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Car-sharing services are developing at an ever-increasing pace. Taking into account the reduction of carbon dioxide emissions and pursuit of the sustainable development of transport, implementing electric cars in car-sharing fleets is being proposed. On the one hand, these types of vehicles are referred to as emission-free, but on the other hand, their environmental friendliness is questionable due to the emission of carbon dioxide during the production of energy to power them. Although many scientific papers are devoted to the issue of reducing emissions through car sharing, there is a research gap concerning the real production of carbon dioxide by car-sharing vehicles during car-sharing trips. To fill this research gap, the objective of the article was to analyze the actual level of carbon dioxide emissions from combustion and electric vehicles from car-sharing systems produced when renting rides. The test results showed that the electric car turned out to be significantly less emitting. The use of electric vehicles in car-sharing fleets can reduce carbon dioxide emissions from 14% to 65% compared to using cars with internal combustion engines. However, the key role during car-sharing trips is played by the driving style of the drivers, which has been omitted from the literature to date. This should be properly regulated by service providers and focus on the proper use of energy from electric vehicle batteries, especially at low temperatures. The article provides support for operators planning to modernize their fleet of vehicles and fills the research gap concerning car-sharing emissions.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

Railways play an essential role in China’s development, driving economic and social progress while negatively impacting the environment. Assessing different railway projects’ sustainability can maximize the positive effects and minimize the side effects. This article aims to propose a methodological framework containing a series of indicators to evaluate the sustainability of four railway projects in China. A hybrid MCDM (multi-criteria decision-making) method was adopted to analyze the interrelationships among the dimensions and indicators and then present the projects’ differences. The weights of each indicator in the research were studied according to experts’ advice, and the same indicators were of different importance when placed in different research environments, such as developing and developed countries. This helps to establish a developing-country-centered evaluation indicator system. After comparison, it can be seen that Railway A is the best in terms of sustainability, mainly because it is in an economically developed region of China that is rich in construction resources, and is less constrained by natural conditions than other projects. The results show that although environmental sustainability has been improved over the past few years, its low level of influence still urges people to invest more attention and take more actions with significant effects in this respect. Furthermore, railways show significant differences in economic sustainability, while the differences in social sustainability have reduced considerably. This study provides further insight into the investigated measures and proposes recommendations for improving railway transport in China.

The private motor vehicles are significantly important means of transportation in modern lifestyle, however, these also contribute to a large proportion of the total air pollution and primary energy consumption. In order to develop green transportation system, it becomes imperative to use integrated technologies to achieve reduced emissions and utilize renewable energy. Electric vehicles (EVs) have been considered as one of these technologies to transform the traditional vehicle mix. However, the uptake of EV has been debated on factors like cost, performance (autonomous mileage), charging point infrastructure construction, energy saving, policy and end users’ adaptation. Present study investigates the technology feasibility (which usually refer to EVs’ cost, EV charging, supplier’s customer services quality, EV travel performance) and users’ adaptation of EV in Beijing, which is a key driver for the EV uptake into the Beijing transportation system. The relevant data have been collected and analyzed in the form of questionnaire survey around all of these factors. While considering the user perception and satisfaction, safety of charging and energy bills have also been investigated. According to the data analysis, it has been found the policy of ‘No traffic restrictions for EVs’ (the traffic restrictions means for certain date, from Monday to Friday the motor vehicles with the last register number of 1 and 6, 2 and 7, 3 and 8, 4 and 9, 5 and 0, are restricted to travel, respectively), the availability of the charging infrastructure and technical support are the most significant factors affecting the users’ opinions on using EVs.

As the construction of supporting infrastructure for electric vehicles (EV) becomes more and more perfect, an energy replenishment station (ERS) involving photovoltaics (PV) that can provide charging and battery swapping services for electric vehicle owners comes into the vision of humanity. The operation optimization of each device in the ERS is conducive to improving the service capacity of the ERS, extending the service life of the energy storage batteries (ESB), and enhancing the economic benefits of the ERS. However, traditional model-based optimization algorithms cannot fully consider the stochastic nature of EV owners’ charging and battery swapping demands, the uncertainty of PV output, and the complex operating characteristics of ESB. Therefore, we propose a deep reinforcement learning-based adaptive optimal operation method for ERS considering ESB’s losses. Firstly, a mathematical model of each device in the ERS is established, and a refined energy storage model is introduced to describe ESB’s capacity degradation and efficiency decay. Secondly, to solve the dimensional disaster problem, the state space and action space selection method, and the charging strategy of batteries in the battery swapping station (BSS) are proposed to apply to the ERS, thus modeling the ERS optimization operation problem as a Markov decision process. Then, the solution is performed using a neural network-based proximal policy optimization (PPO) algorithm, consisting of a recurrent neural network that extracts information about the PV outflow trend and a deep neural network used to generate a control policy. Finally, the effectiveness of the proposed method is verified by simulation calculations, which not only enable adaptive decision-making under different PV output scenarios, but also consider the availability of EV battery swapping services, energy storage losses, and the economic benefits of the ERS.

Electric vehicles are an important part of governments’ environmental policies, and therefore understanding the factors affecting their market share is very important. So, this research is designed to investigate the factors affecting electric vehicle adoption, considering the effects of the COVID-19 pandemic and sustainable development level. Effective factors have been investigated in three categories. One is the characteristics of electric vehicles; the other is the impact of the COVID-19 pandemic on demand for these vehicles; and finally, the impact of the level of sustainable development of countries on adopting electric vehicles. Our analysis method is based on grey econometric and grey regression methods. The results show that vehicle dimensions, battery warranty conditions, battery life, and charging facilities are effective factors in the field of vehicle characteristics that can increase the adoption of electric vehicles. Also, the analysis shows that the COVID-19 pandemic has reduced the adoption of electric vehicles. Finally, we have shown that the market share of electric vehicles is higher in countries with a higher sustainable development level because of better economic, social, and cultural infrastructures.

The high growth of the automotive industry reveals the very bright future of this technology and its high penetration effects on the human society. No doubt that the random and volatile charging demand of these devices would affect the power grid optimal operation and scheduling which may be regarded as a new challenge. Therefore, this paper investigates the stochastic scheduling of hybrid AC/DC microgrids considering the plugin hybrid electric vehicles charging demands, distributed all over the grid. Three different charging patterns, called coordinated, uncoordinated and smart charging models with different characteristics for the charger type, capacity and market share are proposed. Moreover, different types of renewable energy sources including wind turbine, solar panel and fuel cell are modeled and considered in the scheduling process of the hybrid microgrid. In order to mitigate the charging effects of electric vehicles on the hybrid AC–DC microgrid operation, some remotely switches are considered in the system which make it possible for changing the topology and power flow way. In order to model the uncertainty effects, a data-driven framework based on point estimate method and support vector machine is developed. This would make it possible to extract out the standard deviation value of the uncertain parameters and reflect their impacts on the microgrid operation problem through the limited concentration points. A novel evolving solution based on flower pollination algorithm is also proposed to solve the problem optimally. An IEEE standard test system is used as the hybrid AC/DC microgrid case study to assess the performance of proposed model.

The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.