Tanker Shipping Research Articles

Advances in risk management: optimum investment portfolios in tanker shipping

Advances in risk management: optimum investment portfolios in tanker shipping

Fleet Repositioning, Flag Switching, Transportation Scheduling, and Speed Optimization for Tanker Shipping Firms

In response to the European Union (EU)’s sanctions on Russian oil products, tanker shipping firms may adopt two strategies to reoptimize their shipping networks. The first strategy is to switch the flag states of tankers that are not eligible to operate on certain routes. The second strategy is to reposition tankers based on their flag states, i.e., moving those tankers that are eligible from other groups to specified routes. To help tanker shipping firms minimize the total operating cost during the planning horizon in the context of EU oil sanctions, including costs of fleet repositioning, flag switching, and fuel, this study investigates an integrated problem of fleet repositioning, flag switching, transportation scheduling, and speed optimization considering the dynamic relationships among fuel consumption, speed, and load. By formulating the problem as a nonlinear integer programming model and applying various linearization techniques to convert the nonlinear model into a linear optimization model solvable by off-the-shelf linear optimization solvers, this study demonstrates the practical application potential of the proposed model, with the longest solution time of less than two hours for a numerical instance with seven routes. Furthermore, through sensitivity analyses on important factors including unit fuel prices, crude oil transportation demand, and the tanker repositioning cost, this study provides managerial insights into the operations management of tanker shipping firms.

Fuzzy Cognitive Mapping Approach to Conduct Deficiency Investigation under SIRE 2.0 Inspection

Ship Inspection Report Programme (SIRE) 2.0 has recently become operational as a new vessel inspection regime in the tanker industry. This study proposes a methodology to analyse and address multiple deficiencies observed during SIRE 2.0 inspections. The methodology is structured based on Fuzzy Cognitive Mapping (FCM) to identify and analyse the causes of deficiencies derived from the International Maritime Organization (IMO) classification scheme, including the factors under key dimensions, such as diminished human performance, marine environment, safety administration, and management. An illustrative case study on a set of deficiencies has been conducted to ascertain the utility of the methodology. The results specifically reveal that inadequate situational communication and awareness, inadequate knowledge of ship procedures, regulations, and standards, inadequate supervision, being unaware of role or task responsibility, poor maintenance, etc. are the potential causes that might lead to the occurrence of deficiency items. Considering the dimension-based distributions of causes, the study highlights integrated preventive action recommendations specific to the analysed deficiency cases. Consequently, the study might help tanker shipping companies manage key challenges with SIRE 2.0 implementations.

Information spillovers between carbon emissions trading prices and shipping markets: A time-frequency analysis

Climate change has become mankind's main challenge. Greenhouse gas (GHG) emissions from shipping are not totally irresponsible for this representing, roughly, 3% of the global total; an amount equal to that of Germany's total GHG emissions. The Fourth Greenhouse Gas Study 2020 of the International Maritime Organization (IMO) predicts that the share of GHG emissions from shipping will increase further, as international trade recovers and continues to grow, alongside with the economic development of India, China, and Africa. China and the European Union have proposed to include shipping in their carbon emissions trading systems (ETS). As a result, the study of the relationship between the carbon finance market and the shipping industry, attempted here for the first time, is both important and timely, both for policymakers and shipowners. We use wavelet analysis and the spillover index methods to explore the dynamic dependence and information spillovers between the carbon finance market and shipping. We discover a long-term dependence and information linkages between the two markets, with the carbon finance market being the dominant one. Major events, such as the 2009 global financial crisis; Brexit in 2016; the 2018 China-US trade frictions; and COVID-19 are shown to strengthen the dependence of carbon finance and shipping. We find that the dependence is strongest between the EU carbon finance market and dry bulk shipping, while the link is weaker in the case of tanker shipping. Nonetheless, carbon finance and tanker shipping showed a relatively stronger dependence when OPEC refused to cut production in 2014, and when the China-US trade disputes led to the collapse of oil prices after 2018. We show that information spillovers between carbon finance and shipping are bidirectional and asymmetric, with the carbon finance market being the principal transmitter of information. Our results and their interpretation provide guidance to governments on whether (and how) to include shipping in emissions trading schemes, supporting at the same time the environmental sustainability decisions of shipping companies.

