• Energy Research
• 11. Sustainability close
• ZENODO close
• Transport Research close

We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel.

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.

Output generated by the RAMP engine for use in the Sector-Coupled Euro-Calliope model. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope. Cooking energy demand profiles (ramp-cooking-profiles): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the RAMP model [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**. Electric vehicle plug-in profiles (ramp-ev-plugin-profiles): Profiles of the percentage of parked electric vehicles, stochastically generated using the RAMP-Mobility model [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*. Electric vehicle energy consumption profiles (ramp-ev-consumption-profiles): Profiles of the electricity consumption of electric vehicles, stochastically generated using the RAMP-Mobility model [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*. * AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE ** (*) + BGR, SRB *** ALB, MKD, GRC, CYP, BIH, MNE, ISL [1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. Energy 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097. [2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. Applied Energy 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676. {"references": ["Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. 'Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model'. Energy 177 (June): 433\u201344. https://doi.org/10.1016/j.energy.2019.04.097", "Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavi\u010devi\u0107, Sylvain Quoilin, and Emanuela Colombo. 2022. 'Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries'. Applied Energy 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676"]}

La creciente tendencia de los vehículos eléctricos (EV) y la construcción de sistemas fotovoltaicos integrados (BIPV) es un medio prometedor para reducir los problemas relacionados con el cambio climático. Las cargas de EV se pueden gestionar a través de un agregador para maximizar el uso de energía verde producida por unidades fotovoltaicas (PV) a través de estrategias de carga inteligentes que explotan la demanda de EV controlable conectada a BIPV. Los trabajos anteriores se han centrado en la coordinación de la carga de vehículos eléctricos en un BIPV inteligente, aunque sin una optimización que fomente la carga de vehículos eléctricos con la energía producida por las unidades fotovoltaicas. Este documento propone una estrategia de agregación que maximiza un índice de energía verde (GEI) para la coordinación de carga inteligente de los vehículos eléctricos, que aprovecha los períodos con alta disponibilidad fotovoltaica para cargar las baterías de los vehículos eléctricos; además, una etapa de posprocesamiento para el GEI proporciona a los propietarios de vehículos eléctricos información sobre el porcentaje de energía cargada, período por período, que proviene de la generación fotovoltaica. Los resultados de un estudio de caso con 510 vehículos eléctricos integrados con 17 BIPV inteligentes muestran que la estrategia optimiza de manera efectiva el uso de la energía producida por las unidades fotovoltaicas para cargar los vehículos eléctricos, contribuye a reducir el consumo de energía no renovable del sector de la construcción y satisface los requisitos de energía de los propietarios de vehículos eléctricos para el transporte. La tendance croissante des véhicules électriques (VE) et de la construction de systèmes photovoltaïques intégrés (BIPV) est un moyen prometteur de réduire les problèmes liés au changement climatique. Les charges de VE peuvent être gérées via un agrégateur pour maximiser l'utilisation de l'énergie verte produite par les unités photovoltaïques (PV) grâce à des stratégies de charge intelligentes qui exploitent la demande de VE contrôlable connectée au BIPV. Les travaux précédents se sont concentrés sur la coordination de la charge des véhicules électriques dans un BIPV intelligent, mais sans optimisation qui encourage la charge des véhicules électriques avec l'énergie produite par les unités photovoltaïques. Cet article propose une stratégie d'agrégation qui maximise un indice d'énergie verte (GEI) pour la coordination de la charge intelligente des VE, qui tire parti des périodes de disponibilité PV élevée pour charger les batteries de VE ; en outre, une étape de post-traitement pour le GEI fournit aux propriétaires de VE des informations sur le pourcentage d'énergie chargée, période par période, qui provient de la production PV. Les résultats d'une étude de cas avec 510 VE intégrés à 17 BIPV intelligents montrent que la stratégie optimise efficacement l'utilisation de l'énergie produite par les unités photovoltaïques pour charger les VE, contribue à réduire la consommation d'énergie non renouvelable du secteur du bâtiment et satisfait les besoins énergétiques des propriétaires de VE pour le transport. The growing trend of electric vehicles (EVs) and building integrated photovoltaics (BIPVs) is a promising means to reduce related climate change issues. EV loads can be managed via an aggregator to maximize the usage of green energy produced by photovoltaic units (PV) through smart charging strategies that exploit controllable EV demand connected to BIPV. Previous works have focused on the EV charging coordination in a smart BIPV, although without an optimization that encourages EV charging with the energy produced by the PV units. This paper proposes an aggregation strategy that maximizes a green energy index (GEI) for the smart charging coordination of EVs, which takes advantage of periods with high PV availability to charge the EV batteries; moreover, a post-processing stage for the GEI provides EV owners with information about the percentage of charged energy, period by period, that comes from PV generation. The results for a case study with 510 EVs integrated with 17 smart BIPVs show that the strategy effectively optimizes the usage of the energy produced by the PV units to charge the EVs, contributes to reduce non-renewable energy consumption of the building sector, and satisfies the EV owners' energy requirements for transportation. الاتجاه المتنامي للسيارات الكهربائية وبناء الخلايا الكهروضوئية المتكاملة (BIPVs) هو وسيلة واعدة للحد من قضايا تغير المناخ ذات الصلة. يمكن إدارة أحمال السيارات الكهربائية عبر مجمع لزيادة استخدام الطاقة الخضراء التي تنتجها الوحدات الكهروضوئية من خلال استراتيجيات الشحن الذكية التي تستغل الطلب على السيارات الكهربائية التي يمكن التحكم فيها والمتصلة بـشركة بي آي بي في. ركزت الأعمال السابقة على تنسيق شحن السيارة الكهربائية في BIPV الذكية، على الرغم من عدم وجود تحسين يشجع شحن السيارة الكهربائية بالطاقة التي تنتجها الوحدات الكهروضوئية. تقترح هذه الورقة استراتيجية تجميع تزيد من مؤشر الطاقة الخضراء (GEI) لتنسيق الشحن الذكي للمركبات الكهربائية، والتي تستفيد من الفترات ذات التوافر الكهروضوئي العالي لشحن بطاريات المركبات الكهربائية ؛ علاوة على ذلك، توفر مرحلة ما بعد المعالجة لمالكي المركبات الكهربائية معلومات حول النسبة المئوية للطاقة المشحونة، فترة تلو الأخرى، التي تأتي من توليد الطاقة الكهروضوئية. تُظهر نتائج دراسة حالة شملت 510 سيارات كهربائية مدمجة مع 17 سيارة ذكية من طراز BIPV أن الاستراتيجية تعمل على تحسين استخدام الطاقة التي تنتجها الوحدات الكهروضوئية لشحن السيارات الكهربائية بشكل فعال، وتسهم في تقليل استهلاك الطاقة غير المتجددة في قطاع البناء، وتلبي متطلبات مالكي السيارات الكهربائية من الطاقة للنقل.

