The literature on climate change in the maritime transport industry has grown rapidly in the last few years. Yet as the research agenda has progressed, scientific debates have become more isolated and fragmented, making it difficult to translate new findings into broader policy debates. This article draws on problematization methodology to help organize the scientific debate on maritime emissions and to identify analytical gaps and challenges. We argue that scholars investigate shipping's emission problem from four distinct analytical perspectives— (1) international laws and regulations, (2) markets and economics, (3) engineering and technology, and (4) authority and legitimacy. Each of these perspectives problematizes maritime emissions in specific ways, leading to different policies and strategies to address the problem. We call for better integrating these four literatures and highlight three crosscutting areas and problems for future research. First, developing institutions that facilitate market and engineering solutions; second, integrating climate mitigation and adaptation research; and third, focusing on justice concerns to ensure an equitable green transition in the maritime industry.
Global climate change, which is largely attributed to human activity, is one of the foremost challenges of the 21st century. In recent times, there have been notable alterations in the Earth's climate, resulting in profound impacts on ecosystems and biodiversity. These alterations are caused by greenhouse gas, such as carbon dioxide, methane, and nitrous oxide. Greenhouse gas emissions are caused by practices such as deforestation, industrial operations, and the combustion of fossil fuels in vehicles, vessels, aircraft, and manufacturing facilities. The maritime and aviation industry is currently responsible for approximately 6% of global greenhouse gas emissions. Due to logistical and economic constraints, these industries are heavily reliant on liquid fuels, making direct electrification options unavailable for large parts of these sectors. As a result, these sectors are considered ‘hard to abate’. Understanding the future climate mitigation challenges associated with the maritime and aviation sectors is crucial in shaping effective policy measures, avoiding stranded assets, and preserving the chance to meet Paris Agreement-compatible emission reduction pathways.
This thesis identifies three main challenges and proposes modelling approaches to address them when modelling decarbonization pathways for the aviation and maritime sectors. From these challenges, research gaps have been identified that this PhD thesis aims to fill. Three models have been developed for the thesis: a maritime optimization model, a maritime demand model, and an aviation demand model. The modelling landscape and methodology vary across models, ranging from econometrics and data science to mathematical optimization.
To overcome the challenges and fill in the research gaps, three corresponding modelling approaches have been successfully applied:
1. Developing a holistic decarbonization modelling landscape. This includes life-cycle representations of technology costs and emissions, the upscaling of bottleneck technologies, the availability of sustainable biomass, and consideration of competing demand from other industries, as well as representations of policy levers such as carbon pricing or improvements to fuel efficiency.
2. Developing demand models that interpret the underlying scenario narrative consistently (SSP framework).
3. Improving the representation of technological learning for low-carbon technologies in energy system models.
The findings acquired by applying these three modelling approaches are valuable for energy modellers, climate scientists, and policymakers and offer unique insights into the inherent system dynamics associated with decarbonization of hard-to-abate sectors. Utilizing this modelling landscape reveals that current decarbonization efforts for hard-to-abate sectors are insufficient.
The maritime industry is a crucial hard-to-abate sector that is expected to depend on high-energy density renewable liquid fuels in the future. Traditionally, decarbonization pathways have been assessed assuming exogenous cost trajectories for renewable liquid fuels based on an exogenous learning curve. While past studies have looked at the impact of endogenizing learning curves for a specific technology utilizing linear approximation, a fully endogenous direct non-linear implementation of learning curves in a detailed sectoral model (maritime industry) that explores dynamics concerning sensitive parameters does not yet exist. Here, we apply an open-source optimization model for decarbonizing the maritime industry and further develop the model by encompassing a nonconvex mixed-integer quadratically constrained programming approach to analyze the impact of endogenized learning curves for renewable fuel costs following an experience curve approach. We find that global greenhouse gas emissions are significantly lower (up to 25% over a 30 year horizon) when utilizing endogenously modeled prices for renewable fuels compared to commonly used exogenous learning frameworks. Furthermore, we find that conventional modeling approaches overestimate the cost of climate mitigation, which can have significant policy implication related to carbon pricing and fuel efficiency requirements. In a broader context, this emphasizes the potential opportunities that can be achieved if policymakers and companies accelerate investments that drive down the costs of renewable technologies efficiently and thus trigger endogenous experience-based learning in real life.
