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In order to achieve the goal of “carbon peak before 2030 and carbon neutrality before 2060”, all industries are fully engaged in energy conservation and carbon reduction work. Carbon emissions from the transportation sector account for nearly 10% of China's carbon emissions[1]. Port is an extremely important part of China's transportation industry, and it is also one of the key areas of energy conservation and emission reduction in the transportation industry. It is very important to carry out research on the status evaluation of port carbon emissions, predict the future trend of carbon emissions, and explore the strategy and path of port carbon peak. In this study, the carbon emission scenario prediction model based on the detailed list of port equipment was established to simulate the port carbon emissions. It is also applied to a northern port in China to study and judge the peak value and time point of carbon emission in the future, and propose the path of carbon peak in the port.

In recent years, carbon emission reduction in the shipping sector has increasingly garnered scholarly attention. This study delves into the pathways for carbon emission reduction in shipping across the Yangtze River, emphasizing fuel alternatives. It initiates by introducing a novel ship carbon emission calculation methodology predicated on voyage data, followed by the development of a predictive model for ship carbon emissions tailored to specific voyages. Then, emission reduction scenarios for various voyage categories are designed and exemplary alternative fuels selected to assess their potential for emission mitigation. Subsequently, scenario analysis is employed to scrutinize the CO2 emission trajectories under diverse conditions, pinpointing the most efficacious route for carbon emission abatement for inland vessels. Finally, the proposed method is applied to the middle and lower reaches of the Yangtze River. The results indicate that accelerating the adoption of alternative fuels for long-distance cargo ships would greatly accelerate the development of environmentally friendly shipping. Under a scenario prioritizing zero-carbon growth, emissions from inland vessels are anticipated to reach their zenith by 2040. These findings can provide theoretical guidance for emission reductions in inland shipping and effectively promote the green and sustainable development of the shipping sector. [ABSTRACT FROM AUTHOR]

In the urban delivery sector, expanding the use of new energy vehicles (NEVs) can have a considerable positive impact on low-carbon sustainable development. To promote the application of newenergy vehicle in logistics industry, China’s Ministry of Transport launched Green Urban Freight Pilot Program in 2017 and has been achieved with promising results. However, the program has also encountered various restrictions and obstacles. This study carried out local investigations in six pilot cities of the Green Urban Freight Pilot Program. Key challenges of new energy logistics vehicles (NELV) adoption such as insignificant road access privileges, low cost-effectiveness, insufficient charging facilities, etc., were further identified through field research, online interviews and panel discussions. Then, detailed policy suggestions and recommendations targeting to national level and local level are proposed to promoting NELVs development.

This paper proposes a new optimization algorithm for backpropagation (BP) neural networks by fusing integer-order differentiation and fractional-order differentiation, while fractional-order differentiation has significant advantages in describing complex phenomena with long-term memory effects and nonlocality, its application in neural networks is often limited by a lack of physical interpretability and inconsistencies with traditional models. To address these challenges, we propose a mixed integer-fractional (MIF) gradient descent algorithm for the training of neural networks. Furthermore, a detailed convergence analysis of the proposed algorithm is provided. Finally, numerical experiments illustrate that the new gradient descent algorithm not only speeds up the convergence of the BP neural networks but also increases their classification accuracy. [ABSTRACT FROM AUTHOR]

Business insolvency in the building and construction industry is a major concern on a worldwide scale, and it is particularly pervasive in the Australian construction industry. Many Australian construction companies frequently uses high levels of borrowing and poor profit margins, which increases the likelihood of insolvency. This paper develops a novel, intelligent insolvency prediction model for the Australian construction companies. The proposed framework with bidirectional long short-term memory (BiLSTM) models and autoencoder techniques contains not only the financial variables but also other important indicators that are linked to the features of the sector that have previously been disregarded. Finally, numerical experiments show that the proposed neural network model outperforms several existing models for predicting the insolvency of construction companies. [ABSTRACT FROM AUTHOR]

