Bunker Prices Research Articles

Can an Emission Trading Scheme really reduce CO2 emissions in the short term? Evidence from a maritime fleet composition and deployment model

Global warming is a major challenge for this planet, and its solution requires efforts throughout society. Maritime transportation, which carries more than 90% of the global trade, plays a critical role in the contribution of greenhouse gas (GHG) emissions. However, the GHGs emitted by the global fleet still fall outside the emission reduction scheme established by the Kyoto Protocol. Alternative solutions are therefore sought. Several market-based measures have been proposed and submitted to IMO for discussion and evaluation. In this paper, we focus on one of these measures, namely the Maritime Emissions Trading Scheme (METS). An optimization model that integrates (global or regional) METS into the classical fleet composition and deployment problem is proposed. This model is used as a tool to study the impact of METS on fleet operations and their CO2 emissions. The results of the computational study suggest that, in the short term, the implementation of METS does not lead to emission reduction in most scenarios. However, in the case of low bunker prices, high allowance costs or global METS coverage, a more significant CO2 decrease in the short term can be expected.

The Impact of Macroeconomic Variables on the Profitability of Korean Ocean-Going Shipping Companies

The objective of this study was to establish whether global macroeconomic indicators affect the profitability of Korean shipping companies by using panel regression analysis. OROA (operating return on assets) and ROA (ratio of net profit to assets) were selected as proxy variables for profitability. OROA and ROA were used as dependent variables. The world GDP growth rate, interest rate, exchange rate, stock index, bunker price, freight, demand and supply of the world shipping market were set as independent variables. The size of the firm was added to the control variable. For small-sized firms, OROA was not affect by macroeconomic indicators. However, ROA was affected by variables such as interest rates, bunker prices, and size of firms. For medium-sized firms, OROA was affected by demand, supply, GDP, freight, and asset variables. However, macroeconomic indicators did not affect ROA. For large-sized firms, freight, GDP, and stock index (SCI; Shanghai Composite Index) have an effect on OROA. ROA was analyzed to be influenced by bunker price and SCI.

Slow Steaming as Part of SECA Compliance Strategies among RoRo and RoPax Shipping Companies

Many geographically peripheral member states of the EU are critically dependent on short sea Roll-on/Roll-off (RoRo) and mixed freight–passenger (RoPax) shipping services for intra-European trade. The implementation of the Sulfur Emission Control Area (SECA) regulation was expected to raise the operating cost for RoRo and RoPax shipping, and slow steaming was proposed as an immediate solution to save the increased cost. Previous research has investigated the issue of slow steaming and SECA using a quantitative approach. However, the reaction of the RoRo and RoPax shipping firms toward slow steaming as a mitigating factor in the face of expected additional SECA compliance costs using qualitative methodology has not been explored yet. In addition, the knowledge regarding the impact of slow steaming on the competitiveness of short sea RoRo and RoPax with respect to service quality is limited. This article has addressed these issues through the analysis of multiple cases focusing on RoRo and RoPax firms operating in the North and Baltic Seas. Overall, our findings suggest that the 0.1% SECA regulation of 2015 requiring the use of higher-priced MGO has not caused slow steaming in the RoRo and RoPax segments to a large extent. The increased bunker prices are partially transferred to the customers via increased Bunker Adjustment Factor and partly borne by the shipowners. We have found that out of 11 case firms in our study only one RoRo and one RoPax firm have reduced vessel speeds to compensate for the additional SECA compliance costs. We conclude that for RoPax and RoRo segment bunker prices, rigorous competition and, most important, different service quality requirements have significantly restricted the potential implementation of slow steaming.

Liner Ship Routing with Speed and Fleet Size Optimization

The shipping route network design is a strategic decision issue for shipping lines because liner ships operate along routes and timetables published months ahead. This paper considers an Integrated Planning Problem (IPP) of determining the speed, route, and number of ships deployed (fleet size) of a ship type. The objective is to maximize a carrier’s profit, which is the revenue minus Cargo Handling Charge (CHC), bunker consumption cost, port charge, cargo inventory cost and ship time cost. We present a nonlinear shortest-path model to represent the considered problem and suggest a Simulated Annealing (SA) algorithm with some analytic methods. Performance evaluation experiments demonstrate that the proposed algorithm works well for small test problems. A number of scenario analyses are further performed with the heuristic algorithm to investigate the effect of exogenous factors, including bunker price, chartering cost, revenue (sea freight rate) and ship size. The analysis results show that strategic decisions for determining the route, fleet size and ship speed can be affected by changes in the factors. The performance tests and scenario analyses show that the heuristic algorithm can be utilized as an efficient and effective planning tool for the liner service in both the current environment and feasible future situations.

