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Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087. Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. Additional details can be found from the published article: https://doi.org/10.1016/j.rser.2022.112087

Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extent This subdataset covers the West Coast CONUS, i.e. CA OR WA For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further information For further information, please see the publication. 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 D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep 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). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extentThis subdataset covers the South CONUS, i.e. AL AR FL GA KY LA MS NC SC TN VA WV For the remaining CONUS, see the related identifiers. Temporal extentThe map is representative for ca. 2018. Data formatThe data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layersNote that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further informationFor further information, please see the publication.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. PublicationD. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. Nature Communications 14, 8014. https://doi.org/10.1038/s41467-023-43755-5 FundingThis 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). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. AcknowledgmentsWe thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Summary Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Fleet Transition Assessment and Decision Support (Geo-FTADS) tool. Relevant Links Link to the online version of the tool (requires creation of a free user account). Link to GitHub repo with source code to produce this dataset and deploy the Geo-FTADS tool locally. Funding This dataset was produced with support from the MIT Climate & Sustainability Consortium. Original Data Sources These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below: Filename(s) Description of Original Data Source(s) Link(s) to Download Original Data License and Attribution for Original Data Source(s) faf5_freight_flows/*.geojson trucking_energy_demand.geojson highway_assignment_links_*.geojson infrastructure_pooling_thought_experiment/*.geojson Regional and highway-level freight flow data obtained from the Freight Analysis Framework Version 5. Shapefiles for FAF5 region boundaries and highway links are obtained from the National Transportation Atlas Database. Emissions attributes are evaluated by incorporating data from the 2002 Vehicle Inventory and Use Survey and the GREET lifecycle emissions tool maintained by Argonne National Lab. Shapefile for FAF5 Regions Shapefile for FAF5 Highway Network Links FAF5 2022 Origin-Destination Freight Flow database FAF5 2022 Highway Assignment Results Attribution for Shapefiles: United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset. License for Shapefiles: This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use. Attribution for Origin-Destination Freight Flow database: National Transportation Research Center in the Oak Ridge National Laboratory with funding from the Bureau of Transportation Statistics and the Federal Highway Administration. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain. Attribution for the 2022 Vehicle Inventory and Use Survey Data: United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070 Attribution for the GREET tool (original publication): Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from this link. Attribution for the GREET tool (2022 updates): Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644 grid_emission_intensity/*.geojson Emission intensity data is obtained from the eGRID database maintained by the United States Environmental Protection Agency. eGRID subregion boundaries are obtained as a shapefile from the eGRID Mapping Files database. eGRID database Shapefile with eGRID subregion boundaries Attribution for eGRID data: United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain. Attribution for shapefile: United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain. US_elec.geojson US_hy.geojson US_lng.geojson US_cng.geojson US_lpg.geojson Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the Station Data for Alternative Fuel Corridors in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy. US_elec.geojson US_hy.geojson US_lng.geojson US_cng.geojson US_lpg.geojson Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: https://afdc.energy.gov/corridors. In the public domain. These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever. daily_grid_emission_profiles/*.geojson Hourly emission intensity data obtained from ElectricityMaps. Original data can be downloaded as csv files from the ElectricityMaps United States of America database Shapefile with region boundaries used by ElectricityMaps License: Open Database License (ODbL). Details here: https://www.electricitymaps.com/data-portal Attribution for csv files: Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal. Attribution for shapefile with region boundaries: ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib. gen_cap_2022_state_merged.geojson trucking_energy_demand.geojson Grid electricity generation and net summer power capacity data is obtained from the state-level electricity database maintained by the United States Energy Information Administration. U.S. state boundaries obtained from this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog. Annual electricity generation by state Net summer capacity by state Shapefile with U.S. state boundaries Attribution for electricity generation and capacity data: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain. electricity_rates_by_state_merged.geojson Commercial electricity prices are obtained from the Electricity database maintained by the United States Energy Information Administration. Electricity rate by state Attribution: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain. demand_charges_merged.geojson demand_charges_by_state.geojson Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this this Data Catalog. Historical demand charge dataset The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance'). Attribution: McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982. eastcoast.geojson midwest.geojson la_i710.geojson h2la.geojson bayarea.geojson saltlake.geojson northeast.geojson Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a public announcement on February 15, 2023 by the United States Department of Energy. The shapefile with Bay area boundaries is obtained from this Berkeley Library dataset. The shapefile with Utah county boundaries is obtained from this dataset from the Utah Geospatial Resource Center. Shapefile for Bay Area country boundaries Shapefile for counties in Utah Attribution for public announcement: United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle. Attribution for Bay area boundaries: San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain. Attribution for Utah boundaries: Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/. License for Utah boundaries: Creative Commons 4.0 International License. incentives_and_regulations/*.geojson State-level incentives and regulations targeting heavy duty vehicles are collected from the State Laws and Incentives database maintained by the United States Department of Energy's Alternative Fuels Data Center. Data was collected manually from the State Laws and Incentives database. Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. In the public domain. These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever. costs_and_emissions/*.geojson diesel_price_by_state.geojson trucking_energy_demand.geojson Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by Moreno Sader et al., and calibrated to the Run on Less dataset for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency. In addition to the data sources outlined in Moreno Sader et al. et al. and the Run on Less dataset, this dataset incorporates: Emission intensity data from the eGRID database, described elsewhere in this metadata. Commercial electricity price data from the US EIA Electricity database, described elsewhere in this metadata. Maximum historical demand charges from the National Renewable Energy Laboratory, described elsewhere in this metadata. Max motor power estimate of 942,900W and frontal area of 10.7 m^s for the Tesla Semi from motormatchup.com. Drag coefficient estimate of 0.36 for the Tesla Semi from notateslaapp.com. Estimates best-in-class truck rolling resistance of 0.0044 from a Rolling Resistance Validation report prepared by the Minnesota Department of Transportation Office of Transportation System Management. Historical diesel prices by state from the United States Energy Information Administration. Estimate of best in class diesel powertrain engine efficiency of 44% from a Fuel Efficiency Technology report by the International Council on Clean Transportation. NACFE Run on Less dataset Historical diesel prices Attribution for original truck model: Moreno Sader K, Biswas S, Jones R, Mennig M, Rezaei R, Green WH. Battery Electric Long-Haul Trucking in the United States: A Comprehensive Costing and Emissions Analysis. ChemRxiv. 2023; doi:10.26434/chemrxiv-2023-48zsc (link to colab notebook included as supplementary material). Attribution for GitHub repository with adapted code for the truck model: MacDonell, D., Moreno-Sader, K., & Biswas, S. (2024). Green_Trucking_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13205854 Attribution for GitHub repository with analysis of the NACFE Run on Less dataset (provides inputs to MacDonell, D., Moreno-Sader, K., & Biswas, S. (2024) cited above): MacDonell, D. (2024). PepsiCo_NACFE_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13173390 Attribution for Run on Less dataset: North American Countil for Freight Efficiency (2023). Run on Less – Electric DEPOT data. Available from: https://runonless.com/run-on-less-electric-depot-reports/ Attribution for data from MotorMatchup: 2022 Tesla Semi Truck Empty Specs. Available from: https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty. Copyright 2024 by MotorMatchup Attribution for data from Not a Tesla App: Not a Tesla App. Everything We Know About the Tesla Semi. 2024. Available from: https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi Attribution for historical diesel prices: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/petroleum/gasdiesel/. In the public domain. Attribution for best in class diesel powertrain efficiency: Delgado O, Rodríguez F, Muncrief R. Fuel Efficiency Technology in European Heavy-Duty Vehicles: Baseline and Potential for the 2020–2030 Time Frame. 2017. Available from: https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf. electrolyzer_operational.geojson electrolyzer_installed.geojson electrolyzer_planned_under_construction.geojson Data on locations and capacities of planned, under-construction, installed, operational electrolyzers was obtained from this DOE Hydrogen Program Record. Data was extracted manually from this DOE Hydrogen Program Record. Attribution: Arjona, Vanessa. DOE Hydrogen Program Record: Electrolyzer Installations in the United States. 2023. Available from https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master. grid_emission_intensity/*.geojson gen_cap_2022_state_merged.geojson trucking_energy_demand.geojson electricity_rates_by_state_merged.geojson demand_charges_merged.geojson demand_charges_by_state.geojson trucking_energy_demand.geojson costs_and_emissions/*.geojson diesel_price_by_state.geojson trucking_energy_demand.geojson U.S. state boundaries obtained from this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog. Attribution: U.S. Department of Commerce, U.S. Census Bureau, Geography Division. State boundaries (generalized for mapping). 2011. In the public domain. refinery.geojson Locations and production rates of hydrogen from refineries are obtained from the following two complementary datasets on the Hydrogen Tools Portal: 1) Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries, and 2) Merchant Hydrogen Plant Capacities in North America Dataset for Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries Dataset for Merchant Hydrogen Plant Capacities in North America Attribution: Copyright © 2024 by H2Tools; H2 Tools is intended for public use. It was built, and is maintained, by the Pacific Northwest National Laboratory with funding from the DOE Office of Energy Efficiency and Renewable Energy's Hydrogen and Fuel Cell Technologies Office. All Rights Reserved. Truck_Stop_Parking.geojson infrastructure_pooling_thought_experiment/*.geojson Obtained from the DOT Bureau of Transportation Statistics's Truck Stop Parking database Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::truck-stop-parking (link for hosted download changes regularly). Attribution: United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::truck-stop-parking. License: This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use. Principal_Port.geojson Obtained from the DOT Bureau of Transportation Statistics's Principal Ports database Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::principal-ports-1 (link for hosted download changes regularly). Attribution: United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::principal-ports-1. License: This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.

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"]}