Maritime transportation is an essential pillar of modern societies, serving as the backbone of global trade. The shipping industry relies heavily on fossil fuels, significantly impacting the environment and contributing to climate change. The International Maritime Organization (IMO) has introduced a strategy to reduce greenhouse gas emissions from international shipping and decarbonize the industry to combat this issue. This strategy aims to accomplish energy efficiency gains, transition to alternative fuels, and implement market-based measures.
Various energy efficiency indicators are in use to monitor the performance of ships, both from technical and operational perspectives. Building upon previous research that identified shortcomings in these indicators, this thesis investigates alternative methods of assessing the energy efficiency of ships. Emphasizing the importance of a benchmarking tool, the primary objective of this thesis is to contribute to the policy debate on reducing emissions in international shipping by developing a comprehensive carbon intensity indicator.
The thesis comprises four articles addressing various approaches to monitoring ship carbon emissions. The first article focuses on the influence of weather conditions on a ship’s energy efficiency, thereby contributing to the ongoing discussion on weather correction factors. Using model-based machine learning techniques, this article illustrates the diverse sea conditions encountered, their impact on energy efficiency, and the necessity of accounting for this diversity through multiple correction factors.
The second and third articles introduce and develop the concept of operational cycles for maritime transportation, drawing inspiration from the driving cycles employed in the automotive industry. The second article describes the process of generating operational cycles for the maritime sector as a novel concept. It validates this concept using real-world data obtained from a fleet of container ships. Building upon this foundation, the third article extends the concept by elaborating more comprehensive cycles that better represent real-world indicators.
The fourth article explores voluntary reporting frameworks in the shipping industry. It focuses on the Clean Cargo case and investigates the needs and interests of its members regarding this private initiative and related reporting framework. The discussion revolves around the role of these voluntary frameworks as complementary approaches to regulatory frameworks towards maritime decarbonization.
Based on the methodology developments and analysis through the thesis, the following key findings and recommendations are presented:
• The weather impact on ships’ fuel consumption prevents an accurate and real assessment of ships’ efficiency. Multiple weather correction factors for energy efficiency indicators introduce a novel approach.
• Inspired by the automotive industry, maritime operational cycles improve the assessment of technical and operational aspects of a ship’s energy efficiency. The cycles reduce the variability inherent to energy
efficiency indicators and are suitable as benchmarking tools.
• Although the IMO regulatory framework remains at the core of the maritime decarbonization strategy, regional regulatory frameworks and private initiatives have demonstrated their capacity to enhance industry
practices and facilitate regulatory developments.
This thesis contributes to enhancing carbon emissions monitoring in the maritime industry by introducing new methodologies and assessments. The resulting proposals are designed to enrich ongoing discussions within the IMO and complement the existing regulatory frameworks.
Studies have indicated that transportation noise is associated with higher cardiovascular mortality, whereas evidence of noise as a risk factor for respiratory and cancer mortality is scarce and inconclusive. Also, knowledge on effects of low-level noise on mortality is very limited. We aimed to investigate associations between road and railway noise and natural-cause and cause-specific mortality in the Danish population. We estimated address-specific road and railway noise at the most (LdenMax) and least (LdenMin) exposed façades for all residential addresses in Denmark from 1990 to 2017 using high-quality exposure models. Using these data, we calculated 10-year time-weighted mean noise exposure for 2.6 million Danes aged >50 years, of whom 600,492 died from natural causes during a mean follow-up of 11.7 years. We analyzed data using Cox proportional hazards models with adjustment for individual and area-level sociodemographic variables and air pollution (PM2.5 and NO2). We found that a 10-year mean exposure to road LdenMax and road LdenMin per 10 dB were associated with hazard ratios (95% confidence intervals) of, respectively, 1.09 (1.09; 1.10) and 1.10 (1.10; 1.11) for natural-cause mortality, 1.09 (1.08; 1.10) and 1.09 (1.08; 1.10) for cardiovascular mortality, 1.13 (1.12; 1.14) and 1.17 (1.16; 1.19) for respiratory mortality and 1.03 (1.02; 1.03) and 1.06 (1.05; 1.07) for cancer mortality. For LdenMax, the associations followed linear exposure-response relationships from 35 dB to 60–<65 dB, after which the function levelled off. For LdenMin, exposure-response relationships were linear from 35 dB and up, with some levelling off at high noise levels for natural-cause and cardiovascular mortality. Railway noise did not seem associated with higher mortality in an exposure-response dependent manner. In conclusion, road traffic noise was associated with higher mortality and the increase in risk started well below the current World Health Organization guideline limit for road traffic noise of 53 dB.
