Under the mounting pressure to make changes to become more environmentally friendly and sustainable, port authorities have been exploring effective solutions to reduce CO₂ emissions. In this regard, alternative fuels, innovative technology, and optimization strategies are key pathways for ports to transition toward a low-carbon pattern. In this review work, the current development status and characteristics of renewable and clean energy in ports were meticulously analyzed. The CO₂ emission reduction effects and limitations of port microgrids, carbon capture, and other technological operations were thoroughly examined. Lastly, the emission reduction optimization strategies ports could adopt under different scenarios were evaluated. The research findings showed that (1) combining the characteristics of the port and quantifying the properties of different renewable energy sources and low-carbon fuels is extremely necessary to select suitable alternative energy sources for port development; (2) technological advancements, multi-party interests, and policy impacts were the primary factors influencing the development of emission reduction technology methods; and (3) the coordinated optimization of multiple objectives in cross-scenarios was the main direction for ports to achieve sustainable development. This study provides theoretical guidance to ports that are transitioning to a greener pattern, as well as pointing out future research directions and development spaces for researchers.

The bulk shipping market is seasonal, cyclical and highly volatile. Due to the nonstationary and nonlinear nature of price series and the complexity of influencing factors, it is difficult to analyse the fluctuations in the bulk shipping market. In this study, a method based on empirical mode decomposition (EMD) is proposed to investigate the volatility of the Baltic Dry Index (BDI). In this method, the original freight price series is decomposed into several independent intrinsic modes, using EMD first. Then, the intrinsic modes are composed into three components: short-term fluctuations caused by normal market activities, the effect of extreme events and a long-term trend. Numerical experiments indicate that the proposed method can effectively reveal the characteristics of bulk freight price series with different economic meanings and decrease error accumulation. Meanwhile, by decomposition of intrinsic modes, the complexity of the model formulation can be controlled and the operability of the model can be improved.

Global shipping alliances have become an important institution in international seaborne trade. Their raison d’être is higher efficiency and lower costs, to the benefit of the consumer. However, experiences from GSA operations during the COVID-19 supply chain crisis show that GSAs may have considerable market power, not quite aligned with the spirit of the lawmaker who has exempted them from antitrust laws. This raises many questions this paper attempts to answer: What drives the formation, stability and dissolution of GSAs? And have external and internal factors, such as government policies, ship sizes and freight rates, had always the same effect on GSAs over time? We decompose industry concentration (HHI) into seven components. This is done based on the Variational Mode Decomposition model. The components are subsequently reconstructed through gray correlation. Next, a Generalized Additive Model is specified, to analyze the relationships between influencing factors and the evolution of GSAs. We look both at the development (trend) of industry concentration, as well as its fluctuations (cyclicality) over time. We show that effects vary over time, with the same factors having different impacts on GSAs at different times. The paper can assist policymakers in their efforts to regulate and supervise container shipping.