Flood risk assessment approaches have traditionally been dominated by measures of economic damage. However, the importance of understanding the social impacts of flooding are increasingly being acknowledged. Social vulnerability indices have been constructed in various geographical contexts to understand the relative susceptibility of different social groups to flood hazards. However, integrated assessments of social vulnerability, exposure, and hazard information are lacking. Here, we construct a national social vulnerability index (SVI) for Denmark and combine this with direct and indirect social exposure data and coastal flood hazard data to construct a national social flood risk index (SFRI). Results show the spatial distribution of social flood vulnerability and social flood risk in Denmark. Our findings illustrate that including social data in flood risk assessment could significantly change our understanding of flood risk on a national scale. Methodologically, our work introduces a comprehensive flood risk modeling approach that explicitly considers the social impacts of flooding in all model components. The application of this model in Denmark reveals that the social impacts of flooding extend far beyond flooded areas, thus highlighting the importance of explicitly considering direct and indirect social exposure in addition to social vulnerability in flood risk assessment. By introducing a comprehensive, socially specific approach to flood risk assessment that is usable within existing risk management frameworks such as the EU Floods Directive, our work aims to mainstream social wellbeing, resilience, and justice as central considerations in decision making on flood risk management.
This paper explores the potential of using green, autonomous ships in revitalizing inland shipping in Europe against the backdrop of declining market share and the dominance of "economy-of-scale" in waterborne freight transportation. It assesses the economic and environmental viability of converting freight from road to waterborne modalities in broader business ecosystems, specifically along the Rotterdam-Ghent corridor. The analysis leverages operational and commercial insights from logistics firms, ports and terminal operators, combined with data on European goods flows by road, and accounts for operational, financial and environmental variables including realistic scenario building and ecosystem implications. Findings indicate that inland shipping in general and green, autonomous shipping in particular offer both economically and environmentally viable alternatives to road transport. The study calls for further research into green, autonomous ships from an ecosystem perspective as a potential solution to current challenges in sustainable freight transportation.
Results from life cycle assessment (LCA) studies are sensitive to modeling choices and data used in building the underlying model. This is also relevant for the case of fisheries and LCAs of fish products. Fisheries' product systems show both multifunctionality because of the simultaneous co-catch of multiple species and potential constraints to supply due to natural stock limits or socially established limits such as quota systems. The performance of fisheries also varies across seasons, locations, vessels, and target species. In this study, we investigate the combined effect of modeling choices and variability on the uncertainty of LCA results of fish products. We use time series data from official Danish statistics for catch and fuel use of several fisheries disaggregated using a top-down procedure. We apply multiple modeling approaches with different assumptions regarding the type of partitioning, substitution, and constraints. The analysis demonstrates that, in the presence of relevant multifunctionality, the results are substantially affected by the modeling approach chosen. These findings are robust across years and fisheries, indicating that modeling choices contribute to uncertainty more than the variability in fishing conditions. We stress the need for a more careful alignment of research questions and methods for LCA studies of fisheries and recommend a very transparent statement of assumptions, combined with uncertainty and sensitivity analysis. This article met the requirements for a gold-gold data openness badge described at http://jie.click.badges.
The introduction of Marine Non-Indigenous Species (NIS) poses a significant threat to global marine biodiversity and ecosystems. To mitigate this risk, the Ballast Water Management Convention (BWMC) was adopted by the UN International Maritime Organisation (IMO), setting strict criteria for discharges of ballast water. However, the BWMC permits exemptions for shipping routes operating within a geographical area, known as a Same-Risk-Area (SRA). An SRA can be established in areas where a risk assessment (RA) can conclude that the spread of NIS via ballast water is low relative to the predicted natural dispersal. Despite the BWMC's requirement for RAs to be based on modelling of the natural dispersal of NIS, no standard procedures have been established. This paper presents a methodology utilizing biophysical modelling and marine connectivity analyses to conduct SRA RA and delineation. Focusing on the Kattegat and Øresund connecting the North Sea and Baltic Sea, we examine two SRA candidates spanning Danish and Swedish waters. We provide an example on how to conduct an RA including an RA summary, and addressing findings, challenges, and prospects. Our study aims to advance the development and adoption of consistent, transparent, and scientifically robust SRA assessments for effective ballast water management.
Operational cycles for maritime transportation is a new concept to improve the assessment of ships’ energy efficiency and offer benchmarking options among similar ship types and sizes. This work extends previous research to consolidate the methodology, bring more comprehensiveness, and provide a more holistic assessment of these operational cycles. The cycles are designed from noon reports from a fleet of around 300 container ships divided into eight size groups. The comparison between cycles derived from speed and draft with those based on main engine power identifies that the cycles based on speed and draft are more accurate and allow for estimating the Energy Efficiency Operational Index but require more data. The main-engine-power cycles are more effective in benchmarking through the Annual Efficiency Ratio. These cycles reduce the inherent variability of the carbon intensity indicator and present good opportunities as a benchmarking tool for strengthening the regulatory framework of international shipping.