Impact analysis of the traffic convoy system and toll pricing policy of the Suez Canal on the operations of a liner containership over a long-haul voyage

This paper takes an initiative to quantitatively assess the impact of the traffic control scheme and the stepwise toll pricing policy of the Suez Canal on the optimal sailing schedule of a liner containership. We develop a mixed-integer nonlinear programming model for the optimal sailing schedule of a containership over a long-haul voyage via Suez. A case study on a 13000-TEU containership running on the LP4 service of APL shows several managerial insights: a) ignoring the traffic control system at Suez in ship speed optimisation may lead to an infeasible sailing schedule, and underestimate the operating cost (even the bunker cost) of a containership on a long-haul voyage via Suez; b) the optimal ship recovery plan in terms of its sailing speeds is mainly determined by the predefined port time windows, delay situation and Suez-clock time, but not pretty much affected by the levels of bunker price and transit due.

Impact analysis of the traffic convoy system and toll pricing policy of the Suez Canal on the operations of a liner containership over a long-haul voyage

This paper takes an initiative to quantitatively assess the impact of the traffic control scheme and the stepwise toll pricing policy of the Suez Canal on the optimal sailing schedule of a liner containership. We develop a mixed-integer nonlinear programming model for the optimal sailing schedule of a containership over a long-haul voyage via Suez. A case study on a 13000-TEU containership running on the LP4 service of APL shows several managerial insights: a) ignoring the traffic control system at Suez in ship speed optimisation may lead to an infeasible sailing schedule, and underestimate the operating cost (even the bunker cost) of a containership on a long-haul voyage via Suez; b) the optimal ship recovery plan in terms of its sailing speeds is mainly determined by the predefined port time windows, delay situation and Suez-clock time, but not pretty much affected by the levels of bunker price and transit due.

Time Varying Causality Between Cost and Price: An Empirical Analysis on Dry Bulk Shipping Market

Fluctuations of freight income and volatility of bunker prices are together having an important impact on shipping profits because oil, which is the main source of bunker fuel, constitutes substantial operational expense to shipping firms, and it accounts for 34-44% of the whole operation costs. However, as the freight rates in live marketconditions are determined by demand, the determinative characteristics of bunker priceloses its importance. In this respect, it is not wrong to say that the relationship betweenbunker prices and freight rates changes according to time. Therefore, with the aim offilling the gap in the related literature, the relationship between these two variables isexamined by time-variance causality analysis. The data set used in the study consists of318 observations on a monthly basis covering the period January 1992 to June 2018. Asa result of the study 24 causality periods with lengths ranging from 1 month to 13months are determined. Also, it is spotted that they are intersected with BDI in thedeclining regions of the index in general and this result confirms that the causalitybetween the freight rates and the bunker prices is realized in the stagnant marketconditions.

Investigating dependencies among oil price and tanker market variables by copula-based multivariate models

Crude oil price, bunker price, and tanker freight rate are very volatile in recent years. Uncertainties about the complicated relationships among these market variables could impose substantial business risks on investors, shipowners, ship operators, and charterers in oil transportation by tankers. A better understanding of the dependencies among these variables can help improve investment and operational decisions. This study aims to develop copula-based multivariate models for analyzing oil price and tanker market variables, and associated efficient approaches to reveal the time varying effects for the relationships. Given the volatilities in oil prices over the recent years, this study analyzes the impact that the changes in oil price has on bunker price and tanker freight rates. The West Texas Intermediate, Bloomberg Weighted Average Bunker Fuel Price, Baltic Handysize Index, Baltic Supramax Index and Baltic Panamax Index are analyzed using various selected copula models, from 2008 through 2015. Findings reveal that high oil price is a factor causing a weak dependence among the studied variables in the tanker market. The use of copula has more advantages over the conventional methods and can best characterize the relationships for the tanker market variables. The time varying effects in the dependences can be well identified in the copula modeling. The results could provide valuable information for the policy makers and participants in the maritime industry. The understanding of the dynamic relationships among the oil market could help the participants to foresee the potential risks and the policy makers to organize a more efficient market.

