ABSTRACT
The collection and distribution network of ports is the main cause of carbon emissions. The carbon peak is a basic policy in China, and the subsidy policy is one of the common measures used by the government to incentivize carbon reduction. We analyzed the transportation methods and the flow direction of a port and proposed a carbon emission calculation method based on emission factors. Based on the transportation time and the cost, a generalized transportation utility function was constructed, and the logit model was used to analyze the impacts of subsidy policies on transportation, thus calculating the effects of the subsidies on carbon reduction. We used Guangzhou Port as a case study, and calculated the carbon reduction effects in six different subsidy policy scenarios and concluded that the absolute carbon reduction value was proportional to the subsidy intensity. In addition, we constructed a subsidy carbon reduction efficiency index and found that the Guangzhou Port collection and distribution network had higher subsidy carbon reduction efficiency in low-subsidy scenarios. Finally, a sensitivity analysis was conducted on the subsidy parameters, and scenario 8 was found to have the highest subsidy carbon reduction efficiency. This achievement can provide decision support for the carbon emission strategy of the port collection and distribution network.
ABSTRACT
In the maritime industry, The unanticipated COVID-19 viral epidemic is an unforeseeable circumstance, and other nations implemented enormous containment measures to stop the Coronavirus epidemic from spreading around the world. Thus, directly affecting the maritime shipping sector. This paper will discuss the current problems facing the shipping industry, taking into account the congestion problems, delays, and uncertainty timeframes, using the Los Angeles port as a case study. These problems and more were addressed directly by increasing the operating hours, workload, and available staff, and indirectly by looking for alternatives for shipping goods, and creating more cargo space, furthermore, this study will use Monte Carlo simulation to predict the effectiveness of these solutions on the congestion at the port.
ABSTRACT
The transportation industry is characterized as a capital-intensive industry that plays a crucial role in economic and social development, and the rapid expansion of this industry has led to serious environmental problems, which makes the eco-efficiency analysis of the transportation industry an important issue. Previous research paid little attention to the regulatory scenarios and suffered from the incomparability problem, hence this paper aims to reasonably estimate the eco-efficiency and identify its evolutionary characteristics. We measure the eco-efficiency and the corresponding global Malmquist–Luenberger productivity index using a modified model of the data envelopment analysis framework, in which different regulatory constraints are incorporated. Based on the empirical study on the transportation industry of thirty provinces in China, we find that the eco-efficiency of Chinese transportation industry experienced a slight increase during 2015–2016, a sharp decline during 2016–2017, and a continuous rise since year 2017. The Middle Yangtze River area was the best performer among the eight regions in terms of eco-efficiency, while the Southwest area was placed last. The global Malmquist–Luenberger productivity index showed an earlier increase and later decrease trend, which was quite consistent with the reality of the variation of inputs and outputs and the emergence of COVID-19. Moreover, the best practice gap change was found to be the main driven force of productivity. The empirical results verify the practicability of our measurement models and the conclusions can be adopted in guiding the formulation of corresponding policies and regulations.
ABSTRACT
The use of online services has become very popular among consumers nowadays, especially since the outbreak of the Coronavirus. One of the most visible services is the use of online taxi. In this regard, the brand and the logo are the first step of introducing the company, so its crucial role cannot be ignored. In this study, we examine the positive effect of brand logo typeface on brand attitude, consumer satisfaction and strengthening the intention to buy and brand preference by customer satisfaction. The statistical population was randomly selected. We design a conceptual model and then prepare a standard online questionnaire. After collecting the data through the software Smart-PLS3, we start the analysis. According to Cronbach's alpha and AVE, the findings show that the brand logo typeface has a positive and direct influence on customer attitude and satisfaction, and customer satisfaction mediates the purchase intention and brand preference.
