Network effects govern the evolution of maritime trade

Industrial and textile waste trade: Multilayer network and environmental policy effects

Waste management is an international enterprise, and it is important to understand global flows of recyclable materials. The Pollution Haven Hypothesis (PHH) suggests that waste moves from high income nations with stringent environmental policy to low income nations with less environmentally stringent policy, by exploiting low labor and regulatory costs. This paper assesses the PHH thesis for slag/dross and textiles (SDT) wastes in PHH through novel integration of the multilayer network and gravity models. The multilayer network model generates network effect that quantifies interlayer connections of multiple waste trade networks. Instead of North-South movement of waste, North-North, South-South, and even South-North are shown. Results from the gravity model indicate that stringent waste management policies reduce both waste exports and imports. PHH is not found for slags/dross where high income countries are importing the waste, contradicting PHH. On the other hand, PHH is more evident between highly connected hubs and havens in SDT waste trade networks.

Observing network effect of shipping emissions from space: A natural experiment in the world's busiest port

Maritime trade and associated emissions are dynamic in nature. Although shipping emissions contribute significantly to air quality and climate change, their trade-governed dynamics remain less explored due to the lack of observational evidence. Here, we use satellite measurements to capture the redistribution of shipping nitrogen oxides (NOx ) emissions from Shanghai port, the world's busiest port, during a natural experiment posted by the localized COVID-19 lockdown in 2022. Viewing the ports as nodes in a network linked by ship journeys, we quantify a lockdown-induced - 42% reduction in shipping NOx emissions for Shanghai port. We further identify an emission transfer to its neighboring connected ports, confirmed by comprehensive vessel activity observations. Our study highlights the socioeconomic drivers of shipping emissions, which may add additional layers of complexity to air quality management.

The variation of PM2.5 from ship emission under low-sulfur regulation: A case study in the coastal suburbs of Kitakyushu, Japan

From January 1, 2020, the International Maritime Organization (IMO) regulation about the limit of fuel sulfur content to 0.5 % become effective, and ships commonly install sulfur scrubbers or use low-sulfur fuel or liquefied natural gas to replace sulfur-rich heavy fuel oil. In this study, the 4-year PM_2.5 sampling in the coastal suburbs of Kitakyushu, Japan clearly indicated the significant effects of relevant regulation and countermeasures on particle emissions in this receptor site. From the perspective of air quality, an obvious decrease in the mass concentration of ship-emitted particles was observed in 2020, and the contribution of sulfate could reach 60 %. The ammonium concentration was mainly controlled by sulfate and nitrate, and its reduction also could not be ignored, accounting for about 17 %. In terms of public health, the particle exposure risk also changed greatly, mainly due to the reduction of risk levels for As, W, Sb, V, Ni, and Cd; the lowest non-carcinogenic risk and carcinogenic risk for both adults (HI = 1.2 and CR = 5.7 × 10^-5) and children (HI = 9.9 and CR = 1.1 × 10^-4) all occurred in 2020. However, these reduced health risks were still not within the safe level (except for the carcinogenic risk for adults), a fact that requires continued attention. This result exposed the deficiency of current countermeasures regarding the IMO's fuel sulfur content limit in Kitakyushu City, and increasing the proportion of ships using clean fuels (liquefied natural gas, methanol, etc.) would surely alleviate the particle pollution caused by ship emissions.

Water search and rescue (SAR) for ship accidents in China: analysis of 12 years' data

Background

Maritime search and rescue (SAR) remains a great global challenge because of the long distances, harsh environment and complicated trauma. A systematic investigation and analysis of China Maritime Search and Rescue Center (CMSRC) data has been lacking. This study aimed to provide more insightful information for future development of a better maritime and aquatic SAR system in China.

Methods

This retrospective study retrieved and analyzed data on the water traffic volume from The Ministry of Transportation, People's Republic of China website and SAR data on ship accidents between January 1, 2008 and December 31, 2019. Spearman's correlation test was performed to analyze the data for the number of ship accidents, number of persons in distress, number of rescued persons, SAR success rate, and SAR forces. The χ² test was used to assess significant changes in the proportion of ship accident locations, categories, dispatched SAR forces, and location of deaths annually. The Cox Stuart test was applied to determine the trends in the data from 2008 to 2019.

Results

Between 2008 and 2019, a total of 24,013 ship accidents were reported and recorded by the CMSRC surveillance system; 209,948 persons in distress because of ship accidents were reported; 8,051 individual deaths from ship accidents. Water traffic volume and ship carrying capacity increased while the annual number of persons in distress, ship collisions, and ship collision-related deaths decreased. The SAR success rate (96.17%±0.92%) did not improve despite an increase in the number of rescue ships dispatched during this period. Helicopters (92.40±20.58 min) arrived faster than rescue ships (283.75±40.96 min) but the dispatched number of helicopters did not increase during this period. The average arrival time of nearby passing ships (41.90±7.98 min) was the shortest.

Conclusions

CMSRC efforts mitigated the growth of ship accidents despite increasing traffic volume. More dispatched rescue ships did not improve the SAR success rate without saving arrival time. Future SAR protocols may need to increase and strengthen the role of helicopters and nearby passing ships to improve the SAR success rate.

