China's coordinated development reform in 2006 is the starting point of contemporary port reforms and the best platform for exploring the persistent barriers to China's port coordination and the causes behind repeated reform restarts. This study assesses the geographical impact of 2006 reform in coastal port clusters and explains why the reform fell short of expectation. Using 1998-2019 panel data covering 59 coastal ports and 13 types of cargoes, reform impacts are evaluated through spatial distribution, specialization, and growth fluctuations. The results reveal that the reform modestly slowed throughput decentralization across port clusters, it failed to reverse the overall trend. Moreover, intra-cluster specialization also declined slightly since 2006. To further analyze the general and specific factors that influenced the performance, a quasi-DID approach was combined with policy text analysis. Compared with smaller ports, the main ports designated under the National Coastal Port Layout Plan (hereafter 'the Plan') experienced slower throughput growth,a sustained loss of market share, and no notable increase in dominant cargoes. These outcomes are attributable to institutional constraints and lagging concepts. The findings illuminate the constraints and dynamics shaping China's coastal port geography since 2006 and offer actionable policy insights for future regional port integration.
Regional cruise networks possess a multi-scale structure, with several localized hub-and-spoke systems and a limited number of connector ports linking these local systems. Multi-scalar connectivity substantially expands a port's potential to assemble diverse itineraries, a capability that plays a decisive role in determining cruise port performance. Existing studies typically rely on single-scale indicators that overlook the multi-scalar nature and differentiated market access it creates. This study addresses this gap by examining how port roles vary across spatial scales and how these roles influence passenger flows. Using the Mediterranean as a case study, a three-scale structure is identified, which is sub-basin, subregion and entire-basin. Results show that multi-scalar centrality provides markedly stronger explanatory power for passenger distribution. Sub-basin centrality directly increases a port's own passenger volume, whereas subregional and basin-wide centrality generate positive spillover effects for neighboring ports. The spatial division of these roles differs across sub-basins, producing three patterns: a Unipolar Homeport System, a Gateway-Homeport System, and a Multi-Connector System. The study accordingly advances a multi-scalar perspective on cruise networks and underscores the need for planning frameworks based on multi-scale communities and differentiated port development strategies.
This study focuses on two potential consequences of burnout in seafaring: turnover intention, an important issue given the concerns about the shortage of seafarers, and quiet quitting, where employees reduce engagement without formally resigning. Quiet quitting has not yet been studied among seafarers but given the increasing attention to employee disengagement in contemporary workplaces, it represents a significant potential risk, particularly for safety-critical operations. The aim of the study was to test whether turnover intention mediates the relationship between burnout and its four dimensions (exhaustion, mental distance, cognitive impairment, and emotional impairment) and quiet quitting among seafarers. Data were collected from an international sample of seafarers (N = 508) via an online survey using the Burnout Assessment Tool, Quiet Quitting Scale, and Turnover Intention Scale. Results indicate that turnover intention significantly mediates the relationship between all four burnout dimensions and quiet quitting, with the strongest mediation observed for exhaustion. All direct and indirect effects were statistically significant, supporting the hypothesized partial mediation model. These findings underscore the significance of turnover intention as a mechanism linking burnout to quiet quitting, highlighting the need for preventive measures to mitigate negative outcomes.
Against the backdrop of advancing the United Nations sustainable development goals SDG 7 (affordable and clean energy) and 13 (climate action), measuring and reducing carbon emissions has been critical for ports' decarbonization and sustainable development. This paper integrates data from multiple sources to establish a comprehensive carbon emission evaluation framework for ports, employing system dynamic simulation models to analyze the impact of various emission reduction strategies. And a dual modeling approach integrates bottom-up and top-down methodologies to respectively estimate emissions from in-port ships and port facilities, predicting forward emissions for multiple scenarios using artificial neural networks. A case study of the world's busiest container port-Shanghai Port is presented to demonstrate the proposed evaluation framework. The result shows that strategies, such as adopting alternative fuels, implementing government subsidies, and promoting carbon trading schemes, are effective methods for reducing carbon emissions. This study develops a data-driven framework to identify the most effective emission-reduction strategies providing a foundation for port managers and operators to assess and improve the sustainability performance of their operations. The results also offer insights for policymakers to reduce port carbon emissions and enhance sustainable port development.
Coastal shipping systems play a vital role in sustaining global trade but face growing maritime safety challenges under increasing traffic density and complex operating environments. This study develops an integrated Geographic Information System (GIS) and machine learning (ML) framework to map coastal accident risks and support differentiated governance. Using China's coastal waters as a representative case, 648 reported accidents (2013-2024) were analyzed. Spatial clustering identified 13 high-risk zones, while interpretable ML models highlighted crew allocation, voyage planning, and improper operation as dominant factors associated with collision severity. Interactive risk maps visualize accident types and spatial heterogeneity, enabling intuitive exploration of risk hotspots. Beyond technical contributions, the framework offers practical insights for maritime policy and management by linking spatial analytics with differentiated governance. It enables regulators and port authorities to prioritize inspections, allocate resources efficiently, and tailor safety interventions to local risk contexts. Overall, the study demonstrates how data-driven and interpretable methods can strengthen adaptive maritime governance and contribute to safer, more resilient coastal shipping systems.