The large increase in the on-road vehicle population in China has raised sustainability concerns regarding air pollution prevention, energy conservation, and climate change mitigation. Vehicle emission inventory is an irreplaceable tool to characterize the temporal and spatial distribution of the air pollutant and provide guidance to the policy makers with effective vehicle emission controls. This review paper reviewed two kinds of typical vehicle emission inventories. The top-down vehicle emission inventories is calculated based on the static datasets (e.g., vehicle population, vehicle kilometer traveled, and fuel consumption). These inventories could track historical emissions abatement progress and examine potential benefits from future regulations. The technological evolution in intelligent transportation systems have facilitated emission inventories to satisfy the increasing sophisticated management demand. The bottom-up link-level vehicle emission inventories are development based on the availability of the real-world traffic profiles. To simulate the temporal and spatial patterns with high-resolution, traffic demand model and machine learning methods are employed to elucidate traffic emissions.

Transportation is one of the important fields of carbon emission. Promoting the Carbon Peak in transportation industry is a key point to accelerate the green and low-carbon transformation and promote the high-quality development of the transportation industry. The transportation sector covers a wide range of fields, and there are many coordination departments. The local transportation authorities still lack a strong grasp in carrying out the Carbon Peak work. It is urgent to further improve the Carbon Peak system and mechanism in the transportation sector. This paper refines the current situation and characteristics of carbon emission in the transportation field of Inner Mongolia Autonomous Region, summarizes the progress of Carbon Peak in the transportation field, analyzes the constraints of the system and mechanism of Carbon Peak in the transportation field, and puts forward suggestions to further improve the system and mechanism of carbon emission reduction in the transportation field of Inner Mongolia Autonomous Region.

Due to the lack of air and traffic monitoring data in China, this study employed a remote sensing (RS) system and air quality monitoring sensors to investigate the air pollutant concentrations, traffic profiles and meteorological data in order to characterize roadside pollutant concentrations and identify their influence factors for a major road in Lanzhou. The results suggested that the temporal variations of concentrations of SO2, NO2, PM2.5, PM10 and CO peaked at 5:00-7:00 while O3 peaked at 17:00. The concentrations of roadside air pollutants were lower on weekdays than weekends except O3. The concentrations of PM2.5 and PM10 declined with the height of the monitoring sites increased, peaking at 2-meter height whereas SO2, NO2, CO, and O3 reached their highest concentrations at 4-meter height. The concentrations of roadside NO2 and CO show positive correlations with the volume of heavy-duty trucks (HDTs) and light-duty passenger vehicles (LDPVs), respectively. This study provided scientific support to the establishment of roadside air quality monitoring systems and the assessment of the environmental and health impact from vehicle emissions.

By considering the symbiosis between rest behavior with charging behavior during intercity traveling, the inconvenience of battery electric vehicles (BEVs) confined by the battery capacity can be relieved. This paper proposed a quantitative analysis method for revealing this phenomenon from an accessibility perspective. This proposed method involved a calculation model of intercity travel time for electric vehicle by considering the current configuration of electric vehicle charging facilities and energy consumption with travel speed. Based on the highway network and charging pile network in Beijing-Tianjin-Hebei region, a case study has been analyzed to verify the feasibility of the model. The results show that the accessibility performance of BEVs and fuel vehicles is close in Beijing Tianjin Hebei region, which has relatively perfect charging facilities. And by joint the charging behavior and rest behavior, there is almost no difference between BEV models with a large range and advanced charging technology and fuel vehicles in intercity travel. In addition, the charging facilities along the expressway in the region can basically satisfy the experience of intercity travel for BEVs, and further increasing the number of charging stations cannot significantly improve the accessibility of electric vehicles.

