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The combination of car-sharing and electric vehicles can increase the acceptance of electric vehicles and facilitate car-sharing to be a more sustainable means of transport. However, this also poses more challenges for the good planning of electric car-sharing systems. To assist car-sharing companies in improving the planning decisions, this paper developed an evaluation framework from a comprehensive view. In the first step, four evaluation criteria were identified according to the planning process: construction of stations; routine inspection; vehicle usability and relocation management; and the maintenance and replacement of stations. Then, a combinatorial method based on analytic hierarchy process (AHP), cost-benefit analysis (CBA), and Voronoi diagram (VD) is developed to determine the relative weight of the four criteria and evaluate the alternative. Finally, the evaluation framework was applied in a realistic case of EVCARD, which is the most influential electric car-sharing company in China. The performance of two different operational districts of EVCARD—Jingan and Changning—were compared. The results showed that vehicle usability and relocation management is the greatest criterion influencing the planning performance of the electric car-sharing system in China, and that routine inspection is a negligible but important factor. According to the relative scores, Jiagan District performed better than Changning district.

Ship-related marine oil spills pose a significant threat to the environment, and while it may not be possible to prevent such incidents entirely, effective clean-up efforts can minimize their impact on the environment. The success of these clean-up efforts is influenced by various factors, including accident-related factors such as the type of accident, location, and environmental weather conditions, as well as emergency response-related factors such as available resources and response actions. To improve targeted and effective responses to oil spills resulting from ship accidents and enhance oil spill emergency response methods, it is essential to understand the factors that affect their effectiveness. In this study, a data-driven Bayesian network (TAN) analysis approach was used with data from the U.S. Coast Guard (USCG) to identify the key accident-related factors that impact oil spill clean-up performance. The analysis found that the amount of discharge, severity, and the location of the accident are the most critical factors affecting the clean-up ratio. These findings are significant for emergency management and planning oil spill clean-up efforts.

Air traffic control systems play a critical role in ensuring the sustainable and resilient flow of air traffic. The air traffic sector serves as a fundamental topological unit and is responsible for overseeing and maintaining the system’s sustainable operation. Examining the structural characteristics of the air traffic sector network is a useful approach to gaining an intuitive understanding of the system’s sustainability and resilience. In this paper, an air traffic sector network (ATSN) was established in mainland China using the complex network theory, and its motif characteristics were analyzed from a microscopic perspective. Additionally, subgraph resilience was defined in order to describe the network topology by analyzing changes in subgraph motif concentration and subgraph residual concentration. Our empirical findings indicated that motifs exhibit high connectivity, while anti-motifs are found in subgraph structures with low connectivity. The motif concentration of subgraphs can efficiently reflect the distribution of heterogeneous subgraph structures within a network. During the process of resilience evaluation, the subgraph motif concentration remains relatively stable but is sensitive to the transition state of the network from disturbance to recovery. The resilience of the system at the macroscopic scale is aligned with the resilience of each heterogeneous subgraph structure to some extent. Topological indicators have a more significant impact on the resilience of the ATSN than air traffic flow characteristics. This study has the outcome of uncovering the preference for connection among nodes and the rationality of sector structure delineation in ATSNs. Additionally, this research addresses the fundamental mechanism behind the network disturbance recovery process, and identifies the connection between network macro- and microstructure in the resilience process.

Current autonomous intersection control strategies are facing issues, such as lack of foresight, frequent occurrence of deadlock, and low control system efficiency. To address these issues, a vehicle–road cooperative autonomous intersection control strategy based on reducing vehicle conflict relationships is proposed in this study. First, a conflict relationship graph that can describe the driving conflict relationship between vehicles is constructed. Second, the complement of the maximum clique in the conflict relationship graph is solved to determine the set of accepted vehicle reservation requests, enabling more vehicle reservation requests to be successfully processed in unit time while ensuring safe driving at the intersection and improving intersection throughput efficiency. Third, based on the maximum clique method, a taboo search method is used to search the neighborhood, thus improving the quality of the final solution with a smaller search cost. Simulation results show that compared to other control strategies, such as the FCFS (First Come First Served) strategy, the traffic signal control strategy (Traffic-Light), and the control strategy based on greedy algorithm search (Batch-Light), the proposed strategy can considerably reduce the average vehicle waiting time by 42%, 19%, and 10%, respectively, as well as increasing the number of vehicles passing through the intersection per unit of time by 35%, 20%, and 12%, respectively. These results demonstrate the effectiveness of the proposed strategy in improving the throughput of the intersection and reducing the average vehicle waiting time.

