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Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. This dataset features a detailed map of material stocks for the whole of Germany on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Temporal extent The map is representative for ca. 2018. Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. The dataset features area and mass for different street types area and mass for different rail types area and mass for other infrastructure area, volume and mass for different building types Masses are reported as total values, and per material category. Units area in m² height in m volume in m³ mass in t for infrastructure and buildings Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

The European Commission (EC) has set ambitious CO2 emission reduction objectives for the transport sector by 2050. In this context, most decarbonisation scenarios for transport foresee large market penetration of electric vehicles in 2030 and 2050. The emergence of electrified car mobility is, however, uncertain due to various barriers such as battery costs, range anxiety and dependence on battery recharging networks. Those barriers need to be addressed in the 2020–2030 decade, as this is key to achieving electrification at a large scale in the longer term. The paper explores the uncertainties prevailing in the first decade and the mix of policies to overcome the barriers by quantifying a series of sensitivity analysis scenarios of the evolution of the car markets in the EU Member States and the impacts of each barrier individually. The model used is PRIMES-TREMOVE, which has been developed by E3MLab and constitutes a detailed energy-economic model for the transport sector. Based on model results, the paper assesses the market, energy, emission and cost impacts of various CO2 car standards, infrastructure development plans with different geographic coverage and a range of battery cost reductions driven by learning and mass industrial production. The assessment draws on the comparison of 29 sensitivity scenarios for the EU, which show that removing the barriers in the decade 2020–2030 is important for electrification emergence. The results show that difficult policy dilemmas exist between adopting stringent standards and infrastructure of wide coverage to push technology and market development and adverse effects on costs, in case the high cost of batteries persists. However, if the pace of battery cost reductions is fast, a weak policy for standards and infrastructure is not cost-effective and sub-optimal. These policies are shown to have impacts on the competition between pure electric and plug-in hybrid vehicles. Drivers that facilitate electrification also favour the uptake of the former technology, the latter being a reasonable choice only in case the barriers persist and obstruct electrification.

A “Well-to-Propeller” Life Cycle Assessment of maritime transport was performed with a European geographical focus. Four typical types of vessels with specific operational profiles were assessed: a container vessel and a tanker (both with 2-stroke engines), a passenger roll-on/roll-off (Ro-Pax) and a cruise vessel (both with 4-stroke engines). All main engines were dual fuel operated with Heavy Fuel Oil (HFO) or Liquefied Natural Gas (LNG). Alternative onshore and offshore fuel supply chains were considered. Primary energy use and greenhouse gas emissions were assessed. Raw material extraction was found to be the most impactful life cycle stage (~90% of total energy use). Regarding greenhouse gases, liquefaction was the key issue. When transitioning from HFO to LNG, the systems were mainly influenced by a reduction in cargo capacity due to bunkering requirements and methane slip, which depends on the fuel supply chain (onshore has 64% more slip than offshore) and the engine type (4-stroke engines have 20% more slip than 2-stroke engines). The combination of alternative fuel supply chains and specific operational profiles allowed for a complete system assessment. The results demonstrated that multiple opposing drivers affect the environmental performance of maritime transport, a useful insight towards establishing emission abatement strategies.

The growing popularity of traveling with electric scooters in recent years is, on the one hand, a great opportunity to shift from the current forms of mobility to a new service among the public, but it is also a huge challenge for urban centers and operators. One of the main challenges related to electric scooters is the growing demand for high energy consumption, which is currently a big issue due to the growing interest in electromobility used in mobility as a service (MaaS) systems. However, in order to properly estimate the level of energy consumption of scooters, there is a real need for reliable scientific information on the actual electricity consumption of electric scooters used in shared mobility systems. An extensive literature analysis indicates the lack of research conducted in this area. Noting this research gap, studies were conducted to determine the factors influencing the energy consumption of an electric scooter. The study attempts to determine these values on the basis of the proposed research experiment. The scientific experiment was based on a three-factor plan; an experimental plan. The author conducted the research by driving and measuring electric scooters in order to compare the actual consumption with the data provided by vehicle manufacturers. The result of the research was to determine the influence of factors that influence the significant energy consumption of electric scooters from shared mobility systems. Interestingly, the results generated show that the electricity requirements for six scooters corresponded to one electric car. This proposal raises the question of whether scooters are the best form of electric mobility for the environment. In addition, the work also includes guidelines for scooter users and recommendations for shared mobility operators on the energy consumption of electric scooters.