The Belt and Road Initiative (BRI) entails investments to improve overland (rail) transport between Europe and China. This paper introduces a microscopic Multi-Commodity Flow Service Selection Problem for freight transport under the BRI and provides a decision tool for shippers to make door-to-door service plans. The minimizing objective function considers transportation costs, in-transit inventory costs, and carbon emissions. A series of sampled data of each provincial region of China are collected from Chinese multimodal transport operators. Results show that inland regions are strongly attracted to the rail mode for shipments to Europe. However, the “last mile” (including “first mile”) transport from the shipper to the long-haul transport terminal strongly influences this choice, and carbon emissions are strongly influenced by the available last mile transport links. Under the dual impact of in-transit inventory and carbon emission costs, regions that prefer rail to maritime are much further east than suggested by previous literature.
The European maritime transport policy recognizes the importance of the waterborne transport systems as key elements for sustainable growth in Europe. A major goal is to transfer more than 50% of road transport to rail or waterways within 2050. To meet this challenge waterway transport needs to get more attractive and overcome its disadvantages. Therefore, it is necessary to develop new knowledge and technology and find a completely new approach to short sea and inland waterways shipping. A key element in this is automation of ships, ports and administrative tasks aligned to requirements of different European regions. One main goal in the AEGIS project is to increase the efficiency of the waterways transport with the use of higher degrees of automation corresponding with new and smaller ship types to reduce costs and secure higher frequency by feeders and provide multimodal green logistics solutions combining short sea shipping with rail and road transport.
Policy makers often need support for evaluating transportation and logistics system performance, and for understanding the long-term effects and relationships between transportation investments, system performance and economic growth, both at the regional and national levels (Banister and Berechman, 2001; Laird and Venables, 2017). The economic evaluation of system performance, risks and barriers come paradigmatically together during the processes of transport systems investment choice decision
In this study, the periodic train timetabling problem is formulated using a time-space graph formulation that exploits the properties of a symmetric timetable. Three solution methods are proposed and compared where solutions are built by what we define as a dive-and-cut-and-price procedure. An LP relaxed version of the problem with a subset of constraints is solved using column generation where each column corresponds to the train paths of a line. Violated constraints are added by separation and a heuristic process is applied to help to find integer solutions. The passenger travel time is computed based on a solution timetable and Benders’ optimality cuts are generated allowing the method to integrate the routing of the passengers. We propose two large neighborhood search methods where the solution is iteratively destroyed and repaired into a new one and one random iterative method. The problem is tested on the morning rush hour period of the Regional and InterCity train network of Zealand, Denmark. The solution approaches show robust performance in a variety of scenarios, being able to find good quality solutions in terms of travel time and path length relatively fast. The inclusion of the proposed Benders’ cuts provide stronger relaxations to the problem. In addition, the graph formulation covers different real-life constraints and has the potential to easily be extended to accommodate more constraints.
The shipping industry is associated with approximately three quarters of all world trade. In recent years, the sustainability of shipping has become a public concern, and various emissions control regulations to reduce pollutants and greenhouse gas (GHG) emissions from ships have been proposed and implemented globally. These regulations aim to drive the shipping industry in a low-carbon and low-pollutant direction by motivating it to switch to more efficient fuel types and reduce energy consumption. At the same time, the cyclical downturn of the world economy and high bunker prices make it necessary and urgent for the shipping industry to operate in a more costeffective way while still satisfying global trade demand. As bunker fuel bunker (e.g., heavy fuel oil (HFO), liquified natural gas (LNG)) consumption is the main source of emissions and bunker fuel costs account for a large proportion of operating costs, shipping companies are making unprecedented efforts to optimize ship energy efficiency. It is widely accepted that the key to improving the energy efficiency of ships is the development of accurate models to predict ship fuel consumption rates under different scenarios. In this study, the ship fuel consumption prediction models presented in the literature (including the academic literature and technical reports, which are a typical type of “grey literature”) are reviewed and compared, and models that optimize ship operations based on fuel consumption prediction results are also presented and discussed. Current research challenges and promising research questions on ship performance monitoring and operational optimization are identified.