10,000 repetitions, average = 34.36, standard
deviation = 16.91). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.69 Histogram of panamax annual grain loadings for voyage
contracts (10,000 repetitions, Min = 1, Max = 4).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.70 Histogram of panamax spot duration (voyage and time
charter trip contracts) (10,000 repetitions, average = 53,
standard deviation = 14). Source Author’s plot using
data from CRSL (2015) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.71 Histogram of panamax voyage costs (10,000 repetitions,
basic technology upgrade: average = 1.371, standard
deviation = 0.879, advanced technology upgrade:
average = 1.296, standard deviation = 0.827).
Source Author’s plot using data from CRSL (2016a, b)
and the excel add-in by Barreto and Howland (2006) . . . . .
. . 143
. . 143
. . 143
. . 144
. . 144
. . 144
. . 145
. . 145
List of Figures
Figure F.72 Histogram of panamax voyage RFR for basic technology
upgrade (10,000 repetitions, average = 34.59, standard
deviation = 6.55). Source Author’s plot using data from
CRSL (2016a, b) and the excel add-in by Barreto and
Howland (2006). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.73 Histogram of panamax voyage RFR for advanced
technology upgrade (10,000 repetitions, average = 33.67,
standard deviation = 6.08). Source Author’s plot using
data from CRSL (2016a, b) and the excel add-in
by Barreto and Howland (2006) . . . . . . . . . . . . . . . . . . . . .
Figure F.74 Histogram of panamax annual grain loadings for time charter
trip contracts (10,000 repetitions, Min = 1, Max = 6).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.75 Histogram of effort level for panamax time charter trip
contract (10,000 repetitions, average = 0.110, standard
deviation = 0.284). Source Author’s plot using data
from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.76 Histogram of agent’s reservation price for panamax time
charter trip contract (10,000 repetitions, average = 13,390,
standard deviation = 9,554). Source Author’s plot using
data from CRSL (2016a) and the excel add-in by Barreto
and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure F.77 Histogram of panamax equilibrium time charter trip contract
for corn and basic technology upgrade (10,000 repetitions,
average = 12,300, standard deviation = 7,795). Source
Author’s plot using data from CRSL (2016a) and the excel
add-in by Barreto and Howland (2006) . . . . . . . . . . . . . . . .
Figure F.78 Histogram of panamax equilibrium time charter trip contract
for corn and advanced technology upgrade (10,000
repetitions, average = 12,361, standard deviation = 7,865).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.79 Histogram of panamax equilibrium time charter trip contract
for wheat and basic technology upgrade (10,000 repetitions,
average = 12,266, standard deviation = 7,822).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
Figure F.80 Histogram of panamax equilibrium time charter trip contract
for wheat and advanced technology upgrade (10,000
repetitions, average = 12,341, standard deviation = 7,883).
Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006) . . . . .
xxxi
. . 145
. . 146
. . 146
. . 146
. . 147
. . 147
. . 147
. . 148
. . 148
xxxii
List of Figures
Figure F.81 Histogram of panamax RFR for time charter trip contract
and basic technology upgrade (10,000 repetitions, average =
11,489, standard deviation = 2,394). Source Author’s plot
using data from CRSL (2016a, b) and the excel add-in
by Barreto and Howland (2006) . . . . . . . . . . . . . . . . . . . . . . . 148
Figure F.82 Histogram of panamax RFR for time charter trip contract
and advanced technology upgrade (10,000 repetitions,
average = 11,924, standard deviation = 2,589).
Source Author’s plot using data from CRSL (2016a, b)
and the excel add-in by Barreto and Howland (2006) . . . . . . . 149
List of Tables
Table 2.1
Table 2.2
Table 4.1
Table 4.2
Table 4.3
Table 4.4
Table 4.5
Table 6.1
Table 6.2
Table 6.3
Table 6.4
Table 6.5
Table 6.6
Table 6.7
Table 6.8
Table 6.9
Upper bound of the fuel savings that may result from
the implementation of different measures . . . . . . . . . . . . . . .
Allocation of costs and responsibilities under voyage
and time charters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Examples of defects in the technical condition of a vessel
or her equipment rendering her unseaworthy . . . . . . . . . . . .
Standardized charter party forms to be surveyed . . . . . . . . . .
List of common Clauses contained in the standard charter
party forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Coding sub categories for vessel energy efficiency in time
charter parties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Coding sub categories for vessel energy efficiency
in voyage charter parties . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Surplus fractions and equilibrium prices for handymax
period contracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Parameters for evaluating the significance of change
for handymax period contracts . . . . . . . . . . . . . . . . . . . . . . .
Surplus fractions and equilibrium prices for handymax
period contracts (sensitivity analysis) . . . . . . . . . . . . . . . . . .
Agent’s preference characteristics and reservation prices
for handymax voyage contracts . . . . . . . . . . . . . . . . . . . . . . .
Surplus fractions and equilibrium prices for handymax
voyage contracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Parameters for evaluating the significance of change
for handymax voyage contracts . . . . . . . . . . . . . . . . . . . . . . .
Agent’s preference characteristics and reservation prices
for handymax voyage contracts (sensitivity analysis) . . . . . .
Surplus fractions and equilibrium prices for handymax
voyage contracts (sensitivity analysis) . . . . . . . . . . . . . . . . . .
Surplus fractions and equilibrium prices for handymax
time charter trips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
..
16
..
18
..
..
35
40
..
41
..
42
..
43
..
75
..
76
..
77
..
78
..
78
..
80
..
81
..
81
..
84
xxxiii
xxxiv
List of Tables
Table 6.10 Parameters for evaluating the significance of change
for handymax time charter trip contracts . . . . . . . . . . . . . . . .
Table 6.11 Surplus fractions and equilibrium prices for handymax
time charter trip contracts (sensitivity analysis) . . . . . . . . . . .
Table 6.12 Surplus fractions and equilibrium prices for panamax
period contracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 6.13 Parameters for evaluating the significance of change
for panamax period contracts . . . . . . . . . . . . . . . . . . . . . . . .
Table 6.14 Surplus fractions and equilibrium prices for panamax
period contracts (sensitivity analysis) . . . . . . . . . . . . . . . . . .
Table 6.15 Agent’s preference characteristics and reservation prices
for panamax voyage contracts . . . . . . . . . . . . . . . . . . . . . . . .
Table 6.16 Surplus fractions and equilibrium prices for panamax
voyage contracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 6.17 Parameters for evaluating the significance of change
for panamax voyage contracts . . . . . . . . . . . . . . . . . . . . . . . .
Table 6.18 Agent’s preference characteristics and reservation prices
for panamax voyage contracts (sensitivity analysis) . . . . . . .
Table 6.19 Surplus fractions and equilibrium prices for panamax
voyage contracts (sensitivity analysis) . . . . . . . . . . . . . . . . . .
Table 6.20 Surplus fractions and equilibrium prices for panamax
time charter trip contracts . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 6.21 Parameters for evaluating the significance of change
for panamax time charter trip contracts . . . . . . . . . . . . . . . . .
Table 6.22 Surplus fractions and equilibrium prices for panamax
time charter trip contracts (sensitivity analysis) . . . . . . . . . . .
Table 6.23 Summary of results from the game theoretical analysis
(average values) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table E.1 Input data for entertaining the probabilistic models . . . . . . . .
Table E.2 Legend—explanations for the parameters
of the statistical distributions . . . . . . . . . . . . . . . . . . . . . . . . .
Table G.3 Work plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
..
85
..
85
..
87
..
88
..
88
..
89
..
90
..
92
..
92
..
92
..
95
..
96
..
97
..
99
. . 119
. . 120
. . 151
Executive Summary
Regulatory imperatives stipulate that shipping needs to improve its environmental
performance and the agreed framework for reducing its air emissions is composed
of three pillars. The first pillar contains operational measures, such as slow
steaming, propeller and hull cleaning, weather routing and voyage planning, etc.
The second pillar includes design measures, such as air lubrication, bulbous bow,
waste heat recovery, hull optimization, propeller nozzles, diesel-electric drives,
counter-rotating propellers, etc. The third pillar refers to market based measures,
such as emissions trading scheme and a levy/fuel charge. It is recognized that the
first two pillars offer greater flexibility since the industrial players are free to select
the optimum combination of measures. Ultimately, a long-term solution is attained
through the improvement of the vessel’s design energy efficiency. Accepting that
any new technology to be installed on-board a vessel encompasses uncertainty with
respect to its energy saving potential, certain mechanisms are deemed appropriate
for inducing confidence and facilitating wider application of energy saving
technologies.
A relevant mechanism that can foster on the ability to systematically address
energy efficiency could be facilitated within the contractual structure of the charter
party agreement. This book is an attempt to explore this call and to provide supporting premise for putting money into the most energy efficient vessel, rather than
giving merit to the standard design. Such provision holds implications on the
manner that chartering practice has traditionally been arranged. First, the expectations and interests of the parties involved in investing in energy efficient equipment
need to be identified. Second, greater emphasis on monitoring as well as on verifying the vessel’s efficiency is given. Third, any potential conflicting issues with
respect to the freight rate negotiation have to be clarified. To this end, the current
book is focused on the contractual considerations associated with design energy
efficiency improvements without taking into account the technical aspects of the
energy efficient technology investments.
Thus, having set the background of the subject available in the published literature, the aim of the present book is to construct a theoretical proposition on
incorporating design energy efficiency improvements in charter party contracts.
xxxv
xxxvi
Executive Summary
Essential element for shedding light on this topic is the observation of commercial
contracts for the use of the vessel as well as legal instruments for the carriage of
goods by sea. To the author’s best effort and belief, this book has effectively
addressed the following qualitative research objectives:
• Examine if the legal instruments permit the inclusion of clauses into the existing
charter party contracts that can clarify the investment responsibility and obligation of design energy efficiency improvements;
• Identify if any clauses included in the charter party contracts are accounting for
design energy efficiency improvements;
• Determine which vessel design parameters incorporated in the charter party
contract clauses are associated with the topic under investigation;
• Propose any amendments or additions to the relevant charter party contract
clauses that could contain the vessel’s energy efficiency specification;
Furthermore, the following quantitative research objectives have been prominently tackled:
• Suggest a mechanism design of the energy efficiency sharing scheme and its
connection to the chartering procedure;
• Demonstrate the practicality of the designed mechanism within the dry bulk
shipping sector and a specific commodity.
The book’s qualitative research objectives are met by adopting a qualitative,
inductive and exploratory approach where coherence is achieved through the
grounded theory method, a formalized strategy to collect and analyze data (observed
premises) which is composed of two sequential stages. The process begins first with
the initial coding (first stage) of legal instruments on the carriage of goods by sea.
The purpose of this activity is to investigate if it is permitted to regulate the rights and
responsibilities of design energy efficiency improvements. In addition, it seeks to
identify any wedge for incorporating such term in the charter party contract through
the relevant academic literature. Second, the analysis of the relationship of the
contracted parties with respect to energy efficiency specification clauses indicates the
type of cases (voyage and time charters) to select for further data collection.
Consequently, since the research is concentrated around these two types of
contracts, the focused coding (second stage) is supported by sufficient sampling
of the standard agreement forms. In this stage, content analysis is employed for
uncovering themes within the clauses by objectively and systematically searching
for certain subjects within the text (i.e., energy efficiency specification).
Furthermore, it is determined which themes are associated with the vessel’s design
parameters. This process continues until additional data collation does not contribute to the coding of the topic under investigation and any new theoretical lines
of enquiry are not possible to be offered. The latter are reflected by proposing any
amendments or additions to the relevant themes (charter party contract clauses) that
could contain the vessel’s energy efficiency specification.
Delving into the quantitative research objectives, game theory is chosen to
model the interplay dynamics of the chartering negotiations between a shipowner
Executive Summary
xxxvii
(carrier) and a charterer (shipper). Hence, the game participants are two. Depending
on the type of contract (voyage and time charter), game theory can be adjusted to
appropriately represent the incentive mixture for energy efficiency (mechanism
design). The process is composed of two stages. First, the principal–agent problem
is utilized for determining the difference between the two players’ reservation prices
(hire for time and freight for voyage charter) as well as their information asymmetry
related to energy efficiency. This forms the basis where the negotiations can begin.
Second, the two players are given the opportunity to split the difference between
them with alternating offers which resembles a bargaining game and results to the
contracted price. Then, for the given vessel revenue performance, discounted cash
flow analysis is performed to investigate whether the EEISS is profitable and the
expense can be justified. Tantamount to proving the practical validity of the outlined mathematical concepts, an illustrative case study contributes to their clear
demonstration. The working example is focused on seaborne grain transport since it
is acknowledged that grain is one of the most important commodities around the
world for human as well as animal feed.
Briefly, the work reported in this book points out that the current legal instruments do not inhibit any constraint for including a rider-clause related to design
energy efficiency improvements within the charter party contracts. Furthermore, the
time and voyage charter party contracts content analysis indicates that reference to
design energy efficiency improvements is not made explicit, whereas they are
indirectly linked to the vessel’s performance (fuel consumption) which is emphasized only on the former. The quantitative analysis results and through the case
study within the grain ocean transportation, show that the mechanism design
endorses strongly the time charter rather than the voyage contracts. This finding is
attributed to the latter contract’s functionality, i.e., transport is bought on a lump
sum basis covering all the incurred costs. Additionally, given the current dry bulk
(grain) freight market conditions and a fixed investment horizon (15 years), only the
basic technology expenditure ($1.5 million) for the smaller vessels (handymax) can
be recovered. Most notably, the models presented in this book need to be populated
when the market conditions improve, so that new areas of thought and investigation
are revealed. In the best sense, it is expected that by encouraging the incorporation
of a standard rider-clause related to the vessel’s design energy efficiency specification, the responsibility between the involved parties for a sustainable supply chain
will be clarified. Remarkably, an incentive will be created for implementing and
investing in design energy efficiency improvements in both time as well as voyage
charters.
Part I
Prologue
Chapter 1
Introduction
Abstract In addition to regulatory compliance and to irradiate environmental
stewardship across the maritime industry, appropriate mechanisms need to be in
place so that the ocean freight service is delivered in an energy efficient manner.
This chapter is aimed at explaining the book’s motivation, the adopted research
approach which is distinguished between qualitative as well as quantitative, and it
offers an outline of the embodied content.
1.1
Preamble
Although shipping is considered to be a small contributor to atmospheric emissions—
approximately 3.1–2.8% of annual global CO2 and GHG respectively, Smith et al.
(2014)—in comparison to other transportation modes and public utilities such as
power stations, due to more stringent regulatory requirements its environmental
performance needs to be improved (IMO 2012c). Albeit to the expected growth of
world economy and associated transport demand, in the long term, it is made clear
that shipping should be engaged to a reduction target of at least 50% by 2050
compared to 2010 levels (Pachauri et al. 2014). This emissions’ reduction potential
can be attained through a pathway consisting of three pillars, with an indicated value
as estimated by Strand (2011). Accordingly, the first pillar is related to business as
usual or operational efficiency improvements (i.e. slow steaming, propeller and hull
cleaning, weather routing and voyage planning, etc.) leading to 14% decrease. The
second one is associated with the introduction by IMO of the EEDI that encourages
design improvements for new vessels (i.e. air lubrication, bulbous bow, waste heat
recovery, hull optimization, propeller nozzles, diesel-electric drives, counter-rotating
propellers, etc.) yielding 12% reduction. The third one corresponds to market based
measures, it is currently under development and the discussions within IMO are
concentrated on two aspects: emissions trading scheme and a levy (fuel charge) with
anticipated outcome of 32%. The abatement of the remaining 43% emissions will
depend on the impact of the successful implementation of the aforementioned
measures (Strand 2011).
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6_1
3
4
1.2
1
Introduction
Design Energy Efficiency Contracting
in Charter Parties
The documents containing all the terms and conditions of the contract between a
charterer or shipper and a ship-owner or carrier are defined as charter party
agreements. These documents encompass various clauses setting out the rights and
responsibilities of both parties in relation to freight or hire payment and contingencies for strikes, general, average and war. They also contain details such as
amount of freight, lay time, demurrage, the vessel’s construction, speed and consumption, without any reference to the vessel’s energy efficiency specification
(Brodie 2015; Gorton et al. 2009; Panayides 2014). In general, energy efficiency is
associated with reduced fuel consumption for a vessel’s propulsion and power
generation systems and this entails benefits for the company charged with the fuel
bill (Psaraftis and Kontovas 2014). As long as the fuel efficiency is monitored and
verified, the provision of evidence of compliance to the shipping emissions’ regulations adopted by IMO enhances the environmental friendly performance of the
carriage of goods by sea and the supply chain to a larger degree (Lai et al. 2013;
Azevedo et al. 2011). To obtain the energy efficiency gain, design alterations are
required implying additional cost conferred on the ship-owner’s shoulder.
However, the current contracts do not include any clauses for investing in energy
efficient technologies and thus providing details on the vessel’s energy efficiency
specification (Gorton et al. 2009), an incentive that could expedite the successful
implementation of EEDI.
To further reap the effectiveness of the global shipping community’s efforts for
greener performance, it has been argued by Johnson and Andersson (2016) as well
as Psarros and Mestl (2015) that the contractual structure of the charter party
agreements should foster on the ability to systematically address energy efficiency.
In this sense, supporting premise for investing into the most energy efficient vessel
could be provided, rather than giving merit to the standard design. Moreover, the
clauses included in the charter party agreements are associated with bunkers, performance, speed and consumption, but no reference to the energy efficiency
specification of the vessel is made explicit (Hill 2003). Such provision could firstly
identify the expectations of the parties involved in investing in energy efficient
equipment. Secondly, it could give greater emphasis on monitoring and verifying
the vessel’s efficiency. Thirdly, it could clarify any potential conflicting issues with
respect to the freight rate negotiation. Indeed, it has been advocated by Lun et al.
(2015) who were inspired by the contribution of Rai et al. (2012) on contractual
flexibility, that contractual agreements specifying the vessel’s energy efficiency can
become the core mechanism for enhancing environmental performance. In addition,
the rights, duties and responsibilities between the business signatories can provide a
mutual understanding of the common obligation for adopting greener shipping
practices. Thus, the wider acceptance of energy efficiency clauses will strongly
depend upon recognizing that such incentive is strongly connected with social
corporate responsibility goals (Locke 1968). These elements are already embraced
1.2 Design Energy Efficiency …
5
to every organization’s strategic objectives and the possible adoption is anticipated
to depend on how change processes are usually implemented, where the pattern
could be either incremental (slow and steady) or abrupt (sudden and immediate)
(Dacin et al. 2002).
The inclusion of energy efficiency within the charter party agreements would be
expected to entail compliance with environmental regulations (i.e. reduction of
shipping emissions) and contribute to environmentally sustainable shipping operations (Lun et al. 2015). Unfortunately, detailed research on this aspect is rather
limited, with the exemption of the studies by Lindholm (2014) as well as
Rehmatulla and Smith (2015a), where operational efficiency improvements have
been investigated. It has been argued in their work that the clauses for operational
efficiency (i.e. “slow steaming”, “virtual arrival”) have been drafted mainly for
resolving the uncertainties regarding the goal of delivery (cargo/vessel) in each
voyage leg. Moreover, these clauses are not concentrated on the implementation
effectiveness of speed reduction as a measure for dealing with the shipping emissions’ issue. However, touching the rigidity and incompleteness of charter party
agreements with respect to design efficiency improvements is a topic that has not
attracted research interest so far. Therefore, the current book attempts to propose
any necessary changes or additions to the relevant clauses so that energy efficiency
is adequately addressed from a design point of view.
1.3
Outline of the Book
The project work conducted in the present book is focused on the contractual
considerations associated with design energy efficiency improvements without
taking into account the technical aspects of the energy efficient technology
investments. In the following subsections, the statements that the research sets out
to achieve are mentioned together with the project’s main beneficiaries.
Furthermore, details on the research design and the type of data used are also
included and finally the project’s structure is offered.
1.3.1
Aim and Objectives
The aim of the work reported herein is to investigate whether the aspect of design
energy efficiency improvements is made explicit in the charter party agreements.
Additionally, the means that would clarify the investment responsibility between
the involved parties are explored besides with the proposition of a suitable incentive
mechanism. In this context, the objectives of the proposed research can be distinguished into qualitative and quantitative. The qualitative research objectives can be
underlined as follows:
6
1
Introduction
• Examine if the legal instruments permit the inclusion of clauses into the existing
charter party contracts that can clarify the investment responsibility and obligation of design energy efficiency improvements;
• Identify if any clauses included in the charter party contracts are accounting for
design energy efficiency improvements;
• Determine which vessel design parameters incorporated in the charter party
contract clauses are associated with the topic under investigation;
• Propose any amendments or additions to the relevant charter party contract
clauses that could contain the vessel’s energy efficiency specification (design
energy efficiency improvements);
Furthermore, the quantitative research objectives can be accentuated along the
following:
• Suggest a mechanism design of the energy efficiency sharing scheme and its
connection to the chartering procedure;
• Demonstrate the practicality of the designed mechanism within the dry bulk
shipping sector and a specific commodity.
1.3.2
Research Design and Methodology
As mentioned already in the previous subsections, the proposed topic has not been
touched upon and therefore exploratory research is utilized in the current book. This
means that an inductive approach is adopted due to the lack of published research
and lack of knowledge with the purpose to develop a better insight into the
aforementioned qualitative research objectives (Wilson 2014). In addition, the study
is largely qualitative since the intent is to explore the complex set of factors surrounding the proposed topic and present the varied beneficiaries perspectives
(charterer or shipper and ship-owner or carrier). Thus, the literature is used to frame
the problem (i.e. contractual considerations associated with design energy efficiency
improvements) and to understand the beneficiaries’ relationship (i.e. their responsibilities and obligations) (Creswell 2009; Saunders et al. 2012).
The problem’s theoretical ideas are emerged out of the collection and analysis of
data, frequently cited as grounded theory method. The purpose of utilizing the
grounded theory method is based on the fact that the process is comprised of two
sequential stages. Firstly, the initial coding with reference to the legal regimes for
the carriage of goods by sea is intended to explore and generate properties as well as
dimensions related to design energy efficiency improvements. In this respect, the
first research objective of examining if the legal instruments permit the inclusion of
clauses into the existing charter party contracts that can clarify the investment
responsibility and obligation of design energy efficiency improvements is met.
Whilst, the second stage known as focused coding, builds on the first stage findings
and through the analysis of the standard (generic) charter party forms available from
the website of BIMCO. Hence, the previous set of findings is not only
1.3 Outline of the Book
7
complemented, but also expanded (i.e. identifying if any clauses included in the
charter party forms are accounting for design energy efficiency improvements and
determining which vessel design parameters incorporated in the charter party
clauses are associated with design energy efficiency improvements). At this point,
the course of the study has demonstrated that the second and third objectives have
been achieved.
For the latter stage, content analysis of the contracts is employed as a technique
for identifying subjects, terms as well as attributes within the contract clauses and
seeking contrasts under certain data component parts (i.e. vessel description) from
which a theoretical elaboration can begin to emerge. The resulting theory for the
work conducted in this book is the fourth research objective, the proposal of any
amendments or additions to the relevant charter party contract clauses that could
contain the vessel’s energy efficiency specification. This operation continues until a
point is reached where the collation of new data no further illuminates any theoretical concept and has been supported by the combination of the findings of the
two stages (initial and focused coding), giving harmonizing strength of the result
(Bryman 2012).
Having set the theoretical foundation of elaborating clauses related to the vessel’s energy efficiency specification within the charter party agreements, the reader
is now prepared to tackle the quantitative research objectives utilizing game theory
as the main tool. It is noted that this book is not intended to exhaust all possible
contributions that have ripened within the scientific domain of game theory.
Instead, the goal is to provide initial clues of designing an incentive mechanism (an
area which has thrived in the context of game theory) and offer its methodological
adaptation to chartering negotiations. Initially, the analysis is focused on aligning
the shipper’s (charterer’s or principal’s) profit maximization objectives when delegating the task of ocean transportation to a carrier (ship-owner or agent). The agent
possesses information (i.e. vessel’s energy efficiency) that either their actions can be
unobserved by the principal (moral hazard), or their valuation can be ignored by the
principal (adverse selection). The former situation resembles the time charter, whilst
the latter reminds the voyage charter contract. These information problems impose
costs to the principal, so that the informed agent is motivated to reveal as well as
credibly communicate their private knowledge and an efficient contract is designed.
Hence, the incentive mechanism is aimed at defining the principal’s desirable
allocative expenses that need to be implemented in order to diminish the impact of
information costs (Laffont and Martimort 2002).
Consequently, with the preferences between the principal and the agent being
assigned, it is inescapable to establish how the two parties could possibly cooperate
under favorable terms. The negotiation process involves a series of alternating
offers between the bargainers who are concerned about the time at which an
agreement is reached. This entails that delay costs are incurred by both players. The
player with the longest patience to conclude the negotiation and complete the
vessel’s fixture is understood to enjoy the strongest bargaining power. As a result,
the gains from the cooperation are divided between the players in a share equivalent
to their bargaining power (Osborne and Rubinstein 1990). The numerical solutions
8
1
Introduction
to the adapted game models originate from CRSL fixtures sample which contains
data associated with freight, cargo type and quantity, as well as hire rate and period.
Given the fact that the literature on the addressed issue is non-existent (the
inclusion of energy efficiency specification clauses in charter party contracts) and
although no conclusive answers will be provided, at least some direction for further
research can be drawn. Furthermore, it is expected that the conducted work in this
book is accurate enough and the material presented herein is supported by multiple
sources of evidence/references, hence the reader will be convinced for its scientific
merit (Wilson 2014). Indeed, Sugden (2011) states that for any sequence of actual
observations of the real world, there is infinity of different patterns where that
sequence fits. Then, inductive inferences can be grounded albeit to prior selection of
a small subset of patterns which are conforming to prior expectations about the
world and its regularities (i.e. environmental performance). In turn, this implies that
despite the fact that very little is known or understood about the mechanism for
projecting this pattern, reasonable confidence has been built from what has been
already observed (Sugden 2011).
1.3.3
Skeleton of the Book
The current work is divided into seven chapters, which are structured as follows:
the first chapter, Introduction, sets the landscape for the topic under investigation
(design energy efficiency improvements) and addresses the motivation to conduct
the research study (environmental awareness and regulatory compliance). In
addition, it presents the aim as well as objectives of the study and gives an overview
of the selected research design together with the corresponding research method.
The first part of the book forms the qualitative section (Chaps. 2 through 4), and
expose the reader to the qualitative study. There, the intent is to exhibit the principles that allow the elaboration of energy efficiency specification clauses within the
charter party agreements. As a start, the second chapter, Literature review, is
devoted to present the connection of design energy efficiency improvements to the
maritime regulatory framework. Then, the types of charter party agreements that are
commonly used within shipping are described, the parties’ duties are outlined and a
short discussion on the barriers that could inhibit the introduction of the vessel’s
design energy efficiency improvements follows.
In the third chapter of the present work, Methodology, the framework of the
conducted research is described, where essential element is the grounded theory
method. Grounded theory is a formalized strategy to collect and analyze data
(observed premises) which enables to answer the particular research questions and
meet the study’s objective. This entails that a qualitative inductive and exploratory
approach is adopted for seeking a theoretical generalization in a sense that there is a
gap in supporting the logic argument for including the element of design energy
efficiency improvements in the charter party agreements. Furthermore, the methods
for data collation and analysis are explained, followed by arguments and evidence
1.3 Outline of the Book
9
embracing their choice. Nevertheless, despite the fact that the study is based on
desk research, the findings are emerging from two sequential stages which not only
complement each other, but also strengthen their validity.
The fourth chapter, Analysis and findings, is concerned with presenting how the
application of the grounded theory method is carried out. For the first stage, the
analysis and its results are grounded on the initial coding of the legal instruments on
the carriage of goods by sea in order to categorize dimensions as well as properties
relevant for design energy efficiency improvements. Additionally, it seeks to
identify if it is permitted to regulate the rights and responsibilities of design energy
efficiency improvements. This formulation sets the premise for the second stage,
which is the content analysis of the existing standard charter party contract forms.
In a nutshell, the sequential work intends to determine any clauses associated with
design energy efficiency improvements and propose any revisions that could
incorporate the vessel’s energy efficiency specification.
The second part of the book constitutes the quantitative section (Chaps. 5 and 6)
which drives the reader to the quantitative study. Essentially, the effort is put
towards designing an incentive mechanism for energy efficiency and testing its
profundity through a case. The core ingredients of the mechanism are revealed at
chapter five, Chartering negotiations for energy efficiency, which consist of the
principal-agent and bargaining models, together with investment appraisal techniques. The mathematical expressions are kept as simple as possible, so that the
reader does not have to invoke any advanced game theory as well as economic and
financial concepts. Yet, to the author’s best knowledge, the synthesis is sufficiently
powerful that clearly demonstrates how the existing chartering practice can be
revolutionized.
In Chap. 6, Application to ocean grain transportation, the reader is invited to
navigate with data through the repertoire of approaches outlined in the previous
chapter. The integration of such insight streams from the exhibited material is so
immanent, that any residual hesitation on the successful implementation of the
proposed incentive mechanism would have been removed. This achievement is
translated into a satisfaction for retaining the book’s targeted and promised research
objectives.
The last chapter, Conclusions, limitations and recommendations, is dedicated for
concluding the performed work, addressing suggestions for further research and
making recommendations drawn upon the second chapter’s literature review.
Ultimately, it should be stressed that while the book concludes, the presented topics
have been investigated with a humble perception. At the same time, the matter
should not be interpreted as complete or final and undoubtedly is open to
improvement. Supplementary material supporting the quantitative section is contained in the Appendices.
Part II
Qualitative Part
Chapter 2
Literature Review
Abstract In the book’s second chapter, a background related to design energy
efficiency improvements and their connection to the maritime regulatory framework
is presented. Consequently, a subsection is devoted to the types of charter party
agreements that are commonly used within shipping and a brief on the parties’
duties is provided. Then, a short discussion on the barriers that could inhibit the
introduction of the design energy efficiency improvements is given.
2.1
Preamble
Although the energy saving potential of new technologies would be higher than the
one offered by existing ones, the substitution of the latter by the former could be
delayed albeit to proven results. The adoption of new energy efficient technologies
is followed by further competence building entailing that previous expertise
becomes obsolete and familiarization efforts are required (Mulder 2005). Moreover,
the acceptance of new technologies is governed by each user’s risk perception on
performance. These obstacles could be overwhelmed when the regulations
are shaped towards addressing not only safety, security and environmental protection standards, but also nurturing innovation, growth and advancement needs
(Herzenstein et al. 2007).
2.2
Design Energy Efficiency Improvements
In response to tackling the climate change in maritime transport, amendments to the
MARPOL Convention were adopted in July 2011 and were entered into force in
January 2013. To this end, a new chapter containing regulations aimed at improving
the energy efficiency of vessels was added on Annex VI, the dedicated part of the
Convention related to the prevention of air pollution from vessels. These changes
made mandatory the introduction of the EEDI for new vessels which is concerned
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6_2
13
14
2 Literature Review
with design improvements. Furthermore, they stipulated the establishment of a
mechanism for all vessels (SEEMP) in order to use operating measures that improve
the energy efficiency of the vessel and requirements for survey and certification,
including the format for IEEC (IMO 2012a). During the EEDI’s development,
conducted research (Kristensen and Lützen 2012; Ozaki et al. 2010) was associated
with this regulation’s thorough analysis and such technical detail will not be covered herein.
The EEDI regulation is believed to be a proactive performance based mechanism
designed to induce technical measures for improving the energy efficiency of
vessels and represents an expression of CO2 emissions per ton-mile of transport
work. Its non-prescriptive nature is attributed to the fact that the industry players are
free to choose the optimum combination of energy saving technologies, as long as
the required energy efficiency level is attained and compliance with the regulation is
obtained (Otsubo 2015). In defense of this view, Chang (2012) and Smith (2012)
concluded that for a long term solution to the issue of shipping emissions, the
regulations need to address energy efficiency in the design of vessel engines and
hulls. Besides, the effects of the application of the new technologies need to be
taken into consideration without affecting basic vessel design parameters such as
deadweight capacity and speed (Chang 2012; Smith 2012). Intuitively, these
parameters are vessel and trade specific, thus embrace distinctive characteristics of
an ocean freight service. Hence, through the EEDI, it should be attempted to thrust
ship-owners to invest in vessels that have state of the art technologies for reducing
fuel (energy) consumption and should not be interpreted to penalize vessels with
higher installed power. Then, the operation of the vessel is affected by the SEEMP
which supplements EEDI and impacts the fuel (energy) consumption (Stevens et al.
2015).
