Climate change poses a threat to the survival of the human race. Increased interest in climate change, carbon peaks, and carbon neutralization and rising recognition of the challenges inherent to highlighting this issue provides the opportunity to carry out a bibliometric study to identify what research can generate ideas regarding climate change, carbon peaks, and carbon neutralization. As expected, it may align with the dual goals of the Chinese government agenda in terms of a carbon peak and carbon neutralization in 2030 and 2060, respectively. The recent argument has induced calls for improved transparency and standardization in the approaches adopted to synthesize climate change, carbon peak, and carbon neutralization research. Nevertheless, key questions are still unanswered, namely, what are the key contributions that the research community has produced in relation to climate change, carbon peaks, and carbon neutralization? Have their contributions been inclined toward specific geographical areas, directions, and themes? As such, software tools for bibliometric analysis, VOSviewer, and Python were used to conduct a systematic quantitative analysis of the relevant literature on climate change, carbon peaks, and carbon neutralization. The results show that carbon peaks and carbon neutralization have received wide attention from academic scholars. In the meantime, China faces the unfolding challenges of economic, technological, and political factors that need to be addressed to achieve carbon peak and carbon neutralization. This study provides policy implications for achieving China’s emission reduction targets.

Maritime transport serves as the backbone of international trade, accounting for more than 90% of global trade. Although maritime transport is cheaper and safer than other modes of transport, it often means long sailing distances, which often results in substantial fuel consumption and emissions. Liner shipping, a vital component of maritime transport, plays an important role in achieving sustainable maritime operations, necessitating the implementation of green liner shipping practices. Therefore, this study formulates a nonlinear integer programming model for a multi-fuel engine selection optimization problem to optimally determine ship order choice in terms of the fuel engine type, fleet deployment, fuel selection, and speed optimization, with the aim of minimizing the total weekly cost containing the weekly investment cost for ship orders and the weekly fuel cost. Given the complexity of solving nonlinear models, several linearization techniques are applied to transform the nonlinear model into a linear model that can be directly solved by Gurobi. To evaluate the performance of the linear model, 20 sets of numerical instances with, at most, seven routes are conducted. The results show that among 20 numerical instances, 16 sets of numerical instances are solved to optimality within two hours. The average gap value of the remaining four sets of numerical instances that cannot be solved to optimality within two hours is 0.51%. Additionally, sensitivity analyses are performed to examine crucial parameters, such as the weekly investment cost for ordering ships, the ship ordering budget, and the potential application of new fuel engine types, thereby exploring managerial insights. In conclusion, our findings indicate that equipping ships with low-sulfur fuel oil engines proves to be the most economical advantageous option in the selected scenarios. Furthermore, ordering ships with low-sulfur fuel, oil + methanol + liquefied natural gas engines, is beneficial when the weekly investment cost for such engines does not exceed $13,000, under the current parameter value setting.

Maritime transportation is vital for the movement of cargo between different continents and distant locations but can be disrupted by the frequent occurrence of pirate attacks. Based on the pirate attacks from July 1994 to December 2019, a spatial analysis of pirate attacks using a Geographic Information System (GIS) was conducted in the present study using the data available for tankers, dry bulk carriers, container vessels, general cargo vessels, and tugs. The adoption of the kernel density analysis was intended to identify the spatial pattern of global pirate attacks. The research results demonstrated that the pirate attacks showed a clustering pattern and were mostly associated with areas experiencing economic depression, a high unemployment rate, and social unrest. Accordingly, spatiotemporal hot spot analysis was carried out to recognize the changing directions of cold spots and hot spots over a period of time. The waters off Somalia, the Strait of Malacca, the Philippines, the Bay of Bengal, the Gulf of Guinea, and the northwest of South America were found to be the common locations of pirate attacks. The cold and hot spots of pirate attacks on the three key vessel types, including tankers, dry bulk carriers, and container vessels, were found to be similar. When considering the same area, the trends of cold and hot spots of different vessel types being attacked were substantially different. This study can provide a useful guideline for the International Maritime Organization and other relevant organizations in the world to design and implement targeted strategies to combat and mitigate pirate attacks. Additionally, the introduction of a GIS may help to envision the spatial and temporal distribution of pirate attacks and to explore the characteristics of pirate behaviors at sea and the patterns of piracy.

The greenhouse effect caused by carbon dioxide (${\mathrm{C}\mathrm{O}}_2$) emissions has forced the shipping industry to actively reduce the amount of ${\mathrm{C}\mathrm{O}}_2$ emissions emitted directly into the atmosphere over the past few years. Carbon capture, utilization, and storage (CCUS) is one of the main technological methods for reducing the amount of ${\mathrm{C}\mathrm{O}}_2$ emissions emitted directly into the atmosphere. ${\mathrm{C}\mathrm{O}}_2$ transport, i.e., shipping ${\mathrm{C}\mathrm{O}}_2$ to permanent or temporary storage sites, is a critical intermediate step in the CCUS chain. This study formulates a mixed-integer programming model for a carbon storage and transport problem in the CCUS chain to optimally determine ship allocation, ship departure scheduling, and ${\mathrm{C}\mathrm{O}}_2$ storage and transport. Taking advantage of the structure of the problem, we transform the mixed-integer programming model into a simpler model that can be computed efficiently. To evaluate the performance of the simpler model, numerous computational experiments are conducted. The results show that all small-scale instances (each with 10 power plants) and medium-scale instances (each with 30 power plants) can be solved optimality by Gurobi within 14.33 s. For large-scale instances with 60 and 65 power plants, feasible solutions with average gap values of 0.06% and 6.93% can be obtained by Gurobi within one hour, which indicates that the proposed methodology can be efficiently applied to practical problems. In addition, important parameters, including the unit fuel price, the time-charter cost, and the ship sailing speed, are examined in sensitivity analyses to investigate the impacts of these factors on operations decisions. In summary, a lower fuel price, a lower charter cost, or a higher ship sailing speed can increase the profit of the CCUS chain.

The two-dimensional strip-packing problem (2D-SPP) emerges as a notable variant of the cutting and packing (C&P) problem, aiming to optimize the arrangement of small rectangular items within unique strips with a fixed width and infinite height to minimize the usage of height. Despite extensive academic exploration, applying 2D-SPP solutions in industrial settings remains challenging. Two significant issues, often overlooked in academia yet frequently encountered in industrial contexts, are the uncertain demand for items, exacerbated by the bullwhip effect, and the need for diverse types of strips to cater to varying customer needs. Our paper addresses this academia–industry gap by proposing a robust optimization model for the uncertain 2D-SPP with variable-sized bins, aiming to manage the demand fluctuations within a box uncertainty set framework. Additionally, we employ the contiguous one-dimensional relaxation technique in conjunction with column generation to tighten the lower bound of the problem, thereby augmenting solution accuracy. Furthermore, we leverage the Karush–Kuhn–Tucker (KKT) condition to transform the model into a more tractable form, subsequently leading to an exact solution. Based on datasets from a real-life plastic-cutting company, comprehensive experiments validate the effectiveness and efficiency of our proposed relaxation method and algorithm, showcasing the potential for an improved industrial application of 2D-SPP solutions.