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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.

The paper represents the analysis, which has helped to establish the usage of gas hydrate technologies in the methane conversion. This gas could be obtained in different ways. Possibilities and sources for the gas obtaining have been demonstrated. Use of other environmentally friendly sources to support operation in such systems in terms of joint energy complex has been considered. The necessary kinetic connections to provide operational sustainability of all the constituents have been given. The approach helps evaluate quantitatively the priority of its physicochemical transformations to obtain gas hydrates artificially. It is possible to transport methane at considerable distances when it is solidified. Actually, in this case there is no necessity to build costly compressor stations and pipelines for its transportation to consumers. The approach is extremely important for mining regions as it helps prolong the operating period and working out of the abandoned and off-balance coal reserves. In this case, it is proposed to apply special gasification technologies tending to maximum methane recovery. The proposed solutions give the possibility to define the trends of our further research. They will be highlighted in the following authors’ studies.

Introduction: During the COVID-19 lockdown significant improvements in urban air quality were detected due to the absence of motorized vehicles. It is crucial to perpetuate such improvements to maintain and improve public health simultaneously. Therefore, this exploratory study approached bicycle infrastructure in the case of Munich (Germany) to find out which specific bicycle lanes meet the demands of its users, how such infrastructure looks like, and which characteristics are potentially important. Methods: To identify patterns of bicycle infrastructure in Munich exploratory data is collected over the timespan of three consecutive weeks in August by a bicycle rider at different times of the day. We measure position, time, velocity, pulse, level of sound, temperature and humidity. In the next step, we qualitatively identified different segments and applied a cluster analysis to quantitatively describe those segments regarding the measured factors. The data allows us to identify which bicycle lanes have a particular set of measurements, indicating a favorable construction for bike riders. Results: In the exploratory dataset, five relevant segment clusters are identified: viscous, slow, inconsistent, accelerating, and best-performance. The segments that are identified as best-performance enable bicycle riders to travel efficiently and safely at amenable distances in urban areas. They are characterized by their width, little to no interaction with motorized traffic as well as pedestrians, and effective traffic light control. Discussion: We propose two levels of discussion: (1) revolves around what kind of bicycles lanes from the case study can help to increase bicycle usage in urban areas, while simultaneously improving public health and mitigating climate change challenges and (2) discussing the possibilities, limitations and necessary improvements of this kind of exploratory methodology.

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.

To take advantage of the high tidal current velocity near the free surface, the horizontal axis turbine is installed, which inevitably causes hydrodynamic characteristics to effect the turbine by the waves. In this article, we established a numerical calculation method for the hydrodynamic load of a horizontal axis turbine under wave-current conditions. Based on the numerical calculation results, the hydrodynamic loads were decomposed and the influence rules of wave parameters and blade tip immersion depth on the hydrodynamic load were obtained. The study found the following: (1) the multi-frequency fluctuations based on the rotation frequency and incident wave frequency occurred in instantaneous values of the axial load coefficients and energy utilization ratios, and the fluctuation amplitude decreased with the increase of the blade tip immersion depth; (2) the fluctuation amplitude, according to rotation frequency, changed less with the increase of wave period and wave height, and was smaller according to wave frequency; (3) the fluctuation amplitude based on wave frequency increased linearly with the increase of wave height and wave period. The research results can provide the basis and reference for the design and engineering application of tidal current power station.

The power coupling equation and energy consumption model for enhancing the fuel economy and power performance of plug-in hybrid electric trucks (PHETs) are proposed based on the economic velocity planning strategy (EVPS-DSIDP), which takes into account the driving style and an improved dynamic programming (IDP) algorithm. This strategy employs a fuzzy controller to identify the driving style, and optimizes the efficiency and accuracy of the conventional dynamic programming (DP) algorithm by associating decision variables, dynamically adjusting the discretization step size, and restricting the state space. Additionally, a penalty function is introduced to enhance the robustness of the DP algorithm. Under our EVPS-DSIDP, the variation of velocity is liberated from the constraints of fixed driving conditions, and directly correlates with road information and driving styles, which is of significant importance for addressing energy management issues in real-time traffic conditions. Moreover, the proposed IDP algorithm can improve computational efficiency while ensuring calculation accuracy, thereby greatly enhancing the potential for the practical application of this algorithm in real-world vehicle scenarios. The simulation results demonstrate that compared to the rule-based control strategy, the application of the proposed EVPS-DSIDP in the economy velocity planning strategy can achieve an average reduction of 2.88% in economic costs and 10.6% in travel time across different driving styles. This approach offers a more comprehensive optimization of both fuel economy and power performance.