Additionally, it is asserted that with the input of industrial stakeholders
(equipment manufacturers, shipbuilders, ship-owners, verifiers, etc.), technology
roadmaps for exploring potential benefits and challenges of radical new developments or retrofit options can be identified for different types of vessels (Gilbert
2014; Vergara et al. 2012). It is also recognized that such flexibility, sets the
requirements and inherently specifications for future vessel designs albeit to the
feedback from new vessels allowing a dynamic change on the required EEDI and
thus stringent control (Ančić and Šestan 2015). The latter will be facilitated through
the need to demonstrate the performance of each technology in terms of (reduced)
energy usage for a given vessel under a range of operational scenarios and environmental conditions (Calleya et al. 2015). Further lines of argument have been
urged by Devanney (2011) to design vessels that are capable of attaining the
contracted speed (albeit to the allowable margin) during market peaks, including
heavy weather and without reaching the engine’s limits in order to avoid any
machinery failures. Reference is given to vessels with large propeller diameter
(hence increased propulsion efficiency) due to a combination of large bore size
(translated into higher power per cylinder), lower rotational speeds and greater fuel
economy (Devanney 2011).
2.2 Design Energy Efficiency Improvements
2.2.1
15
Design Energy Efficiency Specification
Energy efficient technologies concerned with the vessel’s design can be categorized
depending on their characteristics and effects on EEDI, i.e. reduction of main
engine and auxiliary power in terms of fuel consumption (IMO 2013):
1. Reduction of main engine power:
• Cannot be separated from overall performance of the vessel;
–
–
–
–
Low friction coating,
Bare optimization,
Rudder resistance,
Propeller design.
• Can be treated separately from the overall performance of the vessel;
– Hull air lubrication system (an air carpet is created beneath the vessel’s hull
via air injection to reduce frictional resistance—it can be switched-off),
– Wind assistance (sails, Flettner Rotors, kites—subjected to wind condition).
2. Reduction of auxiliary power:
• Effective at all time;
– Waste heat recovery system (exhaust gas heat recovery and conversion to
electric power).
• Depending on ambient environment;
– Photovoltaic cells.
Although the reader may argue that the aforementioned technologies are farfetched, recent research in conjunction with simulated experiments as well as full
scale trials provides evidence that some of them can be adopted in short term, while
others need refinements and adjustment of production capacity to lower their costs.
For instance, this is quite relevant for hull air lubrication where up to 13% of net
energy savings have been recorded (Fukuda et al. 2000; Mizokami et al. 2013;
Kawakita et al. 2015; Kumagai et al. 2015). However, a lower energy saving
potential has been observed (4%) for other technical improvements such as propeller design and hull coating (Armstrong 2013). This result coincides with the duct
(Kim et al. 2015) as well as rudder with bulb and fins appendages (Hai-long et al.
2016) predicted propulsive efficiency from simulated calculations. With respect to
the other technologies, only desk studies have been published and it is worth
pointing out the efforts of Fujiwara et al. (2005), Luyu et al. (2010), Traut et al.
(2014), Viola et al. (2015) for wind assisted propulsion, Lindstad et al. (2013),
Marzi and Gatchell (2012) for hull optimization, Livanos et al. (2014) for waste
heat recovery and Glykas et al. (2010) for photovoltaic cells.
16
Table 2.1 Upper bound of
the fuel savings that may
result from the
implementation of different
measures
2 Literature Review
Technology/design improvement
Low friction coating
Bare optimization
Rudder resistance
Propeller design
Air lubrication
Wind assistance
Waste heat recovery
Photovoltaic cells
Source Fulton et al. (2009)
Fuel efficiency gain (%)
<5
<9
<4
<4
<15
<25
<10
<4
Description of the energy saving equipment is contained in the EEDI technical
file, where the potential energy saving (i.e. reduced percentage of fuel consumption)
is indicated for modifying the design of existing installations, replacing existing
equipment or adding new equipment and modifying the operating conditions of an
existing installation (IMO 2012b). Admittedly, it is challenging to track the energy
saving potential and in the absence of any tangible results the following tentative
values can be realized as shown in Table 2.1. It is claimed that when these measures
are combined, an overall up to 20–30% reduction in CO2 emissions on average
from the existing fleet can be anticipated (Fulton et al. 2009). It should be
emphasized that this uncertainty nurtures the need for transparent monitoring of
efficient vessel operations where the collation of real time data can be fed up into
bottom up models for quantifying more accurately the energy usage on-board
vessels (Gilbert and Bows 2012).
There is indeed evidence to support the latter statement by the initiative of
industrial players to equip vessels with data acquisition systems installed within the
machinery and navigation systems. The purpose is firstly to monitor the performance of their fleets along various routes and verify the simulated results during the
vessel’s design phase as well as provide a cornerstone to comply with the
requirements of the SEEMP (Marty et al. 2012; Baldi et al. 2014; Perera et al. 2015;
Fun-sang and Caprace 2015; Bialystocki and Konovessis 2016; Deligiannis 2016).
Secondly, to develop a methodology for benchmarking and analyzing real time
measurements based on probabilistic methods (Petersen et al. 2012; Sasa et al.
2015; Trodden et al. 2015; Bocchetti et al. 2015; Meng et al. 2016; Kakuta et al.
2016; Orihara et al. 2016). As a matter of fact, much more visibility, clarity and
scrutiny than ever before can be immortalized by such evolvement, and with no
exaggeration, simple regulatory compliance will not be adequate in the future.
Essentially, in turn this may acquaint the business and economic benefits (i.e.
enforce performance guarantee and energy efficiency specification clauses) of design improvements between charterers and owners as well as facilitate better
communication of the financial benefits which can be expected from installing
energy efficient technologies (Rehmatulla and Smith 2015a).
2.3 Types of Charter Party Agreements
2.3
17
Types of Charter Party Agreements
From a functional point of view (i.e. volume of cargo to be transported from one
location to another or the need for an extra vessel for a period of time) the freight
market has two different types of transaction. One is the freight contract (voyage
charter) in which the shipper or charterer buys transport from the ship-owner at a
fixed price per ton of cargo. The second is the time charter (contract) under which
the vessel is hired by the day with the length of the hire taken to complete a single
voyage (trip charter) or a period of months or years (period charter) (Gorton et al.
2009; Stopford 2009). The freight contract suits players who prefer to pay an agreed
sum and leave the management of the transport, hence commercial risk to the
ship-owner (spot market). The time charter is for experienced ship operators who
prefer to manage the transport themselves and it creates the business of industrial
shipping based upon the charterers’ decision to carry the commercial risk (Stopford
2009). There are other types of contracts such as contract of affreightment or
consecutive voyages which correspond to a hybrid form of voyage charter; whereas
the bareboat or demise charter amounts to the lease of the vessel from the owner to
the charterer. The latter is really a financing arrangement and it is not related to the
use of the vessel for transporting cargoes (Gorton et al. 2009; Stopford 2009).
To this end, it is not unreasonable to suggest only two types of contracts for the
carriage of goods by sea since from the inaugural work of Pirrong (1993) it is
reported that long term contracting is interchanged between spot deployments. In
addition, the contractual specificities depend on the characteristics of the cargo
flows, the nature of the markets for the transported commodity, the geographic
dispersion of supply sources and the characteristics of the vessels employed in that
trade. For instance, there is little incentive to haggle over the services of an obsolete
vessel when her contract expires because the quasi gains are trivial (Pirrong 1993).
Similarly, it is advocated by Nomikos and Giamouzi (2013) as well as Fagerholt
et al. (2010) that vessels are allocated by considering the best performing contract
(whether a spot or a period arrangement will be signed or extended) not only in
terms of profitability and avoidance of significant freight rate risks, but also
accounting for strategic positioning aspects. For example, during a market upturn,
ship-owners prefer chartering their vessels under (short) voyage contracts as
opposed to time charter contracts (thus maintaining resource flexibility). Though at
the same time, charterers switch to hire vessels under (lengthy) time charter contracts as opposed to voyage contracts (preferring thus resource commitment)
(Axarloglou et al. 2013). What is really remarkable however, is that, fuel consumption as perceived to be correlated to technology advancements, was found to
be completely unrelated to time charter rates during buoyant market conditions.
Thus, different consumption rates impacting on charterers’ costs mattered only
marginally for contract rates (Köhn and Thanopoulou 2011).
The commercial operation of a vessel always involves certain costs and
responsibilities which are allocated in a slightly different way under the type of
charter, as shown in Table 2.2. Under a voyage charter, the ship-owner contracts to
18
2 Literature Review
Table 2.2 Allocation of costs and responsibilities under voyage and time charters
Voyage charter ($/t)
Voyage
costs
Despatch/demurrage
Costs that may be shared in
Loading/discharging
different ways
Stevedoring/trimming
Certain costs to be paid by
Port charges, fees
charterer
Cleaning holds
Costs of the owner
Cargo claims
Canal transit dues
Bunker fuel
Other voyage expenses
Operating
Wages
costs
Provisions
Maintenance
Repairs
Stores and supplies
Lube oil
Water
Insurance (Hull, War,
P&I)
Overheads
Capital
Capital repayment
costs
Interest on own and
borrowed capital
Depreciation on invested
capital
Brokerage
Source Gorton et al. (2009) and Stopford (2009)
Time charter
($/day)
Costs of the
charterer
Costs of the
owner
carry a specific cargo in a specific vessel for a negotiated price per ton which covers
all the expenses incurred during the voyage, such as voyage (including cargo
handling and port/canal charges) costs, operating costs and capital costs. The time
charter is an agreement between owner and charterer to hire a vessel, complete with
crew, for a fee per day, month or year. In this case, the ship-owner is responsible for
the capital and operating costs, whereas the charterer pays all incurred expenses
associated with the voyage (Gorton et al. 2009; Stopford 2009).
In an attempt to balance the interests of the ship-owner and charterer, as well as
the fact that sea trade is performed internationally albeit to special and hazardous
conditions, where it is practically impossible for one party to supervise the work of
the other daily, legal regimes (i.e. Hague-Visby Rules, Hamburg Rules, Rotterdam
Rules) were enacted to govern the contracts for the carriage of goods by sea (i.e.
bills of lading). This development introduced a set of contractual duties which was
to be used as a base for performance (Dockray and Thomas 2007; Singh 2011). The
implied duties cover seaworthiness, care for the cargo, deviation, reasonable dispatch and not to ship dangerous cargo, whilst for the first three exempting liability
must be transparently attained. The suitable incorporation of the Rules into the
charter party is secured by a clause paramount, or setting out sections of the Rules
2.3 Types of Charter Party Agreements
19
in the charter party, or the provision that any set of Rules that applies to any bill of
lading issued under the charter party shall also apply (Baatz 2014; Todd 2016).
2.4
Barriers on Implementing the Design Energy
Efficiency Improvements
As it has been shown from Table 2.2, due to the different responsibilities and costs
allocated in voyage and time charters, the carrier or ship-owner (agent) and the
shipper or charterer (principal) are not provoked into a steady implementation of
design energy efficiency improvements. In essence, this can be explained by the fact
that the two players have different profit maximization goals from the trade-off
between the cost of energy efficiency and the cost of measuring the performance.
Moreover, assessing the vessel’s performance accurately within a practical amount
of time (i.e. contract’s duration) insurmountably creates uncertainty for curbing any
varying levels of cost sharing (Eisenhardt 1989; Jensen and Meckling 1976). To
further elaborate on this point, it is asserted by Sorrell et al. (2004) as well as Fleiter
and Plötz (2013) that owning to constraints on time, attention and the lack of ability
to process information that may lead to cost effective opportunities, the players do
not make decisions in the manner assumed in economic models (i.e. life cycle cost
consideration). As a consequence, additional costs related to management overheads, information collation and analysis may not be accounted for, thus implying
uncertainty and lack of confidence (bounded rationality). In turn, the players may
neglect energy efficiency opportunities, even when given good information and
appropriate incentives are available or even known (Sorrell et al. 2004; Fleiter and
Plötz 2013).
The latter constitute an important reason why energy efficiency improvements
can be hampered. The most prominent example of split incentives is the investor—
agent/user—principal dilemma, where the carrier or ship-owner—agent is responsible for building the vessel, but the shipper or charterer—principal under a time
charter contract receives the benefit in the form of lower energy bills. In this case,
the agent would not receive a financial reward for the expensive energy efficiency
improvements. On the other hand, the principal, who would receive the benefit and
thus have an incentive to install energy efficient equipment, does not own the vessel
and cannot install it (Sorrell et al. 2004; Fleiter and Plötz 2013). Convergence of
such inconsistent objectives would create agency costs being translated into
incentives, whereas a premium (remuneration) would be required to compensate for
these additional costs (Arnold 2010; Watson and Head 2013).
To attach more credibility in bounded rationality and split incentives, the works
of Balland et al. (2015), Jafarzadeh and Utne (2014) as well as Rojon and Dieperink
(2014) were based on interviews with shipping experts, where it was concluded that
the absence of objective and verified data in combination with the harsh economic
climate were inherently forcing short term survivability. Hence, such behavior
20
2 Literature Review
clearly inhibits any motivation for investing in design energy efficient improvements. Similarly, Lun et al. (2014) and Agnolucci et al. (2014) concur that the
emphasis on operational efficiency in the current charter party agreements for
enforcing warranties and legal legitimacy weaken the promotion of information
monitoring systems as well as the cooperation between principals and agents
concerning energy efficient equipment. However, from the perspective of contractual complexity, agreements encompassing only performance measures may
induce the agent to engage in dysfunctional action, i.e. maximize own margins and
dampen the incentive of paid bunkers, which is not necessarily in line with the
principal’s objective of minimizing transport cost (Baker 1992).
Armstrong and Banks (2015) suggest that although the industry is acquainted
with such information systems, still the lack of shared goals and objectives between
commercial and technical stakeholders (principals and agents) prevents the realization of energy efficiency. In this respect, reference is given to the misconception
of performance monitoring. Voyage performance has more “commercial” nature
related to voyage cost minimization, revenue maximization and efficient vessel
utilization. Whereas, vessel performance has more “technical” nature and is associated with hull and propeller condition so as to plan the appropriate maintenance
activities (Armstrong and Banks 2015). Additionally, energy efficient technologies
are impeded with the information from performance monitoring equipment
remaining imperfect due to absence of any reliable benchmark for crediting the
energy saving potential (Poulsen and Sornn-Friese 2015). Therefore, it is plausible
that the technologies will be selected based on the trade-off between bunker price
and equipment cost, rather than their energy saving potential (Stevens et al. 2015).
In general, any agent would like to reimburse the energy efficiency investment
expenditure, but such effort can be obstructed by the commercial imperatives of the
observed chartering practice (see Sect. 2.3). It is not rare that costly technologies
cannot be implemented to some types of charter contracts either due to short
duration, or lack of control over operations. Moreover, the negotiated price does not
mirror the economic benefit of the claimed fuel efficiency, even if the vessel is
resold (Rehmatulla and Smith 2015b). To further reflect on that, the advantages
granted by trading on the spot market (flexible employment) do not allow acute
recognition of energy efficiency. Admittedly, the realization of fuel savings is a time
consuming process which can take years of assessment and evaluation, whilst such
charters usually expire long before these effects can be genuinely diagnosed. By
contrast, vessels trading under long term contractual relationships (bareboat and
long period charters); have experienced tremendous energy efficient performance
(Poulsen and Johnson 2016). Though, the latter is attributed more on operational
initiatives rather on implementing design measures where the principal may not be
willing to share the cost with the agent. Instead, the agent may not be directly
rewarded for energy efficiency, but at least it is likely to improve the possibility of
winning a charter and obtain better fleet utilization. Nevertheless, principals gradually have given emphasis on the agent’s energy efficiency and in some cases have
influenced the vessel’s design (installed wind assistance and air lubrication system).
2.4 Barriers on Implementing the Design Energy Efficiency Improvements
21
However, the time charter duration is too short (usually half to one year) for
recouping the investment which may require a longer payback period (Rehmatulla
et al. 2015).
Knowing that long term contracts (time charters) are fit for incentive screening
through their performance clauses and short term contracts (voyage charters) allow
a better matching between the abilities of agents and the needs of principals
(Macho-Stadler et al. 2014), it is necessary to provide a mechanism that specifies in
detail the agent’s characteristics (energy efficiency specification and costs). This
should be subject to the available evidence (energy reduction potential) and their
relationship is not only emerged through profit maximization (incentive compatibility), but also is blended with general utility aspects, i.e. greener lifecycle performance (Page 1991; Ochs and Roth 1989). The extent to which such undertaking
(i.e. inclusion of clauses related to the design energy efficiency equipment specification of the vessel and investment sharing) is plausible will be argued in the
fourth chapter.
Chapter 3
Methodology
Abstract This chapter is aimed at presenting the framework of the conducted
qualitative research. Essential element is the observation of commercial contracts
for the use of the vessel (charter party) as well as legal instruments for the carriage
of goods by sea with the result being a theory generalization. This entails that a
qualitative inductive and exploratory approach is adopted in a sense that there is a
gap in the logic argument between the conclusion and the observations being made.
The coherence for this reasoning is achieved through the grounded theory method, a
formalized strategy to collect and analyze data (observed premises) which enables
to answer the particular research objectives and meet the book’s aim.
3.1
Preamble
It is recognized that in social, physical or mathematical sciences knowledge is
evolved from the reciprocal analysis of theory (modeling) and research, where data
can be collated either by a sample survey or a literature review. During such
endeavor, different decisions are made, merely subjective, with respect to variables,
levels of detail and sample space. The inferred ideas and hypotheses furnished by
the analysis may then need to be tuned so that either the model is harmonized with
the data to illustrate the state of affairs, or the model is modified to better describe
the data. This creative iteration between theory and research adds objectivity with
the aim to converge as soon and reliable as possible (Box 1980).
3.2
Research Design
The formulation of clauses in the commercial contract for the use of a vessel
between a carrier or ship-owner and a shipper or charterer is quite complex and
tailored to the specific trade. This means that subject to the book’s qualitative
research objectives, the examination of the legal instruments will allow to
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6_3
23
24
3
Methodology
understand the two parties’ obligations and render under which set of circumstances
the inclusion of clauses that can clarify the responsibility of design energy efficiency improvements is permitted. Thus, it is of concern to explore the way in
which the two parties’ commercial affairs are regulated and offer suggestions as to
how they may be improved within the existing and present practice of the two
parties’ business roles. Furthermore, the investigation of the standardized charter
forms is paramount in determining if design energy efficiency improvements are
included as a subject term in the contractual clauses and which vessel parameters
already incorporated in the clauses can be related to design energy efficiency.
Essentially, such research objectives are hard to tackle without seeking insights
from published interpretations of the legal instruments (secondary data) and an in
depth inquiry of standardized charter forms (primary data). This interpretive philosophy is associated with qualitative research and is aimed at establishing trust,
access to meanings and informed understanding of the addressed topic, i.e. design
energy efficiency improvements within the charter part clauses.
This research is inductively built from objective interpretations of the meanings
of collected data and honors a style that portrays the importance of recognizing the
complexity of the investigated situation. It means that the researcher is the protagonist during the whole process of delving into new layers of information into a
sequential and iterative fashion (Creswell 2009; Saunders et al. 2012). Its procedural regimen is aimed at firstly achieving a level of understanding that transcends
the results and secondly at identifying matured areas as well as areas where more
knowledge and practice recommendations need to be developed. Its universal
applicability of systematic synthesis of research findings to accumulate, organize
and interpret information is demonstrated by contributions from multiple disciplines, i.e. entrepreneurship (Rauch et al. 2014), family research (Goldberg and
Allen 2015), health psychology (Bishop 2015), corporate governance (McNulty
et al. 2013), policy making (Ritz 2011), economics (Starr 2014), as well as management research (Klag and Langley 2013). Commonly to these studies, the main
investigated issues of design energy efficiency as mentioned previously, need to be
exposed from a critical realism perspective with the descriptive findings based on
verbal data rather than numbers or statistics and being generalized at a later stage,
aspects that can be effectively delineated through qualitative rigor.
An inductive approach is employed to develop a richer theoretical perspective
since the investigation of the inclusion of energy efficiency specification clauses in
charter party contracts is relatively new and is not covered in the existing academic
literature, as discussed in the introduction. To this end, albeit to the shortage of
published research and the absence of a theory supporting the view that design
energy efficiency improvements shall be included in the charter forms, an inductive
procedure allows to deal with the book’s qualitative research objectives.
Furthermore, it allows a good fit to address the aforementioned knowledge gap
between the commercial roles and implied obligations of the involved parties as
well as gain familiarity with that topic of which is little currently understood.
Consequently, the developed theoretical proposition needs to be tested and verified
through the detailed investigation of standardized charter forms for drawing
3.2 Research Design
25
conclusions (i.e. how design energy efficiency improvements can be incorporated in
the charter clauses). Hence, as argued by Saunders et al. (2012), the whole process
is based on the extensive collection of verbal data which is simultaneously explored
for building a theory to guide the subsequent work. Because of the interactive
nature of the verbal data collection and analysis, the theory emerges as a result of
the research process (Saunders et al. 2012).
Such approach can be iterative since once the phase of theoretical reflection on a
set of verbal data has been carried out, further verbal data may be collected in order
to establish the conditions in which a theory will and will not hold. This weaving
back and forth between the theory which is created from the legal instruments
interpretations (secondary data) and the research of the standard charter forms
(primary data) contains a deductive element too. Inherently, this feature is particularly evident in grounded theory (Bryman 2012) a formalized strategy of collecting and analyzing data, as it will be explained in the following subsection.
Briefly, the literature is analyzed further during the secondary data analysis, whilst
the findings are expanded by the primary data. By sequence, the process has to
identify any verbal characteristics in the charter forms that account for design
energy efficiency improvements, as well as the determination of which vessel
parameters incorporated in the charter clauses are of relevance. Although admittedly a bulk of desk research is performed, the findings are emerging from two
sequential stages (i.e. legal instruments and charter forms analyses) which not only
complement each other, but also strengthen their validity.
It is this logical, as well as rational and scientific inference of observations that
enables the creation of new forms of knowledge (obligations and responsibilities of
the charterer or shipper and the ship-owner or carrier). In addition, the innovative
creation of new possibilities for objectively reconstructing themes, categories and
patterns in the data (related to design energy efficiency) could bring forth deeper
reflections on the observed information (the proposal or not of amendments in the
charter clauses) (Bendassolli 2013). Furthermore, the primary purpose of the
inductive approach is to allow research findings to emerge from the frequent,
dominant, or significant themes, categories and patterns inherent in raw verbal data,
without the restraints imposed by structured methodologies in which it is not
possible to revisit the developed theory (Thomas 2006). This iterative criterion is
evident in the current study where verbal data is collected from the interpretations
of legal instruments. Then, a theory generalization upon design energy efficiency
improvements is drawn. Successively, more verbal data is collected from the
standard charter forms (i.e. obligations and responsibilities of the charterer or
shipper and the ship-owner or carrier) and is analyzed further. Next, the theory is
re-examined again, until an acceptable conclusion between theory and data is
accomplished.
The book’s objectives pose exploratory research since they are open and seek to
discover what is happening in practice about the implied obligations and responsibilities of the charterer or shipper and the ship-owner or carrier. Moreover, the
book’s objectives have the purpose to gain insight concerning the inclusion of
energy efficiency specification clauses within charter party agreements due to the
26
3
Methodology
scarcity of the conducted research on this topic, as it has already been pointed out.
This type of research is a valuable way to clarify the understanding of this book’s
particular study and is performed through document search of journal articles,
standard charter contracts and interpretations of legal instruments. In this respect, a
diverse set of data is used to examine meanings that may not have been considered
when the written material was firstly produced. It is mainly desk research where
cost effectiveness can be achieved with the minimum expense. Additionally, the
time intensive nature of document search and collection has the advantage of being
flexible and adaptable to change as a result of new verbal data that appear; hence
new insights are occurred than those originally anticipated (Saunders et al. 2012;
Wilson 2014).
This characteristic is applied to the study documented in the current book, where
the initial verbal data collection from the interpretation of legal instruments and
journal articles is followed by a second verbal data collection of standard charter
forms. The purpose is to explore the existing coverage of design energy efficiency
and then generalize the proposition (i.e. incorporation of design energy efficiency
improvements in the charter clauses). The adopted sequential exploratory design is
illustrated at Fig. 3.1, where it involves a first stage of secondary data collection
(interpretation of legal instruments) and a theory building augmented with evidence
from journal articles. Afterwards, a second stage of primary data collection is
carried out from a sample of more than 50 standardized charter forms. The intent of
the second stage is that the results from the first analysis (theory building) can help
to inform the survey of the standardized forms and consequently the latter findings
are fed into the theory developed initially.
st
1 Stage: Interpretation
of legal instruments
Theory building: Design
energy efficiency in charter
clauses
nd
2 Stage: Standardized
charter forms sample
Findings
Evidence: Journal
articles
Fig. 3.1 Simple schematic of the adopted sequential exploratory research design. Note the
inductive element of theory creation from data and the iteration in the second stage for revisiting
the theory. Source Author
3.2 Research Design
3.2.1
27
Research Method
In connection with the previous discussion, it is believed that the adoption of a
qualitative inductive and sequential exploratory research design requires a method
of data collection, analysis, and comparison from different sources. Such adoption
leads to insightful conclusions as well as it provides the necessary consolidated and
validated outcome. The fact that multiple data resources are used (journal articles,
contracts and legal instruments) and their subsequent analysis provides a stronger
foundation for theory building which is also referred to as grounded theory method
(Creswell 2009; Bryman 2012). It is a strategy of inquiry where a general theory is
derived grounded in multiple stages of data collection, refinement and connection
of categories of information for maximizing the associated similarities and differences. In order to obtain convergence of research findings (the inclusion of design
energy efficiency improvements within the charter contracts), triangulation by
sources of data permits the researcher to clarify the perceived meanings and verify
the interpreted information (Creswell 2009; Bryman 2012). For instance, the
findings from the legal instruments (first source) and journal articles (second
source) are elaborated and confirmed by examining evidence from different types of
standardized charter forms (third source). To this end, triangulation from these three
sources is utilized to build a coherent justification for establishing patterns that are
based on converging perspectives. This process can be claimed as adding to the
validity of the current book’s study (Creswell 2009; Bryman 2012).
Reliability is ensured by the method’s application consistency across different
disciplines and projects, for instance family research (LaRossa 2005), legal studies
(Outhwaite et al. 2007), data analytics (Urquhart et al. 2010), education and
learning (Thomas and James 2006), artificial intelligence (Castellani et al. 2003),
nursing research (Engward and Davis 2015), human resource capital
(Storberg-Walker 2007), logistics management (Mello and Flint 2009), managerial
action (Partington 2000), as well as consumer research (Stall-Meadows and Hyle
2010). In a similar point of view, the method adapted to the current book’s study is
unique in appraising the flexible nature of the wide range of evidence from multiple
sources that is required to address and to acquire a deeper understanding of design
energy efficiency improvements as a contractual term.
The process of data collection, analysis and comparison consists of two principal
stages: initial coding of legal instruments as well as journal articles and focused
coding supported by sufficient sampling of contracts, which is illustrated at Fig. 3.2.
During the initial coding, the analysis of the relationship of the contracted parties
with respect to energy efficiency specification clauses indicates the type of cases
(voyage and time charters) to select for further data collection. This coding involves
recording of identifiable subjects (properties or dimensions), where similar subjects
are grouped together (see Fig. 3.2 for the developed labels). This purposive sampling continues until theoretical saturation is achieved and occurs when the data
collection ceases to reveal any new relevant observations as well as the relationship
between the parties has been verified (Bryman 2012; Saunders et al. 2012). Since
28
3
Methodology
“seaworthiness” is identified as a core subject around which research is concentrated, a particular focus is provided (focused coding of standardized charter forms)
from which new data is collected and analyzed to pursue theoretical lines of
enquiry.
For the examination of charter party contracts, content analysis is selected as the
technique for uncovering generic categories within the clauses by objectively and
systematically searching for certain ideas within the text (i.e. vessel description,
bunkers). The text is coded in terms of certain subjects and themes so that the
categorization of the topic under investigation is sought, understanding is increased
and knowledge is accumulated. At the next level, sub categories (see Fig. 3.2)
within similar concepts are classified together (i.e. energy saving devices, fuel
consumption, etc.) and the recording continues as long as it is reasonable and
possible. The categorization within the focused coding not only registers observations that are similar or related, but also allows a comparison between the
Initial Coding: Interpretation of legal instruments and journal articles
Themes:
Time &
Voyage
charters
Properties:
Charterer’s/
Shipper’s &
Shipowner’s/
Carrier’s
obligations
Dimensions:
Seaworthiness
Due care &
despatch
Safe port
Cargo
Freight
Focused Coding: Sampling of standardized charter forms
Sub Categories:
Energy saving devices
Fuel Consumption
Speed
Bunkers
Generic Categories:
Vessel description
Bunkers
Main Categories:
Seaworthiness
Fig. 3.2 Simple schematic conceptualizing the principal stages of data collection, analysis and
comparison. Note that the reciprocal arrows indicate the comparison element (triangulation)
between initial and focused coding. Source Author
3.2 Research Design
29
observations unfolded during the initial coding. In this respect, it is implied that
data is analyzed and simplified to form categories that are relevant to the topic
under investigation i.e. design energy efficiency improvements as a term included in
the charter clauses (Bryman 2012).
As shown in Fig. 3.2, the initial and focused coding scheme and the affiliated
sampling procedure can be clearly set out so that replications and follow up studies
are feasible. It is this transparency that often causes content analysis to be referred
to as an objective technique of analysis. Furthermore, it is highly flexible that it can
be applied to a wide variety of different kinds of documents and can allow information to be generated that is ambiguously hidden in the text (Bryman 2012).
Published examples in academic research using content analysis and affirming its
applicability can be found in nursing and health research (Elo and Kyngäs 2007), as
well as social sciences literature (Zhang et al. 2013). In a maritime context,
Rehmatulla and Smith (2015a) have employed this method to evaluate the influence
of implementing speed reduction measures within the charter party contracts.
However, it should be made crystal clear that their research was focused on
operational measures and not on design energy efficiency improvements which is
the subject matter investigated in the current book (see Sect. 1.2). By using these
elements it is asserted that the process of data collection and analysis becomes
increasingly focused, leading to the generation of a contextually based theoretical
explanation. It is emphasized that the analysis is interactive, flexible and less prescriptive; it develops from constantly comparing data to codes and codes to data in
order to construct higher levels of abstraction and is shaped by the objective
interpretation of these constant comparisons. Thus, two central features of grounded
theory are that, firstly it is concerned with the development of theory out of the
collated data. Whereas secondly, the approach is iterative, or recursive, as it is
sometimes called, meaning that data collection and analysis proceed in tandem,
repeatedly referring back to each other (Bryman 2012; Saunders et al. 2012).
Chapter 4
Analysis and Findings
Abstract This chapter is concerned with reporting the results grounded on the
methodology outlined in the previous chapter. In this respect, firstly the initial
coding of legal instrument interpretations on the carriage of goods by sea investigates if it is permitted to regulate the rights and responsibilities of design energy
efficiency improvements. Furthermore, it seeks to identify any wedge for incorporating such term in the charter party contract through the relevant law literature.
Secondly, since the research is concentrated around voyage and time charters, the
focused coding is supported by sufficient sampling of the standard agreement
forms. To this end, content analysis is employed for uncovering themes within the
clauses by objectively and systematically searching for certain subjects within the
text (i.e. energy efficiency specification). Moreover, it is determined which themes
are associated with vessel design parameters. This process continues until additional data collation does not contribute to the coding of the topic under investigation and any new theoretical lines of enquiry are not possible to be offered.
4.1
Preamble
A theory on how to support the view that design energy improvements can be
included in the charter party agreements has been already emerged and the reader is
disposed to witness its applicability. It is formulated by analyzing the possible and
thinkable moral reasons for the conclusion that such proposal is acceptable. Further,
a richer conclusion is derived from the coherent and consistent set of legal premises,
where the inclusion of a clause specifying the design energy efficiency obligation is
justifiable and rational. This follows the logical expectation that within the juristic
paradigm a morally and legally reasonable responsibility is created by the taken for
granted principle of pollution prevention (Peczenik 1988).
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6_4
31
32
4.2
4 Analysis and Findings
Initial Coding: Themes, Properties and Dimensions
This part seeks to indicate the type of charter parties as well as categories (similar
subjects grouped together) to select for further data collection. Consequently, the
relationship of the contracted parties with respect to the energy efficiency specification is determined. The latter is reflected by proposing any amendments or
additions to the relevant themes (charter party contract clauses) that could contain
the vessel’s energy efficiency specification.