Abstract Plug-in electric vehicles (PEVs) are a promising option for greenhouse gas (GHG) mitigation in the transport sector - especially when the fast decrease in carbon emissions from electricity provision is considered. The rapid uptake of renewable electricity generation worldwide implies an unprecedented change that affects the carbon content of electricity for battery production as well as charging and thus the GHG mitigation potential of PEV. However, most studies assume fixed carbon content of the electricity in the environmental assessment of PEV and the fast change of the generation mix has not been studied on a global scale yet. Furthermore, the inclusion of up-stream emissions remains an open policy problem. Here, we apply a reduced life cycle assessment approach including the well-to-wheel emissions of PEV and taking into account future changes in the electricity mix. We compare future global energy scenarios and combine them with PEV diffusion scenarios. Our results show that the remaining carbon budget is best used with a very early PEV market diffusion; waiting for cleaner PEV battery production cannot compensate for the lost carbon budget in combustion vehicle usage.

Transport can play a key role in mitigating climate change, through reducing traffic, emissions and dependency on private vehicles. Transport is also crucial to connect remote areas to central or urban areas. Yet, sustainable and flexible transport is among the greatest challenges for rural areas and rural–urban regions. Innovative transport concepts and approaches are urgently needed to foster sustainable and integrated regional development. This article addresses challenges of sustainability, accessibility, and connectivity through examining complementary systems to existing public transport, including demand-responsive transport and multimodal mobility. We draw upon case studies from the Metropolitan Area of Styria, Ljubljana Urban Region and rural Wales (GUSTmobil, REGIOtim, EURBAN, Bicikelj, Bwcabus, Grass Routes). In-depth analysis through a mixed-methods case study design captures the complexity behind these chosen examples, which form a basis for analysing the effects of services on accessibility for different groups, connectivity to public transport and usability as a “first and last mile” feeder. We further explore the weaknesses of complementary transport systems, including legal, organisational and financial barriers, and offer potential solutions to structure and communicate complementary transport systems to improve access and use. Looking ahead, we use the case studies to anticipate innovative, sustainable “mobility as a service” (MaaS) solutions within and between urban and rural areas and consider how future public policy orientations and arrangements can enable positive change. A main concern of our article and the contribution to scientific literature is through exploring the benefit of well-established multi-level governance arrangements when introducing smaller-scale mobility solutions to improve rural–urban accessibility. It becomes clear that not a one-size-fits-all model but placed-based and tailored approaches lead to successful and sustainable concepts.

Background: The correct design of electric vehicle (EV) charging infrastructures is of fundamental importance to maximize the benefits for users and infrastructure managers. In addition, the analysis and management of recharges can help evaluate integration with auxiliary systems, such as renewable energy resources and storage systems. EV charging data analysis can highlight informative behaviours and patterns for charging infrastructure planning and management. Methods: We present the analysis of two datasets about the recorded energy and duration required to charge EVs in the cities of Barcelona (Spain) and Turku (Finland). In particular, we investigated hourly, daily and seasonal patterns in charge duration and energy delivered. Simulated scenarios for the power request at charging stations (CSs) were obtained using statistical parameters of the Barcelona dataset and non-parametric distributions of the arrivals. Monte Carlo simulations were used to test different scenarios of users’ influx at the CSs, and determine the optimal size of an integrated renewable energy system (RES). Results: This study highlighted the difference between fast and slow charging users’ habits by analysing the occupancy at the charging stations. Aside from the charge duration, which was shorter for fast charges, distinct features emerged in the hourly distribution of the requests depending on whether slow or fast charges are considered. The distributions were different in the two analysed datasets. The investigation of CS power fluxes showed that results for the investment on a RES could substantially vary when considering synthetic input load profiles obtained with different approaches. The influence of incentives on the initial RES cost were investigated. Conclusions: The novelty of this work lies in testing the impact of different approach to design synthetic profiles in the determination of the optimal size of a photovoltaic (PV) system installed at a charging infrastructure, using the economic criterion of the net present value (NPV).