ABSTRACT
Movement restrictions implementation due the COVID-19 pandemic affected people's mobility and and transportation sectors globally. Tourism, retail, transportation, and hospitality also suffered significant consequences that indirectly impacted a country's economic stability. Hence, this paper presents the Malaysian experience with the effects of the pandemic on transportation services and travel behaviour. Online sources, secondary data and a questionnaire survey were employed to collect the relevant data. These data were further analysed and presented in this paper. In general, transportation sectors involving air, rail and public bus showed decreased demand and services. A severe drop in services was observed in air transportation which is more than 97%. Most people prefer not to use public transportation to avoid the risk of COVID-19 infection. Noticeable changes in travel behaviour and mode preference during and pre-pandemic are observed, particularly on trips related to work/school. Key pandemic factors concerning transportation were fear of infection and social distance. Hence, most people prefer to use a private vehicle. The changes in transportation service and travel behaviour result in severe losses in the transportation sector in Malaysia.
ABSTRACT
To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM2.5, PM10, and NO2 in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O3 increased by 45.1% in comparison;the concentrations of PM2.5, PM10 and NO2 emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively;the concentrations of PM2.5, PM10 and NO2 emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM2.5 and PM10 emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO2. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing.
ABSTRACT
Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s-1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s-1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory.Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.
ABSTRACT
The global outbreak of coronavirus disease has become one of humanity's current greatest challenges and may arguably surpass climate change in the short-term. The virus's rapid dispersion through the transportation sector, its disruption to human health and global economies, has been remarkable. The energy sector has also been impacted, as it has seen episodic low prices of oil, particularly in April 2020. This scenario is due to less demand for oil amid various containment measures and related health policies of governments worldwide. The performance of existing oil and gas contracts, especially time-bound supply contracts, has been rendered impracticable in some parts of the world. This has resulted in parties invoking force majeure clauses as an excuse. However, the legitimacy of coronavirus as an acceptable force majeure has emerged topically controversial. This article, adopting an analytical approach, makes a case for coronavirus as a typical instance of force majeure for energy contracts or sales and purchase agreements ("SPAs"), such as liquefied natural gas ("LNG") contracts and energy purchase agreements ("EPAs"), and which can only avail defense depending on parties' contractual force majeure provision. This article offers an understanding of force majeure alongside required fundamentals. Likewise, it highlights current debates about force majeure and likely impacts on future energy law contracts.
ABSTRACT
Any collision between wildlife (including birds) with an aircraft is known as a wildlife strike. Most of the time such a strike has no operational impact but occasionally it could result in delays because of a technical check or even cause damage to aircraft. The COVID-19 pandemic severely affected Europe's transport sector. Air connectivity collapsed worldwide and the demand on the airports operated by Fraport Greece (FG) decreased significantly during the pandemic years (2020–21). With less air traffic and more stay-home orders, a variety of bird species were attracted to the green, quieter areas of the airports. The European Aviation Safety Agency (EASA) issued warnings concerning the increased presence of wildlife hazards in European airports. This case study aims to communicate to the aviation industry the wildlife strike prevention strategy implemented at the airports operated by FG during the pandemic years. The fundamentals of wildlife management, airport ecology, flight safety and sustainability are presented in a structured way to provide the reader with many relevant messages. The management implications, together with the best practices of this preventive wildlife strike strategy, are discussed for their possible wider use in the aviation industry. [ABSTRACT FROM AUTHOR] Copyright of Journal of Airport Management is the property of Henry Stewart Publications LLP and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
The purpose of this research is to test the fraud heptagon research model in detecting fraud in financial statements using eight Beneish M-Score ratios. The data research method uses regression linear methods in terms of testing research hypothesis. Statistical data processing in this study uses the SPSS version 26 program. The object of this research are Indonesia's Transportation and Logistic Industry companies listed on the IDX within 2019-2021. The total sample of this research are sixteen companies or forty eight samples for three years of research which have been determined by purposive sampling method based on certain criteria's. The results of this study indicate that the fraud heptagon research model simultaneously affects the detection of fraud in financial statements. © 2022 ACM.
ABSTRACT
Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms.