Global Container Port Network Linkages and Topology in 2021

The maritime transport of containers between ports accounts for the bulk of global trade by weight and value. Transport impedance among ports through transit times and port infrastructures can, however, impact accessibility, trade performance, and the attractiveness of ports. Assessments of the transit routes between ports based on performance and attractiveness criteria can provide a topological liner shipping network that quantifies the performance profile of ports. Here, we constructed a directed global liner shipping network (GLSN) of the top six liner shipping companies between the ports of Africa, Asia, North/South America, Europe, and Oceania. Network linkages and community groupings were quantified through a container port accessibility evaluation model, which quantified the performance of the port using betweenness centrality, the transport impedance among ports with the transit time, and the performance of ports using the Port Liner Shipping Connectivity Index. The in-degree and out-degree of the GLSN conformed to the power-law distribution, respectively, and their R-square fitting accuracy was greater than 0.96. The community partition illustrated an obvious consistence with the actual trading flow. The accessibility evaluation result showed that the ports in Asia and Europe had a higher accessibility than those of other regions. Most of the top 30 ports with the highest accessibility are Asian (17) and European (10) ports. Singapore, Port Klang, and Rotterdam have the highest accessibility. Our research may be helpful for further studies such as species invasion and the planning of ports.

Resilience Analysis of Maritime Silk Road Shipping Network Structure under Disruption Simulation

As an important hub in the maritime transportation system, ports are vulnerable to events such as terrorist attacks, security accidents and bad weather. The failure of port nodes to function effectively affects the connectivity and efficiency of the shipping network and impedes trade between countries. In view of this, in this paper, we constructed the Maritime Silk Road shipping network based on route data and used transmissibility and diversity to represent the resilience of the network and nodes. Then, we analyzed the variation characteristics of resilience using disruption simulation and identified 9 dominant nodes and 15 vulnerable nodes that could help to accurately determine the factors that affect the resilience of the MSR shipping network structure. The results show that the Maritime Silk Road shipping network structure is vulnerable, and the failure of ports to function has different effects on network transmissibility and diversity. In terms of node transmissibility and diversity, there are differences in the resistance of port nodes to interventions. In addition, the failure of dominant ports to function and the emergence of vulnerable ports are significant factors that weaken the resilience of the network structure. When dominant ports are interrupted, this greatly affects the resilience of the network structure. It is necessary to reduce the possibilities of the failure of dominant ports. Vulnerable ports are weaknesses in the resilience of the network structure, which weaken the ability of the network to function. The centrality of these ports should be strengthened, and their relation to regional and trans-regional links should be enriched. The research results provide a scientific basis for ensuring the structural resilience of the Maritime Silk Road shipping network.

Global shipping network dynamics during the COVID-19 pandemic's initial phases

Catastrophic incidents can significantly disrupt supply chains, but most of these disruptions remain localized. It was not until the onset of COVID-19 that a disruption in our lifetimes achieved a global magnitude. In order to contain the pandemic, governments around the world resorted to closing borders, shutting down manufacturing plants, and imposing lockdowns, which resulted in disrupted production capabilities and weakened consumer spending. The effects of these measures have been clearly visible in global transport networks, where disruptions ripple through the system and serve as a precursor to the disruptions in the broader economy. In this study, we use liner shipping schedule cancellations, a form of serious transport network disruption, as distress signals of the pandemic's impact on global supply chains. Our study applies a three-stage approach and provides insights into operator behaviors when under distress. We show that the pandemic challenged service network integrity and that network disruptions first clustered in Asia before rippling along main trade routes. Agile liner shipping operations, aided by planned service suspensions, prevented the collapse of the global maritime transport networks and indicated the maritime industry's ability to withstand even major catastrophic incidents.

Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation

This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. While the integration of time within GIS has long been a domain of major interest, alternative modelling and data manipulation approaches derived from graph and knowledge-based principles provide many opportunities for many application domains. We first survey graph principles and how they have been applied to GIS and a few representative domains to date. A comprehensive analysis of the principles behind K-graphs, respective data representation and manipulation capabilities is discussed. The perspectives offered by a close integration of ST-graphs and K-graphs are explored. The whole approach is illustrated and discussed in the context of maritime transportation.

Topological clustering of multilayer networks

Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. In this paper, we introduce the concepts of topological data analysis to studies of complex multilayer networks and propose a topological approach for network clustering. The key rationale is to group nodes based not on pairwise connectivity patterns or relationships between observations recorded at two individual nodes but based on how similar in shape their local neighborhoods are at various resolution scales. Since shapes of local node neighborhoods are quantified using a topological summary in terms of persistence diagrams, we refer to the approach as clustering using persistence diagrams (CPD). CPD systematically accounts for the important heterogeneous higher-order properties of node interactions within and in-between network layers and integrates information from the node neighbors. We illustrate the utility of CPD by applying it to an emerging problem of societal importance: vulnerability zoning of residential properties to weather- and climate-induced risks in the context of house insurance claim dynamics.

Topological clustering of multilayer networks

Multilayer network clustering is used in such diverse areas as optimal islanding of critical infrastructures, analysis of trade agreements, and monitoring ecological interaction patterns. We propose a perspective on multilayer network clustering based on the concept of shape. By invoking the machinery of topological data analysis, we first study a shape of each node neighborhood and then group nodes based on how similar shapes of their local neighborhoods are. The significance of this methodology can be viewed through an emerging problem of sustainability of house insurance to climate risks. The topological perspective opens possibilities for more systematic, robust, and mathematically rigorous integration of higher-order network properties and their interplay to the analysis of complex networks.