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.
This chapter presents the latest development in digital platforms for data sharing in Maritime Informatics as discussed in chapter 1—Responding to humanitarian and global concerns with digitally enabled supply chain visibility. Specifically, we use the TradeLens digital data sharing platform as a case study to illustrate the key actors in containerised global transport and the technical set-up (including the utilisation of a hybrid cloud, permissioned blockchain, and data exchange standards), the benefits and challenges for the individual types of actors, and the overall potential and future challenges of the TradeLens platform.
The potential of data sharing platforms is dependent on the wide adoption of the ecosystem. Today, there is a high interest for the TradeLens ecosystem, and many actors have already adopted the platform, due to the vast variety of benefits it provides to all actors in global trade. Regardless, some actors seem to face internal obstacles to adopting the platform, which are either low or high technical advancement. For these actors, a paradigm shift is necessary to move from a reactive to a proactive scheme enabled by a near real-time supply and logistics data network. Finally, we discuss the challenges of network collaboration.
This study presents a novel approach to forecast freight rates in container shipping by integrating soft facts in the form of measures originating from surveys among practitioners asked about their sentiment, confidence or perception about present and future market development. As a base case, an autoregressive integrated moving average (ARIMA) model was used and compared the results with multivariate modelling frameworks that could integrate exogenous variables, that is, ARIMAX and Vector Autoregressive (VAR). We find that incorporating the Logistics Confidence Index (LCI) provided by Transport Intelligence into the ARIMAX model improves forecast performance greatly. Hence, a sampling of sentiments, perceptions and/or confidence from a panel of practitioners active in the maritime shipping market contributes to an improved predictive power, even when compared to models that integrate hard facts in the sense of factual data collected by official statistical sources. While investigating the Far East to Northern Europe trade route only, we believe that the proposed approach of integrating such judgements by practitioners can improve forecast performance for other trade routes and shipping markets, too, and probably allows detection of market changes and/or economic development notably earlier than factual data available at that time.
Enhancing environmental sustainability in maritime shipping has emerged as an important topic for both firms in shipping-related industries and policy makers. Speed optimization has been proven to be one of the most effective operational measures to achieve this goal, as fuel consumption and greenhouse gas (GHG) emissions of a ship are very sensitive to its sailing speed. Existing research on ship speed optimization does not differentiate speed through water (STW) from speed over ground (SOG) when formulating the fuel consumption function and the sailing time function. Aiming to fill this research gap, we propose a speed optimization model for a fixed ship route to minimize the total fuel consumption over the whole voyage, in which the influence of ocean currents is taken into account. As the difference between STW and SOG is mainly due to ocean currents, the proposed model is capable of distinguishing STW from SOG. Thus, in the proposed model, the ship’s fuel consumption and sailing time can be determined with the correct speed. A case study on a real voyage for an oil products tanker shows that: (a) the average relative error between the estimated SOG and the measured SOG can be reduced from 4.75% to 1.36% across sailing segments, if the influence of ocean currents is taken into account, and (b) the proposed model can enable the selected oil products tanker to save 2.20% of bunker fuel and reduce 26.12 MT of CO2 emissions for a 280-h voyage. The proposed model can be used as a practical and robust decision support tool for voyage planners/managers to reduce the fuel consumption and GHG emissions of a ship
The power system of an all-electric ship (AES) establishes an independent microgrid using the distributed energy resources, energy storage devices, and power electronic converters. As a hybrid energy system (HES), the power system of an AES works as a unified system where each part can affect the reliability of the other parts. The systemic reliability centered maintenance (SRCM), which efficiently enhances the reliability and safety of the AES by identifying optimal maintenance tasks of the AES, is considered in this article to apply to the entire system. In order to calculate the reliability and optimal maintenance schedule, the Markov process and Enhanced JAYA (EJAYA) are utilized. A layer of protection analysis (LOPA), which is a risk management technique, is adopted to assess the safety of the system. A hybrid molten carbonate fuel cell, photovoltaic (PV), and lithium-ion battery are considered as energy sources of the AES. Based on two common standards, DNVGL-ST-0033 and DNVGL-ST-0373, the suggested maintenance planning method can be used in industrial applications. Eventually, in order to validate the proposed method, a model-in-the-loop real-time simulation using dSPACE is carried out. The obtained results show the applicability and efficiency of the proposed method for improving reliability and safety.