4.2.1
Themes: Time and Voyage Charters
The transactions for the services for transporting cargoes by sea are distinguished
into two types. The freight contract (voyage charter); where the shipper or charterer
buys transport from the ship-owner at a fixed price per ton of cargo and it is suited
for those who prefer to pay an agreed sum for the service whilst the management of
the transport is left to the ship-owner. The time charter; where the vessel is hired by
the day and it is appropriate for experienced ship operators who prefer to manage
the transport themselves (Gorton et al. 2009; Stopford 2009). There are other types
of contracts such as contract of affreightment or consecutive voyages which correspond to a hybrid form of voyage charter. Whereas the bareboat or demise charter
amounts to the lease of the vessel from the owner to the charterer, which is really a
financing arrangement and it is not related to the use of the vessel for transporting
cargoes (Gorton et al. 2009; Stopford 2009).
4.2.2
Properties: Rights and Responsibilities (Obligations)
of Shippers or Charterers and Carriers
or Ship-Owners
Since from a theoretical perspective the charter parties are classified as a contract
for the use of the vessel, they are not subject firstly to the statutory assignment
contained in the COGSA 1992 and secondly to the mandatory application of the
Hague and Hague-Visby Rules which are associated with contracts for the carriage
of goods such as the bill of lading. The provisions of these regulations however can
be incorporated in the contract if it is chosen by the signatory parties. For instance,
the inclusion by virtue of a clause in charters linking contractually the carrier or
ship-owner with the bills of lading holder on the terms of the charter party, which is
incorporated into the bill of lading, provided that the regulations effect the necessary statutory or mandatory assignments (Baughen 2010).
4.2 Initial Coding: Themes, Properties and Dimensions
33
Without dealing here with all details, under common law, the shipper or charterer has the obligation to nominate a safe port, not to ship dangerous cargoes, to
provide cargo and pay for the bought service. Whilst under the international conventions, the shipper or charterer has the duty to provide accurate information about
the transported goods (Hague-Visby Rules Art. III, r. 5/Art. IV r. 3 and 6).
Furthermore, the Hamburg Rules [Art. 17(1), 12 and 13] have the potential to cover
a wider scope of information i.e. relevant information as to the dangerous nature of
the goods, as well as the necessary precautions to be taken in transporting the
goods. In addition, the Rotterdam Rules [Art. 27, 28, 29(1), 30, 31, 32] state goods
that are likely to become a danger to persons, property or the environment and the
notification as to the nature of the goods shall be done in accordance with any law,
regulations or other requirements of public authorities that apply during any stage
of the intended carriage of the goods (Singh 2011; Todd 2016).
As far as the obligations of the carrier or ship-owner under common law, these
can be summarized as follows (Singh 2011; Baatz 2014; Todd 2016). The provision
of a seaworthy vessel (i.e. fit for receiving the assigned cargo and performing the
intended voyage in terms of her physical condition and equipment, competence/
efficiency of her master and crew, adequacy of stores and documentation).
Additionally, the cargo is carried to the designated port without deviating from the
agreed route albeit to any lawful justifications for doing so and that the carrier’s or
ship-owner’s contractual obligations are conducted with reasonable dispatch. The
international conventions stipulate that a bill of lading is issued as a receipt for the
goods received (Hague-Visby Rules Art. III, r. 3), the exercise of due diligence to
make the vessel seaworthy, the vessel is properly manned and equipped, the holds
and other parts of the vessel for which goods are carried are fit for the purpose
(Hague-Visby Rules Art. III, r. 1). Further, the cargo is carefully and properly
loaded, handled, stowed, carried, kept, cared for and discharged (Hague-Visby
Rules Art. III, r. 2) and a wider range of permissible deviations is offered
(Hague-Visby Rules Art. IV, r. 4).
The Hamburg Rules do not give the carrier or ship-owner as many exceptions
from liability as the Hague and Hague-Visby Rules. In particular, the carrier or
ship-owner is not exonerated from liability arising from negligence in navigation or
management of the vessel. The Rotterdam Rules cover a wider range of persons
associated with the transport of goods, the period of carrier’s or ship-owner’s
liability is extended to a door to door service (i.e. under the custody of a port
terminal), include the duty to receive the goods for transport and to deliver them to
the consignee at destination (Singh 2011; Baatz 2014; Todd 2016). The importance
of the aforementioned lies in the fact that the shipper or charterer is responsible for
the nature of the goods loaded on the vessel with the technical specification of the
vessel ultimately considered as the obligation of the carrier or ship-owner and not
the shipper’s or charterer’s.
34
4.2.3
4 Analysis and Findings
Dimensions: Breaking Down Groupings Dealing
with the Vessel
Depending on the type of contract, the vessel may have a more or less central
position in the charter party agreement. In time charters the vessel is a central factor
whereas in voyage charters the vessel is not as essential and the agreements are
concluded before it is known what vessel will be used. Since during a voyage
charter the vessel’s performance is at the carrier’s or ship-owner’s risk, the only
parameter of the vessel’s description is her cargo capacity. Other details or qualities
such as principal dimensions, air draught, cargo handling equipment, number of
hatches an their dimensions need to be specified in advance for the economical and
practical planning of the loading, carrying and discharging of a cargo (Gorton et al.
2009; Wilson 2010). The efficiency of the chartered vessel is paramount to the time
charterer because the success of the commercial enterprise may depend on it.
Therefore, the specifications of the vessel are set in detail, the most important of
which are normally those relating to speed, loading capacity and fuel consumption.
Charterers or shippers require that they have correct and sufficient information
about the vessel not only because it is necessary to form an opinion about her
commercial value, but also due to the fact that cargoes to be loaded and ports to be
visited are not known beforehand. In addition, when the vessel is chartered for a
long period, a more detailed description is needed and the shippers or charterers get
copies of the general arrangement plan as well as other plans that give information
about the vessel and her construction (Gorton et al. 2009; Wilson 2010).
Of these points, it is usually stated in any charter party that the vessel shall be kept
in a seaworthy condition. The concept of seaworthiness can be described as having
three aspects: (i) seaworthiness from the technical point of view, (ii) cargoworthiness, and (iii) seaworthiness for the intended voyage. The design energy efficiency
improvements are of interest to the first issue which includes the vessel’s design and
condition in hull and machinery, and also her stability. In addition, the vessel must
be technically equipped and furnished with certificates necessary for her to be able to
call at a certain port or a certain country without risk of delay as well as being kept in
good order and condition during the charter period (Gorton et al. 2009). It is well to
note that such undertaking reflects technology advancements which are matter of
construction and relatively depend on the charter’s character. By the same token, if
the vessel is not classed as represented, she may be enunciated as a handicap to
maritime commerce and either the charterer is compelled to pay extra insurance
premiums to protect the shipped cargo, or refuse to load the vessel (Vandeventer
1975). Until now, case examples or rulings related to design energy efficiency
improvements (see Table 2.1 for an explanation of vessel related technologies)
rendering technical unseaworthiness do not exist (see Table 4.1 for relevant
4.2 Initial Coding: Themes, Properties and Dimensions
35
Table 4.1 Examples of defects in the technical condition of a vessel or her equipment rendering
her unseaworthy
Defect
Case
Leaking hull
Hull fracture
Lyon v Mells [1805] 1 KB 697
Danske Sukkerfabrikker v Bajamar Compania
Naviera, The Torenia [1983] 2 Lloyd’s Rep 211
Itoh & Co Ltd v Atlantska Plovidba, The Gundulic
[1981] 2 Lloyd’s Rep 511
McFadden v Blue Star Line [1905] 1 KB 697
Steel v State Line; Dobell v Steamship Rossmore Co
[1905] 2 QB 408
Havelock v Geddes (1809) 10 East 555
The Glenfruin (1885) 10 PD 103
SNIA v Suzuki (1924) 29 Com Cas 284
Project Asia Line Inc v Shone, The Pride of Donegal
[2002] EWHC 24; [2002] 1 Lloyd’s Rep 659
Guinomar of Conakry v Samsung Fire & Marine
Insurance Co Ltd, The Kamsar Voyager [2002] 2
Lloyd’s Rep 57
Fyffes Group Ltd and Caribbean Gold Ltd v Reefer
Express Lines Pty Ltd & Reefkrit Shipping Inc, The
Kriti Rex [1996] 2 Lloyd’s Rep 373
The Vortigern [1899] P 140
Island Tug and Barge v Makedonia, The Makedonia
[1962] P 190
Vinmar v Theresa [2001] 2 Lloyd’s Rep 1
Rey Banano del Pacifico Co v Transportes Navieros
Ecuatorianos, The Isla Fernandina [2000] 2 Lloyd’s
Rep 15
Maori King v Hughes [1895] 2 QB 550
Leaking hatch covers
Leaking sea valve
Porthole not capable of being closed at
sea
Neglecting to put in a nail
Crankshaft with flaw in weld
Defective propeller
Insufficient spare parts
Unsuitable spare parts
Sludge in lubricating oil
Insufficient supply of fuel
Contaminated fuel
Contaminated cargo tanks and lines
Inadequate charts and navigation aids
Defective refrigerating machinery
where the cargo was frozen meat
Ship with no dunnage mats to protect
dry cargo
Pumps inadequate for cargo
Source Dockray and Thomas (2007)
Hogarth v Walker [1899] 2 QB 401
Stanton v Richardson (1872) LR 7 CP 421
examples regarding the vessel’s technical condition according to the common law
doctrine), meaning that the parties signing the charter agreement are free to adopt a
solution making their wishes clear (Dockray and Thomas 2007). However, the
ground for any voluntary contractual inclusion of a design energy efficiency
improvement clause that also exemplifies a sharing scheme of the incurred cost will
be discussed in the next sub-section.
36
4.3
4 Analysis and Findings
Theory Building: Balancing the Interests for Design
Energy Efficiency Improvements
It has been already reported that the existing legal instruments and contractual
charter party practices assign the duty and responsibility of the vessel’s construction
(hence design energy efficiency improvements) to the carrier or ship-owner.
However, it is true to say that socially responsible behavior nurtured through
improved environmental risk management (i.e. a reduction in emissions and pollutants) decreases the probability of environmental crises that can negatively affect a
firm’s expected cash flows (i.e. lawsuits, clean-up costs of environmental accidents,
fines, reputation damage). Hence, investing in energy efficient technologies
demonstrates environmental commitment and creates “moral capital”, or goodwill
which provides insurance-like protection in the occurrences of negative events or
crises. In addition, stakeholders including shippers or charterers and consignees are
requiring carriers to adopt promulgated environmental policies striving for minimum environmental damage when handling their shipments (Drobetz et al. 2014a;
Lai et al. 2011).
The aforementioned statements can be supported by Lys et al. (2015) who found
that corporate social responsibility activities (i.e. reduction of environmental
impact) are considered as investments which are positively associated with economic returns. Such investments within a wide range of industries including
transportation, utilities, agriculture, communications, minerals, retailers among
others, have generated at least 7% increase in the operating cash flows difference
between two consecutive years. Also, it is suggested by the results of Clarkson et al.
(2013) and Plumlee et al. (2015) that industries with high environmental expenditures (i.e. oil and gas, metals and mining, chemicals, pharmaceuticals, utilities,
food and beverage) have lowered their cost of capital and equity by 17 and 6.5%
respectively. Although these studies are not referred to the shipping industry per se,
it can be argued that initiatives to improve the energy efficiency of a fleet and
reduce its greenhouse gas emissions can enhance the stakeholder value of any
company.
The efforts shaping the achievement of environmental and thus sustainable
legitimacy in the society should be also coerced by those who have exercised
pervasive control over the carrier’s activities, such as budgeting, commercial
contracts, voyage instructions and port assignment (Anderson and Rue 2001). Thus,
under the negligence theory (doctrine) of common law, if a time or voyage charterer
breached a duty of care which proximately caused pollution damage to third parties,
the charterer would be held liable for such damage. By virtue, the above issue was
raised only in one recorded case (The American Trader oil spill); where the court
declined to dismiss the action against entities of cargo owner and charterer, see for
example Slaven v. BP America Inc. [786 F. Supp. 853, 1993 AMC 455 (C.D. Cal.
1992)] as well as Holifield v. BP America Inc. [786 F. Supp. 840, 842, 1992 AMC
456, 457 (C.D. Cal. 1991)] (Anderson and Rue 2001). This entails that time or
voyage charterers would of course remain responsible for contractual obligations
4.3 Theory Building: Balancing the Interests for Design Energy …
37
arising out of cargo operations and vessel movement directions as well as for their
own independent negligence causing damage or injury to third parties.
Nevertheless, the exercise of these rights is not inconsistent with the vessel owner’s
control of the vessel and her crew (Anderson and Rue 2001). Although the
aforementioned apply to oil pollution, there is a clear moral basis that such utterance can be chosen to express a single shared meaning of the contracting parties’
common intention for socially responsible performance. Ultimately, this reflects the
value of decreasing their environmental footprint in terms of air emissions and thus
creating rights and obligations which increase the abilities of the parties to plan and
facilitate commerce in a sustainable manner (Leggatt 2015).
By instinct, the promotion of environmental stewardship in charter party contracts can be traced in one respect through the infusion of a sub-clause in the charter
party agreements referring to the use of performance monitoring systems provided
by the charterers, upon mutual agreement between the parties beforehand. This
provision is intended not to place any additional burden on the owners, but merely
to indicate that if the use of such equipment aids has been agreed by the parties,
then the owners are also under the obligation to use them optimally (Hunter 2012).
The corollary of this is that under the common purpose of air emissions reduction,
both parties have the duty to carry on negotiations in good faith for pursuing the
inclusion of a sharing scheme for the incurred costs of design energy efficiency
improvements. Entering the bona fides point and in view of the facts known, it
would be expected that the parties act in honest as well as reasonable grounds to
conclude their bargaining (Tetley 2004). In this way, to advance own interest, that
party is entitled, if the co-operative regime for negotiation could not appropriately
serve long term benefits, to threaten to withdraw from further negotiations, in the
hope that the opposite party may seek to reopen the negotiations by offering better
terms (Hoskins 2014). Hence, such good will clauses may provide agreements with
valuable flexibility, allowing a contractual venture to continue in changed circumstances (i.e. extension). The formation of legally binding relations of economic
significance justifies a robust approach to determine susceptible enforceable provisions in construing the terms of their agreement, identifying its boundaries and
distinct obligations (Hoskins 2014).
From a general contract law perspective, when establishing the necessary elements for the formation of a charter party (fixture), the name and characteristics of
the vessel constitute among others, the essential terms consented to by owner and
charterer in order to constitute agreement on the use of the vessel (Andreu 2001;
Anderson 2001). Pursuant to the fixture, the parties negotiate and fine tune the
particulars of the essential terms (details), which can also involve deciding specifics
on energy efficiency and associated costs. The negotiations conclude upon concurrence of remaining particularities and the contract is deemed binding (Andreu
2001; Anderson 2001). Upon acceptance, the success of the prospective commercial enterprise is assured by declaring commitment to what has been agreed, thus
promised and the self-imposed obligation that a return is expected for keeping the
promised premises intact (Fried 1981). In line with the touchstone of contract law,
the charter party cannot be disintegrated upon disagreement to minor details, since
38
4 Analysis and Findings
all terms are equally important. At the same time, it could be argued that “details”
are not considered as an acknowledgement of the intent to continue negotiations,
but a condition subsequent which satisfies the contract. As discussed previously,
details under the common purpose of environmental stewardship can be introduced
as an essential term for the conduct of the voyage or inaugurated as a main term at
the onset of negotiations and there should be an agreement between the parties
before the charter party is “fixed” (Andreu 2001; Anderson 2001).
It should be noted that design energy efficiency as a term should have compensatory nature in defining the willingness and ability of the two parties to bear
risk (cost). Apparently, such element requires that the size of awards be modified so
as to share the risk according to their respective degrees of risk aversion or
approximating Pareto efficiency, i.e. the most important risks are separated from the
trivial ones (Trimarchi 1991). On this reason, it is important to establish a legal rule
(charter clause) that is based on a consideration of the effects deriving from the
performance of the energy efficient equipment, where areas for a concerted effort
(sharing scheme) are identified. Contracting in advance is necessary because of the
very nature of the equipment performance which requires time to be fulfilled (i.e. in
realizing the benefits of design energy efficiency improvements). Essentially, an
efficient cost allocation is achieved so that the contract is performed as long as the
incurred cost is less than the obtained profit (Pareto efficiency). Yet, the implication
of this legal rule is to provide an incentive which reasonably aims at solving
effectively the energy efficiency sharing scheme by influencing the behavior of the
parties at the time they negotiate the contract (Trimarchi 1991).
4.4
Focused Coding: Content Analysis of Charter
Party Contracts
The theory elaborated through the initial coding is now refined by a more detailed
analysis of predetermined subjects as collated from the available charter party
forms. It is an iterative process aimed at providing validity of the previous proposal
that the vessel’s energy efficiency specification could be contained as a theme by
amending or adding relevant clauses.
4.4.1
Information Sources
Charter parties are commonly drafted using highly standardized forms specific to
the particular trades and business needs of the parties which are available from
BIMCO website (BIMCO 2015). It is because the shipping industry is unique in the
fact that such contracts are negotiated within a few days or even hours; hence these
forms have been established to facilitate the process efficiently. To cater both
parties’ needs, these forms are being amended so that good chartering practice is
4.4 Focused Coding: Content Analysis of Charter Party Contracts
39
captured and the evolved market dynamics (bargaining power and balance between
the charterer and the owner) are reflected (Collins 2000; Humphries 2014). A list of
the 56 analyzed contracts is summarized on Table 4.2, where they are categorized
according to market (dry and wet bulk, liner and general) and the themes from the
initial coding (time and voyage charters, see Fig. 3.2).
4.4.2
Content Analysis
The method for data collection and analysis was presented and explained in the
previous chapter and bringing in mind the conceptual schematic of Fig. 3.2, the
purpose of content analysis is to organize the qualitative data under higher order
headings and group them by creating categories. As shown from Table 4.2, more
than fifty standard forms are currently in use, most of which are voyage charter
parties covering various trades (i.e. coal, iron ore, fertilizer, timber, minerals and
sands, chemicals, liquefied natural gas, crude oil and products). There are also
standard forms for tanker charter parties, partly because of the specific characteristics of this type of carriage and partly reflecting the relatively stronger bargaining
power of tanker charterers (oil major companies). In addition, some very large
charterers (dry bulk) have their own forms of charter parties, whilst general standardized forms as well as for the liner markets exist.
4.4.2.1
Qualitative Data Organization: Main Category
The standard forms are supplemented by a myriad of additional clauses (so-called
“rider-clauses”), some of which have attained standardized wording themselves and
many which are drafted on an ad hoc basis (UNCTAD 1990). An overview of the
normal clauses observed in the surveyed standard charter parties is given at
Table 4.3, that by no means can be considered as exhaustive. This can be explained
by the fact that firstly the various trades have different characteristics of carriage as
well as clients’ expectations. Secondly, large companies and charterers also prepare
their own private (“in-house”) tailor-made contracts which are not publicly available. From the initial coding, the subject of “seaworthiness” from a technical point
of view was identified as a dimension that reflects the fitness of the vessel to
withstand the expected hazards of the contemplated voyage laden with cargo. This
entails that the vessel shall be equipped and be maintained in a thoroughly efficient
state throughout the duration of the charter.
4.4.2.2
Qualitative Data Organization: Generic Categories
There are clauses in charter parties referring to the vessel’s dimensions and capabilities, that are important as far as trading is concerned: the vessel’s capacity to
40
4 Analysis and Findings
Table 4.2 Standardized charter party forms to be surveyed
Market
Time charters
Voyage charters
Dry bulk
ASBATIME
BALTIME
BHPBTIME
GENTIME
NYPE
Wet bulk
BIMCHEMTIME
BPTIME
EXXONMOBILTIME
GASTIME
INTERTANKTIME
SHELLLNGTIME
SHELLTIME
BOXTIME
ROPAXTIME
AMWELSH
AUSTWHEAT
BHPBVOY
BLACKSEAWOOD
CEMENTVOY
COAL-OREVOY
FERTICON
FERTISOV
FERTIVOY
GRAINCON
HYDROCHARTER
MUNTAJATCHARTER
MURMAPATIT
NANYOZAI
NIPPONCOAL
BIMCHEMVOY
BISCOILVOY
BPVOY
EXXONMOBILVOY
GASVOY
Liner
General
NIPPONORE
NORGRAIN
NUBALTWOOD
PANSTONE
POLCOALVOY
QAFOCHARTER
RIODOCEORE
RUSWOOD
SCANCON
SOVCOAL
SOVCONROUND
SOVORECON
SYNACOMEX
WORLDFOOD
YARACHARTER
GIIGNLLNGVOY
INTERCONSEC
SHELLVOY
TANKERVOY
CRUISEVOY
GENCON
NUVOY
Source BIMCO (2015)
carry cargo, the vessel’s ability to enter a port and be berthed, the basis on which
dues are calculated. With respect to time charters, there are clauses specifying the
vessel’s performance as well as the amount of fuel and diesel required to be
on-board at the time of delivery and redelivery. The bunker clause in voyage
charters allows the ship-owner to proceed to any port on or off the route to take on
bunkers. These observations have also been documented by Brodie (2015).
4.4.2.3
Qualitative Data Organization: Sub Categories
The clauses referring to the vessel’s description and performance are considered to
be related to the vessel’s energy efficiency design specification, whilst their qualitative data systemization and interpretation provide a sound ground for comparison, as shown in Tables 4.4 and 4.5. The results indicate that the focus of the time
charter contracts on the vessel’s performance (coded by the vessel’s parameters fuel
consumption and speed) is around protecting the commercial relationship between
the two parties. Moreover, no merit is given to the vessel’s design energy efficiency
4.4 Focused Coding: Content Analysis of Charter Party Contracts
Table 4.3 List of common clauses contained in the standard charter party forms
Time charter party
Preamble
Vessel description
Duration of period/description of trip(s)
Trading intentions/limits
Cargo intentions/exclusions
Vessel condition
Owner’s responsibilities
Charterer’s responsibilities
Delivery and redelivery
Bunkers
Segregated ballastb
Hire
Off-hire
Vessel performance
Vessel maintenance
Cargo claims
Masters and officers
Logbooks
Supercargo/victualling
Pollution
Oil major acceptabilityb
Emergency responseb
Salvage
Laying-up
Arbitration
Lien
Assignment
Exceptions
ITOPFb
Requisitions
Bills of lading
Stevedore damagea
Stevedores, pilots, tugs
Commissions
Loss of vessel
Protecting clauses
Signatures
Voyage charter party
Preamble
Name and brief description of the vessel
Condition of the vessel
Cargo quantity and description
Loading place
Loading port orders/rotation
Discharging places and port orders/rotation
Laydays and cancelling
Freight
Cost of loading/discharging
Berthing, pumping and mooringb
Notice of readiness/time counting
Loading/discharging rates
Excepted periods
Demurrage/dispatch
Notices
Vessel’s gear
Grab discharge/stevedore damagea
Overtime
Shifting seaworthy trim
Cleaning/gradesb
Clean ballastb
Inert gasb
Crude oil washingb
Cargo separations and tallying
Dues and taxes
Port agents
Bills of lading
Lightening
General average
ITOPFb
Strikes
Exceptions
Commissions
Protecting clauses
Lien and cesser
Ice
War risks
Signatures
a
Common only to the dry bulk market/bCommon only to the wet bulk market
Source Compiled by the Author from BIMCO (2015)
41
42
4 Analysis and Findings
Table 4.4 Coding sub categories for vessel energy efficiency in time charter parties
Market
Contract form
Bunkers
Fuel
consumption
Energy
saving
devices
Speed
Dry
bulk
ASBATIME
BALTIME
BHPBTIME
GENTIME
NYPE
BIMCHEMTIME
✓
Fuel oil
Grades
Grades
✓
Sulphur
content and
grades
✓
Grades
✓
Grades
Sulphur
content and
grades
Grades
Sulphur
content and
grades
Sulphur
content and
grades
✓
✓
✓
✓
✓
✓
X
X
X
X
X
X
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
X
X
X
X
X
✓
✓
✓
✓
✓
✓
✓
X
X
✓
✓
✓
X
✓
Wet
bulk
BPTIME
EXXONMOBILTIME
GASTIME
INTERTANKTIME
SHELLLNGTIME
Liner
SHELLTIME
BOXTIME
ROPAXTIME
Source Developed by the Author
specification, despite the fact that fuel consumption is inherently connected to the
vessel’s energy efficiency. In addition, reference to the bunkers’ quality is associated with the charterer avoiding any claims and indemnities arising from engine
damages (Gorton et al. 2009; Plomaritou 2014). By contrast, in voyage charter
contracts, the bunkers and speed interpretation embrace the ship-owner’s or carrier’s right when bunker prices have risen and it has sometimes become difficult to
find bunkers. This is in accord to deviate for the purpose of getting bunkers or to
proceed at reduced speed so that to get a lower bunker consumption (Gorton et al.
2009). Since the vessel’s owner is responsible for the voyage costs, fuel consumption and thus the vessel’s performance is not taken into account, yielding that
design energy efficiency is not negotiated as a term when fixing the vessel.
However, in a very limited number of voyage contracts and particularly for wet
bulk, a reward as increased freight is allocated when sailing at higher speed, hence
emphasizing on fuel consumption (Table 4.5).
4.4 Focused Coding: Content Analysis of Charter Party Contracts
43
Table 4.5 Coding sub categories for vessel energy efficiency in voyage charter parties
Market
Contract form
Bunkers
Fuel
consumption
Energy saving
devices
Speed
Dry
bulk
AMWELSH
AUSTWHEAT
BHPBVOY
BLACKSEAWOOD
CEMENTVOY
COAL-OREVOY
FERTICON
FERTISOV
FERTIVOY
GRAINCON
HYDROCHARTER
MUNTAJATCHARTER
✓
X
✓
X
✓
✓
✓
✓
✓
✓
X
Sulphur
content
X
X
✓
✓
✓
✓
✓
✓
X
✓
✓
✓
✓
✓
X
X
X
X
✓
X
✓
✓
X
✓
✓
✓
✓
X
X
X
X
✓
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
✓
X
✓
X
✓
X
X
X
✓
X
X
✓
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
✓
✓
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
✓
✓
✓
✓
X
X
X
✓
X
X
X
X
X
X
✓
✓
X
X
✓
✓
X
✓
✓
✓
✓
Wet
bulk
MURMAPATIT
NANYOZAI
NIPPONCOAL
NIPPONORE
NORGRAIN
NUBALTWOOD
PANSTONE
POLCOALVOY
QAFOCHARTER
RIODOCEORE
RUSWOOD
SCANCON
SOVCOAL
SOVCONROUND
SOVORECON
SYNACOMEX
WORLDFOOD
YARACHARTER
BIMCHEMVOY
BISCOILVOY
BPVOY
EXXONMOBILVOY
GASVOY
GIIGNLLNGVOY
INTERCONSEC
SHELLVOY
TANKERVOY
(continued)
44
4 Analysis and Findings
Table 4.5 (continued)
Market
Contract form
Liner
General
CRUISEVOY
GENCON
NUVOY
Source Developed by the Author
4.5
Bunkers
Fuel
consumption
Energy saving
devices
Speed
✓
X
X
X
X
X
X
X
X
X
X
✓
Revisiting the Theory from the Initial Coding
The previous subsection provided evidence that the incorporation of the vessel’s
design energy efficiency improvements (i.e. installed energy saving devices) has not
received attention when preparing the charter party contract, due to the established
functional and commercial use of the vessel. It is encouraging though to note that
reference to the bunkers’ sulphur content and grades (for time charters in particular)
can be from one hand translated as an indication on behalf of the shippers or
charterers to transport their goods without any disruption. Hence, by necessity
comply with Regulation 14 of MARPOL’s Annex VI on Emission Control Areas
(IMO 2012a). From the other hand, it could be argued that this is related to a
protecting strategy minimizing the commercial risk arising from the vessel’s
operation. In this point, it should be stressed that the promotion of environmental
stewardship strengthens the corporate social responsibility image of both charterers
or shippers and ship-owners or carriers. Given the need for increased transparency
and reporting, such attribute shall not be pursued by adhering only to operational
measures (i.e. bunkers, slow steaming, etc.). Further improvement in reducing the
environmental footprint of shipping can be additionally attained by the consideration of design energy efficiency measures or devices (see Sect. 2.2.1) that could be
included in the contract as a sub-clause referring to the vessel’s design energy
efficiency specification.
Having design energy efficiency as a negotiated term from the initial stages of
the chartering process is expected to provide an incentive supporting both parties’
environmental commitment. Additionally, it furnishes a means for clarifying the
costs’ and rewards’ distribution of the will to invest in design energy efficiency
measures. These parameters can represent the substantive (technical and economic)
constituent of negotiations. What is more, an activity is sketched where the two
parties with diverging interests search for agreement despite a difference in their
points of view as well as traditional behavior emanating from agency theory
(Pannebakker 2013). This is illustrated by the charterer or shipper who would like
to minimize the transportation cost, whilst the ship-owner or carrier would like to
maximize income (see Sect. 2.4). Indeed, such clause can be instrumental in firstly
having the capacity to enable both parties and empower them for advancing the
collective interest of environmental stewardship. Secondly, it stimulates the continuous improvement of the vessel’s energy efficiency and emulates the reinvestment into efficiency upgrades. Thirdly, it determines the legal status of the
4.5 Revisiting the Theory from the Initial Coding
45
obligation to comply with air emissions regulations. This constructive tailoring can
also be interpreted as a form of control restraining the traditional behavior of both
charterers or shippers and ship-owners or carriers by changing the principles that
govern their long established relationship (Messenger 2016).
Another argument put forward for the implicit reasoning of design energy efficiency as a rider-clause becoming legally operative, is the consideration of close
analogies within the doctrine of precedent in oil and air pollution (see Sect. 4.3).
These are concerned with an intelligible rationale nested within the wider body of
law used to help resolve unsettled issues. The conjecture by reliance depends on an
intuitive appreciation of the similarity between previous rulings on relevant cases
and the case at hand. On an orthodox understanding, the ruling holds that given the
facts (i.e. vessel movements and a duty of care breach) certain legal consequences
follow (liability to pay damages to third parties arising from pollution) (Lamond
2014). Hence, the binding of rulings and the rationales for those rulings creates a
close regard for internal doctrinal coherence between analogy and precedence.
Because at a higher level of generality air pollution as a novel context is comparable
to oil pollution, the view is taken that the outcome of the latter cases should be
followed unless the facts of the air pollution case provide some basis for reaching a
different conclusion (Lamond 2014). This cornerstone of common law reasoning
constitutes a good rational canon for incorporating a vessel design energy efficiency
specification rider-clause pointing the desirability to eliminate the legal
consequences.
Part III
Quantitative Part
Chapter 5
Chartering Negotiations for Energy
Efficiency
Abstract The fifth chapter introduces the reader to the quantitative part, which is
devoted to answering the fundamental question how to design the energy efficiency
sharing scheme mechanism and its connection to the chartering procedure. In this
respect, game theory offers a wide range of models that can be adjusted to
appropriately represent the incentive mixture for energy efficiency. Firstly, the
principal-agent problem is utilized for determining the difference between the two
players’ reservation prices (hire for time and freight for voyage charter) as well as
their information asymmetry related to energy efficiency. This forms the basis
where the negotiations can begin. Secondly, the two players are given the opportunity to split the difference between them with alternating offers which resembles a
bargaining game and results to the contracted price. Then, for the given vessel
revenue performance, discounted cash flow analysis is performed to investigate
whether the EEISS is profitable and the expense can be justified.
5.1
Preamble
Since the empirics of chartering are so complex, it is not attempted to develop any
theoretical models, but instead apply well established concepts and approaches. The
chartering negotiations between a ship-owner/carrier and a charterer/shipper are
aimed at inaugurating consensus. Thus, the entailed interplay dynamics is modeled
through game theory, a mathematical study of strategic decision making between
intelligent and rational players. It treats the general principles that prescribe how
each participant within a social or trade exchange behaves in every situation which
may conceivably arise. For each specific alternative, the participant attaches a
quantity (utility) that describes own fulfilment of needs and wishes about certain
aspects of the world (i.e. compensation). Consequently, every participant is influenced by the anticipated reactions of the others to the participant’s own utilities and
choices and they in turn reflect the other participant’s expectation of that participant’s own actions. This characterizes rational behavior, where in concrete
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6_5
49
50
5 Chartering Negotiations for Energy Efficiency
instances a choice between various alternatives has to be made (i.e. agency costs,
accept offer or counteroffer) by either one of the participants, or by some device
subject to chance denoted as nature (Von Neumann and Morgenstern 2004).