Since the outbreak of COVID-19, its impacts on the maritime transportation and logistics field have been multi-dimensional. In addition to the green shipping corridor proposed by the Clydebank Declaration in the United Kingdom in 2021, port digitalisation and decarbonisation of the maritime industry have become focal issues in the field. The industry needs a new framework to offset the negative impacts of the pandemic and to accommodate integrated technologies comprising of artificial intelligence (AI), blockchain, cloud systems, internet of things (IoT) and others, which have been applied to the industry. Having considered these circumstances, this paper aims to propose the 6th-generation ports model with smart port (6GP) as a new framework for the port logistics industry in the post-COVID-19 period. The proposed 6GP contributes to providing business development strategy and port development policy for stakeholders in the industry in the post-pandemic era reflecting focal challenges such as digitalisation, decarbonisation, sustainability and smart transformation. It also contributes to expanding port devolution theory from the fifth-generation ports (5GP) to 6GP.
An efficient extreme ship response prediction approach in a given short-term sea state is devised in the paper. The present approach employs an active learning reliability method, named as the active learning Kriging + Markov Chain Monte Carlo (AK-MCMC), to predict the exceedance probability of extreme ship response. Apart from that, the Karhunen-Loève (KL) expansion of stochastic ocean wave is adopted to reduce the number of stochastic variables and to expedite the AK-MCMC computations. Weakly and strongly nonlinear vertical bending moments (VBMs) in a container ship, where the former only accounts for the nonlinearities in the hydrostatic and Froude-Krylov forces, while the latter also accounts for the nonlinearities in the radiation and diffraction forces together with slamming and hydroelastic effects, are studied to demonstrate the efficiency and accuracy of the present approach. The nonlinear strip theory is used for time domain VBM computations. Validation and comparison against the crude Monte Carlo Simulation (MCS) and the First Order Reliability Method (FORM) are made. The present approach demonstrates superior efficiency and accuracy compared to FORM. Moreover, methods for estimating the Mean-out-crossing rate of VBM based on reliability indices derived from the present approach are proposed and are validated against long-time numerical simulations.
The climate emergency has prompted rapid and intensive research into sustainable, reliable, and affordable energy alternatives. Offshore wind has developed and exceeded all expectations over the last 2 decades and is now a central pillar of the UK and other international strategies to decarbonise energy systems. As the dependence on variable renewable energy resources increases, so does the importance of the necessity to develop energy storage and nonelectric energy vectors to ensure a resilient whole-energy system, also enabling difficult-to-decarbonise applications, e.g. heavy industry, heat, and certain areas of transport. Offshore wind and marine renewables have enormous potential that can never be completely utilised by the electricity system, and so green hydrogen has become a topic of increasing interest. Although numerous offshore and marine technologies are possible, the most appropriate combinations of power generation, materials and supporting structures, electrolysers, and support infrastructure and equipment depend on a wide range of factors, including the potential to maximise the use of local resources. This paper presents a critical review of contemporary offshore engineering tools and methodologies developed over many years for upstream oil and gas (O&G), maritime, and more recently offshore wind and renewable energy applications and examines how these along with recent developments in modelling and digitalisation might provide a platform to optimise green hydrogen offshore infrastructure. The key drivers and characteristics of future offshore green hydrogen systems are considered, and a SWOT (strength, weakness, opportunity, and threat) analysis is provided to aid the discussion of the challenges and opportunities for the offshore green hydrogen production sector.
The BBNJ Agreement will affect legal frameworks for the conservation of marine biological diversity in various regions of the world ocean and the marine Arctic is no exception. As biological diversity in the marine Arctic is particularly vulnerable, the implications of the BBNJ Agreement for the conservation of biological diversity in the marine Arctic deserves serious consideration. Of particular note is the procedure for an environmental impact assessment (EIA). Given that damage to the environment may be irreversible, it is a prerequisite to conduct an EIA before authorizing planned activities, with a view to preventing environmental harm. An EIA constitutes a crucial element in the conservation of the marine environment, including biological diversity. Hence, this article examines the potential implications of the procedure for an EIA as set out under the BBNJ Agreement for the conservation of biological diversity in the marine Arctic beyond national jurisdiction.
An adaptive machine learning framework is established for an implicit determination of the performance degradation of a ship due to marine growth, i.e., biofouling. The framework is applied in a case study considering telemetry data of a cruise ship operating predominantly in the Caribbean Sea. The dataset encompasses seven years including three dry-docking intervals and several in-water cleaning events. The COVID-19 period receives special focus due to the drastic change in the operational profile. A main outcome of the study is a comparison of the derived performance estimate to the corresponding results of the industry standard ISO 19030. Additional aspects of the present study include the use of special regularization techniques for incremental machine learning and the increase of transparency through the implementation of prediction intervals indicating model uncertainty. Overall, it is found that the developed machine learning framework shows good agreement with the industry standard underlining its plausibility.