Due to the increasing impacts of ships pollutants on the environment and the preventive laws that are tightening every day, the utilization of all-electric ships is a recent emerging technology. Being a promising technology, the usage of fuel cells as the main energy resource of marine vessels is an interesting choice. In this article, an all-electric hybrid energy system with zero emission based on fuel cell, battery, and cold-ironing is proposed and analyzed. To this end, actual data of a ferry boat, including load profiles and paths, are considered to assess the feasibility of the proposed energy system. The configuration of the boat and energy resources as well as the problem constraints are modeled and analyzed. Finally, the boat's energy management in hourly form for a one-day period is implemented. The improved sine cosine algorithm is used for the power dispatch optimization, and all models are implemented in MATLAB software. Based on the analysis results, the proposed hybrid system and the energy management method have high performance as an applicable method for the marine vessels. In addition, to be a zero-emission ship, the proposed system has an acceptable energy cost.
The utilization of green energy resources for supplying energy to ships in the marine industry has received increasing attention during the last years, where different green resource combinations and control strategies have been used. This article considers a ferry ship supplied by fuel cells (FCs) and batteries as the main sources of ship's power. Based on the designers' and owners' preferences, different scenarios can be considered for managing the operation of the FCs and batteries in all-electric marine power systems. In this article, while considering different constraints of the system, six operating scenarios for the set of FCs and batteries are proposed. Impacts of each proposed scenario on the optimal daily scheduling of FCs and batteries and operation costs of the ship are calculated using a mixed-integer nonlinear programming model. Model predictive control (MPC) is also applied to consider the deviations from hourly forecast demand. Moreover, since the efficiency of FCs varies for different output powers, the impacts of applying a linear model for FCs' efficiency are compared with the proposed nonlinear model and its related deviations from the optimal operation of the ship are investigated. The proposed model is solved by GAMS software using actual system data and the simulation results are discussed. Finally, detailed real-time hardware-in-the-loop (HiL) simulation outcomes and comparative analysis are presented to confirm the adaptation capability of the proposed strategy.
Nowadays, sea traveling is increasing due to its practicality and low-cost. Ferry boats play a significant role in the marine tourism industry to transfer passengers and tourists. Nevertheless, traditional ferry ships consume massive amounts of fossil fuels to generate the required energy for their motors and demanded loads. Also, by consuming fossil fuels, ferries spatter the atmosphere with CO2 emissions and detrimental particles. In order to address these issues, ferry-building industries try to utilize renewable energy sources (RESs) and energy storage systems (ESSs), instead of fossil fuels, to provide the required power in the ferry boats. In general, full-electric ferry (FEF) boats are a new concept to reduce the cost of fossil fuels and air emissions. Hence, FEF can be regarded as a kind of dc stand-alone microgrid with constant power loads (CPLs). This article proposes a new structure of a FEF ship based on RESs and ESSs. In order to solve the negative impedance induced instabilities in dc power electronic based RESs, a new intelligent single input interval type-2 fuzzy logic controller based on sliding mode control is proposed for the dc-dc converters feeding CLPs. The main feature of the suggested technique is that it is mode-free and regulates the plant without requiring the knowledge of converter dynamics. Finally, we conduct a dSPACE-based real-time experiment to examine the effectiveness of the proposed energy management system for FEF vessels.
All-electric ships, and especially the hybrid ones with diesel generators and batteries, have attracted the attention of maritime industry in the last years due to their less emission and higher efficiency. The variant emission policies in different sailing areas and the impact of physical and environmental phenomena on ships energy consumption are two interesting and serious concepts in the maritime issues. In this paper, an efficient energy management strategy is proposed for a hybrid vessel that can effectively consider the emission policies and apply the impacts of ship resistant, wind direction and sea state on the ships propulsion. In addition, the possibility and impact of charging and discharging the carried electrical vehicles’ batteries by the ship is investigated. All mentioned matters are mathematically formulated and a general model of the system is extracted. The resulted model and real data are utilized for the proposed energy management strategy. A genetic algorithm is used in MATLAB software to obtain the optimal solution for a specific trip of the ship. Simulation results confirm the effectiveness of the proposed energy management method in economical and reliable operation of the ship considering the different emission control policies and weather condition impacts.
Due to environmental and economic issues as well as the high performance of marine vessels, efficient energy using has been becoming more demanding. Also, in order to have a zero-emission ship, the utilization of a fuel cell combined with energy storage such as batteries gets more and more attention. In this work, a zero-emission hybrid energy system, including fuel cells, batteries, and cold-ironing, is employed to have an environmentally friendly vessel, and to create condition in which ship operates with high performance, both energy management and components sizing of fuel cells and batteries using real data of ferry boat and intelligent optimization method are done simultaneously. In addition, all constraints related to energy management and component sizing with the topography of the boat and electric power sources are represented and analyzed thoroughly. Ultimately, hourly energy management and component sizing for one specific day are considered in this work, and to optimize this problem, the Improved Sine Cosine Algorithm (ISCA) is utilized. According to obtained results, the proposed energy management and component sizing result in the high-performance ship which could be utilized in the marine industry.