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Abstract The kinetics of methane hydrate decomposition was studied using a semibatch stirred-tank reactor. The decomposition was accomplished by reducing the pressure on a hydrate slurry in water to a value below the three-phase equilibrium pressure at the reactor temperature. The data were obtained at temperatures from 274 to 283 K and pressures from 0.17 to 6.97 MPa. The stirring rates were high enough to eliminate mass-transfer effects. Analysis of the data indicated that the decomposition rate was proportional to the particle surface area and to the difference in the fugacity of methane at the equilibrium pressure and the decomposition pressure. The proportionality constant showed an Arrhenius temperature dependence. An estimate of the hydrate particle diameters in the experiments permitted the development of an intrinsic model for the kinetics of hydrate decomposition.

Women’s empowerment is a powerful engine for personal and societal economic development and well-being. Nevertheless, gender biases in physical infrastructure investments lead to negative consequences for women and children that reduce their empowerment and limit their economic benefits. Public fixed-route buses, such as those in Washington, DC, illustrate how physical transportation infrastructure has innate gender biases. These young residents likely depend on strollers to travel longer than a few blocks. The Washington Metropolitan Area Transit Authority (WMATA) runs the public transportation system in Washington, DC. In 2021, 7% of DC’s 720,000 residents were under five. WMATA maintains a fleet of approximately 1595 buses, 95% of which banned the onboarding of open strollers until recently. This ban directly limited the use of Metro buses for the caregivers of young children, primarily women. It also reduced the opportunities for these caregivers to participate in DC’s economic life. In neighborhoods dependent on buses for essential mobility, the stroller ban reduces employment, healthcare, social service, educational, and recreational offerings beyond walkable distances. This paper examines the publicly available discussions and actions that led to the updated stroller policy and offers opportunities for improving caregiver transit access in Washington, DC, and, by extension, other cities worldwide.

With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained.

The development of public charging infrastructure is crucial to support mass electric vehicle (EV) adoption. Although many cities worldwide have already installed an initial network of public chargers, it is often unclear whether the current supply of infrastructure is in line with demand and how many more charging stations are required to cope with future EV growth. In this sense, transactional charging data on the existing network can help answer these questions. We present a novel method that uses historical charging data as input to obtain answers to the following questions: (a) How many more chargers are required to meet future demand? and (b) Where should these new chargers be installed? By mining the individual charging behavior of EV drivers, we show that overflow dynamics can be found between charging stations. That is, when a preferred charging station is fully occupied, it is found that EV drivers divert to other charging stations nearby. Identifying these dynamics allows us to simulate the impact of a demand increase on the charging infrastructure network more accurately. We found the number of new chargers required to be significantly lower when considering overflow dynamics. Our simulations indicate that if demand is doubled, 30%–50% fewer charging points are needed compared with a situation in which overflow dynamics are neglected but the same failure rate is still maintained (i.e., percentage of failed charging sessions in the network). Determining the exact number of chargers will depend on the failure rate policymakers are willing to accept, reflecting the trade-off between charging convenience and utilization.

Currently, the adoption of electric vehicles (EV) draws much attention, as the environmental issue of reducing carbon emission is increasing worldwide. However, different countries face different challenges during this transition, particularly developing countries. This research aims to create a framework for the transition to EV in Indonesia through Agent-Based Modeling (ABM). The framework is used as the conceptual design for ABM to investigate the effect of agents’ decision-making processes at the microlevel into the number of adopted EV at the macrolevel. The cluster analysis is equipped to determine the agents’ characteristics based on the categories of the innovation adopters. There are 11 significant variables and four respondents’ clusters: innovators, early majority, late majority, and the uncategorized one. Moreover, Twitter data analytics are utilized to investigate the information engagement coefficient based on the agents’ location. The agents’ characteristics which emerged from this analysis framework will be used as the fundamental for investigating the effect of agents’ specific characteristics and their interaction through ABM for further research. It is expected that this framework will enable the discovery of which incentive scheme or critical technical features effectively increase the uptake of EV according to the agents’ specific characteristics.

AbstractThis paper assesses how parameter uncertainties in the model for rise velocity of CO2 droplets in the ocean cause uncertainties in their rise and dissolution in marine waters. The parameter uncertainties in the rise velocity for both hydrate coated and hydrate free droplets are estimated from experiment data. Thereafter the rise velocity is coupled with a mass transfer model to simulate the fate of dissolution of a single droplet.The assessment shows that parameter uncertainties are highest for large droplets. However, it is also shown that in some circumstances varying the temperature gives significant change in rise distance of droplets.

In this study, a mixed integer linear programming model that integrates multimodal transport—truck and rail—into the switchgrass-based bioethanol supply chain was formulated. The objective of this study was to minimize the total cost for cultivation and harvesting, infrastructure, the storage process, bioethanol production, and transportation. Strategic decisions, including the number and location of intermodal facilities and biorefineries, and tactical decisions, such as the amount of biomass shipped, processed, and converted into bioethanol, were validated by using North Dakota as a case study. It was found that the multimodal transport scenario was more cost effective than a single mode of transport (truck) and resulted in a lower cost for bioethanol. A sensitivity analysis was conducted to demonstrate the impact of key factors in the decision to use multimodal transport in a switchgrass-based bioethanol supply chain and on the cost of bioethanol.

This paper reports on a review of the European literature about the impacts of having an electrically-assisted bike available to use, together with results from a trial in the UK city of Brighton, where 80 employees were loaned an electrically-assisted bike for a 6–8 week period. In the Brighton trial, three-quarters of those who were loaned an e-bike used them at least once a week. Across the sample as a whole, average usage was in the order of 15–20 miles per week, and was accompanied by an overall reduction in car mileage of 20%. At the end of the trial, 38% participants expected to cycle more in the future, and at least 70%said that they would like to have an e-bike available for use in the future, and would cycle more if this was the case. This is consistent with the results of the European literature which shows that when e-bikes are made available, they get used; that a proportion of e-bike trips typically substitutes for car use; and that many people who take part in trials become interested in future e-bike use, or cycling more generally