New challenges in fleet deployment considering EU oil sanctions

<abstract> <p>Due to European Union (EU) oil sanctions, tanker shipping companies need to redeploy their tankers by moving tankers between ship routes with the consideration of flag states of tankers, but the literature lacks quantitative methods for this problem. To fill this research gap, this paper studies an integrated problem of fleet deployment, fleet repositioning, round trip completion, and speed optimization with the consideration of flag states of tankers. The problem is formulated as a nonlinear integer programming model to minimize the total cost, including the fleet repositioning cost, the mismatch cost, and the fuel cost, during the planning period while satisfying the total crude oil transportation demand of each voyage and the minimum shipping frequency. Some linearization methods are used to transform the nonlinear model to a linear one which can be directly solved by Gurobi. The average solving time required for 17 computational instances is 4.5 minutes, which validates the effectiveness of the proposed model. Sensitivity analyses, including the influences of the unit fuel price, the total crude oil transportation demand, the mismatch cost of completing a round trip by a deployed tanker, and the repositioning cost for each deployed tanker, on operations decisions, are conducted to obtain managerial insights.</p> </abstract>

A Neural Network approach for integrating banks’ decision in shipping finance

Forecasting refers to the process of predicting future trends by lying on data from the past. An error in forecasting can lead to significant business losses especially in banking industry where decisions are taken in a highly volatile and uncertain environment due to the dynamic changes in world economy. In this paper, we study both the effectuations of the exogenous factors in the tanker shipping-related financial market and the modulation of the credibility coefficient as an internal factor in shipping banks that may affect their decision to either increase or decrease loans within tanker shipping sector by adopting the artificial neural network technique. Within this context, we modeled a unique network that adjusts 88 macroeconomic indices to the real data of 89 shipping banks within a period of T = 5 years time. The main contribution of this study is the understanding of the relation between bias and either exogenous or unpredictable factors in the market as a key factor in the financing decision policy of a shipping bank for the forthcoming year T + 1.

GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA

Due to decarbonization and greenhouse gas (GHG) emission reduction attempts nowadays, liquefied natural gas (LNG) has become widely used as an alternative marine fuel. As Japan is the top global LNG importer and one of the largest crude oil importers, this study focuses on LNG and tanker shipping and their emissions in Japan, and import volumes. In this study, the emission estimation model is constructed based on the Holtrop-Mennen power prediction method. Using automatic identification system (AIS) data, fuel consumption and GHG emissions are estimated. Next, long term GHG emission is predicted using hthe Japan trade statistics. Combining the vessel movement data and trade statistics, GHG emission in Japan is projected to decline over years for tankers, and to remain stable for LNG carriers. The results could be considered in formulating environmental and trade policy. It is hoped the study will provide useful insights for zero emission projects and implementations in Japan.

Influencing factors of crude oil maritime shipping freight fluctuations: a case of Suezmax tankers in Europe–Africa routes

PurposeThe paper aims to identify the contributors to freight rate fluctuations in the Suezmax tanker market; this study selected the refinery output, crude oil price, one-year charter rate and fleet development as the main influencing factors for the market analysis.Design/methodology/approachThe paper used the vector error correction model to evaluate the degree of impact of each influencing factor on Suezmax tanker freight rates, as well as the interplay between these factors.FindingsThe conclusion and results were tested using the 20-year data from 1999 to 2019, and the methodology and theory of this paper were proved to be effective. Results of this study provide effective reference for scholars to find the law of fluctuations in Suezmax tanker freight rates.Originality/valueThis paper provides a decision-making support tool for tanker operators to cope with fluctuation risks in the tanker shipping market.