5.2
Theoretical Applications of Games Within Chartering
In recent years, game theory has gained an increased focus on capturing key aspects
of maritime commerce such as the interactions of shippers and carriers, supply
chain integration, fleet deployment, route implementation, chartering and freight
decisions (Yang et al. 2010; Shi and Voß 2011). When arranging the employment
of a vessel, the charterer or shipper would like to minimize the transportation cost,
whereas the ship-owner or carrier would like to maximize income, thus the two
players have opposite goals. Due to the possibility to bid for cargoes and on
realizing that the lowest offer will win, the latter (ship-owners or carriers) are price
takers since they accept the freight rate offered in the market as given (McConville
1999). The power for reaching an agreement is not uniform, as some charterers may
have stronger power than others and similarly some owners may have more power
than others. For example, weak owners will opt for lower rates from stronger
parties, whilst charterers not fixing cargoes on unknown numerous intermediaries or
to owners not perceived to be weak will pay higher rate. Hence, the freight rate over
a particular cargo is the outcome of iterated negotiations that happen at the same or
approximately the same time in different places, balancing expectations of overall
demand and supply in a particular market segment or the entire market. The process
thus consists of a game where the two players negotiate over a known market price
until the charter contract rate is achieved (equilibrium) and both parties are satisfied
(Karakitsos and Varnavides 2014).
This novel approach on determining the price for the shipping service (freight) as
well as modeling the contracting procedure has not attracted yet the meticulous
attention of academic research, though very few studies have been published. The
value of information in terms of vessel quality while negotiating for a voyage
charter has been investigated by Bergantino and Veenstra (2002), whilst the effect
of contractual variation in vessel speed and fuel consumption as translated through
an underperformance claim during a time charter has been explored by Veenstra
and van Dalen (2011). The division of the equilibrium freight rate’s payoff (profit or
loss) between the charterer and the ship-owner during the pre-fixture process
(reservation vs market price) of a voyage charter has been determined by Suh and
Park (2010). Tezuka and Ishii (2008) examine the formation of the equilibrium
freight rates in the spot market; whereas later this work is extended further by
incorporating the behavior of ship-owners who wish to balance the supply of
shipping services (Tezuka and Ishii 2011). Tezuka et al. (2012) address how to
utilize shipping derivatives to describe the risk attitudes of ship-owners and charterers, whilst Ishizaka et al. (2012) augment their study with the impact of the
market’s volatility on the shares of listed shipping companies. The strategic
5.2 Theoretical Applications of Games Within Chartering
51
decisions of two competing ship-owners on investing in fleet expansion through the
influence of freight rates on vessel speed and bunker is analyzed by Kou and Luo
(2016).
With respect to contract performance, the game theoretical analysis conducted
by Zhou (2010) investigated the seller’s (charterer’s) payoff when repudiation to the
charter party occurs due to own decision to breach the agreement subject to a
cancellation clause. Anderlini et al. (2007) modeled the magnitude of the effects of
unforeseen contingencies (i.e. force majeure) on profit maximization by excusing
performance of the contract between a risk neutral buyer (shipper) and a risk averse
seller (carrier). The selection (matching) of cargoes by carriers and vessels by
shippers through a price game considering also the time where the vessel is
available to satisfy the transportation demand was studied by Peng et al. (2016).
Their work focused on how the non-dominant market players manage to gain
bigger surplus by adjusting the bidding time charter hire until advantageous pairing
is achieved and the equilibrium price is settled. In the following, it is demonstrated
how game theory can be applied within charter contracts to allocate the investment
sharing scheme of design energy efficiency improvements.
5.3
Game Theory Model for Sharing
Scheme Determination
Game theory is understood to provide valuable insights into the mechanisms that
may encourage viewing design energy efficiency improvements as a way of
obtaining competitive advantage through a differentiation focus, rather than simply
regulatory compliance where only a license to operate is assigned. Intuitively, the
investment in innovative technologies achieves environmental friendliness that is
sufficiently valued by both buyers and sellers of ocean freight to allow a price
premium, increases commitment to service and can improve brand recognition as
well as customer loyalty (Johnson et al. 2011). By placing a weighting on the
freight rate it is possible to perceive the optimum reward for energy efficiency
which determines a fair service payment and resolves the level of contribution
between the contractual parties. The magnitude of ratios governs the sharing
scheme and it is proportional to the strength of motivation and thus bargaining
power to reduce or remove inequity, an unpleasant experience causing tension
(Mullins et al. 2013).
The initial bid from a ship-owner/carrier (agent) might specify the vessel’s name,
deadweight, the date and place when she is open, possible voyage and an idea of the
rate required (agent’s reservation price). A charterer/shipper (principal) would state
the size and type of the cargo, readiness and laycan dates, voyage and also the rate
sought (expected market price—principal’s reservation price). Once a potential
match is found between a vessel and a cargo, the agent is able to estimate the costs
of the proposed voyage and the exchange of correspondence becomes more
52
5 Chartering Negotiations for Energy Efficiency
detailed. Since the rate is subject to the market’s behavior (trough: low rates or
peak: high rates) as well as the availability of other committed cargoes and vessels
in the area, it is possible that the rate may change when the agent makes a bid at a
later stage (Brodie 2015).
From a practical point of view, negotiating the sharing scheme for energy efficiency investments within a charter contract can fall under the standard routine of
any chartering procedure (Gorton et al. 2009; Panayides 2014; Collins 2000). To
put things into perspective, it contains the establishment of a stipend component,
plus a series of “offers” and “counter offers” albeit to the obligation that each party
has to respond within a fixed time limit. It consists of two parts, firstly determination of the ship-owner’s/carrier’s reservation price and secondly bargaining for
splitting the surplus (difference in reservation prices). The former can be analyzed
in the context of a principal—agent relationship, since both parties may not be
equally aware of the energy efficiency performance. Hence the two players possess
asymmetric information. For instance, the principal (charterer/shipper) is less- or
un-informed, due to coarser information; whilst the agent (ship-owner/carrier) is
better- or well-informed, due to finer information (Rasmusen 2007). When the
parties are in agreement, further consultations take place about the details and the
wording of the respective clause (sharing scheme). A diagrammatic representation
of the mechanism design is depicted in Fig. 5.1, with each part described in the
subsequent sections.
Principal – Agent energy efficiency investment information asymmetry for the reserved freight rate
Charterer /
Shipper
(Principal)
Charter Party
Contract with
EEISS clause
Ship-owner /
Carrier (Agent)
Bargaining the energy efficiency investment equilibrium freight rate
Fig. 5.1 Simple schematic illustrating the contract mechanism design for the EEISS. Source
Author
5.3 Game Theory Model for Sharing Scheme Determination
5.3.1
53
Principal—Agent Part
The energy efficiency investment clause implies that the buyer—B (charterer/
shipper—principal) pays a fixed proportion of the installed technology cost (incentive contract) so that the seller—S (ship-owner/carrier—agent) can appropriate
the return. Hence, the seller’s reservation price should account for the energy
efficiency expenditure during the initial bid and form the basis of a good negotiable
point. In order to design the optimal response, the buyer’s difficulties in monitoring
and observing the seller’s character (i.e. energy efficiency performance) shall be
considered. Such setting falls under the general family of principal-agent problems
and of interest are two categories. The first one is when the principal (buyer) has
complete information concerning the agent’s (seller’s) character, without being able
to control it (moral hazard with hidden action). The second one is when the principal has incomplete information about the character of the agent, who has to be
induced to sincerely report details (adverse selection with screening) (Levin 2003).
Bearing in mind that there are two types of transactions for the services for
transporting cargoes by sea (Gorton et al. 2009; Stopford 2009), the aforementioned
principal-agent problem applications are outlined hereunder.
5.3.1.1
Time Charter
In the time charter, the charterer/shipper (principal) hires per day a ship-owner’s/
carrier’s (agent’s) vessel for satisfying own transport needs. The price reflects the
market’s behavior and is denoted as FRM. The agent’s task is to determine the
customized premium accounting for energy efficiency that will form the basis for
the negotiations. The principal cares for and knows the vessel’s performance (fuel
consumption), but is not aware of the agent’s effort level (cost for improved energy
efficiency). This situation resembles the moral hazard problem with hidden action
and is portrayed by the simple game tree in Fig. 5.2. It is assumed that the principal
(B) offers the agent (S) a contract and an effort level π (high or low) has been
chosen by the agent. The result of the decided effort level is stated as “Nature—N”
(Rasmusen 2007).
accept
contract
B
S
Market freight rate: FRM
effort, π
S1
N
Forming
the basis
of negotiations
Reserved freight rate: FR RT
Fig. 5.2 Simple diagram showing the game tree for the principal (B)—agent (S) problem of moral
hazard with hidden action between a charterer/shipper B and a ship-owner/carrier S. Source
Adapted by Author from Rasmusen (2007)
54
5 Chartering Negotiations for Energy Efficiency
Then, it is necessary to strike the best possible effort level π (percentage of FRM)
that balances the interests of both contracted parties. The model utilized here builds
on Holmström (1979) with the analytical solution presented in Bolton and
Dewatripont (2005). The agent’s effort level affects the principal’s gains from trade
[commodity price (cp: $/t) × commodity quantity (cq: t)] in terms of reduced
bunker fuel expenses [contract duration (d: days) × bunker price (bp: $/t) × bunker
consumption (bc: t/day)] in comparison to the standard vessel. The vessel with
design energy efficiency improvements is a costly decision to the agent; hence the
principal has to compensate the agent for the incurred investment. The compensation level C(π) should be as appropriate as to induce the agent to maintain the
contract with the greater possible return to the principal. In this respect, the principal expects to earn a higher residual profit when the compensation level strictly
increases with high technology cost (tc: $) but at a decreasing rate (Miller and
Whitford 2007). The charterer’s/shipper’s [B(π)] and ship-owner’s/carrier’s [S(π)]
utility functions as well as the compensation level can be given as:
BðpÞ ¼ cp cq d bp bc d FRM d FRM e1p
ð5:1Þ
S(pÞ ¼ d FRM þ d FRM e1p tc
ð5:2Þ
C(pÞ ¼ d FRM þ d FRM e1p
ð5:3Þ
The best possible effort level π can be derived from Bolton and Dewatripont
(2005):
C0 ðpÞ ¼
B00 ðpÞ
k S00 ðpÞ þ B00 ðpÞ
ð5:4Þ
B0 ðpjFRH Þ
S0 ðpjFRH Þ
ð5:5Þ
with:
k¼
where FRH is associated with the historical development of freight rates and it
represents the agent’s outside option (i.e. keep the vessel running). Equations (5.4)
and (5.5) cannot be solved analytically and the computer program Mathcad (PTC
2014) will be utilized to find the best possible effort level π. In conclusion, the
agent’s reserved freight rate FRRT yields:
FRRT ¼ FRM ð1 þ pÞ
5.3.1.2
ð5:6Þ
Voyage Charter
In the voyage charter, the charterer/shipper (principal) buys transport from the
ship-owner/carrier (agent) with the freight rate agreed on a lump sum basis per ton of
5.3 Game Theory Model for Sharing Scheme Determination
55
cargo. This amount covers all the incurred expenses for the contract’s duration, such
as voyage, operating and capital costs (Gorton et al. 2009; Stopford 2009). The price
reflects the market’s behavior and is denoted as FRM. The agent’s task is to determine
the customized premium accounting for energy efficiency that will form the basis for
the negotiations. Admittedly, the agent is selected for enhancing the principal’s
corporate responsibility disclosure in terms of environmental stewardship. The
principal does not know the agent’s environmental stewardship attributes, since the
agent is privately informed on the investment expenditure with cost tc ($). In
response to that, the agent provides credentials with the anticipation to obtain a
premium for the vessel’s design energy efficiency improvements. This state of affairs
can be highlighted by the adverse selection problem with screening and is rendered
by the simple game tree in Fig. 5.3. “Nature—N” is defined as the decision to invest
in environmental stewardship, π (high or low). The principal (B) as an uninformed
player, offers the agent (S) a contract that might depend on the agent’s environmental
stewardship attribute, which is communicated as a cue (installed technologies
described in SEEMP) and is an observable characteristic (Rasmusen 2007).
The prime concern is to derive the best, i.e. profit maximizing value of π
(percentage of FRM) that the agent will be able to induce the principal to choose.
Thus, the agent is accorded an “informational rent” from the principal so that the
private information on environmental stewardship is elicited. The analysis of this
problem had been credited to Mirrlees (1971), which is rigorously explored by
Bolton and Dewatripont (2005) and the model reported herein is inspired by their
work. The principal has to offer for all types of agent’s environmental stewardship
(low and high) the same menu of options, being reflected with preference characteristics σL and σH respectively (σL < σH):
rL ¼ e
tcL
cq
cp
ð5:7Þ
tcH
cq
rH ¼ e cp
ð5:8Þ
where tci: technology cost i 2 {L: Low, H: High } ($), cp: commodity price ($/t)
and cq: commodity quantity (t). The agent is of type σL with probability β 2 [0, 1]
and of type σH with probability (1 − β). The probability β can also be interpreted as
high, π
N
B
low, π
contract
S1
Market freight rate: FRM
accept
S2
Forming
the basis
of negotiations
Reserved freight rate: FRRV
Fig. 5.3 Simple diagram showing the game tree for the principal (B)—agent (S) problem of
adverse selection with screening between a charterer/shipper B and a ship-owner/carrier S. Source
Adapted by Author from Rasmusen (2007)
56
5 Chartering Negotiations for Energy Efficiency
the proportion of agents of type σL which can be obtained through vetting, for
instance RightShip (2016).
If the agent has attained environmental certification (publicly available), the
principal can infer that boosting corporate responsibility is not too difficult and will
consequently want to reduce the informational rent on environmental stewardship.
This directly implies that the commitment to partial disclosure (i.e. SEEMP) is
ex-ante incentive compatible and individually rational for both parties. The principal’s ex-post information rent can be eliminated because the value of misrepresenting ex-post information is diminished. Thus, revealing less information in the
first period of the negotiations is beneficial to the agent and reduces the principal’s
informational rents at a decreasing rate (Krähmer and Strausz 2015). The principal’s
preferences can be expressed by the utility function:
cp
B ðpÞ ¼ ri cp cq pi e1FRM pi cq FRM ð1 þ pi Þ
|fflffli{zfflffl}
ð5:9Þ
i2fL; H g
Assuming that the agent’s marginal cost (technology cost (tc, $)/cargo quantity
(cq, t)) is given by mc, the profit from transporting the cargo can be evaluated by:
Si ðpÞ ¼ cq FRM ð1 þ pi Þ cq mc
|ffl{zffl}
ð5:10Þ
i2fL; H g
The optimum pair value of π (πL < πH) can be entertained from (Bolton and
Dewatripont 2005):
cp
1FRcp pH
M
rH cp cq e
pH e
¼ mc
ð5:11Þ
FRM
cp
mc
1FRcp pL
1FRcp pL
M
M
[ mc
rL cp cq e
pL e
¼
1b rH rL
FRM
1 b rL
1FRcp pH
M
ð5:12Þ
If the denominator in (5.12) is not positive, the optimum solution involves
πL = 0, while the other agent’s type remains determined by (5.11) (Bolton and
Dewatripont 2005). Equations (5.11) and (5.12) cannot be solved analytically and
the computer program Mathcad (PTC 2014) will be utilized to estimate the best
possible rent pair π. In conclusion, the reserved freight rate yields:
FRRV ¼ FRM ð1 þ b pL þ ð1 bÞ ½pH ðpH pL Þ pL Þ
ð5:13Þ
5.3 Game Theory Model for Sharing Scheme Determination
5.3.2
57
Bargaining Part
In this subsection, the equilibrium freight rate is determined accounting for the
negotiations related to offsetting the expectations of energy efficiency. The model is
based on the bargaining work of alternating offers from Rubinstein (1982) as
exemplified by Bierman and Fernandez (1998). Let’s consider a charterer/shipper
(buyer—B) and a ship-owner/carrier (seller—S) who are bargaining over the
division of a surplus q (difference of their reservation prices QB = FRM and
QS = FRRT/V respectively, QB < QS) with alternate offers. The delay in reaching an
agreement imposes a cost on the negotiations which is associated with the discounting of future trade gains relative to present trade gains and is referred to as
discount factor (impatience). It is admitted that the energy efficiency investment
sharing will not be the same for the contract parties (asymmetric impatience),
implying that their discount factors δB and δS are not necessarily equal, though are
strictly positive and no greater than one (Bierman and Fernandez 1998).
Certainly, the delay cost hurts more the ship-owner/carrier rather than the charterer/shipper (δB < δS) due to the assigned capital. This entails that the ship-owner/
carrier can expect less generous share (θB > θS) since the charterer/shipper has the
incentive to reduce its partition, partly reflecting own relatively stronger bargaining
power. Hence, it may be better for the charterer/shipper to wait for a larger expected
share (translated into soared gain) than to increase the probability of settling early
with a smaller expected share. As an outcome, it will be the weaker party’s
(ship-owner/carrier) negotiation flexibility to accept the other party’s proposal
(Ellingsen and Miettinen 2014). Then, there is a unique perfect equilibrium freight
rate agreed after an infinite number of rounds (Fig. 5.4), where each party’s fraction
(weight) of the surplus is Bierman and Fernandez (1998):
1 dS
1 d S dB
ð5:14Þ
dS ð1 dB Þ
1 dS dB
ð5:15Þ
hB ¼
hS ¼
The discount factors can be calculated as the reduction of gains per day from
trade for each party, normalized by the technology investment and vessel cost per
day. In the case of charterer/shipper, the delay is associated with holding physical
stocks of the commodity (cargo to be transported) over the negotiation period and is
defined as cost of carry (Pindyck 2001):
dB ¼
icpcq
365 þ sc
tc þ vp
365
ð5:16Þ
With i: the interest rate, cp: commodity price ($/t), cq: commodity quantity (t),
sc: storage cost ($/day), tc: technology cost ($) and vp: vessel price ($). The storage
58
5 Chartering Negotiations for Energy Efficiency
Negotiations begin: QB , QS
Round 0:
B
δB|0 , δS|0 , FRE|0
offer
S
Round 1:
accept
δB|1 , δS|1 , FRE|1
counteroffer
End: Fixture
confirmed,
main terms
& freight
rate agreed
End: Fixture
confirmed,
main terms
& freight
rate agreed
accept
B
Round 2:
δB|2 , δS|2 , FRE|2
counteroffer
S
accept
counteroffer
………
End: Fixture
confirmed,
main terms
& freight
rate agreed
………
Round n∞:
δB| n∞ , δS| n∞ , FRE| n∞
counteroffer
End: Negotiations terminated
reject
S
accept
End: Fixture
confirmed,
main terms
& freight
rate agreed
Fig. 5.4 Simple diagram showing the game tree for bargaining between a charterer/shipper B and
a ship-owner/carrier S with alternating offers and asymmetric impatience. Source Adapted by
Author from Bierman and Fernandez (1998)
cost can be provided as a percentage of the commodity cost, where the coefficient ω
has been estimated in Symeonidis et al. (2012) and Frankel (2014):
sc ¼
1 ex cp cq
365
100
ð5:17Þ
For the ship-owner/carrier, the delay is due to the lost earnings le ($/day) and
depreciation cost dc ($/day):
5.3 Game Theory Model for Sharing Scheme Determination
dS ¼
le þ dc
tc þ vp
365
59
ð5:18Þ
The declining balance depreciation is chosen because it is impossible to reduce
the vessel’s value to zero at the end of her expected useful life. Under this method,
the depreciation amount charged every year is an amount less than the previous
year; hence a fixed rate is applied to an annually changing vessel value. This entails
that larger amounts are appropriated during the initial years of the vessel’s life. So,
the depreciation rate rdep per annum basis can be given from Liapis and Kantianis
(2015):
rdep ¼ 1 rv 1n
ac
ð5:19Þ
where rv: vessel’s residual value ($), for instance resale (second hand) or scrap
price, ac: vessel’s acquisition cost ($) and n: vessel’s expected useful life (operational horizon).
It follows from the aforementioned discussion, as supported by the influential
work of Akerlof (1982) that the negotiated (equilibrium) freight rate (FRET/V) the
charterer/shipper is agreeing to pay is more than the minimum market freight rate
FRM. However, the ship-owner/carrier is accepting a fixture not as much as the
reservation price and is less than the maximum rate at which the transportation
service can be sold (hence opt for the smaller surplus fraction):
FRET=V ¼ FRM þ hS q
ð5:20Þ
To ensure that the contractual freight rate has been drawn-up accordingly, hence
the sharing scheme clause will perform as expected; it is necessary to investigate
how the energy efficiency premium affects the revenues of the vessel. This aspect is
dealt with in the subsequent section.
5.4
Energy Efficiency Investment Appraisal
To assure that the aforementioned EEISS justifies the capital expenditure and leads
to the greatest increase in the value of the ship-owner/carrier, it is necessary to
consider the magnitude of expected cash flows, their timing (through discounting)
and their associated risk (through the selected discounting rate). To this end, the
NPV method of investment appraisal is considered appropriate since it results in a
discounted stream of cash in-/out-flows and its foundation is attributed to
Hirshleifer (1958). Reasons supporting its wider applicability cover: (i) changes in
the discount rate can be easily incorporated, (ii) cash flows of different sign in
successive periods can be accommodated, (iii) mutually exclusive investments can
be selected and (iv) it is assumed that the generated cash flows can be reinvested
60
5 Chartering Negotiations for Energy Efficiency
elsewhere at a rate equal to the cost of capital. Cash flows occurring during a time
period are assumed to occur at the end of the period, whilst the initial investment
occurs at the start of the first period. Intuitively, a positive value indicates that the
investment is expected to provide a return in excess of the cost of capital (target rate
of return to discount the relevant cash flows) (Watson and Head 2013). The generic
algebraic formula for NPV can be represented as:
NPV ¼ I0 þ
C1
C2
C3
Cn
þ
þ
þ...þ
2
3
ð1 þ r Þ ð1 þ r Þ
ð1 þ r Þn
ð1 þ r Þ
ð5:21Þ
where I0 is the initial investment, C1, C2, C3, …, Cn are the cash flows generated in
years 1, 2, 3,…, n and r is the cost of capital or required rate of return (Watson and
Head 2013).
It is generally accepted that in the investment appraisal, a WACC should be used
as the discount rate, since it provides an indication of ‘financial gearing’ or
‘leverage’ between the main components of finance: debt (FD) and equity (FE)
capital (Modigliani and Miller 1958). Debt reflects the borrowed capital, whereas
equity represents own capital and thus ownership stake in the business (Ross et al.
2013). Each of these components is supplied at a cost, which are then combined
(Brigham and Houston 2013):
rWACC ¼
FD
FE
rD ð1 T Þ þ
ðrRF þ bS RPM Þ
FD þ FE
FD þ FE
ð5:22Þ
where: rD (1 − T) is the tax adjusted cost of debt since interest on debt is tax
deductible, and rRF + βS RPM is the equity cost component equal to the risk free
rate (average investment return) plus a market risk premium RPM (usually estimated
by government securities) scaled up or down by a coefficient βS to reflect the
systematic risk (economic growth, inflation and exchange rates changes) (Brigham
and Houston 2013). By way of reference, the equity cost component is estimated by
the CAPM and is based on the ground breaking work of Sharpe (1964). It is an
equilibrium asset pricing theory showing that the equilibrium expected return on all
risky assets is a function of their covariance with the market portfolio (Ross et al.
2013). It should be borne in mind that the choice of the appropriate risk premium as
well as systematic risk, financial gearing level and risk free rate has attracted a wide
range of academic research (see Mason et al. 2016 for a review) and these issues are
considered out of the current book’s scope. Though, it is emphasized that knowing
where informed judgement has been employed in the cost of capital calculation and
what is reasonable range, are more important aspects than scientific precision
(Arnold 2010).
From (5.22), it is evident that shipping companies (common to any borrower)
would like to minimize the cost of capital by asking the highest leverage (debt to
value) possible, since debt is cheaper than equity, i.e. 4–8%, whereas creditors will
seek a premium on higher debt to value ratios (Brauner and Illingworth 2009).
Indeed, it was recently reported (Albertijn et al. 2011) that shipping companies have
5.4 Energy Efficiency Investment Appraisal
61
traditionally operated with fairly leveraged capital structures which are accompanied with lower operating leverage albeit to market volatility. This entails that the
companies are exposed to larger operational risk which sets constraints in the
companies’ debt capacity and restricting their access to the public debt market (i.e.
bonds). Hence, in order to avoid facing higher financial distress costs, the more
flexible but nominally more expensive bank debt is preferred in a sense that is more
efficient in rewriting debt contracts with banks than with bondholders. Although the
banks are becoming increasingly risk averse, asset-backed securities that contain
shipping loans remain attractive since generally good collateral is provided and thus
debt capacity is increased (Albertijn et al. 2011).
The loan proposal can be placed at 65% of the vessel’s current market value with
1% arrangement fee charged in the first year, a tenor of 7 years and repayment in
equal annual instalments. With 600 basis points (6.0%) the bank’s funding rate
(Germany et al. 2014), the cost of debt can be made at a ‘spread’ over the bank’s
funding rate, for example LIBOR, plus 200 basis points (2.0%) (Stopford 2009).
For realizing the tax benefit, the OECD values on corporate tax can be used (OECD
2016). As far as the equity component cost parameters, the average investment
return of shipping companies has been reported in Stopford (2009), the systematic
risk for shipping has been estimated by Drobetz et al. (2014b) and the risk premium
can be obtained from Dimson et al. (2015).
The RFR (FRR) is a criterion for investment appraisal within shipping and is a
variation of the NPV formula. By setting NPV = 0 it provides information on the
cost level that needs to be covered by freight revenue (break-even point).
Furthermore, it can be used to determine whether the contracted freight rate as
estimated by (5.13) and (5.20) is profitable. The algebraic formula for obtaining
FRR can be expressed in the following way (Schinas and Kewitsch 2015):
NPV ¼ 0 !
n
X
d ½FRR ð1 bf Þ Xt CapExt OpExt VCt ðrWACC þ 1Þn
t¼1
ð5:23Þ
where: δ is a coefficient reflecting the annual number of commodity loadings, Xt is
the expected payload in year t (voyage charter) or the expected hire period (time
charter), bf is the brokerage fee (2.5% on average), CapExt, OpExt and VCt (only
for voyage charter) are the expected costs in year t, rWACC the cost of capital used
for discounting and n the vessel’s expected life (operational horizon). Only relevant
cash flow components in (5.23) need to be included, with a typical classification
constituting of capital costs—CapEx (loan principal and interest, arrangement fee
and depreciation), operating costs—OpEx (manning, insurance, repairs and maintenance, stores, spares and supplies, management and administration) and voyage
costs—VC (bunkers, cargo handling, port charges and canal dues) (Stopford 2009;
Gorton et al. 2009).
The latter costs are quite challenging to be estimated, since voyage details are
unknown. To deal with this obstacle, it is claimed by Stopford (2009) that bunkers
account for the majority of the voyage costs (3/4) with the remaining quarter
62
5 Chartering Negotiations for Energy Efficiency
associated with the other constituents. Hence, for a known bunker cost, it can be
argued that the voyage costs could be estimated from the bunker costs, multiplied
by a factor of 4/3. The bunker costs are a function of the vessel’s fuel consumption
which in turn is approximately proportional to the third power of vessel’s speed.
This cubic relationship has been accepted as reasonable assumption for relating
changes in speed Vi and consumption bci to the vessel’s design speed V0 and
consumption bc0 (Alderton 1981; Ronen 1982). Common practice allows a reasonable margin for the time charter contract speed (Collins 2000; Gorton et al.
2009) which has been reported by Veenstra and van Dalen (2011) to be in the range
of 0.3—0.5 knots (depending on vessel type) and a deviation of 0.3 knots. It is
argued then that the adjusted fuel consumption can be estimated as:
Vi
bci ¼ bc0 V0
3
ð5:24Þ
The vessel over her expected investment life wears out in equal proportions;
hence this cost should be deducted from the revenues each year [reference (5.19)].
Apparently, a longer investment horizon results into less annual depreciation
(Stopford 2009).
An indication of whether the energy efficiency sharing scheme increases the
value (wealth) of the agent (carrier or ship-owner) has been given by (5.23). Since
there is great deal of uncertainty about future cash flows, it is appropriate to
investigate their risk related to the time distance from the scheme’s implementation
date as associated with the incurred technology cost (project). Hence, for those
cases where the contracted (equilibrium) freight rate is larger than the minimum
required freight value, it is worthy to determine the number of years it is expected to
take to recover the cost expenditure from the forecasted net discounted cash flows
resulting from a capital investment project. This estimated timeframe is defined as
payback period which provides information about a company’s liquidity and risk. It
can be argued that the shorter the payback, the greater the project’s liquidity and the
lesser the company’s exposure to risk. Furthermore, in situations of capital shortage
(limited funds) there is an advantage in receiving a return earlier than later since
reinvestment in other profitable opportunities is allowed (Arnold 2010; Watson and
Head 2013). For a given annual cash flow output at time t yielding NPVt, the
payback period TPB can be estimated as (Kim et al. 2013):
TPB ¼ t NPVt
NPVt þ 1 NPVt
ð5:25Þ
where t* is the last year that the NPV has a negative value and t* + 1 is the first year
that the NPV becomes positive and remains positive. Due to the inherent uncertainty of the forecasted discounted cash flows and to include the random variation,
it is assumed that the payback period is normally distributed (Weingartner 1969).
5.5 Conceptualizing the Generated Knowledge
5.5
63
Conceptualizing the Generated Knowledge
The ancillary clause inclusion is created by the parties’ asymmetric information on
a variety of issues (investment cost, vessel performance, environmental attributes)
in addition to bargaining power. Under this view, game theory is useful in structuring the contractual provisions of the EEISS since not only the potential gains
from trade are realized, but also the fiduciary obligations of the parties are specified.
Thus, the contractual equilibrium is produced through an immutable rule set in a
manner that the parties’ welfare is enhanced and their private information is optimally allocated (Ayres and Gertner 1992). Accordingly, the incentive mechanism
designed through game theory is established bona fide since it generates value for
both players as well as it allows pre-commitment to intrinsically plain and
unequivocal language. On this conjecture, any player who has institutionalized a
reputation of honesty will have a competitive advantage by being offered more
attractive contracts. Nevertheless, it is transcended from common sense that when
writing down extra few sincere sentences while negotiating the main terms of the
charter party agreement, adds little to the cost (economies of scale) (Rasmusen
2001).
In this setting, it is conventional wisdom for the many players participating
regularly in the market that the mechanism is standardized in some fashion.
Certainly, it is recognized by the contracting parties that such ‘boilerplate’ leaves
blank space to be filled with the essential economic terms. Besides, the intention is
to provide well-defined and successful ways of dividing the trade surplus, to reduce
the social costs of the parties’ strategic behavior, to promote an ongoing cooperation and to encourage the desire to maintain goodwill (Katz 1990). This emphasis
has been sustained through the game models outlined in the current chapter, which
can usefully guide the development of a rider clause related to the vessel’s design
energy efficiency improvements. Under the auspices of such clause, it is then
possible to predict how aware the players are of the extent to which they are
ignorant about the vessel’s energy efficiency. From the analysis portrayed in the
current chapter, the reader is persuaded that this regime has been comprehensively
treated. Given that sellers (ship-owners or carriers) are bearing the cost of the
(innovative) technology installed onboard, it is to their interest to provide a level of
energy efficiency beyond the minimum and to alert buyers (charterers or shippers)
to the fact they have done so. Once the effort and attributes are learned, the insights
gained the game model presented herein can help highlight the incentive mechanism design and illuminate the terms in which the parties choose to contract.
Understandably, during the course of negotiation, a norm is imposed that is subject
to the players’ protection against unusually oppressive terms and commercial reasonableness (Katz 1990).
Explicitly, the aforementioned lines of argument while bargaining the EEISS
clause stipulate that evidence shall be provided with respect to the vessel’s performance, taking into account the data available from the vessel’s condition
monitoring systems. The contractual provisions can be detailed with the
64
5 Chartering Negotiations for Energy Efficiency
indispensable guidance of BIMCO’s distilled experience on chartering aspects
(BIMCO 2016). For example, the wording of the clause shall be formulated by
considering how slow steaming influences the operation of the installed energy
efficiency equipment (technical and safety recommendations published by the
manufacturer/designer). Furthermore, subject to the Master’s obligation to ensure
the safety of the vessel, her crew, cargo and protection of the marine environment,
due diligence shall be exercised to prevent contradiction with other clauses (i.e.
utmost and/or due dispatch). Additionally, the contracted parties shall establish the
elements to comply with (basis of measurement, means of verification and term of
application) and the remedies for violation which constitute a breach of the contract.