Human pressures on marine ecosystems significantly increased during last decades. Among the intense anthropic activities, industrial fisheries have caused the alteration of habitats, the reduction of biodiversity and the main fish stocks. The aim of this research, carried out in the Adriatic Sea, was to test a repeatable Marine Spatial Planning framework aimed at enhancing fisheries sustainability through the application of Decision Support Tools and the composition of a catalog of possible measures. The use of these tools proved very useful to identify possible criticalities and facilitate an effective exchange with fisheries stakeholders, local authorities, and fishermen, whose involvement was an indispensable step in the process. Tool-based analyses allowed to assess the spatial footprint of a range of anthropogenic pressures from human activities (e.g., fisheries, maritime traffic, and aquaculture). Within this multi-pressure scenario, special attention was paid to fishing-related disturbances and potential conflicts across different fishing métier and with other sectors. Specifically, results highlighted the spatial features of the major fishing pressures (e.g., abrasion from trawling) affecting essential fish habitats, marine mammals and turtles in the study area. A portfolio of possible management measures is identified for the study area. It provides clear evidence that, in order to mitigate emerging conflicts and cumulative impacts, it is necessary to combine and integrate different types of measures: spatial measures modulated over time, monitoring and control, actions to fill knowledge gaps, concertation—involvement—co-management actions, improvement of governance systems, actions to support innovation in the sector, etc. Given the complex set of measures discussed, this work can provide a useful contribution to the management of fisheries both at local and regional level, fostering the transition to sustainable fisheries.

Urban logistics implementation causes environmental pollution; therefore, it is necessary to consider the impact on the environment when carrying out such logistics. Electric vehicles are alternative vehicles that reduce the impact on the environment. For this reason, this study investigated which electric vehicle has the best indicators for urban logistics. An innovative approach when selecting such vehicles is the application of a fuzzy–rough method based on expert decision making, whereby the decision-making process is adapted to the decision makers. In this case, two methods of multi-criteria decision making (MCDM) were used: SWARA (stepwise weight assessment ratio analysis) and MARCOS (measurement alternatives and ranking according to compromise solution). By applying the fuzzy–rough approach, uncertainty is included when making a decision, and it is possible to use linguistic values. The results obtained by the fuzzy–rough SWARA method showed that the range and price of electric vehicles have the greatest influence on the selection of an electric delivery vehicle. The results of applying the fuzzy–rough MARCOS method indicated that the Kangoo E-Tech Electric vehicle has the best characteristics according to experts’ estimates. These results were confirmed by validation and the application of sensitivity analysis. In urban logistics, the selection of an electric delivery vehicle helps to reduce the impact on the environment. By applying the fuzzy–rough approach, the decision-making problem is adjusted to the preferences of the decision makers who play a major role in purchasing a vehicle.

The pursuit of sustainability has been at the forefront of contemporary planning initiatives. However, most recent research has focused on the environmental and economic aspects of developing sustainable urban environment, whilst largely neglecting the social aspects. Contemporary political thinking in the UK often disregards the potential of the urban infrastructure to improve social equity. The aim of this study was to analyse the impact of transport infrastructure on a variety of social measures, in an empirical and ideologically unbiased fashion, using both quantitative and qualitative methods. We selected “Tramlink” as a case study: a light-rail system in the London Borough of Croydon which began operation in 2000. We used quantitative methods, including advanced spatial statistics, to produce a more detailed analysis of social equity than has been previously published. This acknowledges that determining localised issues can produce more informed and effective policy interventions. Our results demonstrate that the physical properties of transport infrastructure and the non-physical attributes of society, in combination, help to create opportunities for individuals to succeed. We also find that in order to reduce the negative effects of austerity, public money could be more effectively spent if diverted to areas that are most in need which can be highlighted through localised investigations.

Vehicle electrification has been promoted as an effective way to tackle environmental issues and the energy crisis worldwide. Being the largest auto market, China witnessed a dramatic increase of sale volume and market share of electric vehicles recently, while the incentives kept decreasing. Normative factors have been found to effectively explain consumers’ intention to adopt electric vehicles, but the mechanism remains to be discovered. One of the culture’s orientations, namely collectivism, has been proved to have significant impact on consumption behaviors, but the influence of collectivism on intention to adopt electric vehicles in China needs further discussion. Based on 433 questionnaires collected from Chinese consumers, this study adopted the Value–Belief–Norm (VBN) framework and examined collectivism as a moderator variable on the relationship between personal norms and intention to adopt electric vehicles. Data were analyzed using IBM SPSS 27 and PLS-SEM (SmartPLS 3). The results show that the VBN framework successfully explains the intention to adopt electric vehicles of Chinese consumers, and collectivism has a significant positive moderating effect on the relationship between personal norms and intention to adopt electric vehicles. Insights and suggestions from theoretical and managerial perspectives on how to accelerate electric vehicle adoption are discussed for marketers, policymakers and industry practitioners.