Marine insurance claims analysis using the Weibull and log-normal models: Compensation for oil spill pollution due to tanker accidents

In marine insurance, the identification of the distribution of the claim size processes from given observations is of major importance. We evaluate the claim amounts due to accidental oil spill volume from tankers. Previous studies on oil spills were mainly concentrating on the relation between spill amounts and some accident factors. The trend of the spill claims is a major concern to the sustainability of insurance funds, tanker shipping and the whole oil industry. By analysing the real claim data series of IOPC (International Oil Pollution Compensation Funds), we identify the trends and reveal findings which enable policy-makers and insurers for the sufficient financial protection from oil spill damage. This study introduces the Weibull model and the log-normal model to the framework for oil spill claims. The Weibull model and the log-normal model are obtained with Markov chain Monte Carlo (MCMC) simulation method. The statistical properties of oil spill claims are determined in the time series of oil spill claims. Moreover, the relations among claim properties of different settings are examined. The log-normal of 3 groups of gross tonnage is found the best fit according to R-squared statistics and the mean squared errors.

VaR as a mitigating risk tool in the maritime sector: An empirical approach on freight rates

<abstract> <p>Shipping freight rates fluctuation is considered as one of the most important risk factors that participants face in the tanker shipping market (ship-owners, charterers, traders, hedge funds, banks and other financial institutions) in order to watch its evolution. This study examines freight rates for two of the most popular clean and dirty tanker routes; TC2 and TD3 from 22 May 2007 to 21 September 2015, using daily spot and future prices. The full data sample is divided into two sub periods, from 22 May 2007 to 13 August 2013 (in sample period) on which the model estimation section is based and from 14 August 2013 to 21 September 2015 (out of sample period) over which the Value at Risk is measured and backtesting process was performed. In all cases tested, there are observed high peaks and fat tails in all distributions. We apply a range of VaR models (parametric and non-parametric) in order to estimate the risk of the returns of TC2 route and TD3 route for spot, one month and three months future market. Backtesting tools are implemented in order to find the best fit model in terms of economic and statistical accuracy. Our empirical analysis concludes that the best fit models used for mitigating risk are simple GARCH model and non-parametric model. The above outcome seems to be valid a) for spot returns as well as for future returns and b) for short and long positions. In addition to the aforementioned conclusions, it is observed high freight rate risk at all routes. Our results are useful for risk management purposes for all the tanker shipping market participants and derivatives' counterparties.</p> </abstract>

Fuzzy Multiple-Criteria Decision Making Based Evaluation of the Incident Analysis Forms Used in Internal Reporting Systems: A Case Study of Tanker Shipping Companies

Under the International Safety Management (ISM) Code, shipping companies are required to build a Safety Management System and develop procedures that will regulate the incident analysis process. By these procedures, the shipping companies develop an incident analysis form and use it on all ships within their fleets. Although these forms serve a common purpose, their structure shows some differences between companies. Thus, time may be wasted when transferring the data or insufficient data may be obtained. This paper examines incident analysis forms used for tanker ships whose hazardous cargos make them of particular importance. The features of incident analysis forms used on tankers of a shipping company were specified, and the fuzzy analytic hierarchy process (FAHP) method presented their importance levels. Expert opinion was collected by a questionnaire. The results revealed that the highest priorities belong to the sections allocated for details about the ship and environmental conditions and root cause analysis technique. The least essential sub-criteria were related to the structure and variety of the forms. Based on the study results, which captured the current practice and the sector’s expectations, tanker shipping companies’ existing incident analysis forms could be revised. The revised documents would help to achieve complete information and prevent loss of time in information exchange between companies. Bringing a common approach to the analysis of incidents in tanker shipping would increase safety in the shipping industry.

Forecasting oil tanker shipping market in crisis periods: Exponential smoothing model application

This paper fills the gap in the literature of applying an exponential smoothing model in the oil shipping market forecasting. The author refines the adaptive combined model with B-criterion based on Brown's model with modification by Trigg and Leach. Forecasting the values of the average time-charter equivalent of a tanker along 6 different routes of oil transportation in the world ocean during the crisis period 2015–2019. The accuracy of the proposed method is superior to naive, autoregression methods and machine learning models in all used error metrics. The obtained accuracy in 71% of cases is available for commercial use by operators and charterers of the tanker fleet.