Such protection measures and controls shall be clearly stated so that the charter
party contract is mutually maintained and the rise of any claim leading to costly and
unpredictable arbitration awards is avoided (BIMCO 2016). The most important
merit of the energy efficiency clause is that both parties have ownership of environmental stewardship, thus achieving brand recognition. It is also a proactive
approach (industry mediated standard) which can ensure that technological innovations can be realized.
Chapter 6
Application—Ocean Grain Transportation
Abstract Having outlined the principal-agent and bargaining games as foundation
tools for modeling the chartering negotiations inclusive of the EEISS appraisal, we
are now in position to appreciate the capabilities of this approach. The forthcoming
developments in the text indicate how informed decision making can be realized
when these models are entertained with data. By necessity, this chapter is concentrated on demonstrating the practical viability of the suggested techniques
through an illustrative case study, where the material is presented in a gradual quest
for problem solving. The working example is focused on seaborne grain transport
since it is acknowledged that grain is one of the most important commodities
around the world.
6.1
Preamble
The difficulty of understanding the feasibility of alternatives or searching for the
next alternative involves additional procedural features beyond those captured by
the utilized cognitive processes and the finer the collated information is filtered.
Decisions are made rationally when a strict preference relation over the various sets
of alternatives exists and depends on the identity (classification) of the best alternative encountered so far. Therefore, maximization of the expected utility is used as
a measure of performance when a choice is made that satisfies the necessary
requirements for pursuing a fair and honest determination of EEISS (Salant 2011).
The latter is evidently treated through the selected case study where, as acclaimed
by Thomson (1883), when any entity is measured and quantified, then, it is possible
for anyone to be acquainted with its meanings as well as properties, and this is
considered the starting point of systematically studying its structure.
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6_6
65
66
6.2
6 Application—Ocean Grain Transportation
Trading Grains by Sea
Grains are grasses cultivated for their grains or seeds; constitute the third largest
commodity transported by sea (Fig. 6.1a) and they provide food energy to humans
as well as animals. Grains are categorized in cereal (wheat, corn/maize, rice, barley,
oats, sorghum and rye), soya beans and coarse used for animal feedstuff, oils and
fats. Of these, corn/maize and wheat are the most important. Although the transported volumes follow the overall trend of world population (subject to many
fluctuations), they are more closely related to the income (Fig. 6.1b). Countries
with low income per capita rely more on grains for direct human consumption. As
the income per capita is increasing, consumers can afford to switch from rice to
wheat, which is a staple part of a diet and is treated as luxury. When the income is
higher, the demand slowly turns to more expensive foods like meat and dairy
products, implying a more extensive use of grains for livestock feeds (Tamvakis
2007).
The main suppliers and receivers of seaborne grain trades are illustrated in
Fig. 6.2 together with their growth potential. Asia represents 32% of total deliveries
(a) Growth of five major bulk commodities
(b) Growth of grain shipments
Fig. 6.1 Time development of seaborne grain trades. Source Author’s plot using data from CRSL
(2016) and World Bank (2016)
6.2 Trading Grains by Sea
67
(a) Major exporters
(b) Major importers
Fig. 6.2 The structure of ocean grain trades. Source Author’s plot using data from CRSL (2016)
and is by far the largest import region. Africa, the Middle East, Europe and Latin
America are the second tier importers with 21, 14, 11 and 9% market share
respectively. Europe has the highest import growth, followed by the increasing role
of Asia, Africa, the Middle East and Latin America, albeit to the aforementioned
commentary. On the export side, the Americas (both North and South) dominate
with about 60% of total exports, whilst Brazil is increasingly contributing after
2006, hence explaining the wide spread. As can be seen, the United States and
Brazil are playing steadily increasing, but diagonally opposite roles. Europe and
Australia are the second tier exporters with a market share close to 10% each. The
higher growth of Europe as an exporter can be explained by its self-sufficiency in
grains and thus its transformation into a frequent exporter albeit to a revised
agricultural policy. For Australia, the agricultural sector although it is still important, it has lost its position as the country’s top export income earner, giving place
to manufacturing and mining (Tamvakis 2007).
68
6 Application—Ocean Grain Transportation
The seaborne trades in grains naturally follow in the short term seasonal variations in different parts of the world such as harvests, with crop failures being the
main source of unexpected trough trade flows. Additionally, grain trading emphasis
is being periodically switched between a number of highly scattered and dispersed
import markets that generate considerable requirements among shippers and traders
for eminently flexible vessel selection policies. Since these fluctuations are not
predictable, planning the transport is very difficult and complex, with shippers
relying more on the spot market (Stopford 2009). It is estimated that 60% of
seaborne grain trade is transported under voyage and trip charters, whilst the
remainder are shipped on relatively short time charters (Pirrong 1993; Jonnala et al.
2002). In the longer term, the grain trade is influenced by income, prices and yields’
performance. Hence, the grain logistics system is designed so that its elements work
together as efficiently as possible, whereas the sea transport is just one stage in the
supply chain moving bulk grain between producers and consumers (Stopford 2009).
Figure 6.3 shows a typical scenario of a bulk grain supply chain. Seeds are
bought by farmers from a seed company. After crop production, grains are harvested and moved from the field to a storage bin. During high harvests or when
demand is low, these storage facilities may become inadequate and any available
covered storage is handled by elevator companies. Grain elevators market bulk
commodities against generic grade standards, thus grains are commingled to meet
buyer specifications and to maximize profit. The grains are either directly sold to a
processor, or are shipped for overseas export. The infrastructure commonly used for
storing grains usually has tall and cylindrical shape. From the storage elevator, the
grains are fed to railcars, trucks or barges and shipped to a terminal. Then, the
grains are transferred into an elevator where they await to be loaded on a bulk
Fig. 6.3 Elements of a generic grain logistics network. Source Author’s drawing based on Abis
et al. (2014) and WESTAC (1998)
6.2 Trading Grains by Sea
69
Fig. 6.4 Loading and storage facilities at a grain export terminal with a bulk carrier berthed.
Photo credit Courtesy of Bunge Limited
carrier (see Fig. 6.4). Naturally, the elevator must hold enough grain to fill the
vessel (Stopford 2009; Thakur and Hurburgh 2009). It is noted that the grain when
carried in bulk by sea, is subjected to the vessel’s movements and is liable to shift
transversely across the cargo holds. To eliminate such behavior characteristic, the
cargo holds are filled and trimmed to feed as much grain as possible, reaching their
highest level (see Fig. 6.5). This levelling (trimming) provides stable stowage
during the vessel’s sea passage (House 2005). At the end of the voyage, the process
is reversed: the grains are offloaded from the vessel either with grab (see Fig. 6.6)
or pneumatic or mechanical systems and moved to storage elevators (silos). From
the silos, the grains are shipped into a flour mill, grinding facility or feed compounder. At the processing plant, grains are either processed into a final product
with the addition of other ingredients, or are mixed with other products while
undergoing many processing steps. The finished product (i.e. flour) is either
packaged for the consumer market, or shipped in bulk by rail and truck to bakeries,
other large industrial users or farmers (Stopford 2009; Thakur and Hurburgh 2009).
It is emphasized that storage bins contain grains from many different sources and
elevator companies buy grain from the farmers with different quality characteristics
(i.e. moisture, test weight, foreign material, etc.). When in storage, a part or entire
content of an elevator’s silo can be transferred to other silos in order to avoid
spoilage due to environmental conditions, usually increased temperature. Finally,
the grains for the outgoing shipments are blended from several bins so that the
customer specifications for quality are met and the finished product for consignment
70
6 Application—Ocean Grain Transportation
Fig. 6.5 Cargo hold being loaded and filled with grain by a loading spout. Photo credit Courtesy
of Cargill Inc.
Fig. 6.6 Grain being discharged from a bulk carrier’s cargo hold. Photo credit Courtesy of
BayWa AG
6.2 Trading Grains by Sea
71
Fig. 6.7 Handymax bulk carrier berthed alongside a grain export terminal and being loaded.
Photo credit Courtesy of Columbia Grain Inc.
is prepared (Thakur et al. 2010). In this respect, the arrangement of ocean grain
transport requires careful scheduling from different sources and involves the
avoidance of any penalties for faulty consignments as well as demurrage charges,
which grow rapidly with large cargoes. For this reason, it is more difficult to
introduce large vessels into the grain trade than into the iron ore and coal trades and
there is often congestion (Stopford 2009).
As mentioned earlier, bulk carriers are heavily employed in the grain seaborne
trade with the individual consignments (parcel sizes) clustered in two ranges. The
first one is around 25,000–45,000 tones with handymax bulk carriers (see Fig. 6.7)
the main source for transport, and the second one is around 50,000–70,000 tones
which is carried in panamax bulk carriers (see Fig. 6.8) (Stopford 2009). For the
interested reader, generic vessel designs are exhibited in Appendix A. The panamax
vessel reflects the cargo quantity where economies of scale can be reached for
obtaining the minimum ocean transport cost (Jonnala et al. 2002; Park and Koo
2004). Besides, the attainment of even larger scale economies has introduced the
ultramax and kamsarmax types which belong to the handymax and panamax range,
though with increased cargo capacity (JSEA 2016). In view of the two grain parcel
sizes, the application of the models described in the previous chapter refers to these
two vessel sizes.
72
6 Application—Ocean Grain Transportation
Fig. 6.8 Panamax bulk carrier berthed alongside a grain export terminal and being loaded. Photo
credit Courtesy of Groupe Sica Atlantique
6.3
Dealing with the Inherent Uncertainties
The utilized data (i.e. quantities to populate the game theoretical models) are
transformed into information with the expression of frequency estimates, out of
which knowledge is derived about their properties. A measure of how much confidence is known about the data and thus a measure of the attached uncertainty is
the use of probability (Modarres 2006). It is realized that the critical component in
these probabilistic characterizations are the choices made in selecting the fitted
probability distributions. Moreover, these approximations represent a simplification
of the real world, which do not necessarily describe the natural complexity.
However, it is believed that the logical derivations are useful in terms of encapsulating knowledge with practical reality (Box 1976) and the following subparagraphs describe how uncertainty is handled.
6.3.1
Parameter Uncertainties
Parameter uncertainties are those associated with the values of the quantities of
interest and are due to unknowns about correct input. These involve limited data
and information, inaccuracies in the assumptions used to infer the actual quantity of
interest from the observed readings, estimation of parameters through small and/or
6.3 Dealing with the Inherent Uncertainties
73
non-representative samples. As stated earlier, they are typically characterized by
establishing probability distributions on the parameter values (Modarres 2006). In
this respect, the uncertainty for all monetary parameters (commodity prices, time
and voyage charter rates, bunker and vessel prices, depreciation, lost earnings,
operational and voyage costs) is represented by the log-normal distribution. The
reason for choosing this probabilistic characterization is not only because the distribution is defined for positive values (Forbes et al. 2011), but also salient features
of price behavior can be captured (i.e. extreme outcomes occur more frequently)
which is quite important within shipping (Pirrong 2015).
Following Psarros and Vassalos (2009), Gaussian distributions are used for
capturing the uncertainties of interest rates. The vessel’s speed and bunker consumption are manifested by a Weibull distribution (Aldous et al. 2015), since
decreasing, constant, or increasing levels can be portrayed (Forbes et al. 2011). The
vessel’s voyage as well as hire duration are depicted by a Gamma distribution
(Chen et al. 2011) due to its positive space and shape flexibility (Forbes et al. 2011).
Gaussian distributions are assigned to the vessel’s bale capacity, principal dimensions (Diez and Peri 2010) as well as commodity quantity and density (Chen et al.
2011) because most values are expected to cluster closely around a central mean
(Bury 1999). Vessel calls and inherently number of voyages are assumed to follow
the Poisson distribution which occur randomly at a normally distributed constant
rate (Bury 1999; Vose 2009) and are in consistency with Alvarez et al. (2010). The
approach outlined in Vose (2009) is employed for estimating the parameters of the
chosen distributions (see Appendix B).
6.3.2
Uncertainty Propagation
The probabilistic characterization of the utilized data allows finding the distribution
characteristics (mean and variance) of a function (i.e. the game theoretical as well as
investment appraisal models) by treating the dependent and independent elements
as random variables. Such technique to perform the uncertainty propagation is
based on Monte Carlo simulation and involves the random sampling of each
independent variable’s probability distribution within the model to produce many
trials of the dependent variable’s distribution. The reconstructed distribution’s
shape is procured from calculated values for the model outcome and therefore
reflects the probability of the values that could occur (Modarres 2006; Vose 2009).
A procedure for Monte Carlo simulation is given in Appendix C.
6.3.3
Parameter Significance of Choice
The probabilistic characterizations and the uncertainty propagation from which the
game theoretical as well as investment appraisal models are entertained entail that
74
6 Application—Ocean Grain Transportation
their parameters’ significance of choice should be determined. Thus, the objective
of a sensitivity analysis is to identify and rank the parameters in the acceptable
range which are mostly responsible for realizing the models’ target output (Saltelli
et al. 2006). The effects of input variables such as charter rates are measured by
modifying them by several folds, factors, or even one or more order of magnitudes
one at a time, and measure relative changes observed in the model’s results. This
process is repeated using different values for the same parameter until those
parameters whose change leads to the highest impact in the final model’s values are
determined. As an outcome, attention is focused to those parameters that need better
characterization of uncertainty which in turn strengthens the quality and validity of
the performed calculations (Modarres 2006). The specification of the amount of
change to be applied to each parameter is outlined in Appendix D.
6.4
Energy Efficiency Sharing Scheme for Handymax
Vessel
The models outlined in the previous chapter are populated with data from CRSL
(2016a, b). It is assumed that the investment horizon is fifteen years since the vessel
is sold to new owners at the end of the period. The calculations are considering the
transport of corn and the more expensive wheat, as well as the options of basic and
advanced technological upgrades.
6.4.1
Time Charter
The charterer’s or shipper’s (principal’s) reservation price can be determined
through a sample of handymax period fixtures from CRSL (2016a) between August
2012 and December 2015. For that period, out of a sample of 364 transactions, 49
fixtures were related to the carriage of grains with a rate of FRM = 11,228 $/d (see
Appendix E for the distribution parameters). This value represents also the market
price.
6.4.1.1
Principal—Agent Part
By applying (5.4) and (5.5), the optimum effort level is 9%, whereas from (5.6) the
ship-owner’s or carrier’s (agent’s) reservation price accounting also for the energy
efficiency effort is FRRP = 12,193 $/d (see Appendix F for the simulated result,
Figs. F.1 and F.2).
6.4 Energy Efficiency Sharing Scheme for Handymax Vessel
75
Table 6.1 Surplus fractions and equilibrium prices for handymax period contracts
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
6.4.1.2
θΒ (%)
θS (%)
FRE ($/d)
78.5
78.5
21.5
21.5
11,434
11,448
78.7
78.6
21.3
21.4
11,408
11,446
Bargaining Part
As per (5.14) and (5.15), the surplus between the principal’s and agent’s reservation
prices is split by the respective percentages shown in Table 6.1 (see Appendix F for
the simulated result). From (5.20), the equilibrium rate is also shown in Table 6.1
(see Appendix F for the simulated result, Figs. F.4, F.5, F.6, F.7, F.8, F.9, F.10 and
F.11). It is observed that the surplus fraction and equilibrium rate are indifferent
(inelastic) to the commodity prices and technology costs. For the sake of consistency, the inventory interest rate is assumed to be 5.35% (Dunsby et al. 2008) whilst
the inventory holding coefficient ω has been estimated as −0.062 for corn and
−0.254 for wheat (Symeonidis et al. 2012; Frankel 2014). Data from CRSL (2016)
are used for the estimation of commodity prices, vessel prices, earnings and grain
capacity (see Appendix E for distribution parameters). The values for the cargo’s
density are considered from Sewell (1999) (see Appendix E for distribution
parameters) and the vessel’s depreciation is estimated from (5.19) (see Appendix F
for the simulated results, Fig. F.3). The basic technology upgrade (i.e. low friction
coating or bare optimization or appendages or a combination of these) is assumed to
cost around $1.5 million, whereas the advanced upgrade (basic plus air lubrication
or wind solutions) is assumed to cost around $4 million (Eason 2015).
6.4.1.3
Investment Appraisal
The simulated results of (5.22) provide a capital cost of 10.9% (see Appendix F,
Fig. F.12), whereas the operating costs are in the range of 5,181 $/d (Gardiner et al.
2009) (see Appendix E for distribution parameters). Furthermore, the operating
costs are assumed to escalate by 2.5% on an annual basis, with an anticipated
increase of 5% to current levels during the tenth year (Gardiner 2014, 2015). By
applying (5.23), the minimum RFR for basic and advanced technology upgrade is
11,936 and 12,693 $/d respectively (see Appendix F for simulated results,
Figs. F.13 and F.14). Comparing these values with those from Table 6.1 it is
obvious that the equilibrium freight rate is not profitable for the basic and advanced
technology expenditure with probability 66 and 86% respectively. It is worthwhile
76
6 Application—Ocean Grain Transportation
Fig. 6.9 Handymax period rates cumulative probability curves. Source Plotted by Author
noting that the agent’s (ship-owner’s) reservation price (Sect. 6.4.1.1) can justify
only the cost of the basic technology upgrade by 55% (Fig. 6.9).
6.4.1.4
Sensitivity Analysis
The peculiar results of the previous paragraph deserve the further investigation of
the equilibrium freight rate subject to the volatility of the market charter rates. For
lower prices it is straightforward that the EEISS would still not be profitable.
Though, the models could be entertained by considering an increase on the prices
with the technique outlined in Appendix D. The new values are shown in Table 6.2.
Upon these changes, the optimum effort level is 9.7% and the agent’s reservation
price yields FRRP = 12,762 $/d. The surplus fraction percentages between the
Table 6.2 Parameters for evaluating the significance of change for handymax period contracts
Variable
Corn price ($/t)
Wheat price ($/t)
Period rate ($/d)
Newbuilding price ($ 106)
15 years price ($ 106)
Earnings ($/d)
Source Calculated by Author
Model
Distribution Parameters
Mean
Variance
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
m
m
m
m
m
m
=
=
=
=
=
=
257
328
11,563
25.9
10.6
11,598
s2
s2
s2
s2
s2
s2
=
=
=
=
=
=
582
562
5,5552
1.542
4.172
3,3342
6.4 Energy Efficiency Sharing Scheme for Handymax Vessel
77
Table 6.3 Surplus fractions and equilibrium prices for handymax period contracts (sensitivity
analysis)
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
θΒ (%)
θS (%)
FRE ($/d)
78.4
78.3
21.6
21.7
11,853
11,810
78.4
78.4
21.6
21.6
11,854
11,837
principal’s and agent’s reservation prices as well as the equilibrium rate are shown
in Table 6.3. Again, it is observed that the surplus fraction and equilibrium rate are
indifferent (inelastic) to the commodity prices and technology costs. The fraction
percentages are almost similar to those estimated previously, whilst, the rate values
have been moderately increased.
The minimum RFR for basic and advanced technology upgrade is 11,647 $/d
(standard deviation = 1,241) and 12,222 $/d (standard deviation = 1,527) respectively. From a first instance it could be claimed that the basic technology upgrade
EEISS can be justified. However, considering the variation, it can be concluded that
only from a marginal point of view the cost is covered. As far as the advanced
technology upgrade is concerned, even with slightly improved freight market, still
the energy efficiency sharing scheme is not profitable.
6.4.2
Voyage Charter
The charterer’s or shipper’s (principal’s) reservation price can be determined
through a sample of handymax voyage fixtures from CRSL (2016a) between
August 2012 and December 2015. For that period, out of a sample of 169 transactions, 79 fixtures were related to the carriage of grains with a rate of FRM = 38.4
$/t and transported cargo quantity of 52,883 t (see Appendix E for the distribution
parameters). This value represents also the market price.
6.4.2.1
Principal—Agent Part
Before determining the ship-owner’s or carrier’s (agent’s) reservation price, it is
necessary to estimate the agent’s preference characteristics, which are obtained by
applying (5.7) and (5.8) and are shown in Table 6.4. The assigned values reflect
different commodity prices as well as basic and advanced technology upgrade. The
probability β of the agent’s preference type is assumed to be 24% (Cargill 2016).
78
6 Application—Ocean Grain Transportation
Table 6.4 Agent’s preference characteristics and reservation prices for handymax voyage
contracts
σL (−)
σH (−)
Basic technology upgrade
1.034
1.145
0.658
0.13
0.37
38.65
Advanced technology upgrade
1.095
1.444
−0.013
0
0.33
38.61
Basic technology upgrade
1.026
1.107
0.749
0.18
0.24
38.43
Advanced technology upgrade
1.070
1.313
0.274
0.19
0.27
38.70
Den (−)
πL (%)
πH (%)
FRRV ($/t)
Commodity: corn
Commodity: wheat
Source Calculated by Author
Prior to continuing, it is important to explore the denominator’s sign of the right
part of (5.12) ðDen ¼ 1 ððð1 bÞ=bÞ ððrH rL Þ=rL ÞÞÞ. Furthermore, in the
absence of relevant information, an agent of low preference σL is assumed to spend
25% of the cost needed for basic and advanced technology upgrades respectively.
The negative value only for the advanced technology upgrade with corn transported
as cargo stipulates that πL = 0. The environmental stewardship can be evaluated
from (5.11) and (5.12) (see Appendix F for the simulated results, Figs. F.15, F.16,
F.17, F.18, F.19 and F.20). It can be observed that the values differ marginally from
the principal’s reservation price, which coincides with the freight contract’s functionality, i.e. transport is bought from the agent on a lump sum basis and covers all
the incurred costs (see Sect. 2.3).
6.4.2.2
Bargaining Part
As per (5.14) and (5.15), the surplus between the principal’s and agent’s reservation
prices is split by the respective percentages shown in Table 6.5 (see Appendix F for
the simulated result). From (5.20), the equilibrium rate is also shown in Table 6.5
(see Appendix F for the simulated result, Figs. F.21, F.22, F.23, F.24, F.25, F.26,
F.27, F.28). It is observed that the surplus fraction and equilibrium rate are indifferent (inelastic) to the commodity prices and technology costs. Additionally, the
Table 6.5 Surplus fractions and equilibrium prices for handymax voyage contracts
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
θΒ (%)
θS (%)
FRE ($/t)
78.6
78.7
21.4
21.3
38.42
38.48
78.7
78.6
21.3
21.4
38.41
38.52
6.4 Energy Efficiency Sharing Scheme for Handymax Vessel
79
surplus fractions are similar to those of Table 6.1. For the sake of consistency, the
same assumptions concerning the inventory interest rate and holding coefficient,
technology upgrades and vessel’s depreciation hold.
6.4.2.3
Investment Appraisal
The number of voyages per year of a handymax vessel is assumed to be ten
(10) (Psarros 2009) with a factor of 0.4 related to grain cargoes and an average
voyage duration of 48 days (CRSL 2015, 2016a) (see Appendix E for distribution
parameters and Appendix F for simulated results, Figs. F.29 and F.30). The voyage
costs are determined indirectly from (5.24) with data from CRSL (2016a, b), taking
fuel cost in the range of 584 $/t, speed in the range of 14.4 knots (considering in
addition the margin of 0.5 ± 0.3 knots, see Sect. 5.4) and fuel consumption in the
range of 31.7 t/d (see Appendix E for the distribution parameters) accounting also
for the fuel efficiency gains. It is acknowledged that the fuel savings are vessel,
weather and route specific, so based on the values of Table 2.1 and Eason (2015), it
is conservatively assumed that 10 and 15% fuel consumption efficiency can be
obtained for the basic and advanced technology upgrade respectively (see
Appendix F for the simulated results, Fig. F.31).
Furthermore, the previous assumptions concerning the capital and operating
costs hold here as well. By applying (5.23), the minimum RFR for basic and
advanced technology upgrade is 36.5 and 35.7 $/t respectively (see Appendix F for
simulated results, Figs. F.32 and F.33). Comparing these values with those from
Table 6.5 it is obvious that the equilibrium freight rate is profitable for the technology expenditure. However, the variability can create some concerns and by
plotting the cumulative probability graphs of the equilibrium and RFRs (Fig. 6.10)
the comparison can be interpreted directly. As shown, the probability of the RFR
Fig. 6.10 Handymax voyage rates cumulative probability curves. Source Plotted by Author
80
6 Application—Ocean Grain Transportation
being larger than the equilibrium prices is around 42–46%. This entails that the cost
of basic and advanced technology upgrades can be justified only by a range
between 54 and 58%.
6.4.2.4
Sensitivity Analysis
The peculiar results of the previous paragraph deserve the further investigation of
the equilibrium freight rate subject to the volatility of the market voyage rates.
Lower and higher prices are considered for evaluating the significance of change on
the RFR and exploring whether the EEISS would still be profitable. Therefore, the
models are entertained by considering different prices with the technique outlined in
Appendix D. The new values are shown in Table 6.6.
Upon these changes, the agent’s preference characteristics and environmental
stewardship together with the reserved prices are shown in Table 6.7. It is evident
that the values differ marginally from the market prices, entailing that this observation coincides with the freight contract’s functionality as commented in
Sect. 6.4.2.1. The surplus fraction percentages between the principal’s and agent’s
reservation prices as well as the equilibrium rate are shown in Table 6.8. Again, it is
observed that the surplus fraction and equilibrium rate are indifferent (inelastic) to
the commodity prices and technology costs.
The minimum RFR for basic and advanced technology upgrade with low prices
is 35.18 $/t (standard deviation = 3.8) and 35.18 $/t (standard deviation = 4.5)
respectively. By contrast, the minimum RFR for basic and advanced technology
upgrade with high prices is 37.91 $/t (standard deviation = 4.1) and 37.85 $/t
(standard deviation = 4.4) respectively. In order to check whether the EEISS could
be justified, the cumulative probability graphs are plotted together (Fig. 6.11). Vis á
vis, for low and high prices the probability of RFR being larger than the equilibrium
prices is between 44 and 46%. This indicates that the cost of basic and advanced
Table 6.6 Parameters for evaluating the significance of change for handymax voyage contracts
Variable
Model
Distribution parameters
Mean
Corn price ($/t)
Lognormal
Wheat price ($/t)
Lognormal
Voyage rate ($/t)
Lognormal
6
Newbuilding price ($ 10 )
15 years price
($ 106)
Lognormal
Lognormal
Variance
Lower
Upper
Lower
m = 211
m = 257
s2 = 812
m = 277
m = 37
m = 25.1
m = 9.2
m = 328
m = 39.8
m = 25.9
m = 10.6
Upper
2
2
2
2
s = 79
s = 11
s2 = 582
s2 = 562
s2 = 152
2
2
s2 = 1.542
2
2
s2 = 4.172
s = 1.09
s = 2.96
Earnings ($/d)
Lognormal
m = 9,900
m = 11,598
s2 = 4,6952
s2 = 3,3342
Bunker price
($/t)
Lognormal
m = 537
m = 632
s2 = 2592
s2 = 3652
Source Calculated by Author
6.4 Energy Efficiency Sharing Scheme for Handymax Vessel
81
Table 6.7 Agent’s preference characteristics and reservation prices for handymax voyage
contracts (sensitivity analysis)
σL (−)
σH (−)
Basic technology upgrade
1.035
1.150
0.656
0.20
0.04
37.04
Advanced technology upgrade
1.098
1.454
−0.030
0
0.24
37.03
Basic technology upgrade
1.029
1.122
0.717
0.05
0.40
39.86
Advanced technology upgrade
1.079
1.358
0.276
0.08
0.09
39.85
Basic technology upgrade
1.027
1.112
0.736
0.02
0.29
37.05
Advanced technology upgrade
1.073
1.329
0.254
0.09
0.01
37.04
Basic technology upgrade
1.023
1.094
0.775
0.14
0.06
39.83
Advanced technology upgrade
1.061
1.271
0.379
0.05
0.36
39.85
Den (−)
πL (%)
πH (%)
FRRV ($/t)
Commodity: corn—low price
Commodity: corn—high price
Commodity: wheat—low price
Commodity: wheat—high price
Source Calculated by Author
Table 6.8 Surplus fractions and equilibrium prices for handymax voyage contracts (sensitivity
analysis)
Commodity: corn—low price
Basic technology upgrade
Advanced technology upgrade
Commodity: corn—high price
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat—low price
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat—high price
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
θΒ (%)
θS (%)
FRE ($/t)
79.4
79.4
20.6
20.6
37.03
37.02
78.4
78.2
21.6
21.8
39.86
39.82
79.6
79.6
20.4
20.4
37.04
37.03
78.5
78.4
21.5
21.6
39.81
39.82
technology upgrades can be justified by 54–56%, which is slightly reduced than the
previous estimation, but it still provides robust result.
6.4.2.5
Investment Recovery
By applying (5.25), the payback period is estimated as 12.4 years (standard deviation 3.1 years) for the basic technology upgrade and 20.6 years (standard
82
6 Application—Ocean Grain Transportation
(a) Low prices
(b) High prices
Fig. 6.11 Handymax voyage rates cumulative probability curves (sensitivity analysis). Source
Plotted by Author
deviation 7.2 years) for the advanced technology upgrade. From Fig. 6.12 it is
illustrated that for the vessel’s expected operational horizon (15 years), the cost
expenditure can be recovered up to 80% for the basic, whilst up to 22% for the
advanced technology upgrade.
6.4 Energy Efficiency Sharing Scheme for Handymax Vessel
83
Fig. 6.12 Handymax
technology investment
payback period cumulative
probability curves for voyage
contracts. Source Plotted by
Author
6.4.3
Time Charter Trip
The charterer’s or shipper’s (principal’s) reservation price can be determined
through a sample of handymax time charter trip fixtures CRSL (2016a) between
August 2012 and December 2015. For that period, out of a sample of 3,317
transactions, 324 fixtures were related to the carriage of grains with a rate of
FRM = 13,692 $/d (see Appendix E for the distribution parameters). This value
represents also the market price.
6.4.3.1
Principal—Agent Part
The assumptions of Sect. 6.4.2.3 related to a handymax vessel’s annual number of
trips and average trip duration also hold herein. A factor of 0.1 is allocated for grain
trips per year (CRSL 2016a) (see Appendix E for distribution parameters and
Appendix F for simulated results, Fig. F.34). By applying (5.4) and (5.5), the
optimum effort level is 12.8%, whereas from (5.6) the ship-owner’s or carrier’s
(agent’s) reservation price accounting also for the energy efficiency effort is
FRRP = 15,478 $/d (see Appendix F for the simulated result, Figs. F.35 and F.36).
6.4.3.2
Bargaining Part
As per (5.14) and (5.15), the surplus between the principal’s and agent’s reservation
prices is split by the respective percentages shown in Table 6.9, which are similar to
the fraction values presented in Table 6.1. From (5.20), the equilibrium rate is also
shown in Table 6.9 (see Appendix F for the simulated result, Figs. F.37, F.38, F.39
and F.40). Again, it is observed that the surplus fraction and equilibrium rate are
indifferent (inelastic) to the commodity prices and technology costs. The assumptions of Sect. 6.4.1.2 related to the inventory holding and interest rate, cargo
density, technology upgrades, vessel earnings, prices and grain capacity also hold.
84
6 Application—Ocean Grain Transportation
Table 6.9 Surplus fractions and equilibrium prices for handymax time charter trips
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
6.4.3.3
θΒ (%)
θS (%)
FRE ($/d)
78.5
78.5
21.5
21.5
14,040
14,083
78.6
78.5
21.4
21.5
14,211
14,114
Investment Appraisal
The capital and operating costs are assumed to be the same as in Sect. 6.4.1.3,
whereas the number of grain trips and their respective duration are in consistency
with the values in Sect. 6.4.3.1. By applying (5.23), the minimum RFR for basic
and advanced technology upgrade is 11,534 and 10,615 $/d respectively (see
Appendix F for simulated results, Figs. F.41 and F.42). Comparing these values
with those from Table 6.9 it is obvious that the equilibrium freight rate is profitable
for the basic and advanced technology expenditure. However, to remove any
doubts, by comparing the rate cumulative probability plots the results can be elucidated outspokenly. As indicated by Fig. 6.13, the probability of the equilibrium
time charter trip rates being larger than the minimum required values for basic and
advanced technology upgrades is between 82 and 84%.
Fig. 6.13 Handymax time charter trip rates cumulative probability curves. Source Plotted by
Author
6.4 Energy Efficiency Sharing Scheme for Handymax Vessel
6.4.3.4
85
Sensitivity Analysis
It is straightforward that for higher prices the EEISS would still be profitable.
Though, to investigate the significance of change in the price volatility, the models
could be entertained by considering a price reduction with the technique outlined in
Appendix D. The new values are shown in Table 6.10.