This paper evaluates how policy shaped the emergence of electric mobility in three countries, Norway, the Netherlands and Denmark, between 2010 and 2015. Whereas previous studies have looked at the effects of separate policy instruments, this paper gives insights in the interaction effects of instruments on the diffusion of battery electric cars between five policy areas. Based on analysis of synergetic, contradictory and pre-conditional effects, we find that an effective policy mix includes: fiscal incentives that mirror the actual carbon footprint of the respective vehicles; non-fiscal demand-side incentives; centrally financed and/or coordinated charging infrastructure; clarity regarding the choice of technology that will be supported. Moreover, development of a domestic, e-mobility-related industry and a high share of renewable energy strengthens the legitimization of e-mobility support. The findings help designing policy mixes in the transition to electric mobility.

In the context of global suburbanization and population aging, a low-speed, automated vehicle (LSAV) system provides essential mobility services in suburban residential areas. Although extensive studies on shared autonomous vehicle (SAV) services have been conducted, quantitative investigations on the operation of suburban LSAV systems are limited. Based on a demonstration pilot project of an autonomous vehicle called “Slocal Automated Driving”, we investigated the performance of an SAV system considering several scenarios in Kozoji Newtown, a suburban commuter town in Japan. The agent-based simulation results revealed that 40 LSAVs can satisfy the demands of 2263 daily trips with an average wait time of 15 min. However, in the case of a high-speed scenario, the same fleet size improved the level of service (LOS) by reducing the average wait time to two and a half minutes and halving the in-vehicle time. By contrast, the wait time in terms of the average and 95th percentile of the no-sharing ride scenario drastically deteriorated to an unacceptable level. Based on the fluctuations of hourly share rates, wait times, and the number of vacant vehicles, we determined that preparing for the potential fleet insufficiency periods from 7:00–13:00 and 15:00–18:00 can improve the LOS.

The main interest of this paper is to analyze the mobility acceptance factors of an automated shuttle bus last-mile service. There is limited research on the passengers’ perception of security and safety of automated mobility, whereas prior research is mostly based on surveys interested in attitudes towards self-driving vehicles, without being linked to the experience. We, on the other hand, are interested in passengers’ feeling of security and safety, after taking a ride with an automated shuttle in an open urban environment. For studying this, we conducted an automated shuttle bus last-mile pilot during a four-month period in the city of Tallinn in late 2019. The method is a case study focusing on one city with several tools for data collection applied (surveys, interviews, document analysis). The pilot, open and free for everybody, attracted approximately 4000 passengers, out of which 4% responded to the online feedback survey. For studying the operational capacity, we had a panel interview with operators of the shuttle service, in addition to analyzing daily operational log files. The results indicate that passengers’ perceived feeling of security and safety onboard was remarkably high, after taking a ride (and lower without a ride, in a different control group). The bus was operated only if operational capacity was secured, thus having significant downtime in service due to environment, technology and traffic-related factors.

Understanding the nature of consumers’ environmental behaviors will help design better environmental policies for a sustainable future. Drawing on the responsible environmental behavior (REB) theoretical framework, we disentangle the effects of social and psychological environmental factors on Europeans’ behaviors, considering that living contexts vary from country to country. Using data on attitudes to the environment sourced from the 2017 Eurobarometer, we measure the socio-psychological factors and environmental behaviors using exploratory factor analysis. A multilevel model measures the effect of individual-level environmental factors and analyzes the impact of the country context on Europeans’ environmental behaviors. Results show that the three tested environmental behaviors (eco-friendly purchasing, public transport use, and reduced resource consumption) are explained by individual-level environmental factors as well as by country differences, but the effects differ depending on the behavior considered. We also find that the effects of knowledge, attitudes, and perceived behavioral control are mediated by a set of social indicators (age, gender, education, and income). We conclude with a discussion of the implications for policymakers.