Tanker freight rates and economic policy uncertainty: A wavelet-based copula approach

This study examines the dynamic relationship between global economic policy uncertainty (GEPU) and tanker freight rates in both time- and frequency-domains. The tanker shipping market is shown to be susceptible to global political and economic risks. Given the recent high economic policy uncertainty pertaining to the world and the important role maritime transport plays in the global oil trade, it is a vital task to evaluate the dependence dynamics between economic policy uncertainty and tanker freight rates. The return series are first decomposed into different frequency components using wavelets in order to identify the dependence structure in different time scales. Second, the average and tail dependence behaviors are depicted using a wide range of both static and time-varying copula functions. The empirical results demonstrate significant time variations in the dependence structure between GEPU and tanker freight indices over different time scales. The results of this study convey significant policy implications and strategic planning for energy transport players.

Freight rate co-movement and risk spillovers in the product tanker shipping market: A copula analysis

Tanker shipping is a crucial sector carrying energy products across regions worldwide. This paper examines freight rate return dependencies across six major clean product tanker shipping routes by the copula-GARCH model. Using information from marginal and copula models, extreme risk spillover effects between different routes are further examined by the conditional Value-at-Risk (CoVaR) measure. Results reveal that the product tanker shipping market is regionalized and segmented by geographical locations. The dependency is high for different size ships operating in the same trade routes, while freight rates of ships having same sizes operating in adjacent regions have higher dependencies than operating in distant areas. Extreme co-movements exist in most return pairs with significant tail dependence. We further document significant extreme risk spillovers especially between adjacent or same trading routes and such spillover effects are found to be asymmetric in bearish and bullish markets. Our findings provide implications for shipping risk management.

The Potential and Limits of Environmental Disclosure Regulation: A Global Value Chain Perspective Applied to Tanker Shipping

Abstract Exploring how transnational environmental governance and the operation of global value chains (GVCs) intersect is key in explaining the circumstances under which mandatory disclosure can improve the environmental footprint of business operations. We investigate how the governance dynamics of the tanker shipping value chain (a major emitter of greenhouse gases) limits the effectiveness of the European Union (EU) monitoring, reporting, and verification (MRV) regulation, which mandates the disclosure of greenhouse gas emissions for ships calling at EU ports. Although MRV seeks to help shipowners and ship managers save fuel and reduce emissions, it does not address the complexity of power relations along the tanker shipping value chain and currently cannot disentangle how different actors influence the design, operational, commercial, and ocean/weather factors that together determine fuel consumption. In particular, the EU MRV neglects to reflect on how oil majors exert their power and impose their commercial priorities on other actors, and thus co-determine fuel use levels. We conclude that, in its current form, the EU MRV is unlikely to lead to significant environmental upgrading in tanker shipping. More generally, we argue that regulators seeking to facilitate environmental upgrading need to expand their focus beyond the unwanted behaviors of producers of goods and providers of services to also address the incentive structures and demands placed on them by global buyers.

Modelling Stock Returns and Risk Management in the Shipping Industry

We estimate the impact of macroeconomic risk factors on shipping stock returns, using a quantile regression (QR) model. We regress the excess return of a portfolio for the container, dry bulk, chemical/gas, oil tanker, and diversified shipping sectors on the world market portfolio excess return, volatility index, and changes in the oil price, exchange rate, and interest rate. The sensitivities of stock returns to the risk factors differ across quantiles and shipping segments and are found to be significant for the volatility index, world market portfolio return, exchange rate, and changes in long-term interest rate with variation over quantiles. This provides evidence of asymmetric and heterogeneous dependence between stock returns and certain macroeconomic risk variables. The results of the study also suggest that standard OLS regression is inadequate to uncover the risk-return relation.