Upon these changes, the optimum effort level is 11.2% and the agent’s reservation price yields FRRP = 14,546 $/d. The surplus fraction percentages between
the principal’s and agent’s reservation prices as well as the equilibrium rate are
shown in Table 6.11. Again, it is observed that the surplus fraction and equilibrium
rate are indifferent (inelastic) to the commodity prices and technology costs. The
fraction percentages are almost similar to those estimated previously, whilst, the
rate values have been decreased.
The minimum RFR for basic and advanced technology upgrade is 10,744 $/d
(standard deviation = 2,876) and 11,008 $/d (standard deviation = 2,928) respectively. With these values, it could be claimed that the basic technology upgrade
EEISS can be justified. From the cumulative probability curves it can be interpreted
that the equilibrium time charter trip rates can be higher than the minimum required
values by 93–95%, which validates even further the investment appraisal’s result.
Table 6.10 Parameters for evaluating the significance of change for handymax time charter trip
contracts
Variable
Corn price ($/t)
Wheat price ($/t)
Trip rate ($/d)
Newbuilding price ($ 106)
15 years price ($ 106)
Earnings ($/d)
Source Calculated by Author
Model
Distribution Parameters
Mean
Variance
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
m
m
m
m
m
m
=
=
=
=
=
=
211
277
13,087
25.1
9.2
9,900
s2
s2
s2
s2
s2
s2
=
=
=
=
=
=
812
792
63402
1.092
2.962
4,6952
Table 6.11 Surplus fractions and equilibrium prices for handymax time charter trip contracts
(sensitivity analysis)
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
θΒ (%)
θS (%)
FRE ($/d)
79.5
79.5
20.5
20.5
13,455
13,416
79.6
79.6
20.4
20.4
13,398
13,510
86
6 Application—Ocean Grain Transportation
Fig. 6.14 Handymax
technology investment
payback period cumulative
probability curves for time
charter trip contracts. Source
Plotted by Author
6.4.3.5
Investment Recovery
By applying (5.25), the payback period is estimated as 12.1 years (standard deviation 2.7 years) for the basic technology upgrade and 20.6 years (standard deviation 12.2 years) for the advanced technology upgrade. From Fig. 6.14 it is
illustrated that for the vessel’s expected operational horizon (15 years), the cost
expenditure can be recovered up to 86% for the basic, whilst up to 32% for the
advanced technology upgrade.
6.5
Energy Efficiency Sharing Scheme for Panamax Vessel
The models outlined in the previous chapter are populated with data from CRSL
(2016a, b). Similarly to the handymax vessel case, it is assumed that the investment
horizon is fifteen years since the vessel is sold to new owners at the end of the
period. The calculations are considering the transport of corn and the more
expensive wheat, as well as the options of basic and advanced technological
upgrades.
6.5.1
Time Charter
The charterer’s or shipper’s (principal’s) reservation price can be determined
through a sample of panamax period fixtures from CRSL (2016a) between August
2012 and December 2015. For that period, out of a sample of 851 transactions, 132
fixtures were related to the carriage of grains with a rate of FRM = 9,635 $/d (see
Appendix E for the distribution parameters). This value represents also the market
price.
6.5 Energy Efficiency Sharing Scheme for Panamax Vessel
6.5.1.1
87
Principal—Agent Part
By applying (5.4) and (5.5), the optimum effort level is 9.6%, whereas from (5.6)
the ship-owner’s or carrier’s (agent’s) reservation price accounting also for the
energy efficiency effort is FRRP = 10,541 $/d (see Appendix F for the simulated
result, Figs. F.43 and F.44).
6.5.1.2
Bargaining Part
As per (5.14) and (5.15), the surplus between the principal’s and agent’s reservation
prices is split by the respective percentages shown in Table 6.12 (see Appendix F
for the simulated result). From (5.20), the equilibrium rate is also shown in Table 6.
12 (see Appendix F for the simulated result, Figs. F.45, F.46, F.47, F.48, F.49, F.
50, F.51 and F.52). It is observed that the surplus fraction and equilibrium rate are
indifferent (inelastic) to the commodity prices and technology costs. For the sake of
consistency, the assumptions of Sect. 6.4.1.2 associated with the inventory interest
rate, holding coefficient, vessel depreciation, technology upgrade costs and cargo
density also hold herein. Data from CRSL (2016a) are used for the estimation of
commodity prices, vessel prices, earnings and grain capacity (see Appendix E for
distribution parameters).
6.5.1.3
Investment Appraisal
Similarly to the previous application, a capital cost of 10.9% (see figure X of
Appendix F) is utilized, whereas the operating costs are in the range of 6,324 $/d
(Gardiner et al. 2009) (see Appendix E for distribution parameters) and assumed
escalation as in Sect. 6.4.1.3. By applying (5.23), the minimum RFR for basic and
advanced technology upgrade is 14,803 and 16,529 $/d respectively (see
Appendix F for simulated results, Figs. F.53 and F.54). Comparing these values
with those from Table 6.12 it is obvious that the equilibrium freight rate is not
profitable for the basic and advanced technology expenditure.
Table 6.12 Surplus fractions and equilibrium prices for panamax period contracts
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
θΒ (%)
θS (%)
FRE ($/d)
83.3
83.2
16.7
16.8
9,819
9,807
83.3
83.4
16.7
16.6
9,803
9,826
88
6.5.1.4
6 Application—Ocean Grain Transportation
Sensitivity Analysis
For lower prices it is straightforward that the EEISS would still not be profitable.
Though, the models could be entertained by considering an increase on the prices
with the technique outlined in Appendix D. The new values are shown in
Table 6.13.
Upon these changes, the optimum effort level is 12.4% and the agent’s reservation price yields FRRP = 11,058 $/d. The surplus fraction percentages between
the principal’s and agent’s reservation prices as well as the equilibrium rate are
shown in Table 6.14. Again, it is observed that the surplus fraction and equilibrium
rate are indifferent (inelastic) to the commodity prices and technology costs. The
fraction percentages are almost similar to those estimated previously, whilst, the
rate values have been moderately increased.
The minimum RFR for basic and advanced technology upgrade is 14,808 $/d
(standard deviation = 2,191) and 16,513 $/d (standard deviation = 4020) respectively. From these values it is indicated that the basic as well as advanced technology upgrade EEISSs are not profitable and cannot be justified.
Table 6.13 Parameters for evaluating the significance of change for panamax period contracts
Variable
Corn price ($/t)
Wheat price ($/t)
Period rate ($/d)
Newbuilding price ($ 106)
15 years price ($ 106)
Earnings ($/d)
Source Calculated by Author
Model
Distribution Parameters
Mean
Variance
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
m
m
m
m
m
m
=
=
=
=
=
=
257
328
9,799
27.7
10.9
8,428
s2
s2
s2
s2
s2
s2
=
=
=
=
=
=
582
562
3,6572
1.952
4.092
2,5372
Table 6.14 Surplus fractions and equilibrium prices for panamax period contracts (sensitivity
analysis)
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
θΒ (%)
θS (%)
FRE ($/d)
83.3
83.3
16.7
16.7
10,110
10,100
83.4
83.4
16.6
16.6
10,026
10,058
6.5 Energy Efficiency Sharing Scheme for Panamax Vessel
6.5.2
89
Voyage Charter
The charterer’s or shipper’s (principal’s) reservation price can be determined
through a sample of panamax voyage fixtures from CRSL (2016a) between August
2012 and December 2015. For that period, out of a sample of 1,197 transactions,
218 fixtures were related to the carriage of grains with a rate of FRM = 34.3 $/t and
transported cargo quantity of 59,507 t (see Appendix E for the distribution
parameters). This value represents also the market price.
6.5.2.1
Principal—Agent Part
Before determining the ship-owner’s or carrier’s (agent’s) reservation price, it is
necessary to estimate the agent’s preference characteristics, which are obtained by
applying (5.7) and (5.8) and are shown in Table 6.15. The assigned values reflect
different commodity prices as well as basic and advanced technology upgrade. The
probability β of the agent’s preference type is assumed to be 14% (Cargill 2016).
Prior to continuing, it is important to explore the denominator’s sign of the right
part of (5.112) ðDen ¼ 1 ððð1 bÞ=bÞ ððrH rL Þ=rL ÞÞÞ. In a similar way to
Sect. 6.4.2.1, an agent of low preference σL is assumed to spend 25% of the cost
needed for basic and advanced technology upgrades respectively. The negative
value for the advanced technology upgrade with corn and wheat transported as
cargo stipulates that πL = 0. The environmental stewardship can be evaluated from
(5.11) and (5.12) (see Appendix F for the simulated results, Figs. F.55, F.56, F.57,
F.58, F.59 and F.60). It can be observed that the values differ marginally from the
principal’s reservation price, which coincides with the freight contract’s functionality, i.e. transport is bought from the agent on a lump sum basis and covers all the
incurred costs (see Sect. 2.3).
6.5.2.2
Bargaining Part
As per (5.14) and (5.15), the surplus between the principal’s and agent’s reservation
prices is split by the respective percentages shown in Table 6.16 (see Appendix F for
Table 6.15 Agent’s preference characteristics and reservation prices for panamax voyage
contracts
σL (−)
σH (−)
Basic technology upgrade
1.029
1.124
0.435
0.27
0.20
34.32
Advanced technology upgrade
1.083
1.369
−0.624
0
0.21
34.33
Basic technology upgrade
1.022
1.091
0.583
0.10
0.12
34.52
Advanced technology upgrade
1.060
1.263
−0.180
0
0.21
34.61
Den (−)
πL (%)
πH (%)
FRRV ($/t)
Commodity: corn
Commodity: wheat
Source Calculated by Author
90
6 Application—Ocean Grain Transportation
Table 6.16 Surplus fractions and equilibrium prices for panamax voyage contracts
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
θΒ (%)
θS (%)
FRE ($/t)
83.2
83.1
16.8
16.9
34.30
34.32
83.3
83.3
16.7
16.7
34.31
34.36
the simulated result). From (5.20), the equilibrium rate is also shown in Table 6.16
(see Appendix F for the simulated result, Figs. F.61, F.62, F.63, F.64, F.65, F.66,
F.67 and F.68). It is observed that the surplus fraction and equilibrium rate are
indifferent (inelastic) to the commodity prices and technology costs. Additionally,
the surplus fractions are similar to those of Table 6.14. For the sake of consistency,
the same assumptions concerning the inventory interest rate and holding coefficient,
technology upgrades and vessel’s depreciation hold.
6.5.2.3
Investment Appraisal
The number of voyages per year of a panamax vessel is assumed to be eight
(8) (Psarros 2009) with a factor of 0.2 related to grain cargoes and an average
voyage duration of 53 days (CRSL 2015, 2016a) (see Appendix E for distribution
parameters and Appendix F for simulated results, Figs. F.69 and F.70). The voyage
costs are determined indirectly from (5.24) with data from CRSL (2016a, b), taking
fuel cost in the range of 584 $/t, speed in the range of 14.4 knots (considering in
addition the margin of 0.3 ± 0.3 knots, see Sect. 5.4) and fuel consumption in the
range of 35.9 t/d (see Appendix E for the distribution parameters) accounting also
for the fuel efficiency gains. It is acknowledged that the fuel savings are vessel,
weather and route specific, so based on the values of Table 2.1 and Eason (2015), it
is conservatively assumed that 10 and 15% fuel efficiency can be obtained for the
basic and advanced technology upgrade respectively (see Appendix F for the
simulated results, Fig. F.71).
Furthermore, the previous assumptions concerning the capital and operating
costs hold here as well. By applying (5.23), the minimum RFR for basic and
advanced technology upgrade is 34.6 and 33.7 $/t respectively (see Appendix F for
simulated results, Figs. F.72 and F.73). Comparing these values with those from
Table 6.16 it is obvious that the equilibrium freight rate is not profitable for the
technology expenditure. However, the variability can doubt the previous observation and by plotting the cumulative probability graphs of the equilibrium and RFRs
(Fig. 6.15) the comparison can be interpreted directly. As shown, the probability of
the RFR being larger than the equilibrium prices is around 50–52%. This entails
6.5 Energy Efficiency Sharing Scheme for Panamax Vessel
91
Fig. 6.15 Panamax voyage rates cumulative probability curves. Source Plotted by Author
that the cost of basic and advanced technology upgrades can be justified only by a
range between 48 and 50%.
6.5.2.4
Sensitivity Analysis
The peculiar results of the previous paragraph deserve the further investigation of
the equilibrium freight rate subject to the volatility of the market voyage rates.
Lower and higher prices are considered for evaluating the significance of change on
the RFR and exploring whether the EEISS would still be profitable. Therefore, the
models are entertained by considering different prices with the technique outlined in
Appendix D. The new values are shown in Table 6.17.
Upon these changes, the agent’s preference characteristics and environmental
stewardship together with the reserved prices are shown in Table 6.18. It is evident
that the values differ marginally from the market prices, entailing that this observation coincides with the freight contract’s functionality as commented in
Sect. 6.5.2.1. The surplus fraction percentages between the principal’s and agent’s
reservation prices as well as the equilibrium rate are shown in Table 6.19. Again, it
is observed that the surplus fraction and equilibrium rate are indifferent (inelastic) to
the commodity prices and technology costs.
The minimum RFR for basic and advanced technology upgrade with low prices
is 31.81 $/t (standard deviation = 6.16) and 32.72 $/t (standard deviation = 4.47)
respectively. By contrast, the minimum RFR for basic and advanced technology
upgrade with high prices is 35.39 $/t (standard deviation = 7.2) and 35.06 $/t
(standard deviation = 6.7) respectively. In order to check whether the EEISS could
be justified, the cumulative probability graphs are plotted together (Fig. 6.16). Vis á
92
6 Application—Ocean Grain Transportation
Table 6.17 Parameters for evaluating the significance of change for panamax voyage contracts
Variable
Model
Distribution parameters
Mean
Variance
Lower
Upper
Corn price ($/t)
Lognormal
m = 211
m = 257
s2 = 812
s2 = 582
Wheat price ($/t)
Lognormal
m = 277
m = 328
s2 = 792
s2 = 562
Voyage rate ($/t)
Lognormal
6
Lower
Upper
m = 33.7
m = 34.8
2
s = 11
2
2
s2 = 122
2
s2 = 1.952
Newbuilding price ($ 10 )
Lognormal
m = 26.8
m = 27.7
s = 1.39
15 years price ($ 106)
Lognormal
m = 9.5
m = 10.97
s2 = 2.912
s2 = 4.092
Earnings ($/d)
Lognormal
m = 7,319
m = 8,428
s2 = 1,8012
s2 = 2,5372
Bunker price ($/t)
Lognormal
m = 537
2
m = 632
s = 259
2
s2 = 3652
Source Calculated by Author
Table 6.18 Agent’s preference characteristics and reservation prices for panamax voyage
contracts (sensitivity analysis)
σL (−)
σH (−)
Basic technology upgrade
1.030
1.128
0.422
0.10
0.25
33.70
Advanced technology upgrade
1.085
1.376
−0.660
0
0.20
33.75
Basic technology upgrade
1.025
1.104
0.530
0.05
0.03
34.86
Advanced technology upgrade
1.069
1.301
−0.325
0
0.20
34.85
Basic technology upgrade
1.023
1.096
0.564
0.11
0.20
33.75
Advanced technology upgrade
1.064
1.276
−0.223
0
0.10
33.73
Basic technology upgrade
1.019
1.080
0.633
0.13
0.03
34.85
Advanced technology upgrade
1.054
1.229
−0.018
0
0.10
34.87
Den (−)
πL (%)
πH (%)
FRRV ($/t)
Commodity: corn—low price
Commodity: corn—high price
Commodity: wheat—low price
Commodity: wheat—high price
Source Calculated by Author
Table 6.19 Surplus fractions and equilibrium prices for panamax voyage contracts (sensitivity
analysis)
Commodity: corn—low price
Basic technology upgrade
Advanced technology upgrade
Commodity: corn—high price
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat—low price
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat—high price
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
θΒ (%)
θS (%)
FRE ($/t)
83.7
83.6
16.3
16.4
33.70
33.75
83.2
83.2
16.8
16.8
34.86
34.85
83.8
83.7
16.2
16.3
33.74
33.71
83.4
83.4
16.6
16.6
34.84
34.86
6.5 Energy Efficiency Sharing Scheme for Panamax Vessel
93
vis, for low prices, the probability of the equilibrium prices being larger than the
required freight is between 60 and 64%, thus the energy efficiency scheme may be
considered as reasonable. However, for high prices, the probability of the required
freight being larger than the equilibrium freight ranges between 51 and 54%. This
indicates that the cost of basic and advanced technology upgrades can be justified
by 46–49%, which is slightly reduced than the previous estimation, but it still
provides robust result.
(a) Low prices
(b) High prices
Fig. 6.16 Panamax voyage rates cumulative probability curves (sensitivity analysis). Source
Plotted by Author
94
6 Application—Ocean Grain Transportation
Fig. 6.17 Panamax
technology investment
payback period cumulative
probability curves for voyage
contracts. Source Plotted by
Author
6.5.2.5
Investment Recovery
By applying (5.25), the payback period is estimated as 19.7 years (standard deviation 10.5 years) for the basic technology upgrade and 24.4 years (standard deviation 15.1 years) for the advanced technology upgrade. From Fig. 6.17 it is
illustrated that for the vessel’s expected operational horizon (15 years), the cost
expenditure can be recovered up to 33% for the basic, whilst up to 27% for the
advanced technology upgrade.
6.5.3
Time Charter Trip
The charterer’s or shipper’s (principal’s) reservation price can be determined
through a sample of panamax time charter trip fixtures from CRSL (2016a) between
August 2012 and December 2015. For that period, out of a sample of 6,086
transactions, 1,576 fixtures were related to the carriage of grains with a rate of
FRM = 12,059 $/d (see Appendix E for the distribution parameters). This value
represents also the market price.
6.5.3.1
Principal—Agent Part
The assumptions of Sect. 6.5.2.3 related to a panamax vessel’s annual number of
trips and average trip duration also hold herein. A factor of 0.25 is allocated for
grain trips per year (CRSL 2016a) (see Appendix E for distribution parameters and
Appendix F for simulated results, Fig. F.74). By applying (5.4) and (5.5), the
optimum effort level is 11%, whereas from (5.6) the ship-owner’s or carrier’s
(agent’s) reservation price accounting also for the energy efficiency effort is
FRRP = 13,390 $/d (see Appendix F for the simulated result, Figs. F.75 and F.76).
6.5 Energy Efficiency Sharing Scheme for Panamax Vessel
95
Table 6.20 Surplus fractions and equilibrium prices for panamax time charter trip contracts
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
6.5.3.2
θΒ (%)
θS (%)
FRE ($/d)
83.2
83.1
16.8
16.9
12,300
12,361
83.4
83.4
16.6
16.6
12,266
12,341
Bargaining Part
As per (5.14) and (5.15), the surplus between the principal’s and agent’s reservation
prices is split by the respective percentages shown in Table 6.20, which are similar
to the fraction values presented in Table 6.12. From (5.20), the equilibrium rate is
also shown in Table 6.20 (see Appendix F for the simulated result, Figs. F.77, F.78,
F.79 and F.80). Again, it is observed that the surplus fraction and equilibrium rate
are indifferent (inelastic) to the commodity prices and technology costs. The
assumptions of Sect. 6.5.1.2 related to the inventory holding and interest rate, cargo
density, technology upgrades, vessel earnings, prices and grain capacity also hold.
6.5.3.3
Investment Appraisal
The capital and operating costs are assumed to be the same as in Sect. 6.5.1.3,
whereas the number of grain trips and their respective duration are in consistency
with the values in Sect. 6.5.3.1. By applying (5.23), the minimum RFR for basic
and advanced technology upgrade is 11,489 and 11,924 $/d respectively (see
Appendix F for simulated results, Figs. F.81 and F.82). Juxtaposing these values
with those from Table 6.20 it is obvious that the equilibrium freight rate is profitable for the basic and advanced technology expenditure. However, to remove any
doubts, by comparing the rate cumulative probability plots the results can be elucidated outspokenly. As indicated by Fig. 6.18, the probability of the minimum
required time charter trip rates being larger than the equilibrium values for basic and
advanced technology upgrades is between 44 and 48%. This entails that the EEISS
can be justified by a probability in the range of 52–56%.
6.5.3.4
Sensitivity Analysis
It is straightforward that for higher prices the EEISS would still be profitable.
Though, to investigate the significance of change in the price volatility, the models
could be entertained by considering a price reduction with the technique outlined in
Appendix D. The new values are shown in Table 6.21.
96
6 Application—Ocean Grain Transportation
Fig. 6.18 Panamax time charter trip rates cumulative probability curves. Source Plotted by
Author
Table 6.21 Parameters for evaluating the significance of change for panamax time charter trip
contracts
Variable
Corn price ($/t)
Wheat price ($/t)
Trip rate ($/d)
Newbuilding price ($ 106)
15 years price ($ 106)
Earnings ($/d)
Source Calculated by Author
Model
Distribution Parameters
Mean
Variance
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
m
m
m
m
m
m
=
=
=
=
=
=
211
277
11,762
26.8
9.5
7,319
s2
s2
s2
s2
s2
s2
=
=
=
=
=
=
812
792
5,8352
1.392
2.912
1,8012
Upon these changes, the optimum effort level is 10.6% and the agent’s reservation price yields FRRP = 13,036 $/d. The surplus fraction percentages between
the principal’s and agent’s reservation prices as well as the equilibrium rate are
shown in Table 6.22. Again, it is observed that the surplus fraction and equilibrium
rate are indifferent (inelastic) to the commodity prices and technology costs. The
fraction percentages are almost similar to those estimated previously, whilst, the
rate values have been moderately decreased.
The minimum RFR for basic and advanced technology upgrade is 12,028 $/d
(standard deviation = 2,661) and 12,571 $/d (standard deviation = 2,745) respectively. With these values, it could be claimed that the basic technology upgrade
EEISS can be marginally justified. From the cumulative probability curves
(Fig. 6.19) it can be interpreted that the minimum required time charter trip rates
can be higher than the equilibrium prices by 39–51%. Hence, it can be depicted that
6.5 Energy Efficiency Sharing Scheme for Panamax Vessel
97
Table 6.22 Surplus fractions and equilibrium prices for panamax time charter trip contracts
(sensitivity analysis)
Commodity: corn
Basic technology upgrade
Advanced technology upgrade
Commodity: wheat
Basic technology upgrade
Advanced technology upgrade
Source Calculated by Author
θΒ (%)
θS (%)
FRE ($/d)
83.7
83.7
16.3
16.3
12,037
12,021
83.8
83.8
16.2
16.2
12,023
12,059
Fig. 6.19 Panamax time charter trip rates cumulative probability curves (sensitivity analysis).
Source Plotted by Author
the investment expenditure could be justified by a probability in the range of
49–51%. Although this value is moderately reduced than the previous estimation, it
is consistent with the investment appraisal’s result.
6.5.3.5
Investment Recovery
By applying (5.25), the payback period is estimated as 20.3 years (standard deviation 16.1 years) for the basic technology upgrade and 27.4 years (standard deviation 18.2 years) for the advanced technology upgrade. From Fig. 6.20 it is
illustrated that for the vessel’s expected operational horizon (15 years), the cost
expenditure can be recovered up to 37% for the basic, whilst up to 25% for the
advanced technology upgrade.
98
6 Application—Ocean Grain Transportation
Fig. 6.20 Panamax
technology investment
payback period cumulative
probability curves for time
charter trip contracts. Source
Plotted by Author
6.6
Commenting Remarks
It is noted that the surplus fractions for the selected commodity (i.e. grains), are in
favor of the charterer/shipper (principal/buyer) and this provides strong evidence
that the arguments put in Sect. 5.3.2 are correct. Moreover, further support is
accorded through the work of Ådland et al. (2016) who investigated the fixture
buyer-seller characteristics. From their work, a degree of charterers’ market (bargaining) power over the ship-owner/carrier (agent/seller) was observed. Thus, it is
understood that the former are large contributors to the determination of the contract
price. Consequently, the risk for not reaching an agreement during the negotiations
is greater for the owner than it is for the charterer. In this sense, there is more
pressure on the owner’s side to settle since the charterer may be willing to give up
more than the owner and fix the cargo with another owner. Even in booming market
conditions, the owner may not be willing to pursue own reservation price because is
fully aware that any final agreement will necessarily benefit the side with the higher
surplus (Fisher et al. 2010).
The previous analysis has also indicated that the EEISS endorses strongly the
time charter rather than the voyage contracts (see Table 6.23 for a summary). As
noted earlier, the effect of energy efficiency on voyage freight rates is insignificant
because of the freight contract’s functionality, i.e. transport is bought from the agent
on a lump sum basis and covers all the incurred costs (see Sect. 2.3). The estimated
premium for energy efficiency in the time charter, taking into account the market
condition from August 2012 until December 2015, is around 1.8–3.0% of the
equilibrium (contract) price, which might be viewed as skimpy. As claimed by
Poulsen et al. (2016), although cargo owners (charterers/buyers) have shaped their
corporate social responsibility agenda to meet their sustainability concerns and are
important potential incubators of promoting environmental behavior, inertia factors
exist that inhibit greener performance. Dry bulk and inherently grain sea transportation is characterized by low consumer visibility (the raw material is considerably processed before the final product is made), emphasis is given on price and
environmental performance into the procurement of shipping services has been
6.6 Commenting Remarks
99
Table 6.23 Summary of results from the game theoretical analysis (average values)
Vessel/contract type
Handymax
Period
Spot
Voyage
Time charter trip
Panamax
Period
Spot
Voyage
Time charter trip
Source Calculated by Author
Energy efficiency
premium
$/d
$/t
% of equilibrium price
206
1.8
420
0.06
0.16
3.0
179
1.8
0.03
258
0.09
2.1
integrated by very few players. On the other hand, carriers (ship-owners/sellers)
encounter increasing pressure from their clients to improve their environmental
performance and in order to provide evidence of the achieved gains; they voluntarily participate in a confusing set of industry and stakeholder initiatives for
benchmarking. Moreover, the lack of a consistent framework for collecting data,
verification methodology of energy efficient technologies, standardized ranking and
rating scheme creates uncertainty among carriers that stalls commitment to environmental stewardship (Poulsen et al. 2016).
Yet, this premium percentage is expected to become higher albeit to the anticipation of increasing grain shipments (doubled by 2050 in comparison to current
levels) to satisfy rising consumer expectations in developing countries.
Additionally, supply chain strategies will be greatly influenced by sustainability
aspects, bunker cost minimization motives, raised awareness of climate change and
the impact of shipping on atmospheric emissions. Besides, the all-embracing
implementation of EEDI measures to ensure a supply of low carbon bulk carriers
needs also to be considered (Dinwoodie et al. 2014). Nevertheless, efforts to reduce
the carbon footprint of the grain supply chain should contemplate improvements in
transportation together with the resultant changes in emissions from production
modifications, with the latter comprising the largest component (O’Donnell et al.
2009). However, it is equitable to say that any adjustment will also strongly depend
on how the freight rates will respond to the return and volatility of commodity
markets as well as the cycles of world economy and commodity consumption
(Kavussanos et al. 2010; Geman and Smith 2012). Table 6.23 results coincide with
the work of Smith et al. (2013) where the impact of energy efficiency on time
charter prices was within the same range and was quantified as being in the region
of 200 $/d.
100
6 Application—Ocean Grain Transportation
The asymmetric behavior of the energy efficiency premium between period and
spot time charter contracts can be explained by the fact that period earnings are
lower than spot earnings as well as contain less uncertainty (less than half standard
deviation in comparison to the spot). This is because the latter involve unemployment risk (the vessel could be unoccupied for a time), relocation cost (incurred
expense when the vessel is relocated after being fixed with different spot contracts)
and due to temporal high demand for shipping services combined with an inelastic
supply of tonnage (Alizadeh and Nomikos 2011). External factors like port conditions and the different trading patterns of each region play also a role for the
observed asymmetry. Besides, as far as the spot market is concerned (trip and
voyage charter), voyage contracts are more volatile, hence higher profit is expected
and the vessel is preferred to be fixed more on such market (Tsioumas and
Papadimitriou 2015). In this respect and under the current market condition, the
result that the energy efficiency sharing scheme is not profitable for the period but it
can be justified for the spot contracts is reasonable.
Outspokenly, basic technology solutions (i.e. low friction coating or bare optimization or appendages or a combination of these) have been also identified by
Tsereklas-Zafeirakis et al. (2016) as preferable due to their profitability and technological maturity. Hence, it could be argued that the results of the current book’s
chapter can vividly be supported as valid. Furthermore, for the expected investment
horizon and the investigated commodity (i.e. grains), only the basic technology
upgrade for the handymax vessel is possible to be recovered. Such an observation is
not surprising since this is related to the flexibility of smaller vessels to operate in
different routes as well as their versatility to perform better in poor market conditions, whilst the operation of larger vessels is more rigid and to secure soaring
employment is not easy. Apparently, optimizing the portfolio of period and spot
contracts in order a fleet to be fully utilized is part of any shipping company’s risk
management policy. For example within a tactical planning context, the possibilities
to charter in additional vessels or to charter out the subset of available vessels to
other companies (even considering vessel lay-up) is decided so that transportation
demand can be met (Nomikos et al. 2013; Pantuso et al. 2014). Though, it should be
pointed out that if an owner is risk averse, a stable income even with a lower
average value will be accepted than a higher variable income. Similarly, the owner
is opting for an energy efficiency premium that balances own needs for secured
vessel employment versus the charterer’s needs to have the commodity transported
(Fisher et al. 2010).
Part IV
Epilogue
Chapter 7
Conclusions, Limitations
and Recommendations
Abstract The research objectives posed the necessity to adapt a qualitative
approach using exploratory document search from which a theory is developed to
examine if it is appropriate to include design energy efficiency improvement clause
in charter party contracts. Subsequently, the book’s work proceeded with the
quantitative research objectives, where game theory is employed to suggest a
suitable incentive mechanism design that may support the pricing aspects of the
clause. The developed conceptual framework is demonstrated through a case study
within seaborne grain transport. In this respect, the book’s current and final chapter
is aimed at concluding the performed research by outlining the results of the
analysis as well as the contribution to the body of knowledge on charter party
contracts. In confidence that academic and industry interest may be generated, this
chapter has also the purpose to address the book’s limitations and to make recommendations drawn upon the book’s second chapter’s literature review.
7.1
Preamble
The maritime industry’s approach to minimizing its emissions’ impact can go
beyond the already adapted operational measures and become more strategically
oriented by exploiting opportunities induced through regulations. Companies can
embrace a proactive approach that improves their commercial positioning to
competitors by the ability to invest largely in technology and reconfigure their
fleets. The competitive advantage produced by a company’s energy efficient vessels
can outperform rivals lacking the scope of energy saving performance beyond the
standard requirements (Porter and Reinhardt 2007). This is related to innovative
technologies that initially may look financially unattractive and cannot make a valid
contribution to corporate growth. However, many of these technologies are valued
only in new applications which are not in the forefront of the rapidly growing needs
of existing markets. Once these innovations are established within their emerging
markets and followed by steep progress, then the next generation market needs can
be satisfied more or better. The cost structure securing investment and funneling
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6_7
103
104
7
Conclusions, Limitations and Recommendations
resources for these technologies plays a critical role in pursuing the effort required
for their acceptance (Bower and Christensen 1995).
7.2
Conclusions
Forthcoming stringent maritime environmental regulations as well as those that
have recently been in force imply the importance of adopting a life cycle approach
to environmental stewardship by investing in the installation of energy efficient
equipment. The argument put forward is not only viewed from an operational
perspective, but also sets actions during the vessel’s design phase. Essentially, an
efficient way of achieving this target is through the creation of incentives, so that the
long established business attitude between charterers or shippers and ship-owners or
carriers is influenced and ultimately changed. In this study therefore, an attempt has
been made to shed light on this challenge by assembling a qualitative and
exploratory research study where the conclusions are grounded on the simultaneous
analyses of data (legal instruments and literature as well as charter party contracts)
and a body of theoretical knowledge is emerged from the observed phenomena.
Thus, to negotiate design energy efficiency improvements from a position of
strength and gain a competitive edge, it is important to grasp a solid understanding
of the relevant commercial, regulatory, legal and contractual principles. It is
explored how the prevailing multi-faceted commercial challenges of the charter
party signatories can be resolved through the international regulatory context on
pollution prevention which underpins maritime law. Moreover, it is clearly
demonstrated how the corresponding framework of reference under case law comes
into play within the governing legal landscape in order to appreciate and provide
wise contract safeguards surrounding environmental protection. The value to
transforming the existing chartering practice and its contractual structure is incisively addressed with a conceptualization commentary.