ABSTRACT
Since it has been established that supply chain management has traditionally been a process-oriented field with not much focus on innovation, in a post-pandemic world, engineering managers have realized that research is needed to identify which aspects of global supply chain management have the greatest need for transformation and could benefit most through the application of entrepreneurial competencies. Along those lines, the authors of this paper have earlier proposed a research model, which includes a mapping between supply chain management process steps and entrepreneurial competencies along with proposed training and organizational changes to facilitate the implementation of a more entrepreneurial approach to supply chain management processes. Using a qualitative, grounded theory approach based on two case studies, the authors focus on two industries. The differences and similarities between the two industries will be compared in this paper;thus, showing engineering managers how the model could be applied to the two different industries considered in this paper. Copyright, American Society for Engineering Management, 2022.
ABSTRACT
IntroductionPublic transport workers have never stopped working during the COVID-19 pandemic. Despite the high personal contact with the public, studies in this job category on COVID-19 risk are scarce.Material and MethodsWe aimed to fill in this knowledge gap by investigating a cohort of over 2,000 employees of the Regional public transport sector in Sardinia, Italy. Incident COVID-19 cases were identified between 1 September 2020 – 6 May 2021 by real-time reverse transcription-polymerase chain reaction tests performed on nasopharyngeal swabs during periodic occupational health surveillance. We applied the age- and gender-specific COVID-19 incidence rates in the regional population at the same time frame to the correspondent strata of the study cohort to calculate the expected COVID-19 events. Age- and gender-adjusted relative risks (RRs) of COVID-19 and relative 95% confidence intervals (95% CIs) were estimated as the ratio between the observed and the expected events for the overall cohort and in two sub-cohorts: bus drivers and the rest of the workers (including administrative staff).ResultsMale bus drivers showed an increased COVID-19 risk (RR = 1.4, 95% C.I. 1.07 – 1.79). There was no excess risk among the rest of the personnel. Women were too few to allow reliable risk estimates.ConclusionsOur study suggests an excess risk of COVID-19 among bus drivers even in a relatively low incidence area, which could imply inadequacy of occupational preventive measures. Further larger studies, with detailed information on occupational and personal determinants, are warranted to disentangle the underlying causal factors and focus preventive strategies.
ABSTRACT
The exponential growth of the worldpopulation in the last decades (approx. eight billion people in 2022), corroborated with spectacular development of various sectors of activity such as transport, construction, information, and communication technology, etc. exert a worrying pressure on limited natural resources. Moreover, climate change, environmental degradation, increasing pollution, recent armed conflicts are increasingly real existential threats to the global population in general and to the old continent in particular. In this context, the European Green Deal (EGD) comes to counteract such unhealthy developments and proposes to transform Europe into a society with a modern, high performing and resource-efficient economy, with a goal of achieving climate neutrality by 2050. EGD involves a series of legislative measures that will support and implement policies on financial and competitive issues related to climate, energy, industry, sustainable and smart mobility, agriculture and biodiversity conservation and protection. From the EGD perspective, the objectives of this research have focused on the transport sector, with a view to ensuring safe, sustainable, green, and smart mobility. The main result of the research consists of the optimization algorithm developed and implemented by authors at one of the largest alliances transport, which calculates the best option for the delivery of goods, respecting the price lists and the proposed carbon dioxide emissions targets. At the same time, in the light of the results and conclusions of the research, the general implications regarding the financial and competitive aspects of the EGD are analysed, as well as the particular ones, specific to the alliance that is the object of the case study.
ABSTRACT
The marine transportation industry, which has made significant steps in recent years to combat climate change and global warming, is heavily influenced by regional and worldwide economic trends. The COVID-19 pandemic, which began in China at the end of 2019 and spread to the rest of the world in the first quarter of 2020, caused the global economy and marine transport to decline by 4.1% in 2020. It is proposed in this study to investigate various scenarios by modelling this circumstance that has unexpectedly occurred using decision assistance technologies.
ABSTRACT
Recently, Covid-19 pandemic has introduced some disturbances in the transportation industry. As a result, some university students are now participating in online internship programs. In our laboratory, we proposed an online campus tour project where each foreign student teleoperates with a robot. The robot prepared is outfitted with a camera, laser range finder sensor, and a mobile monitor to represent the student's physical appearance. The robots and foreign students are connected through the university's internal network. During the tour, we found some flaws and improvements in the current system. Because of the time delay and other difficulties, some students are not able to follow the campus tour project at the same pace as the others. Consequently, a distributed module for automated following features with dynamic order is proposed. The following order is determined by the location of each robot. The proposed module combines ROS Navigation Stack and a novel method of robot formation. In this study, the proposed improvement is evaluated and discussed. © 2022 ICROS.