Current status and future operational models for transit shipping along the Northern Sea Route

The Northern Sea Route (NSR) has received increased international attention during the recent years as an alternative transit corridor for shipping between Europe and East Asia. In 2015, the project “Feasibility and Reliability of Shipping on the Northern Sea Route and Modeling of an Arctic Marine Transportation & Logistics System” was established to perform a comprehensive analysis of the current status and future prospects of NSR transit shipping. The project brought together several partners and numerous participants representing industry, governmental bodies, and research groups from Europe, Asia, and Russia, thus providing a unique and comprehensive overview of the subject. This paper is based on the insights gathered during the project. Firstly, it provides a comprehensive overview of the NSR's current regulations and support services. Secondly, it combines the information on the current status of the route with feedback received from the stakeholders during project discussions for the purpose of establishing several possible future operational models for transit shipping along the NSR. It is concluded that the most probable of the analyzed operational models is a combination of ice-strengthened vessels and independent ice-going cargo vessels. This model requires a decrease in severity of ice conditions to allow for year-round commercial navigation, an increase in bunker prices, further development of maritime infrastructure and icebreaking support, and the development of new maritime insurance models. Additionally, establishing transshipment hubs at each end of the NSR with ice-going cargo vessels sailing between them is also considered to be a viable future option.

Development of early warning index in Korean shipping industry by using signal approach

ABSTRACTThe study aims to investigate and prevent shipping companies’ insolvency that can be derived from depressed market sentiment in the shipping industry. For this purpose, the paper focuses on developing an early warning index. The main factors that contribute to the change in the shipping industry are derived. Sixty independent variables were accounted in the development of early warning index, some of which are shipping, shipbuilding, and finance. Suggestions drew from the result of early warning index are described as below. Firstly, the results of a signal approach towards independent variables showed lowest the signal error value in newbuilding price of containership, bunker price, Commodity Research Bureau (CRB) commodity index, and order book of bulkship. These indicators have proven to be a high level of variation factor during shipping markets’ crisis. Secondly, the early warning index confirmed that it preceded by 6 months compared to probability of default. Thirdly, in order to validate the accuracy of the early warning index, the adequacy of the model was tested using a mean square error and time lag correlations. As a result of the verification, value proved to be at a high level of compliance at 0.097.

Impact of maritime emissions trading system on fleet deployment and mitigation of CO2 emission

Global emissions trading system is an incentive-based instrument purported to achieve environmental objectives through allocating and auctioning GHG emission allowances. This paper investigates the potential impact of an open maritime emissions trading system (METS) on individual containership operators’ fleet composition strategies and CO2 emissions levels. A stochastic programming model that formulates the fleet composition problem is developed. It considers the outcomes (i.e. fleet composition and CO2 emissions of operators) of various scenarios by varying CO2 prices. Real-world data from the industry were collected for case experiments. The results demonstrate that METS can motivate operators to utilise new technologies, deploy more energy-carbon-efficient ships, and even lay up less energy-efficient ships. Furthermore, the efficacy of METS is more pronounced when bunker prices are high. The results also reveal that bunker prices have a larger effect on CO2 reduction than applying stricter CO2 allowance allocation. This study implicates the formulation of METS policies and provides recommendations to enhance the effectiveness of METS to achieve the shipping industry’s impetus to improve its environmental performance. It also offers guidelines for existing containership operators to minimise cost by managing their fleet compositions when METS is implemented.

