Tropical cyclones are highly destructive weather systems, especially in coastal areas. Tropical cyclones with maximum sustained winds exceeding 74 mph (≈119 kph) are classified as typhoons in the Northwest Pacific, whilst the term ‘hurricanes’ applies to other regions. This study aims to investigate the general characteristics of the most devastating and catastrophic tropical cyclones in the USA Europe, and Asia. To achieve the study objectives, the three most devastating typical tropical cyclones in each region were selected. The tropical cyclones were examined based on various features, such as the number of deaths, minimum pressure, highest wind speed, total financial losses, and frequency per year. In contrast to Europe and Asia, the USA has recorded the highest number of catastrophic tropical cyclones. The damage induced by hurricanes Katrina, Harvey, and Maria in the USA totalled approximately USD USD 380 billion. In addition, the present research highlights the demand to improve the public attitude and behaviour toward the impact of climate change along with the enhancement of climate change alleviation strategies. The number of intense tropical cyclones is expected to rise, and the tropical cyclone-related precipitation rate is expected to increase in warmer-climate areas. Stakeholders and industrial practitioners may use the research findings to design resilience and adaptation plans in the face of tropical cyclones, allowing them to assess the effects of climate change on tropical cyclone incidents from an academic humanitarian logistics viewpoint in the forthcoming years.

Inspired by the discontinuation of the blockchain platform TradeLens, co-developed by IBM and Maersk, due to the lack of the involved supply chain stakeholders’ adoption, a critical literature review on the models of supply chain stakeholders’ adoption of blockchain applications was conducted. This review is significant as it provides insights into the exploration of a more universal approach to investigate which factors really influence blockchain adoption, which is a pre-requisite for the technical sustainability of blockchain technology in supply chains. As observed in the review, the technology acceptance model (TAM), the technology–organization–environment (TOE) framework, and the unified theory of acceptance and use of technology (UTAUT) are frequently used in the literature, but little attention has been paid to whether blockchain technology fits the users’ tasks in understanding blockchain adoption in the supply chain. Among the technology adoption theories, task–technology fit (TTF) considers whether a technology fits the tasks, but only two previous studies involved the use of TTF. This study discusses the suitability of these existing models of technology adoption for blockchain applications in supply chains and comes up with a new unified model, namely TOE-TTF-UTAUT. This review also has implications for a more appropriate conceptual research design using mixed methods.

This paper focuses on the analysis of the COVID-19 effects on passenger shipping in Danish waters as an example and aims to analyse the differences in passenger vessel activities and emissions before and after the COVID-19 outbreak. Two sets of Automatic Identification System (AIS) data for the passenger ships sailing in Danish waters associated with the whole year respectively for 2020 and 2019 are used for a comprehensive evaluation of the passenger shipping activities in the region by means of the analysis of variance and bottom-up emission models. A comparison of those results based on the two datasets shows that the COVID-19 pandemic has a major impact on cruise ships, with a significant reduction in the number of ships, average speed, and average draught. In contrast, the pandemic has a smaller impact on ferry-pax only and ferry-ro pax vessels. The effects can also be seen from the fact that, after the COVID-19 outbreak, SOx emissions from cruise ships, ferry-pax only and ferry-ro pax vessels were reduced by 50.71%, 0.51% and 0.82%, respectively. This investigation provides an important reference for policy makers in the marine environment sector.

In the last six years, there has been a rise in research interest with regard to the applications of blockchain technology in supply chains and how these applications bring benefits to supply chain management. In a broader sense, an essential research focus that has been discussed in the literature is the way in which this emerging blockchain technology in supply chains brings sustainable benefits to a community. The rationale for incorporating cloud technology into a blockchain and integrating the blockchain with machine learning for supply chain applications is to maintain technical sustainability. While previous studies suggested and reported sustainable practices of applying blockchain technology in supply chains, the means with which these practices are brought about by the cloud-based blockchain integrated with machine learning (CBML) have not been thoroughly explored in the literature. The case study presented in this paper aims to fill this gap by exploring technically, environmentally, economically, and socially sustainable practices through the use cases of CBML for supply chain management by the international leading container shipping company Maersk. The use cases by Maersk presented in published documents were collected from the Internet and then analyzed. This document analysis was performed in two ways. The first way was a technical review of the blockchain technology used by Maersk with a consideration of technical sustainability to ensure scalability and big data analytics. The other way was to analyze the applications of the CBML by Maersk to indicate how environmental sustainability, economic sustainability, and social sustainability can be achieved. On the other hand, this paper also highlights the negative technical, environmental, economic, and social sustainability impacts caused by Maersk and discusses implications for future research directions.