To this end, it is argued that the book’s aim and objectives have been met, since
the main contribution of the conducted qualitative research has been twofold, firstly
it is related to the development of the methodology itself. Secondly, due to the lack
of academic work and relevant literature, a theoretical proposition on incorporating
design energy efficiency improvements in charter party contracts is described. On
attaining this contribution, answers to the following qualitative research objectives
need to be obtained from the current book:
• Examine if the legal instruments permit the inclusion of clauses into the existing
charter party contracts that can clarify the investment responsibility and obligation of design energy efficiency improvements;
• Identify if any clauses included in the charter party contracts are accounting for
design energy efficiency improvements;
• Determine which vessel design parameters incorporated in the charter party
contract clauses are associated with the topic under investigation;
7.2 Conclusions
105
• Propose any amendments or additions to the relevant charter party contract
clauses that could contain the vessel’s energy efficiency specification.
It is believed that the book’s qualitative part provides answers to the aforementioned that are guided by an inductive sequential exploratory research using qualitative data extracted from documents, where each objective is addressed through
several stages. The whole procedure is based on grounded theory which helped to
establish the framework for promoting the inclusion of design energy efficiency
improvements in charter party contracts. The first objective is answered by the initial
coding of the legal instruments interpretations (first stage—see Sect. 4.2). The
documentary sources augmented with evidence from journal articles enabled the
author (researcher) to acquire an enhanced understanding of the complex nature of
the charter party contracts with respect to the vessel’s energy efficiency specification.
More specifically, the conducted work allows firstly pointing out that the current
legal instruments do not inhibit any constraint for including a rider-clause related to
design energy efficiency improvements within the charter party contracts.
The focused coding (second stage—see Sect. 4.4) of the standardized charter
forms provides input to the second and third objectives. It identifies the areas where
the vessel’s design energy efficiency improvements can be supported as a subject
term of a charter’s rider-clause. This finding is drawn upon the time and voyage
charter party contracts content analysis, which indicated that reference to design
energy efficiency improvements is not made explicit. It is observed that energy
efficiency is indirectly linked to the vessel’s performance (fuel consumption) which
is emphasized only on the former (time charters). The answer to the last qualitative
objective is grounded on the performed research and advances a clear view of the
theoretical proposition on incorporating design energy efficiency improvements in
charter party contracts (see Sects. 4.3 and 4.5). It is expected that such initiative will
clarify the responsibility between the involved parties and will create an incentive
for implementing and investing in design energy efficiency improvements in both
time as well as voyage charters.
To increase the book’s intellectual gravity, the work dwells in the quantitative
part which is an economic inquiry process of the theoretical proposition, i.e.
rider-clause on design energy efficiency improvements. For this purpose, the book
is tasked to satisfy the following quantitative research objectives:
• Suggest a mechanism design of the energy efficiency sharing scheme and its
connection to the chartering procedure;
• Demonstrate the practicality of the designed mechanism within the dry bulk
shipping sector and a specific commodity.
In tribute of the first, the well-established game theory mathematical models on
principal-agent and bargaining are adapted to portray the business attitude between
the charterer (shipper or buyer) and ship-owner (carrier or seller) (see Sect. 5.3).
The work is also supplemented with investment appraisal techniques to foster
justification of the proposed energy efficiency sharing scheme, as specified in a
106
7
Conclusions, Limitations and Recommendations
prospective rider-clause. On this foundation, it is anticipated that such rule is
grounded on commercial reasonableness, the parties’ interests are secured and
inevitably the maritime industry’s environmental commitment is not only conserved, but also recognized. The illustrative case study is concentrated on seaborne
grain trade and treats the last quantitative research objective (see Sects. 6.4 and 6.5).
The results clearly discern the charterer’s bargaining power over the ship-owner
with the surplus fraction (gains from trade) favoring the former. This affirms that the
technical arguments displayed in Sect. 5.3 are correct. The energy efficiency sharing
scheme’s mastery is prominently noticed on the time charter contracts. By contrast,
for the voyage contracts the contribution is trivial due to their functionality, i.e.
transport is bought from the agent on a lump sum basis and covers all the incurred
costs (see Sect. 2.3). Furthermore, the higher energy efficiency premium for the spot
time charters signals their intricate purpose since more employment risk is availed.
As far as the technology investment is concerned, given the current grain market
situation and the fixed operational horizon (15 years), it suffices to say that only the
basic expenditure ($1.5 million) for the handymax bulk carrier can be recovered.
Such finding confers a thorough indication of the smaller vessel’s resource capability to perform satisfactorily under a challenging freight market. In this spirit, the
game theory approach applied to chartering and as outlined in the current book’s
quantitative part can provide an all-encompassing insight of the parties’ information
needs when negotiating the contract. Incredibly, a new ground is offered to make
understandable the incentive mechanism for investing in the vessel’s design energy
efficiency improvements.
7.3
Limitations and Suggestions for Further Research
Common to any research study, the conducted work in this book recognizes a
number of limitations which need to be mentioned. The inductive nature of the
book’s qualitative part has facilitated the creation of a theory related to the incorporation of design energy efficiency improvements in charter party contracts and an
instrumental element is the reliance on document search. Although the qualitative
research objectives have been addressed, a third stage could be added in the
research method examining the industry’s opinion on this aspect. This could be
materialized through the organization of questionnaires as well as interviews with
major stakeholders such as ship-owners or carriers, charterers or shippers, ship
builders, brokers, lawyers, shipping associations, etc. To this end, using multiple
data collection methods will strengthen the triangulation of data sources during the
previous two stages (see Sect. 3.2.1). However, it should be stressed that in the
present work, only the theoretical foundations for supporting the existence of a
rider-clause associated with design energy efficiency improvements in time and
voyage charters are formed.
The difficulty of the negotiations in determining the details of the sharing scheme
(costs, risks and rewards) between the two parties should not be underestimated and
7.3 Limitations and Suggestions for Further Research
107
relies on a thorough quantification. Accordingly, given the fact that the investigated
topic poses challenges into the traditional concept of the commercial enterprise
between the charterers or shippers and ship-owners or carriers, the validity and
practicality of their respective changing financial responsibilities can be elaborated
with the consideration of brokers. In this respect, the rigorous mathematical
framework of game theory provides an excellent premise to extend the models in
Sect. 5.3, see for example Rubinstein and Wolinsky (1987) as well as Biglaiser
(1993). Certainly, the adapted mathematical models could be also specified for
other possible states, for instance competition or pooling between ship-owners and
selection from a menu of charterers or when the ship-owners are able to exercise
market power. Initial thoughts on how to analyze the strategic interaction and
valuate the contract can be pursued from Roger (2016) and Blume et al.
(2009) respectively. Another element that requires closer examination is the
quantification of the agent’s environmental attributes (see Sect. 5.3.2.2) which is a
complex topic by itself. Moreover, the intention of the current book is to introduce
game theory reasoning when negotiating the charter party contract and orchestrate
additional activity in this area.
Appreciating the caveat of reflecting the real world by mathematical models, the
prevailing assumptions in the NPV calculations need to be refined. In order to get
an accurate description for the vessel’s operational profile, backhaul opportunities
of other major dry bulk commodities (as well as the minor bulks) need to be
accommodated. However, this may diverge from the book’s research objectives
since this issue may fall upon drawing inferences on the optimal mix of transported
cargoes and is out of the book’s scope (see Stålhane et al. 2014 as well as Laake and
Zhang (2016) for more information). Without adding unnecessary difficulty, the
inclusion of different values of parameter δ in (5.23) and the relevant changes in the
commodity values as well as freight and hire rates (the principal-agent and bargaining models have to be updated), can solve this aspect. Yet, by investigating a
set of commodities, the models could govern the general principles on how to select
contracts that can recover faster the design energy efficiency investment. The
depicted economic evaluations were concerned only for newbuilding vessels, thus,
valuable insights of the EEISS within the second-hand market could be obtained
when these calculations are also performed for the old tonnage. Furthermore, the
topics discussed herein could be implemented in the capesize sector as well as in the
seaborne oil transportation.
Recognizing that during the last four decades the fuel consumption per transported unit has been reduced by 1.5% on an annual basis (Chen et al. 2010), the
conservative assignment of energy efficiency gains could be relaxed (i.e. 5%
increase of the assumed values). In addition, the impact of trading within ECAs
could be added by considering two different bunkers (heavy fuel and marine gas
oil), although such formulation is not expected to influence the findings. In any
case, the vessel will choose to sail longer distances to avoid or reduce the time spent
within ECAs, hence use less of the more expensive fuel (Fagerholt et al. 2015).
Variable vessel speeds and voyage duration have already been utilized and this
element can be incorporated by increasing the standard deviation of the
108
7
Conclusions, Limitations and Recommendations
distributions. Nevertheless, the aforementioned stemming appeals seem to
painstakingly correspond to the author’s best effort in providing useful insights that
can justify the investment sharing between the charter contract’s signatories. This is
a subject of some controversy as accorded to the existing business setting between
charterers or shippers and ship-owners or carriers.
7.4
Recommendations
Given the lack of a theory supporting the view that design energy efficiency
improvements shall be included in the charter forms; the qualitative stages as
described in the current book, need to be supplemented by a succeeding action stage
that informs the qualitative findings. This requires the collation of primary data
through interviews, questionnaires as well as discussions to obtain the perceptions
of the market (industrial players) and gain insight into their motivations as well as
actions regarding design energy efficiency improvements. Such a comprehensive
study will offer an understanding whether the promotion of environmental stewardship across the value chain for both charterers or shippers and ship-owners or
carriers should be reflected in the charter party agreements. In this way, the compliance of the EEDI regulation for improving the energy efficiency of vessels will
be driven by the industrial players, confirming the regulation’s non-prescriptive
nature. Judged by the aforementioned, the EEDI regulation as a proactive performance based mechanism designed to induce technical measures for improving the
energy efficiency of vessels, gives the industry players the privilege to choose the
optimum combination of energy saving technologies, as long as the required energy
efficiency level is attained and compliance with the regulation is obtained.
In order to transform the traditional concept of the commercial enterprise
between the charterers or shippers and ship-owners or carriers and to encourage
better implementation of the design energy saving improvements, it is suggested
that a standardized rider-clause within the ordinary contracts will ensure the interest
protection of both parties and overcome the agency formation tendency. All in all,
shipping circles (Ballou 2015; Rust 2015) have raised concerns over the current
standardized charter agreements and a better solution that would improve the
business would be overhauling the agreement, in which outdated clauses are
replaced with new terms that motivate taking advantage of technology innovations.
Having said that, and conferring the book’s findings from the quantitative framework of contract theory, the uncertainties emanating through the varying levels of
cost, information sharing as well as the trade-off between energy efficiency and
performance have been clarified. To this end, the book’s material can be viewed as
a pathway towards forming or amending charter contracts where commercial reasonableness guides a fair negotiation.
As shown from the conducted content analysis of time and voyage charters, as
well as the observations identified by the literature (for instance see the relevant
theory on design energy efficiency improvements in Sect. 4.3), the consideration of
7.4 Recommendations
109
the bunkers’ quality in addition to the installed systems for monitoring the vessel’s
performance (fuel consumption) paves the way for the inclusion of new negotiated
terms, for instance the vessel’s design energy efficiency specification when
arranging her employment. Such provision is intended to indicate that design
energy efficiency improvements have been agreed by the parties and there is
implied obligation on the equipment’s optimal utilization. The corollary of this is
that, both parties have the duty to carry on negotiations in good faith for pursuing
the inclusion of a sharing scheme for the incurred costs of design energy efficiency
improvements, since their goals of profit maximization are different. Ultimately the
human element is paramount to the overall effectiveness of the chartering process
and as argued by Kitada and Ölҫer (2015) humans need to be equipped with
satisfactory knowledge and awareness of selecting as well as implementing
appropriate energy efficiency measures.
It should be noted that the contractual flexibility achieved by the inclusion of
clauses in the charter party forms specifying the vessel’s energy efficiency clarifies
the rights, duties and responsibilities between the business signatories concerning
design energy efficiency improvements. Furthermore, it provides a mutual understanding of the common obligation for adopting greener shipping practices and it is
the core mechanism for enhancing environmental performance. In coincidence with
Lister et al. (2015) stakeholders along the maritime value chain have moved
towards a more proactive stance by voluntarily participating in various ‘green
shipping’ initiatives. Definitely environmentally sustainable shipping operations are
given merit, but with these schemes different shipping segments are targeted and the
evaluations rely on self-assessment, therefore a consistent framework for data
collection and verification methodology is lacking (Lister et al. 2015). Although
this entails progress and thus compliance with environmental regulations (i.e.
reduction of shipping emissions), it is inefficient to effectively motivate the
implementation of design energy efficiency improvements. It is believed that the
obtained competitive advantages of enhanced corporate social responsibility and a
focus on sustainability through design energy efficiency will be realized when the
widely used operational measures (i.e. slow steaming, virtual arrival) have already
delivered their emission abatement potential. Consequently, to the author’s ambition this book could be regarded as a starting point to fill the gap on how to support
design measures.
Appendix A
Generic Bulk Carrier Designs
The bulk carrier is a merchant vessel designed for the carriage of cargoes in
homogeneous mass, usually without packaging of any kind and is engaged in
various trades (i.e. iron ore, coal, grains, fertilizers, minerals, etc.) lacking a regular
itinerary. This ocean transportation service is known as tramp shipping. Apart from
achieving economies of scale which in turn facilitated the growth in vessel size, the
increasing popularity of the bulk carrier is attributed to her cargo hold configuration
(see Fig. A.1). As shown, the hatch opening is wide to ease cargo handling, the
hatch is extended above deck to accept more cargo (grain in particular) and the need
for shifting boards is eliminated due to the slopped wing and hopper tanks that can
carry ballast. These tanks are the key, since they result in a self-trimming hold,
which means that the cargoes follow their natural gradient when loaded into the
hold and thereby reducing the expensive stevedore costs for trimming the cargo
(Wijnolst and Wergeland 2009). Bulk carriers employed in the grain trade are of
Fig. A.1 Typical cargo hold
configuration for a single skin
bulk carrier. Note Drawing
not in scale. Source Drawn by
author and based on IACS
(2007)
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6
111
112
Appendix A: Generic Bulk Carrier Designs
Fig. A.2 Generic views of a typical single skin handymax bulk carrier (50,000–65,000 DWT).
Average dimensions (m): L B T: 200 32.26 12.9. Note Drawing not in scale. Source
Drawn by author and based on JSEA (2016)
Fig. A.3 Generic views of a typical single skin panamax bulk carrier (70,000–85,000 DWT)
Average dimensions (m): L B T: 227 32.26 14.1. Note Drawing not in scale. Source
Drawn by author and based on JSEA (2016)
two types: handymax and panamax (see Figs. A.2 and A.3 respectively). It is
observed that the handymax vessel is equipped with cranes (usually grab fitted) and
her cargo space is divided into five holds. Whereas, the panamax vessel is gearless
and her cargo space is divided into seven holds.
Appendix B
Parametric Distribution Fitting
to Observed Data
The most common and flexible technique of finding a theoretical (parametric)
distribution that best fits the observed data is the maximum likelihood estimation.
The estimators of the distribution are the parameters that maximize the joint
probability density (likelihood function) and usually an optimizer is exploited (like
Microsoft Excel Solver) to find the most suitable combination of parameter values.
The likelihood function of a sample x1, x2, x3, …, xn from the probability density f
(X, h) of random variable X, with parameter vector h = (h1, …, hm) can be given as
(Vose 2009):
Lðx1 ; x2 ; . . .; xn jh1 ; . . .; hm Þ ¼ max
n
Y
f ðxi jhÞ
ðB:1Þ
i¼1
Due to the multiplicative form of (B.1) and because the logarithmic transformation is monotonous, it is more convenient to maximize the logarithm of the
likelihood function (Vose 2009):
lðx1 ; x2 ; . . .; xn jh1 ; . . .; hm Þ ¼ max
n
X
logðf ðxi jhÞÞ
ðB:2Þ
i¼1
Upon fitting the theoretical distribution, it is necessary to determine whether the
sample belongs to the parametric distribution (goodness of fit statistic). The
chi-square (v2) statistic measures how well the expected frequency E(i) of the fitted
distribution compares with the observed frequency O(i) of a k-interval histogram of
the observed data and is calculated as follows (Vose 2009):
v2 ¼
k
X
½OðiÞ E ðiÞ2
i¼1
E ðiÞ
ðB:3Þ
Given that m parameters have been estimated from the sample, the result of (B.3)
shall be smaller than the critical value of v21a ðk m 1Þ, i.e. the (1 − a)100
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6
113
114
Appendix B: Parametric Distribution Fitting to Observed Data
percentile of the chi-square distribution with k – m − 1 degrees of freedom. In this
respect, chances are greater than 1 in (100 − a%)−1 that the observed data correspond to the theoretical distribution, which implies that the distribution should not
be rejected (Vose 2009).
Appendix C
Monte Carlo Simulation
Let us consider a general estimation model M which is a function of uncertain
quantities x1, …, xn and is described as (Modarres 2006; Vose 2009):
M ¼ f ðx1 ; . . .; xn Þ
ðC:1Þ
Knowing that the uncertainties of the quantities xi have been characterized and
analyzed through the fitted distributions from the observed data, we want to
determine the distribution of M on the basis of some information about the joint
distribution of x1, …, xn. Apparently, information is available about the marginal
distributions of x1, …, xn which are regarded as random variables, are treated
separately and are considered uncorrelated (independent) from each other. It is the
concept of inverse function gi(fi(xi)) that is employed to generate random samples
from each distribution. Naturally, the generated random number ri is acquired from
a uniform (0, 1) distribution to provide equal opportunity of an xk value being
produced in any percentile range. The result is then fed into the equation to
determine the value to be generated for the distribution (Modarres 2006; Vose
2009):
gi ðfi ðxi ÞÞ ¼ ri ) gi ðri Þ ¼ xk
ðC:2Þ
Hence, we are able to calculate the uncertainty characteristics of M by also
treating it as a random variable. For each xi, a value is selected from its respective
distribution with the assistance of statistical sampling techniques, where each set of
data values is used to find one estimate of M. This is repeated for a large number of
times, i.e. 10,000 (or more to achieve the required level of precision), in order to
obtain many estimate instances of the M’s distribution. Consequently, these values
are used for determining the distribution of M (Modarres 2006; Vose 2009):
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6
115
116
Appendix C: Monte Carlo Simulation
g11 ðr1 Þ; . . .; g1n ðrn Þ ) M1 ¼ f x1k
... ...
... ... ...
gl1 ðr1 Þ; . . .; gln ðrn Þ
...
) M ¼ f xlk
ðC:3Þ
l
It is noted that the performed Monte Carlo simulations are run with the Microsoft
Excel add-in created by Barreto and Howland (2006) and packaged in their book.
Appendix D
Parameter Change Interval
Given a sample of observable quantities y1, y2, …, yn, we would like to draw
conclusions about the population’s location (mean h) and scale (variance r)
parameters which are assumed to be independent. The joint posterior distribution of
h and r can be regarded as being built up from a series of a class of distributions
each centered at y, each of which represents the conditional distribution pðhjr; yÞ of
h for some given r, multiplied by pðrjsÞ, the marginal posterior distribution of r
which has the form of an inverted gamma function (where s2 is the sample’s
variance). It is known that the parent distribution is a member of a class of symmetric distributions which is stipulated by a parameter b. In particular, when b = 0
the normal distribution is chosen, b = 1 the double exponential distribution is
included and b = −1 the uniform distribution is disposed. The posterior distribution
of h can thus be derived supposing a fixed value b0 (Box and Tiao 1962):
"
ðn þ 1Þ
pðh; rjy,b0 Þ ¼ kr
2=ð1 þ b0 Þ #
1 X yi h
exp 2 i r ðD:1Þ
where k is an appropriate normalization coefficient. After taking the logarithms and
upon integration of (D.1), the subsequent conclusions are drawn (Box and Tiao
1965, 1968):
Pn
pffiffiffiffi
2
yÞ2 and
• It is observed that r is distributed as vsv1
v where vs ¼
i¼1 ðyi v = n − 1 degrees of freedom. The limits of the 95% interval can be obtained
from tables of the double tailed chi-squared distribution corresponding to
a = 0.05 and v = n − 1 degrees of freedom. Thus, the lower and upper limits of
r are respectively:
r20 ¼
vs2
v2 ð v Þ
and
r21 ¼
vs2
v2 ð v Þ
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6
ðD:2Þ
117
118
Appendix D: Parameter Change Interval
• It is shown that h follows Student’s t distribution with v = n − 1 degrees of
pffiffiffi
freedom, which is symmetric and centered at y with scaling factor s= n.
Further, the limits of the (1 − a) intervals of tðy; s2 =n; vÞ are given by:
s
y pffiffiffi tðaÞ ðvÞ
n 2
ðD:3Þ
An illustrative example that demonstrates in a practical way how the aforementioned propositions can be applied is offered by Box and Tiao (1992).
Appendix E
Assigned Parametric Distributions
See Tables E.1 and E.2.
Table E.1 Input data for entertaining the probabilistic models
Variable
Model
Distribution parameters
Test
statistic
Corn price ($/t)
Wheat price ($/t)
Grain densitya (t/m3)
Bunker price ($/t)
Handymax vessel
Period rate ($/d)
Period duration (d)
Newbuilding price
($106)
15 years price ($106)
Earnings ($/d)
Grain capacity (m3)
Operating costsb ($/d)
Voyage rate ($/t)
Cargo quantity (t)
Grain voyages (%)
Spot duration (d)
Speed (kn)
Fuel consumption (t/d)
Time charter trip ($/d)
Grain trips (%)
Panamax vessel
Period rate ($/d)
Period duration (d)
Lognormal
Lognormal
Normal
Lognormal
m = 234
m = 302
l = 0.7227
m = 584
s2 = 662
s2 = 642
r2 = 0.0952
s2 = 2962
0.45
0.33
0.04
1.33
36.415
36.415
24.996
36.415
Lognormal
Gamma
Lognormal
m = 11,228
a = 20.3
m = 25.5
s2 = 2,4882
b = 6.6
s2 = 1.252
0.52
1.29
0.57
36.415
36.415
36.415
Lognormal
Lognormal
Normal
Lognormal
Lognormal
Normal
Normal
Gamma
Weibull
Weibull
Lognormal
Normal
m = 9.9
m = 10,749
l = 68,785
m = 5,181
m = 38.4
l = 52,883
l = 40
a = 3.95
a = 32.9
a = 11
m = 13,962
l = 10
s2 = 3.382
s2 = 3,8112
r2 = 9,5512
s2 = 1,4662
s2 = 19.92
r2 = 8,8972
r 2 = 52
b = 12.21
b = 14.4
b = 31.7
s2 = 6,7272
r2 = 0.52
0.51
0.23
11.9
0.21
0.51
0.05
0.84
0.06
2.72
19.13
1.39
0.16
36.415
36.415
124.342
22.362
55.758
55.758
30.144
9.488
124.342
124.342
79.082
30.144
Lognormal
Gamma
m = 9,635
a = 3.5
s2 = 2,7622
b = 75.5
0.67
2.66
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6
Critical
value
79.082
79.082
(continued)
119
120
Appendix E: Assigned Parametric Distributions
Table E.1 (continued)
Variable
Model
Distribution parameters
Lognormal m = 27.3
Newbuilding price
($106)
Lognormal m = 10.2
15 years price ($106)
Earnings ($/d)
Lognormal m = 7,873
Normal
l = 93,687
Grain capacity (m3)
Lognormal m = 6,324
Operating costsb ($/d)
Voyage rate ($/t)
Lognormal m = 34.3
Cargo quantity (t)
Normal
l = 59,508
Grain voyages (%)
Normal
l = 20
Spot duration (d)
Gamma
a = 15.09
Speed (kn)
Weibull
a = 34.6
Fuel consumption (t/d) Weibull
a = 11
Time charter trip ($/d) Lognormal m = 12,059
Grain trips (%)
Normal
l = 25
Source Author’s calculations using data from CRSL
b
Gardiner et al. (2009)
s2 = 1.592
Test
statistic
0.56
Critical
value
36.415
s2 = 3.322
0.22
36.415
0.07
36.415
s2 = 2,0592
r2 = 6,7332 11.88
124.342
s2 = 1,7522
0.21
22.362
1.16
79.082
s2 = 16.62
79.082
r2 = 2,8682 17.11
0.08
30.144
r 2 = 32
b = 3.48
0.79
21.026
b = 14.4
22.07
124.342
b = 35.9
3.72
124.342
124.342
s2 = 7,7002 19.47
0.15
30.144
r2 = 0.92
(2016a, b) except from: aSewell (1999) and
Table E.2 Legend—explanations for the parameters of the statistical distributions
Parameter
l
r
m
s
a
b
a
b
Mean of the normal distribution (location parameter)
Standard deviation of the normal distribution (scale parameter)
Mean of the lognormal distribution (scale parameter)
Standard deviation of the lognormal distribution (shape parameter)
Shape parameter of the Gamma distribution
Scale parameter of the Gamma distribution
Shape parameter (Weibull distribution)
Characteristic value (scale parameter of Weibull distribution)
Appendix F
Simulated Variables
See Figs. F.1, F.2, F.3, F.4, F.5, F.6, F.7, F.8, F.9, F.10, F.11, F.12, F.13,
F.15, F.16, F.17, F.18, F.19, F.20, F.21, F.22, F.23, F.24, F.25, F.26, F.27,
F.29, F.30, F.31, F.32, F.33, F.34, F.35, F.36, F.37, F.38, F.39, F.40, F.41,
F.43, F.44, F.45, F.46, F.47, F.48, F.49, F.50, F.51, F.52, F.53, F.54, F.55,
F.57, F.58, F.59, F.60, F.61, F.62, F.63, F.64, F.65, F.66, F.67, F.68, F.69,
F.71, F.72, F.73, F.74, F.75, F.76, F.77, F.78, F.79, F.80, F.81 and F.82.
F.14,
F.28,
F.42,
F.56,
F.70,
Fig. F.1 Histogram of effort level for handymax period contract (10,000 repetitions, average = 0.090, standard deviation = 0.242). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006)
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6
121
122
Appendix F: Simulated Variables
Fig. F.2 Histogram of agent’s reservation price for handymax period contract (10,000 repetitions,
average = 12,193, standard deviation = 3,852). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.3 Histogram of depreciation for handymax vessel (10,000 repetitions, average = 0.064,
standard deviation = 0.021). Source Author’s plot using data from CRSL (2016a) and the excel
add-in by Barreto and Howland (2006)
Fig. F.4 Histogram of handymax period surplus fraction for corn and basic technology upgrade
(10,000 repetitions, principal: average = 0.785, standard deviation = 0.0595, agent: average = 0.215, standard deviation = 0.0602). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
123
Fig. F.5 Histogram of handymax equilibrium period contract for corn and basic technology
upgrade (10,000 repetitions, average = 11,434, standard deviation = 2,684). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.6 Histogram of handymax period surplus fraction for corn and advanced technology
upgrade (10,000 repetitions, principal: average = 0.785, standard deviation = 0.0588, agent:
average = 0.215, standard deviation = 0.0594). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.7 Histogram of handymax equilibrium period contract for corn and advanced technology
upgrade (10,000 repetitions, average = 11,448, standard deviation = 2,670). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
124
Appendix F: Simulated Variables
Fig. F.8 Histogram of handymax period surplus fraction for wheat and basic technology upgrade
(10,000 repetitions, principal: average = 0.787, standard deviation = 0.058, agent: average = 0.213, standard deviation = 0.0584). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006)
Fig. F.9 Histogram of handymax equilibrium period contract for wheat and basic technology
upgrade (10,000 repetitions, average = 11,408, standard deviation = 2,667). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.10 Histogram of handymax period surplus fraction for wheat and advanced technology
upgrade (10,000 repetitions, principal: average = 0.786, standard deviation = 0.0596, agent:
average = 0.214, standard deviation = 0.059). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
125
Fig. F.11 Histogram of handymax equilibrium period contract for wheat and advanced technology upgrade (10,000 repetitions, average = 11,446, standard deviation = 2,665). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.12 Histogram of capital cost (10,000 repetitions, average = 10.88, standard deviation = 0.545). Source Author’s plot using data from CRSL (2016a), OECD (2016) and the excel
add-in by Barreto and Howland (2006)
Fig. F.13 Histogram of handymax minimum RFR for period contract and basic technology
upgrade (10,000 repetitions, average = 11,936, standard deviation = 1,423). Source Author’s plot
using data from CRSL (2016a, b) and the excel add-in by Barreto and Howland (2006)
126
Appendix F: Simulated Variables
Fig. F.14 Histogram of handymax minimum RFR for period contract and advanced technology
upgrade (10,000 repetitions, average = 12,693, standard deviation = 1,434). Source Author’s plot
using data from CRSL (2016a, b) and the excel add-in by Barreto and Howland (2006)
Fig. F.15 Histogram of environmental stewardship for handymax voyage contract and corn as
cargo (10,000 repetitions, basic technology upgrade and low attribute: average = 0.0013, standard
deviation = 0.0033, basic technology upgrade and high attribute: average = 0.0037, standard
deviation = 0.0097, advanced technology upgrade and high attribute: average = 0.0033, standard
deviation = 0.0113). Source Author’s plot using data from CRSL (2016a) and the excel add-in by
Barreto and Howland (2006)
Appendix F: Simulated Variables
127
Fig. F.16 Histogram of environmental stewardship for handymax voyage contract and wheat as
cargo (10,000 repetitions, basic technology upgrade and low attribute: average = 0.0018, standard
deviation = 0.0047, basic technology upgrade and high attribute: average = 0.0024, standard
deviation = 0.0081, advanced technology upgrade and low attribute: average = 0.0019, standard
deviation = 0.46, advanced technology upgrade and high attribute: average = 0.0027, standard
deviation = 0.0087). Source Author’s plot using data from CRSL (2016a) and the excel add-in by
Barreto and Howland (2006)
Fig. F.17 Histogram of agent’s reservation price for handymax corn voyage contract and basic
technology upgrade (10,000 repetitions, average = 38.65, standard deviation = 20.11). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.18 Histogram of agent’s reservation price for handymax corn voyage contract and advanced
technology upgrade (10,000 repetitions, average = 38.61, standard deviation = 20.12). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
128
Appendix F: Simulated Variables
Fig. F.19 Histogram of agent’s reservation price for handymax wheat voyage contract and basic
technology upgrade (10,000 repetitions, average = 38.43, standard deviation = 19.85). Source
Author’s plot using data from CRSL (2016a) and the Excel add-in by Barreto and Howland (2006)
Fig. F.20 Histogram of agent’s reservation price for handymax wheat voyage contract and
advanced technology upgrade (10,000 repetitions, average = 38.70, standard deviation = 20.36).