확률변동성 모형을 적용한 해운산업의 벙커가격과 환율 리스크 추정

본 연구는 해운기업의 주요 비용요인 벙커 가격과 환율의 불확실성으로 인한 재무적 리스크를 수치화하는 방법론을 2010년 1월 1일부터 2018년 1월 31일까지의 일별자료를 대상으로 적용한다. 기하브라운 운동 (Geometric Brownian Motion 이하 GBM)과 이를 확장한 조건부 이분산성(heteroskedasticity) 및 점프 확산 프로세스(jump diffusion process)에 의존하는 모형으로부터 추정한 현금 흐름 리스크 추정치는 다음 세 가지 학술적 기여로 요약할 수 있다. 첫째, 운임수익률과 같은 단일 변수에 의존한 리스크 분석을 벙커가격과 환율 수익률 변동성과 같이 복합요인으로부터 발생하는 영향으로 분석을 확장하였다. 둘째, 개별기업 수준에서 벙커가격과 환율 리크스 관리의 필요성을 민감도 분석을 통해 현금흐름수준으로 제시하였다. 마지막으로 분석결과가 제시하는 리스크 규모를 근거로 해운기업은 리스크 관리를 위한 수단으로 무엇이 적절한가를 고민해야 할 필요성이 있음을 제기한다.

Jointly optimizing ship sailing speed and bunker purchase in liner shipping with distribution-free stochastic bunker prices

This paper jointly designs the optimal ship sailing speeds on shipping voyages and the optimal amount of bunker fuel to purchase at each port of a shipping network operated by a container liner shipping company. Bunker prices at these ports are assumed to be correlated random variables. Considering the difficulties in calibrating these prices into specific joint probability distribution in practice, this study merely requires some fundamental descriptive statistics information of these bunker prices, including lower and upper bounds, means and covariances, which can be tangibly estimated from historical data. To solve this problem, a mixed integer programming model is first formulated for deterministic bunker prices to minimize the sum of ship operating cost and bunker consumption cost. This model is subsequently extended to incorporate stochastic bunker prices by developing a series of approximation techniques using the bunker price descriptive statistics information. A numerical example based on real-case price data of a liner shipping network from an international shipping company shows that the proposed model is able to simultaneously control the average bunker purchase cost as well as the risk resulting from the extremely high bunker prices.

Understanding volatility and leverage effects in bunker markets

The research analysed the volatility and the leverage effect in the bunker prices in IFO 380 and IFO 180 grades. Limited studies were found on bunker prices using EGARCH and bivariate analysis. The research investigated the causal relationships between IFO 380 and IFO 180 grades of bunkers. Serial correlations were found through using Lagrange multiplier (LM test) and heteroskedastic tests in the bunker price data. The analysis exhibited the model to be highly significant at 1%. Both the grades showed the short and long-run shocks and a positive leverage effect. The recommendations were made to the maritime industry to strategise during the long run and short-run shocks of bunker markets.

Impacts of vessel speed on bunker cost in short sea shipping: A cross-examination

Air emissions of shipping have decreased in SECA regions after 2015. Environmental impacts of regulation policies are identifiable and measurable, as a study from the port of Gothenburg recently exposed. In addition to emissions, it is important to understand how regulations and decisions, focusing on environmental sustainability, impact on the maritime business indicators such as transport costs and marginal revenues. When SECA decision was made, the oil and bunker prices were historically high. Prices were also highly volatile. Numerous studies estimated difficulties for shipping companies, and maritime transport dependent export companies. The oil price dropped dramatically in 2016 remaining at the same level till the early 2018. This empirical case study examines and simulates bunker price data in relation to different vessel speeds. The paper looks at how different speed and oil price combinations impact transport costs and export business. The results of this simulation can be summarized with the notion that the negative economic impacts of the oil price variation can be mitigated to some extent by using lower vessel speeds, thus slow steaming, on short sea shipping (SSS). However, variable relations are not straightforward and they require additional studies.

Can an Emission Trading Scheme Really Reduce CO2 Emissions in the Short Term? Evidence from a Maritime Fleet Composition and Deployment Model

Global warming has become one of the most popular topics on this planet in the past decades, since it is the challenge that needs the efforts from the whole mankind. Maritime transportation, which carries more than 90% of the global trade, plays a critical role in the contribution of green house gases (GHGs) emission. Unfortunately, the GHGs emitted by the global fleet still falls outside the emission reduction scheme established by the Kyoto Protocol. Alternative solutions are therefore strongly desired. Several market-based measures are proposed and submitted to IMO for discussion and evaluation. In this paper, we choose to focus on one of these measures, namely Maritime Emissions Trading Scheme (METS). An optimization model integrating the classical fleet composition and deployment problem with the application of ETS (global or regional) is proposed. This model is used as a tool to study the actual impact of METS on fleet operation and corresponding CO2 emission. The results of the computational study suggest that in the short term the implementation of METS may not guarantee further emission reduction in certain scenarios. However, in other scenarios with low bunker price, high allowance cost or global METS coverage, a more significant CO2 decrease in the short term can be expected.