CFD, comprising multiple types of softwares, involves complex technological theories and is used to solve profound issues with a huge amount of calculation. Given the complexity and profundity, this study selects Fluent, OpenFOAM and Star-CCM+ as representatives to review the application of the CFD simulation technology in the vehicle exhaust dispersion models. Fluent, a commercial CFD software, started relatively early and has so far been the most widely used in the field of motor vehicle exhaust dispersion research; OpenFOAM, is open source and has a large number of models and algorithms. Apart from supporting pure model research, OpenFOAM has broad application prospects in coupling with the mesoscale model WRF to improve the accuracy of pollutant dispersion simulation; Star-CCM+, less applied in the research field of vehicle exhaust dispersion, focuses more on vehicle air conditioning system, vehicle radiator, refueling, vehicle aerodynamic noise source simulation and other related fields. The research provides theoretical basis and scientific reference for the application of CFD in the field of urban block-scale air quality research. In the future, with the rapid development of computer technology and the introduction of new theories and intelligent algorithms, CFD technology is very likely to achieve new breakthroughs and continue to enjoy even wider application in the research of motor vehicle exhaust dispersion.

Public transport (PT) networks face significant challenges in achieving optimal outcomes due to the presence of risk and uncertainty. Despite the importance of optimising PT networks' performance, limited research has applied risk management tools to tackle this issue. In response, this study presents a three-stage framework to optimise PT networks' performance in uncertain conditions. First, we establish a PT criteria matrix using an analytic hierarchy process to develop a criteria model and calculate the criteria weightings. Second, we propose a multi-aspiration-level goal programming approach to optimise a PT network's performance based on the weighted results. To manage uncertainty, we use Monte Carlo simulation to analyse the probability of the optimal solution. Finally, to validate our approach, we apply the three-stage framework to three case study areas in Australia. The results of this research offer significant insights into identifying the likelihood of criteria optimisation scenarios, thereby assisting decision makers in allocating resources for optimising the delivery of PT network performance solutions in accordance with government requirements. [ABSTRACT FROM AUTHOR]

In this paper, we consider a general class of discrete-time optimal control problems subject to all-time-step constraints on the state and control variables. The derivations of the gradient formulas for the cost and constraint functions for this constrained discrete-time optimal control problem are rather involved. We present a simple approach to the derivations of these gradient formulas based on reversed automatic differentiation. On this basis, a numerical algorithm is developed to solve this all-time-step constrained discrete-time optimal control problem. We then consider a class of continuous-time optimal control problems subject to continuous state inequality constraints. This constrained continuous-time optimal control problem is discretized into a discrete-time optimal control problem with all-time-step constraints using the Euler discretization method. Then, the algorithm developed for constrained discrete-time optimal control problem is applied to solve this discretized optimal control problem. Numerical examples are presented to verify the applicability of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, a calculation method system of air pollutant emission inventory of port operation machinery in container terminal is established. Through the special investigation of port machinery manufacturers and engine manufacturers, the average load rate data of main port machinery engines in domestic container terminals are obtained. Taking a domestic container terminal as an example, the dynamic method and fuel consumption method are used to calculate the air pollutant emission inventory of port operation machinery, and the calculation results of the two methods are compared and analyzed. The results show that the calculation method system of air pollutant emission inventory of port operation machinery established in this paper is highly practical, and the calculation results obtained by the two methods are very close.

This paper proposes a method to enhance the accuracy of power load forecasting by considering the variability in the impact of multi‐dimensional meteorological information on power load in diverse regions. The proposed method employs spatio‐temporal fusion (SF) of multi‐dimensional meteorological information and applies the Copula theory to analyze the non‐linear coupling of meteorological information from multiple stations with power load to achieve SF in the spatial dimension. To enhance the accuracy of load forecasting in the time dimension, this paper improves the core parameters of the variational mode decomposition (VMD) using the marine predators algorithm (MPA) and utilizes the weighted permutation entropy (WPE) to construct the MPA‐VMD fitness function for the adaptive decomposition of the load sequence. Moreover, this paper constructs input sets for the long short‐term memory model and the MPA‐LSSVM model by combining each component of the time dimension and each meteorological information of the spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index and reconstructed to obtain the overall prediction results. The analysis results demonstrate that the proposed forecasting method outperforms the traditional forecasting method and effectively enhances the accuracy of power load forecasting. [ABSTRACT FROM AUTHOR]