Source Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland
(2006)
Fig. F.21 Histogram of handymax voyage surplus fraction for corn and basic technology upgrade
(10,000 repetitions, principal: average = 0.786, standard deviation = 0.0587, agent: average = 0.214, standard deviation = 0.0586). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
129
Fig. F.22 Histogram of handymax equilibrium voyage contract for corn and basic technology
upgrade (10,000 repetitions, average = 38.42, standard deviation = 20.22). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.23 Histogram of handymax voyage surplus fraction for corn and advanced technology
upgrade (10,000 repetitions, principal: average = 0.787, standard deviation = 0.0582, agent:
average = 0.213, standard deviation = 0.059). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.24 Histogram of handymax equilibrium voyage contract for corn and advanced technology
upgrade (10,000 repetitions, average = 38.48, standard deviation = 20.59). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
130
Appendix F: Simulated Variables
Fig. F.25 Histogram of handymax voyage surplus fraction for wheat and basic technology
upgrade (10,000 repetitions, principal: average = 0.787, standard deviation = 0.0588, agent:
average = 0.213, standard deviation = 0.0593). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.26 Histogram of handymax equilibrium voyage contract for wheat and basic technology
upgrade (10,000 repetitions, average = 38.41, standard deviation = 20.99). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.27 Histogram of handymax voyage surplus fraction for wheat and advanced technology
upgrade (10,000 repetitions, principal: average = 0.786, standard deviation = 0.0592, agent:
average = 0.214, standard deviation = 0.0589). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
131
Fig. F.28 Histogram of handymax equilibrium voyage contract for wheat and advanced technology upgrade (10,000 repetitions, average = 38.52, standard deviation = 20.81). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.29 Histogram of handymax annual grain loadings for voyage contracts (10,000 repetitions,
Min = 1, Max = 3). Source Author’s plot using data from CRSL (2016a) and the excel add-in by
Barreto and Howland (2006)
Fig. F.30 Histogram of handymax spot duration (voyage and time charter trip contracts) (10,000
repetitions, average = 48, standard deviation = 24). Source Author’s plot using data from CRSL
(2015) and the excel add-in by Barreto and Howland (2006)
132
Appendix F: Simulated Variables
Fig. F.31 Histogram of handymax voyage costs (10,000 repetitions, basic technology upgrade:
average = 1.159, standard deviation = 0.94, advanced technology upgrade: average = 1.094,
standard deviation = 0.87). Source Author’s plot using data from CRSL (2016a, b) and the excel
add-in by Barreto and Howland (2006)
Fig. F.32 Histogram of handymax voyage RFR for basic technology upgrade (10,000 repetitions,
average = 36.51, standard deviation = 5.26). Source Author’s plot using data from CRSL (2016a, b)
and the excel add-in by Barreto and Howland (2006)
Fig. F.33 Histogram of handymax voyage RFR for advanced technology upgrade (10,000 repetitions, average = 35.68, standard deviation = 5.02). Source Author’s plot using data from CRSL
(2016a, b) and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
133
Fig. F.34 Histogram of handymax annual grain loadings for time charter trip contracts (10,000
repetitions, Min = 1, Max = 10). Source Author’s plot using data from CRSL (2016a) and the
excel add-in by Barreto and Howland (2006)
Fig. F.35 Histogram of effort level for handymax time charter trip contract (10,000 repetitions,
average = 0.128, standard deviation = 0.266). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.36 Histogram of agent’s reservation price for handymax time charter trip contract (10,000
repetitions, average = 15,478, standard deviation = 8,510). Source Author’s plot using data from
CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
134
Appendix F: Simulated Variables
Fig. F.37 Histogram of handymax equilibrium time charter trip contract for corn and basic
technology upgrade (10,000 repetitions, average = 14,040, standard deviation = 6,829). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.38 Histogram of handymax equilibrium time charter trip contract for corn and advanced
technology upgrade (10,000 repetitions, average = 14,083, standard deviation = 6,925). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.39 Histogram of handymax equilibrium time charter trip contract for wheat and basic
technology upgrade (10,000 repetitions, average = 14,211, standard deviation = 6,854). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
135
Fig. F.40 Histogram of handymax equilibrium time charter trip contract for wheat and advanced
technology upgrade (10,000 repetitions, average = 14,114, standard deviation = 6,904). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.41 Histogram of handymax RFR for time charter trip contract and basic technology
upgrade (10,000 repetitions, average = 11,534, standard deviation = 4,358). Source Author’s plot
using data from CRSL (2016a, b) and the excel add-in by Barreto and Howland (2006)
Fig. F.42 Histogram of handymax RFR for time charter trip contract and advanced technology
upgrade (10,000 repetitions, average = 10,615, standard deviation = 3,162). Source Author’s plot
using data from CRSL (2016a, b) and the excel add-in by Barreto and Howland (2006)
136
Appendix F: Simulated Variables
Fig. F.43 Histogram of effort level for panamax period contract (10,000 repetitions, average = 0.096, standard deviation = 0.231). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006)
Fig. F.44 Histogram of agent’s reservation price for panamax period contract (10,000 repetitions,
average = 10,541, standard deviation = 3813). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.45 Histogram of panamax period surplus fraction for corn and basic technology upgrade
(10,000 repetitions, principal: average = 0.833, standard deviation = 0.0352, agent: average = 0.167, standard deviation = 0.0354). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
137
Fig. F.46 Histogram of panamax equilibrium period contract for corn and basic technology
upgrade (10,000 repetitions, average = 9,819, standard deviation = 2,798). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.47 Histogram of panamax period surplus fraction for corn and advanced technology
upgrade (10,000 repetitions, principal: average = 0.832, standard deviation = 0.0350, agent:
average = 0.168, standard deviation = 0.0355). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.48 Histogram of panamax equilibrium period contract for corn and advanced technology
upgrade (10,000 repetitions, average = 9,807, standard deviation = 2,812). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
138
Appendix F: Simulated Variables
Fig. F.49 Histogram of panamax period surplus fraction for wheat and basic technology upgrade
(10,000 repetitions, principal: average = 0.833, standard deviation = 0.0350, agent: average = 0.167, standard deviation = 0.0353). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006)
Fig. F.50 Histogram of panamax equilibrium period contract for wheat and basic technology
upgrade (10,000 repetitions, average = 9,803, standard deviation = 2,812). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.51 Histogram of panamax period surplus fraction for wheat and advanced technology
upgrade (10,000 repetitions, principal: average = 0.834, standard deviation = 0.0350, agent:
average = 0.166, standard deviation = 0.0353). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
139
Fig. F.52 Histogram of panamax equilibrium period contract for wheat and advanced technology
upgrade (10,000 repetitions, average = 9,826, standard deviation = 2,790). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.53 Histogram of panamax minimum RFR for period contract and basic technology
upgrade (10,000 repetitions, average = 14,803, standard deviation = 3,406). Source Author’s plot
using data from CRSL (2016a, b) and the excel add-in by Barreto and Howland (2006)
Fig. F.54 Histogram of panamax minimum RFR for period contract and advanced technology
upgrade (10,000 repetitions, average = 16,529, standard deviation = 3,821). Source Author’s plot
using data from CRSL (2016a, b) and the excel add-in by Barreto and Howland (2006)
140
Appendix F: Simulated Variables
Fig. F.55 Histogram of environmental stewardship for panamax voyage contract and corn as
cargo (10,000 repetitions, basic technology upgrade and low attribute: average = 0.0027, standard
deviation = 0.0049, basic technology upgrade and high attribute: average = 0.0020, standard
deviation = 0.0031, advanced technology upgrade and high attribute: average = 0.0021, standard
deviation = 0.0053). Source Author’s plot using data from CRSL (2016a) and the excel add-in by
Barreto and Howland (2006)
Fig. F.56 Histogram of environmental stewardship for panamax voyage contract and wheat as
cargo (10,000 repetitions, basic technology upgrade and low attribute: average = 0.0010, standard
deviation = 0.0020, basic technology upgrade and high attribute: average = 0.0012, standard
deviation = 0.0024, advanced technology upgrade and high attribute: average = 0.0021, standard
deviation = 0.0030). Source Author’s plot using data from CRSL (2016a) and the excel add-in by
Barreto and Howland (2006)
Fig. F.57 Histogram of agent’s reservation price for panamax corn voyage contract and basic
technology upgrade (10,000 repetitions, average = 34.32, standard deviation = 16.44). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
141
Fig. F.58 Histogram of agent’s reservation price for panamax corn voyage contract and advanced
technology upgrade (10,000 repetitions, average = 34.33, standard deviation = 16.87). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.59 Histogram of agent’s reservation price for panamax wheat voyage contract and basic
technology upgrade (10,000 repetitions, average = 34.52, standard deviation = 16.63). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.60 Histogram of agent’s reservation price for panamax wheat voyage contract and
advanced technology upgrade (10,000 repetitions, average = 34.61, standard deviation = 16.71).
Source Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland
(2006)
142
Appendix F: Simulated Variables
Fig. F.61 Histogram of panamax voyage surplus fraction for corn and basic technology upgrade
(10,000 repetitions, principal: average = 0.832, standard deviation = 0.0352, agent: average = 0.168, standard deviation = 0.0351). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006)
Fig. F.62 Histogram of panamax equilibrium voyage contract for corn and basic technology
upgrade (10,000 repetitions, average = 34.30, standard deviation = 16.58). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.63 Histogram of panamax voyage surplus fraction for corn and advanced technology
upgrade (10,000 repetitions, principal: average = 0.831, standard deviation = 0.0351, agent:
average = 0.169, standard deviation = 0.0353). Source Author’s plot using data from CRSL
(2016a) and the Excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
143
Fig. F.64 Histogram of panamax equilibrium voyage contract for corn and advanced technology
upgrade (10,000 repetitions, average = 34.32, standard deviation = 16.85). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.65 Histogram of panamax voyage surplus fraction for wheat and basic technology upgrade
(10,000 repetitions, principal: average = 0.833, standard deviation = 0.0349, agent: average = 0.167, standard deviation = 0.0351). Source Author’s plot using data from CRSL (2016a)
and the excel add-in by Barreto and Howland (2006)
Fig. F.66 Histogram of panamax equilibrium voyage contract for wheat and basic technology
upgrade (10,000 repetitions, average = 34.31, standard deviation = 16.58). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
144
Appendix F: Simulated Variables
Fig. F.67 Histogram of panamax voyage surplus fraction for wheat and advanced technology
upgrade (10,000 repetitions, principal: average = 0.833, standard deviation = 0.0352, agent:
average = 0.167, standard deviation = 0.035). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.68 Histogram of panamax equilibrium voyage contract for wheat and advanced technology
upgrade (10,000 repetitions, average = 34.36, standard deviation = 16.91). Source Author’s plot
using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.69 Histogram of panamax annual grain loadings for voyage contracts (10,000 repetitions,
Min = 1, Max = 4). Source Author’s plot using data from CRSL (2016a) and the excel add-in by
Barreto and Howland (2006)
Appendix F: Simulated Variables
145
Fig. F.70 Histogram of panamax spot duration (voyage and time charter trip contracts) (10,000
repetitions, average = 53, standard deviation = 14). Source Author’s plot using data from CRSL
(2015) and the excel add-in by Barreto and Howland (2006)
Fig. F.71 Histogram of panamax voyage costs (10,000 repetitions, basic technology upgrade:
average = 1.371, standard deviation = 0.879, advanced technology upgrade: average = 1.296,
standard deviation = 0.827). Source Author’s plot using data from CRSL (2016a, b) and the excel
add-in by Barreto and Howland (2006)
Fig. F.72 Histogram of panamax voyage RFR for basic technology upgrade (10,000 repetitions,
average = 34.59, standard deviation = 6.55). Source Author’s plot using data from CRSL (2016a,
b) and the excel add-in by Barreto and Howland (2006)
146
Appendix F: Simulated Variables
Fig. F.73 Histogram of panamax voyage RFR for advanced technology upgrade (10,000 repetitions, average = 33.67, standard deviation = 6.08). Source Author’s plot using data from CRSL
(2016a, b) and the excel add-in by Barreto and Howland (2006)
Fig. F.74 Histogram of panamax annual grain loadings for time charter trip contracts (10,000
repetitions, Min = 1, Max = 6). Source Author’s plot using data from CRSL (2016a) and the excel
add-in by Barreto and Howland (2006)
Fig. F.75 Histogram of effort level for panamax time charter trip contract (10,000 repetitions,
average = 0.110, standard deviation = 0.284). Source Author’s plot using data from CRSL
(2016a) and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
147
Fig. F.76 Histogram of agent’s reservation price for panamax time charter trip contract (10,000
repetitions, average = 13,390, standard deviation = 9554). Source Author’s plot using data from
CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.77 Histogram of panamax equilibrium time charter trip contract for corn and basic technology upgrade (10,000 repetitions, average = 12,300, standard deviation = 7,795). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.78 Histogram of panamax equilibrium time charter trip contract for corn and advanced
technology upgrade (10,000 repetitions, average = 12,361, standard deviation = 7,865). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
148
Appendix F: Simulated Variables
Fig. F.79 Histogram of panamax equilibrium time charter trip contract for wheat and basic
technology upgrade (10,000 repetitions, average = 12,266, standard deviation = 7,822). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.80 Histogram of panamax equilibrium time charter trip contract for wheat and advanced
technology upgrade (10,000 repetitions, average = 12,341, standard deviation = 7,883). Source
Author’s plot using data from CRSL (2016a) and the excel add-in by Barreto and Howland (2006)
Fig. F.81 Histogram of panamax RFR for time charter trip contract and basic technology upgrade
(10,000 repetitions, average = 11,489, standard deviation = 2,394). Source Author’s plot using
data from CRSL (2016a, b) and the excel add-in by Barreto and Howland (2006)
Appendix F: Simulated Variables
149
Fig. F.82 Histogram of panamax RFR for time charter trip contract and advanced technology
upgrade (10,000 repetitions, average = 11,924, standard deviation = 2,589). Source Author’s plot
using data from CRSL (2016a, b) and the excel add-in by Barreto and Howland (2006)
Appendix G
Milestones of the Book
See Table G.1.
Table G.1 Work plan
Event
Key date
Choosing topic
Literature on strategy
Literature on leadership
Literature on innovative technologies and marketing
Literature on finance
Topic refinement
Qualitative part
Book proposal
Quantitative part
Synthesis
Draft manuscript available
Foreword received
Draft book complete
Review comments
Book production
December 2013–January 2014
March 2014–April 2014
July 2014–August 2014
December 2014–January 2015
March 2015–April 2015
February 2015–June 2015
July 2015–October 2015
November 2015
December 2015–April 2016
May 2016–July 2016
August 2016
September 2016
September 2016
October 2016
November 2016
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6
151
Glossary
Adverse selection Principal-agent problem in which the agent has private information about a parameter of his optimization problem (Laffont and Martimort
2002)
Agency cost Cost of preventing agents pursuing their own interests at the expense
of their principals (Arnold 2010)
Agent Player who has the finer information partition (informed player) (Rasmusen
2007)
Asymmetric information Information partition that is different and not worse than
another player’s, hence the useful private information possessed by a player
(Rasmusen 2007)
Bargaining Game situation in which the participants have the possibility of concluding a mutually beneficial agreement albeit to balancing their interests. The
agreement is considered binding upon each participant’s approved acceptance
(Osborne and Rubinstein 1990)
Bill of lading Document issued by a ship-owner to a shipper where it serves four
purposes: (i) a receipt for the goods, (ii) evidence of the contract of carriage,
(iii) document of title, (iv) transfer of constructive possession (Baughen 2010)
Bulk cargo Homogeneous unpacked dry cargo (Brodie 2015)
Bulk carrier Sea going self-propelled vessel which is constructed generally with
single deck, double bottom, hopper side tanks, and top side tanks and with single
or double side skin construction in cargo length area and is intended primarily to
carry dry cargoes in bulk (IACS 2006)
Bunker Vessel’s fuel distinguished into four general categories: (i) marine gas oil,
(ii) marine diesel oil, (iii) intermediate fuel oil, (iv) heavy fuel oil (Brodie 2015)
Buyer Party entering into a sales transaction in return for the contract goods or
service (Baughen 2010)
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6
153
154
Glossary
Capital Funding for a business—can be equity only or equity plus debt (Arnold
2010)
Cargo Goods carried in or on a vessel (Brodie 2015)
Carrier Party who enters into a contract with a charterer or shipper, as the case
may be, for the carriage of cargo (Brodie 2015)
Charter in To hire a vessel from a ship-owner (Brodie 2015)
Charter out To hire a vessel out to a charterer (Brodie 2015)
Charter party agreement Document containing all the terms and conditions of
the contract between a ship-owner and a charterer (Brodie 2015)
Charterer Person or company who hires a vessel from a ship-owner for a period of
time (time charterer) or who reserves the entire cargo space of a vessel for the
carriage of goods from a port or ports of loading to a port or ports of discharge
(voyage charterer) (Brodie 2015)
Chartering negotiation Exchange of correspondence between a ship-owner and a
charterer, often by means of intermediaries acting for each, respectively owner’s
broker and chartering agent, leading to the chartering of a vessel (Brodie 2015)
Clauses Contractual provisions contained in a charter party setting out the rights
and responsibilities of both parties in relation to such matters as payment of
freight or hire and contingencies for strikes, general average and war (Brodie
2015)
Commodity Large quantity of a product moving between two locations on a
regular basis, such as raw materials and foodstuffs, i.e. major and minor bulks
(Arnold 2010; Bowersox et al. 2013)
Consignee Person named in the bill of lading, or waybill, as the person to whom
delivery of the goods loaded thereunder is to be made, usually the buyer
(Baughen 2010; Singh 2011)
Consignor Person entitled to possession of goods at the time that they are loaded
onto a vessel. The bill of lading should be issued to this party even if another
party has made the contract of carriage with the ship-owner, for example a free
on board buyer (Baughen 2010)
Content analysis Approach to the analysis of documents and texts that seeks to
quantify content in terms of predetermined categories and in a systematic and
replicable manner. The term is sometimes used in connection with qualitative
research as well (Bryman 2012)
Cost of capital The rate of return that a company has to offer finance providers to
induce them to buy and hold a financial security (Arnold 2010)
Counteroffer Response to an offer which in some way varies the terms or conditions of that offer (Brodie 2015)
Glossary
155
Deadweight Measurement of a merchant vessel’s earning capacity and includes
the weight of all cargo, oil bunkers, fresh and feed water, stores and effects
(Rawson and Tupper 2001)
Debt Obligation to pay (Arnold 2010)
Discharge To unload a vessel (Brodie 2015)
Discount rate The rate applied to cash flows used to change future values into
present values (Watson and Head 2013)
Energy efficiency Way of managing and restraining the growth in energy consumption. Something is more energy efficient if it delivers more services for the
same energy input, or the same services for less energy input (IEA 2016)
Equity Ownership interest in a firm (Ross et al. 2013)
Firm offer Offer which is not conditional in any way and is binding on the party
making it, provided that it is accepted in full and within any time limit specified
in it (Brodie 2015)
Fixture The conclusion of a contract that results to chartering the vessel (Brodie
2015)
Freight Amount of money paid to a ship-owner or shipping line for the carriage of
cargo (Brodie 2015)
Game The totality of the absolute commands that prescribe how each participant
of social or trade exchange behaves in every economic situation that may
conceivably arise (Von Neumann and Morgenstern 2004)
Game theory The mathematical treatment of economic activities of participants
within a social or trade exchange who behave rationally (Von Neumann and
Morgenstern 2004)
Geared vessel Vessel which is equipped with her own crane(s) or derrick(s)
(Brodie 2015)
Gearless vessel Vessel which is not equipped with her own crane(s) or derrick(s)
(Brodie 2015)
Goods Raw materials as well as (semi)manufactured products (Singh 2011)
Grains Fruits of relatively simple plants in the grass family (Tamvakis 2007)
Grounded theory Iterative approach to the analysis of qualitative data that aims to
generate theory out of research data by achieving a close fit between the two
(Bryman 2012)
Hague, Hague-Visby Rules Mandatory codes governing the contractual terms
applicable to bill of lading contracts (Baughen 2010)
Hamburg Rules See Hague, Hague-Visby Rules
156
Glossary
Handymax vessel Bulk carrier of 35,000–50,000 tons deadweight (Brodie 2015)
Hidden action Type of agent’s private information where the agent can take an
action unobserved by the principal (Laffont and Martimort 2002)
Hidden knowledge Type of agent’s private information where the agent’s cost or
valuation is ignored by the principal (Laffont and Martimort 2002)
Hire Money paid by a charterer to a ship-owner for the hire of a vessel taken on
time charter (Brodie 2015)
Hold Compartment of the vessel’s structure used for carrying cargo (IACS 2006)
Incentive Setting attached to aligning the objectives of a firm’s various members
with the firm’s ownership profit maximization (Laffont and Martimort 2002)
Information partition Collection of a player’s set of different points in the game,
but on different action sets, at which some player or Nature takes an action or the
game ends (Rasmusen 2007)
Information rent Cost attached to the principal for eliciting the agent’s private
information (Laffont and Martimort 2002)
Interest rate Price paid for borrowing money (Ross et al. 2013)
Major bulks The five commodities such as iron ore, coal, grain, bauxite/alumina
and phosphate rock (Asariotis et al. 2015)
Minor bulks Commodities such as agribulks, metals and minerals and manufactures (Asariotis et al. 2015)
Moral hazard Principal-agent problem where the principal may also choose
actions that affect the value of trade or, more generally, the agent’s performance
(Laffont and Martimort 2002)
Nature Pseudo-player who takes random actions at specified points in the game
with specified probabilities (Rasmusen 2007)
Net present value Present value of the expected cash flows associated with a
project after discounting at a rate that reflects the value of the alternative use of
the funds (Arnold 2010)
Ocean freight Element of the freight consisting of the ocean part of a voyage.
Often used to denote the service itself (Brodie 2015)
Offer Chartering proposal that starts the firm negotiations (Gorton et al. 2009)
Operate a vessel To run a vessel (Brodie 2015)
Panamax vessel Bulk carrier of 60,000–70,000 tons deadweight capable of transiting the Panama Canal (Brodie 2015)
Payback Period of time it takes to recover the initial cash put into a project
(Arnold 2010)
Glossary
157
Period Freight market’s sector where seaborne transport is traded for a defined
duration (Stopford 2009)
Player Individual who makes decisions and participates in a game. Each player’s
goal is to maximize own utility by choice of actions (Rasmusen 2007)
Population True nature of the measured phenomenon is known when all possible
measurements on a variable are on hand, thus the possession of complete information (Bury 1999)
Present value Current worth of future cash flows when discounted (Arnold 2010)
Principal Player who has the coarser information partition (uninformed player)
(Rasmusen 2007)
Principal-agent problem Game situation of delegating a task to an agent with
private information who does not act in the best interests of the principal (Arnold
2010; Laffont and Martimort 2002)
Probability Positive measurement of all possible fields of mathematical expectation of a set of precise premises which could take place upon realization of
established conditions which could make it possible (Kolmogorov 1933)
Probability distribution Density of probability for a random variable’s possible
values over its sample space, thus the uncertainty of occurrence of a random
variable’s particular value (Bury 1999)
Qualitative content analysis Approach to documents that emphasizes the role of
the investigator in the construction of the meaning of and in texts (Bryman 2012)
Qualitative research Emphasis is given on words rather than quantification in the
collection and analysis of data (Bryman 2012)
Quantitative research Emphasis is given on quantification in the collection and
analysis of data (Bryman 2012)
Random variable Unknown values of repeated measurements on a quantity of
interest (Bury 1999)
Rational behavior Fact that every player is influenced by the anticipated reactions
of the others to the player’s own measures and volitions and they in turn reflect
the other players’ expectation of that player’s own actions (Von Neumann and
Morgenstern 2004)
Research design Framework for the collection and analysis of data (Bryman 2012)
Research method Technique for collecting data (Bryman 2012)
Research strategy General orientation to the conduct of research (Bryman 2012)
Risk Potential for realization of unwanted, negative consequences of an event
under conditions of uncertainty (Rowe 1988)
158
Glossary
Sample Limited set of actual observations on hand, thus the representation of
incomplete information on the population (Bury 1999)
Screening Adverse selection situation where the uninformed party makes the
contract offers and it must attempt to assess the different pieces of information
the informed party has (Bolton and Dewatripont 2005)
Seller Party entering into a sales transaction in return for the contract price
(Baughen 2010)
Ship management Technical operation aspects that include crewing and supplying
the vessel, keeping her machinery and equipment in working order which, in
turn, means arranging the necessary surveys, keeping her certificates up to date,
and stowing cargoes safely and efficiently (Brodie 2015)
Ship operation Commercial operation aspects that are concerned more with
finding cargoes, negotiating freight rates and bunker prices and appointing ship’s
agents at the ports of call (Brodie 2015)
Ship-owner Person or company who owns vessel(s) (Brodie 2015)
Shipbroker Person who negotiates the terms for the charter of a vessel on behalf
of a charterer or ship-owner (Brodie 2015)
Shipper Person who makes the initial contract of carriage with the carrier.
However, it may also be used to refer to the consignor. This person may not
always have made the initial contract of carriage, as is the case with most free on
board contracts (Baughen 2010)
Signaling Adverse selection situation where the informed party makes the contract
offers and it may attempt to indicate to the other party what it knows through the
type of contract it offers or other actions (Bolton and Dewatripont 2005)
Spot Freight market’s sector where seaborne transport is traded for a single voyage
or trip (Stopford 2009)
Subject details Term qualifying an offer or counteroffer for the charter of a ship
which denotes that only minor details remain to be agreed (Brodie 2015)
Subjects Conditions attached to offers (Brodie 2015)
Terms Commercially important elements when involved in a chartering negotiation (Collins 2000)
Time charter The hire of a vessel for a period of time (Brodie 2015)
Time charter party Document containing the terms and conditions of a contract
between a charterer and a ship-owner for the hire of a vessel for a period of time
(Brodie 2015)
Glossary
159
Tramp shipping Maritime transport concerned with vessels which will call at any
port to carry whatever cargoes, most commonly bulk cargoes, are available
(Brodie 2015)
Trip charter Time charter of a vessel for a specific trip, rather for a period of time
(Brodie 2015)
Uncertainty The absence of information, that which is unknown (Rowe 1988)
Utility Quantity attached to certain aspects of the physical world, thus the satisfaction of a rational economic human’s wants and desires, as expressed on a
scale (Von Neumann and Morgenstern 2004; Rowe 1988)
Vessel Ship or boat (Brodie 2015)
Voyage Journey by sea specified via loading and discharging ports and up to the
position where the ship is expected to start her next employment, that is, so that a
possible passage in ballast should also be included (Gorton et al. 2009)
Voyage charter The use of a vessel’s cargo space for one, or sometimes more than
one, voyage (Brodie 2015)
Voyage charter party Document containing the terms and conditions of a contract
between a charterer and a ship-owner for the use of a vessel’s cargo space for
one, or more than one, voyage (Brodie 2015)
Weighted average cost of capital Discount rate calculated by weighting the cost
of debt and equity in proportion to their contributions to the total capital of the
firm (Arnold 2010)
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Index
A
Adverse selection, 7, 53, 55, 153
Agency costs, 19, 50
Agent, 7, 19–21, 49, 51–56, 62, 65, 74–78, 80,
83, 85, 87–89, 91, 94–96, 98, 105–107,
153, 154, 156, 157
Asymmetric information, 153
Content analysis, 7, 9, 28, 29, 31, 39, 105, 108,
154, 157
Contract, 4, 6, 7, 9, 17, 19, 23, 31, 32, 34, 37,
38, 44, 50–55, 57, 62–64, 98, 104–108,
153–155, 158, 159
Contract law, 37
B
Bargaining, 7, 9, 37, 39, 49, 51, 52, 57, 63, 65,
98, 105–107
Bulk carrier, 111, 153
Bunker costs, 62
Buyer, 51, 53, 57, 68, 98, 105, 154
D
Debt, 60, 61, 154, 159
Design improvements, 3, 14, 16
Discount rate, 59, 60
Distribution, 44, 73–75, 77, 79, 83, 86, 87, 89,
90, 94, 113–115, 117, 118, 157
Duty, 33, 36, 37, 45, 109
C
Capital costs, 18, 55, 61
Carrier, 4, 6, 7, 19, 23, 25, 32–34, 36, 42, 44,
49–54, 57–59, 62, 69, 74, 77, 83, 87, 89,
94, 98, 105, 106, 158
Cash flows, 36, 59, 60, 62, 155–157
Charterer, 4, 6, 7, 17–19, 23, 25, 32–34, 36, 37,
39, 42, 44, 49–54, 57, 59, 74, 77, 83, 86,
89, 94, 98, 100, 105, 106, 154, 156, 158,
159
Chartering negotiations, 9
Charter party, 4–9, 13, 18, 20, 23–25, 28, 29,
31, 32, 34, 36, 37, 44, 51, 63, 64, 103–109,
154, 158, 159
Clauses, 4–9, 16, 21, 23–28, 37, 39–41, 64,
104, 105, 108, 109, 154
COGSA, 32
Commodity, 6, 17, 54, 55, 57, 61, 66, 73, 75,
77, 78, 80, 83, 85, 87–91, 95, 96, 98–100,
105, 107
Common law, 33, 35, 36, 45
E
EEDI, 3, 4, 13–16, 99, 108
EEISS, 49, 59, 63, 76, 77, 80, 85, 88, 91, 95,
96, 98
Effort level, 53, 54, 74, 76, 83, 85, 87, 88, 94,
96
Elevator, 68, 69
Energy efficiency, 4–9, 13, 14, 16, 19–21,
24–27, 29, 31, 34, 36–38, 40, 44, 45, 49,
51–55, 57, 59, 62–64, 74, 77, 83, 87, 93,
94, 98–100, 103–109, 155
Energy efficient technologies, 4, 16, 20, 36, 99
Energy saving equipment, 16
Environmental awareness, 8
Environmental commitment, 36, 44, 106
Environmental stewardship, 37, 38, 44, 55, 56,
64, 78, 80, 89, 91, 99, 104, 108
Equilibrium, 50, 57, 59, 60, 62, 63, 75–81,
83–85, 87, 88, 90–93, 95–99
Equity, 36, 60, 61, 154, 159
Exploratory research, 25
© Springer International Publishing Switzerland 2017
G.A. Psarros, Energy Efficiency Clauses in Charter Party Agreements,
Springer Series on Naval Architecture, Marine Engineering,
Shipbuilding and Shipping 3, DOI 10.1007/978-3-319-50265-6
173
174
F
Focused coding, 6, 7, 27–29, 31, 105
Freight contract, 17, 32, 78, 80, 89, 91, 98
Freight rate, 4, 17, 49–51, 54, 56, 57, 59, 61,
62, 75, 76, 79, 80, 84, 87, 90, 91, 95
Fuel consumption, 4, 15–17, 28, 34, 40, 50, 53,
62, 79, 90, 105, 107, 109
G
Game theory, 7, 9, 49–51, 63, 103, 105–107,
155
Good faith, 37, 109
Grain, 9, 65, 66, 68, 69, 71, 75, 79, 83,
84, 87, 90, 94, 95, 98, 99, 103, 106,
111, 156
Grounded theory, 6, 8, 9, 23, 25, 27, 29, 105,
155
H
Hague-Visby Rules, 18, 32, 33, 155
Hamburg Rules, 18, 33, 155
Handymax, 71, 74, 75, 77, 79, 83, 86, 100,
106, 112
Handymax bulk carrier, 71
Handymax vessel, 156
Hidden action, 53, 156
Hidden knowledge, 156
Hire, 4, 8, 17, 18, 41, 49, 61, 73, 107, 154, 156,
158
I
Implied duties, 18
Incentive, 156
Incentive mechanism, 5, 7, 9, 63, 103, 106
Inductive approach, 24
Information, 7, 19, 20, 24, 25, 27, 29, 33, 34,
49, 50, 52, 53, 55, 56, 61–63, 72, 78,
106–108, 115, 153, 156–159
Information rent, 156
Initial coding, 6, 9, 27, 29, 31, 39, 105
M
Moral hazard, 7, 53, 156
N
Nature, 156
Negotiation process, 7
Negotiations, 7, 37, 44, 49, 50, 53, 55–57, 65,
98, 106, 109, 156
Index
NPV, 59, 61, 62, 107
O
Obligation, 4, 6, 33, 37, 45, 52, 64,
104, 109
Operating costs, 18, 61, 75, 79, 84, 87,
90, 95
P
Panamax, 71, 86, 89, 90, 94, 112
Panamax bulk carrier, 71
Panamax vessel, 156
Period charter, 17
Pollution, 13, 36, 45, 104
Principal, 7, 19, 20, 27, 34, 49, 51–56, 61, 65,
73–75, 77, 78, 80, 83, 85–89, 91, 94–96,
98, 105, 107, 156, 157
Principal-agent problem, 157
Probability, 36, 55, 57, 72, 73, 75, 77, 79, 80,
84, 85, 89–91, 95, 96, 113, 157
Q
Qualitative research, 24
Qualitative research objectives, 5, 6, 23, 24,
104, 106
Quantitative research objectives, 6, 7, 103, 105
R
Rational behavior, 49, 157
RFR, 61, 75, 77, 79, 80, 84, 85, 87, 88, 90, 91,
95, 96
Risk, 17, 34, 36, 38, 44, 50, 51, 59–62, 98,
100, 106
Rotterdam Rules, 18, 33
S
Screening, 21, 53, 55, 158
Seaworthiness, 18, 28, 34, 39
SEEMP, 14, 16, 55, 56
Seller, 51, 53, 57, 98, 105
Sensitivity analysis, 74
Sharing scheme, 6, 35, 37, 38, 49, 51, 52, 59,
62, 77, 100, 105, 106, 109
Ship-owner, 4, 18, 23, 33, 154
Shipper, 4, 6, 7, 17, 19, 23, 25, 32, 33, 44,
49–54, 57, 59, 74, 77, 83, 86, 89, 94, 98,
105, 154
Signaling, 158
Spot market, 17, 20, 50, 68, 100
Index
175
T
Terminal, 68
Time charter, 7, 17–19, 21, 32, 40, 50, 53, 61,
62, 83–85, 94–96, 98–100, 106, 156
V
Voyage charter, 7, 17, 32, 34, 39, 42, 49, 50,
54, 61, 73, 100, 105
Voyage costs, 42, 61, 73, 79, 90
U
Uncertainty, 16, 19, 62, 72, 73, 99, 100, 115,
157, 159
Utility, 159
W
WACC, 60