ABSTRACT
The Corona Virus Disease 2019 (COVID-19) has touched individuals from all walks of life in recent years, and the movement of people and commodities has become a crucial route for the disease's spread. Reasonable control of the transportation industry has become a vital to the epidemic's prevention and control. Simultaneously, a succession of tight blockade inspection measures in the transportation business have significantly restricted the movement of people and commodities, putting a strain on the industry. As a result, it is crucial to study the transportation industry's comprehensive measurement in this environment for future regulation and prevention. In this paper, the transportation industry's freight and passenger transport are used as indicators to assess the economy, and the data source is multi-source time series data from government statistics. To anticipate future economic trends in the transportation business, we chose freight and passenger transportation in the air, land, and marine industries, respectively. Due to the significant amount of missing data, this paper develops a time series data imputation approach for its specific missing situations in order to fill in the gaps and make future prediction tasks easier. In addition, this paper builds a long and short-term memory network to train the data in order to predict future transportation industry economic trends. © 2022 IEEE.
ABSTRACT
The emergence, spread, and outbreak of the COVID-19 pandemic, first in the Wuhan city of China but later in the rest of the world, has affected the lives of people all over the world. This inevitable influence has not left the transportation sector unaffected. Therefore, there is a need to examine whether the choices of citizens have been influenced in terms of their mobility. The choice of the optimal solution for each citizen depends on many factors, such as the price of transportation fuel, the safety of going to work, the availability of public transport, the possibility of risk of infection, etc. The mobility choices of the citizens in the pre-COVID-19 era in comparison with the post-COVID-19 era have changed due to the increased vigilance of the citizens. In addition to citizens, other stakeholders are infectious disease experts/epidemiologists, transportation engineers, etc. This is, therefore, a problem that is offered for analysis using Multi-Actor Multi-Criteria Analysis (MAMCA). This research investigates the mobility choices of people in the post-COVID-19 era using the different stakeholder groups and the MAMCA methodology. Useful results arise concerning the influencing factors of the mobility choices of different stakeholder groups in the post-COVID-19 era.
ABSTRACT
How to control the global temperature rise within 1.5 °C in the post-COVID-19 era has attracted attention. Road transport accounts for nearly a quarter of global CO2 emissions, and the related sulfur dioxide (SO2) emissions also trigger air pollution issues in population-intensive cities and areas. Many cities and states have announced a timetable for phasing out urban-based fossil fuel vehicles. By combining a Markov-chain model with a dynamic stochastic general equilibrium (DSGE) model, the impacts of on-road energy structural change led by phasing out fossil fuel vehicles in the road transportation sector are evaluated. The impact of automobile emissions (both CO2 and SO2) on the environment is evaluated, taking into consideration of variation between cities, regions, and countries. Two other major driving forces in addition to CO2 emissions reduction in promoting fossil fuel vehicles' transition toward net-zero carbon are identified and analyzed with multiple different indicators. Under the framework of the DSGE model, climate policy instruments' effects on economic development, energy consumption, and their link to economic and environmental resilience are evaluated under exogenous shocks as well.
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Puranik et al discuss air transportation which encounters inflationary headwinds on its recovery from pandemic. A strong and growing global air travel demand, supported by the lifting of air travel restrictions in most countries, fueled the global airline industry's continued recovery this year from the impacts of covid-19. In June, the International Air Transport Association projected a total of 3.8 billion passengers for the year--83% of pre-pandemic levels--and a record high air cargo volume of more than 68 million tons, resulting in a global airline industry loss of $9.7 billion this year down from $42.1 billion in 2021. Only the North America region was expected to achieve profitability, with an estimated $8.8 billion profit. In October, member states of the International Civil Aviation Organization adopted the goal of achieving net-zero carbon emissions by 2050, a target that IATA member airlines had committed to in Oct 2021.