Ship routing and freight assignment problem for liner shipping: Application to the Northern Sea Route planning problem

In recent years, the Northern Sea Route (NSR) has attracted significant attention with respect to liner shipping. In this research, we propose a general time-space network-based mathematical formulation to analyze the ship routing and freight assignment problem in liner shipping and apply it to NSR planning problem. To solve the resulting program, we propose a Lagrangian relaxation-based decomposition algorithm that facilitates the network features. Empirical results show that navigation skill, bunker price, delay penalty and service commitment are the primary factors that affect the NSR’s commercial practicability.

Global route choice and its modelling of dry bulk carriers based on vessel movement database: Focusing on the Suez Canal

This paper addresses global route choices for dry bulk carriers, focusing on the competitive situation between the Suez Canal (SC) and its competitors, such as the Panama Canal (PC) and the route via the Cape of Good Hope. The authors first establish a methodology and estimate the actual route choices for dry bulk carriers based on a vessel movement database, focusing on the share of SC transit for each pair of regions where the cargo originates and is destined to. Second, an aggregated logit model is applied to predict the estimated shares by regional pair and is utilised in sensitivity analyses for simulations that consider the recent changes in the dry bulk shipping market such as the PC's expansion, the decline in bunker prices, and the suppression of Somali piracy risk. The results suggest that the proposed methodology is useful for estimating the share of SC transit by regional pair and that the model for describing route choice is validated through not only output indices such as prediction rate but also sensitivity analyses, a time transferability check, and a comparison with the results of a shortest-path model.

Short-term liner shipping bunker procurement with swap contracts

ABSTRACTThe bunker price fluctuations in recent years have severely threatened the stability of liner shipping companies’ operations. As an efficient countermeasure, the swap contract is widely adopted throughout the liner shipping industry to hedge the procurement risk resulting from the bunker price fluctuation. This paper looks at the short-term liner shipping bunker procurement problem with swap contracts (BPPSC), aiming to optimally plan the amount of bunker purchased from the spot market and the amount hedged by the swap contract for several months ahead. This BPPSC is first formulated as a bunker procurement cost mean-variance minimization (MVM) model, and is subsequently solved using a tangible two-step approach developed in this study. In the first step, the movements of the swap contract price and the spot market price of the bunker are described using a calibrated multivariate generalized autoregressive conditional heteroskedasticity (mGARCH) time series model. In the second step, the MVM model is approximated and solved by a price scenario tree constructed from the mGARCH time series model. A numerical example shows that the risk hedging strategy obtained can simultaneously control the bunker procurement cost as well as the procurement risk from price fluctuations. This article is a revised and expanded version of the abridged eight-page paper entitled ‘Optimal hedging for liner bunker procurement’ presented at ‘2015 International Conference on Logistics and Maritime Systems (LOGMS 2015)’, Hong Kong, 27–29 August 2015.

Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm

Short-term prediction of tanker freight rates (TFRs) is strategically important to stakeholders in the oil shipping industry. This study develops a hybrid TFR prediction model based on an artificial neural network (ANN) and an adaptive genetic algorithm (AGA). The AGA adaptively searches satisficing network parameters such as input delay size. The ANN iteratively optimizes a prediction network considering parsimonious variables and time-lag effects as predictors. Three parsimonious variables (crude oil price, fleet productivity and bunker price) are selected by a stepwise regression of TFR variables. The article compares the performance of its hybrid model with two traditional approaches (regression and moving average), as well as with the findings of existing ANN studies. The results of our model (root mean squared error (RMSE)=11.2 WS) are not only significantly superior to the regression approach (RMSE=21.6 WS) and the moving average approach (RMSE=17.5 WS), but are even slightly superior to the results of existing ANN studies (RMSE=14.6 WS–15.8 WS).