Shipping companies and maritime organizations want to improve the energy efficiency of ships and reduce fuel costs through optimization measures; however, the accurate fuel consumption prediction of fuel consumption is a prerequisite for conducting optimization measures. In this study, the white box models (WBMs), black box models (BBMs), and gray box models (GBMs) are developed based on sensor data. GBMs have great potential for the prediction of ship fuel consumption, but the lack of interpretability makes it difficult to determine the degree of influence of different influencing factors on ship fuel consumption, making it limited in practical engineering applications. To overcome this difficulty, this study obtains the importance of GBM input characteristics for ship fuel consumption by introducing the SHAP (SHAPley Additive exPlanations) framework. The experimental results show that the prediction performance of the WBM is much lower than that of the BBM and GBM, while the GBM has better prediction performance by applying the a priori knowledge of WBMs to BBMs. Combining with SHAP, a reliable importance analysis of the influencing factors is obtained, which provides a reference for the optimization of ship energy efficiency, and the best input features for fuel consumption prediction are obtained with the help of importance ranking results.

To cope with severe environmental problems, the government has implemented strict emission control policies, and promoted the use of technology such as shore power (SP) in the form of subsidies. The SP providers of the shipping lines like the ports provide better SP services to shipping lines by improving its reliability, such as safety and standardization. This paper examines a two-echelon maritime supply chain consisting of a port and a shipping line under government green subsidy and explores the subsidy mechanism and its impact. The optimal government subsidy intensity and subsidy reduction point are confirmed using game theory. The system dynamics (SD) method is used to analyze the influence and evolution of practical problems such as government subsidy efficiency, information asymmetry, and inconsistent decision-making periods under multiple games utilizing the optimal response function. The paper shows that both shipper SP preference and decision period affect the SP reliability. When the shipper’s preference is high, the actual shipper’s subsidies and government subsidies are inefficient. Information asymmetry borne by ports and shipping line causes considerable fluctuations in the game and does not affect the actual subsidies received by shippers. Also, shipping line downstream of the supply chain are more affected by it. This paper offers insight for the government to formulate subsidy policies in the maritime supply chain.

While promoting the global economy and trade, ports impose serious pollution on the global ocean and atmosphere. Therefore, the development of ports is restrained by the policies and measures of governments and international organizations used to cope with climate change and environmental protection. With the development of information technology, the operation and expansion of ports is facing forms of green and intelligent reform. This research aims to link the development of green intelligent ports, government policies, and third-party organizations to find the most suitable evolutionary path for the development of green intelligent ports. This paper assumes that governments will push ports to transform into green intelligent ports from the perspective of benefiting long-term interests, that the goal of ports is to maximize their profits, and that third-party organizations will actively promote the development of green intelligent ports. Based on these assumptions, this paper has established an evolutionary game theory model of “government–port–third-party organization” regarding the development of green intelligent ports. The Jacobian matrix of the game theory system was constructed by using the replicator dynamic equation, and local stability analysis was performed to obtain the equilibrium stability point of the entire system. This research reveals the limitations of the development of green intelligent ports without government involvement and explores the ability of third-party organizations to promote the implementation of policies, confirming the role of government regulation and control in promoting the development of green intelligent ports. This paper may be helpful for the development of green intelligent ports in the future. The results show that: (1) The main factors affecting the choice of port strategy are the benefits of building a green intelligent port, the intensity of government regulation, and the quantitative influence of third-party evaluation results on the port strategy selection. (2) Government decision-making plays an important role in port transformation. If the relevant government chooses the wrong strategy, then the transformation of the port will be delayed. (3) Government regulation and control need to change with the change of the evolution stage. (4) Compared with the macro-control policies of the government, the influence of the third-party organization on the port is significantly smaller.

Financial time series are generally high-dimensional, nonstationary, and exhibit heteroscedasticity. To derive a suitable way to cluster financial time series, these characteristics have to be taken into consideration. With this aim, in this article, the financial time series is firstly modeled using generalized autoregressive conditional heteroscedasticity (GARCH) models, where the parameters of GARCH models can represent the dynamic feature of the volatility in each time series. Therefore, the following clustering is realized based on the GARCH model parameters, which can help reduce the dimensionality of the original time series at the same time. Then, to produce semantically sound clustering results, we granulate the parameters based on the axiomatic fuzzy set (AFS) theory and structure them into a collection of meaningful and semantically sound entities, i.e., AFS information granules. Furthermore, the hierarchical structure of AFS information granules is built to realize time series clustering under the framework of granular computing. In the proposed approach, the characteristics of financial time series is fully considered to proceed dimensionality reduction, and the semantic clustering results obtained for different numbers of clusters are guaranteed to be the most informative. In the experiments, an application for clustering the time series coming from Chinese Yuan exchange rates against international currencies is presented to demonstrate the performance of the proposed clustering method. The results of clustering of the proposed method are the same as those of the fuzzy C-means algorithm and the hierarchical clustering with ward linkage, where the clustering results produced by the AFS hierarchical clustering exhibit well-articulated semantics at each level of the hierarchy.

Corporate social responsibility (CSR) in the supply chain has become an increasingly popular research topic, but there are little researches on developing countries or emerging market economies as the research object, let alone providing a framework for assessing CSR in sustainable supply chains in the context of these countries. This paper will make up for these literature gaps. From the perspective of the green supply chain management, this paper integrates various authoritative standards and adopts two methods, “R clustering” and “variation coefficient analysis”, to establish a CSR performance evaluation system of China’s transportation industry. Applying the mean square error index weighting method analyzes the performance of 74 companies in China’s transportation industry in 2018 to verify the rationality of the evaluation system. The results find that this industry generally scores low and the performance of responsibilities of different sub-sectors in this industry is different, but the overall performance trend is improving. This research has implications for China’s transportation industry to improve CSR levels from the perspective of green supply chain management, for managers and stakeholders who are committed to improving China’s CSR green and sustainable development, and for the development of CSR in developing countries and emerging markets.

In the last few decades, the progression of climate change has made people aware of the need to reduce CO₂ emissions. In this study, the effect of this awareness on container transport in Northeast China is used as an empirical case study. Firstly, we propose that the freight demand index, calculated by the entropy weight TOPSIS (technique for order preference by similarity to an ideal solution) method, reflects the degree of container demand in destination cities. Then, we describe five scenarios against the background of China’s container development plan and use them to evaluate the cost and carbon emissions of container rerouting. The overall objective of the study is to assess the effects of changes in port selection on the formation of new routes and multimodal transport. The results show that carbon taxes do not significantly affect multimodal transport networks, and the impact of loading and unloading costs on the total cost is far greater than that of corresponding carbon emissions. Despite the railway transportation capacity of Dalian Port, the results show that Yingkou Port and Dandong Port will expand by 227.8% and 191.4% over 2017, respectively. Therefore, Liaoning Port Group needs to reposition its different ports.