The characterisation and provenance of crude oils stranded on the South Australian coastline. Part I: Oil types and their weathering

Semi-solid crude oil has been known to wash ashore along the South Australian coastline for over 120 years. The early reports pre-date offshore petroleum exploration and tanker shipping activities in Australian waters, suggesting that this stranded oil originates from natural offshore seepage. Three physically distinct varieties are represented: waxy bitumen, asphaltite and tar. In order to distinguish this natural “background” contamination of the coastline from any potential anthropogenic sources of petroleum, such as oil spills, whole-oil GC–MS analysis was employed to identify at least seven geochemically different types of stranded oil, based on a suite of 633 specimens collected from the coastline during three annual surveys of 30 ocean beaches between 2014 and 2016. The waxy bitumens, which in terms of their biomarker alkanes display an atypical pattern of alteration due to weathering in the marine environment, are more severely altered than similar specimens collected 25 years ago.

Revealing the Impact of Increased Tanker Size on Shipping Costs

Purpose: The research objectives refer directly to the key in maritime transport problem of cost economies related to the increasing scale (size) of ships. This article reveals the relationship between an increase in tanker size and shipping cost and its various categories. Design/Methodology/Approach: Economies of tanker size are expressed in terms of the elasticity of daily and unit shipping costs relative to vessel size measured in deadweight. Functions of the shipping daily and unit costs concerning the tanker size were derived by regression, while the parameters were estimated with the ordinary least squares’ method. Findings: Elasticity values for daily and unit shipping costs estimated for tankers within the size range (dwt); 25,000 - 300,000. Tanker daily shipping mean elasticity estimates: another operating cost (labor cost included) (0.262), capital costs (0.407), port costs (0.449), fuel costs (0.575). Revealed tanker unit shipping mean elasticity estimates: full operating costs (-0,67), other operating cost (-0.835), capital costs (-0.690), port costs (-0.649), fuel costs (- 0.523). Practical implications: Elaborated models allow one to estimate savings in shipping costs resulting from handling larger tankers in seaports, which in turn is an important factor in terms of the calculation of the effectiveness of port capacity expansion, as well as in the analysis of competition between ports. The models may be used to study the impact of scale in tanker shipping on the remaining transport links (i.e., ports and hinterlands) and in the routing of sea–land supply chains. Originality/Value: The models for daily and unit costs developed here allow calculating the shipping cost for any tanker size.

Addressing the challenges of Global Sulphur Cap 2020: case study Indonesian tanker shipping

Global Sulphur Cap 2020 (GSC 2020) is a policy that regulates the limitation of sulphur content in ship fuel to meet the global standard of 0.5% starting on January 1st, 2020. This new regulation creates new challenges faced by shipping companies, in particular those which operate ageing ships or operating ships in the so-called Environmental Control Areas (ECA). Five technological options to encompass the GSC 2020 issues are deploying one of three scrubber systems, or switching to environmentally friendlier fuels, i.e. MGO or LNG. The paper outlines a case study of an Indonesian shipping company operating eleven ships, nine tankers and two container ships, with an age between two and 29 years of age. The Multi-Attribute Utility Theory is deployed to provide a recommendation on the best option.

Application of data-mining techniques to predict and rank maritime non-conformities in tanker shipping companies using accident inspection reports

ABSTRACT The application of data mining techniques is an extended practice in numerous domains; however, within the context of maritime inspections, the aforementioned methods are rarely applied. Thus, the application of data-mining techniques for the prediction and ranking of non-conformities identified during vessel inspections could be of significant managerial contribution to the safety of shipping companies, as non-conformities could potentially lead to real accidents if not addressed adequately. Hence, specific data mining methods are investigated and applied in this paper to predict and rank non-conformities on oil tankers using a database recorded by tanker shipping companies in Turkey from 2006 to 2019. The results of this study reveal that specific non-conformities, for instance, inadequate ice operations or inadequate general appearance and condition of hull, superstructure and external weather decks, are not company-based problems, rather they are industry wide issues for all tanker shipping companies.