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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.

Wave power is a potential technology for generating sustainable renewable energy. Several types of wave energy converters (WECs) have been proposed for this purpose. WECs operate in a harsh maritime environment that sets strict limitations on how and when the device can be economically and safely reached for maintenance. Thus, to ensure profitable energy generation over the system life cycle, system reliability is a key aspect to be considered in WEC development. In this article, we describe a reliability analysis approach for WEC development, based on the use of reliability block diagram (RBD) modelling. We apply the approach in a case study involving a submerged oscillating wave surge converter device concept that utilizes hydraulics in its power take-off system. In addition to describing the modelling approach, we discuss the data sources that were used for gathering reliability data for the components used in a novel system concept with very limited historical or experimental data available. This includes considerations of the data quality from various sources. As a result, we present examples of applying the RBD modelling approach in the context of WEC development and discuss the applicability of the approach in supporting the development of new technologies.

Transport is an important source of air pollution and greenhouse gas emissions. Although the applications of information and communication technologies (ICTs) for transport, also known as intelligent transport systems, are seen as having great potential to help reduce emissions from road transport, their exact impact on CO2 emissions are uncertain for decision makers from government to industry. This uncertainty hinders the deployment of such applications. Therefore there is a need for a common evaluation approach to assess the CO2 impact of ICT measures in a systemic and realistic way. In this study, a methodology framework to evaluate the impact of ICT measures on CO2 emissions is explained. The methodology was developed within the European Union FP7 project Amitran. In particular, this study focuses on the outline and the framework architecture of the methodology as well as the required interfaces between the required models. The use of the methodology is demonstrated by applying it to a use case of dynamic traffic light systems. Finally, the efforts made to validate the methodology and make it accessible to users are explained.

Abstract The concept of smart mobility and tourism has evolved from a technology-driven approach to one that focuses on sustainable solutions to address economic, social, and environmental issues. The United Nations (UN) sustainable development goals (SDGs) provide a framework for measuring and tracking progress toward sustainability goals. Key performance indicators (KPIs) are a useful tool for measuring and tracking progress towards these goals, allowing for continuous monitoring and evaluation of progress, identification of areas for improvement, and directing targeted interventions. This research aims to develop an indicators-based framework to evaluate the sustainability of smart and sustainable mobility and tourism in rural areas. Rural areas have often been neglected, or at least less prioritized, in the sustainability development of the mobility sector. The study also seeks to identify the overlap of KPIs between rural tourism and mobility, and how improved green mobility services can enhance sustainable rural tourism. Smart mobility and tourism indicators have a strong mutual relationship in rural communities, driving economic development, improving the quality of life for residents and visitors, and creating more sustainable and livable communities. Smart mobility and tourism indicators also play a crucial role in supporting the UN SDGs by providing data and insights that can inform policy and decision-making. The results of this research conclude how the target and performance setting of projects on sustainable mobility and tourism in rural communities support each other, and how they support achieving SDGs.

The Human Mobility Transition model describes shifts in mobility dynamics and transport systems. The aspirational stage, ‘human urbanism’, is characterised by high active travel, universal public transport, low private vehicle use and equitable access to transport. We explored factors associated with travel behaviour in Africa and the Caribbean, investigating the potential to realise ‘human urbanism’ in this context. We conducted a mixed-methods systematic review of ten databases and grey literature for articles published between January 2008 and February 2019. We appraised study quality using Critical Appraisal Skills Programme checklists. We narratively synthesized qualitative and quantitative data, using meta-study principles to integrate the findings. We identified 39,404 studies through database searching, mining reviews, reference screening, and topic experts’ consultation. We included 129 studies (78 quantitative, 28 mixed-methods, 23 qualitative) and 33 grey literature documents. In marginalised groups, including the poor, people living rurally or peripheral to cities, women and girls, and the elderly, transport was poorly accessible, travel was characterised by high levels of walking and paratransit (informal public transport) use, and low private vehicle use. Poorly controlled urban growth (density) and sprawl (expansion), with associated informality, was a salient aspect of this context, resulting in long travel distances and the necessity of motorised transportation. There were existing population-level assets in relation to ‘human urbanism’ (high levels of active travel, good paratransit coverage, low private vehicle use) as well as core challenges (urban sprawl and informality, socioeconomic and gendered barriers to travel, poor transport accessibility). Ineffective mobility systems were a product of uncoordinated urban planning, unregulated land use and subsequent land use conflict. To realise ‘human urbanism’, integrated planning policies recognising the linkages between health, transport and equity are needed. A shift in priority from economic growth to a focus on broader population needs and the rights and wellbeing of ordinary people is required. Policymakers should focus attention on transport accessibility for the most vulnerable.

Electric vehicles require sufficient public charging infrastructure. This in turn necessitates detailed information on charging demand. In this paper we present a four-step approach to estimating public charging demand of electric vehicles. Previous methods are limited in their ability to provide differentiated results and adapt to future developments. Therefore, we account for user groups (private, carsharing, commercial), technical developments (vehicles, infrastructure), infrastructure availability, and carsharing development (operational area, business models, autonomous vehicles). Our approach also considers the interactions between these factors and allows for scenario analysis yielding the quantity and spatial distribution of public charging demand. We demonstrate our approach for Berlin, Germany. We find that the majority of public charging demand results from carsharing. This demand is concentrated in the city center, even when carsharing is available citywide. Public charging demand for commercial users is relatively low and located outside the city center. For private users, public charging demand shifts to the city center with an increasing market penetration of electric vehicles and technological advancements (increased range, charging speed). Public demand from private users increases dramatically when private infrastructure is absent. Finally, public charging demand shifts to the city center when private users do not have private infrastructure.