• Energy Research
• Open Access close
• Closed Access close
• CN close
• US close
• GB close
• Transport Research close

Car-sharing services are developing at an ever-increasing pace. Taking into account the reduction of carbon dioxide emissions and pursuit of the sustainable development of transport, implementing electric cars in car-sharing fleets is being proposed. On the one hand, these types of vehicles are referred to as emission-free, but on the other hand, their environmental friendliness is questionable due to the emission of carbon dioxide during the production of energy to power them. Although many scientific papers are devoted to the issue of reducing emissions through car sharing, there is a research gap concerning the real production of carbon dioxide by car-sharing vehicles during car-sharing trips. To fill this research gap, the objective of the article was to analyze the actual level of carbon dioxide emissions from combustion and electric vehicles from car-sharing systems produced when renting rides. The test results showed that the electric car turned out to be significantly less emitting. The use of electric vehicles in car-sharing fleets can reduce carbon dioxide emissions from 14% to 65% compared to using cars with internal combustion engines. However, the key role during car-sharing trips is played by the driving style of the drivers, which has been omitted from the literature to date. This should be properly regulated by service providers and focus on the proper use of energy from electric vehicle batteries, especially at low temperatures. The article provides support for operators planning to modernize their fleet of vehicles and fills the research gap concerning car-sharing emissions.

The COVID-19 pandemic has tremendously affected the whole of human society worldwide. Travel patterns have greatly changed due to the increased risk perception and the governmental interventions regarding COVID-19. This study aimed to identify contributing factors to the changes in public and private transportation mode choice behavior in China after COVID-19 based on an online questionnaire survey. In the survey, travel behaviors in three periods were studied: before the outbreak (before 27 December 2019), the peak (from 20 January to 17 March 2020), and after the peak (from 18 March to the date of the survey). A series of random-parameter bivariate Probit models was developed to quantify the relationship between individual characteristics and the changes in travel mode choice. The key findings indicated that individual sociodemographic characteristics (e.g., gender, age, ownership, occupation, residence) have significant effects on the changes in mode choice behavior. Other key findings included (1) a higher propensity to use a taxi after the peak compared to urban public transportation (i.e., bus and subway); (2) a significant impact of age on the switch from public transit to private car and two-wheelers; (3) more obvious changes in private car and public transportation modes in more developed cities. The findings from this study are expected to be useful for establishing partial and resilient policies and ensuring sustainable mobility and travel equality in the post-pandemic era.

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.

Whether marine fish will grow differently in future high pCO2 environments remains surprisingly uncertain. Long-term and whole-life cycle effects are particularly unknown, because such experiments are logistically challenging, space demanding, exclude long-lived species, and require controlled, restricted feeding regimes—otherwise increased consumption could mask potential growth effects. Here, we report on repeated, long-term, food-controlled experiments to rear large populations (>4,000 individuals total) of the experimental model and ecologically important forage fish Menidia menidia (Atlantic silverside) under contrasting temperature (17°, 24°, and 28°C) and pCO2 conditions (450 vs. 2,200 μatm) from fertilization to a third of this annual species' life span. Quantile analyses of trait distributions showed mostly negative effects of high pCO2 on long-term growth. At 17°C and 28°C, but not at 24°C, high pCO2 fish were significantly shorter [17°C: -5 to -9%; 28°C: -3%] and weighed less [17°C: -6 to -18%; 28°C: -8%] compared to ambient pCO2 fish. Reductions in fish weight were smaller than in length, which is why high pCO2 fish at 17°C consistently exhibited a higher Fulton's k (weight/length ratio). Notably, it took more than 100 days of rearing for statistically significant length differences to emerge between treatment populations, showing that cumulative, long-term CO2 effects could exist elsewhere but are easily missed by short experiments. Long-term rearing had another benefit: it allowed sexing the surviving fish, thereby enabling rare sex-specific analyses of trait distributions under contrasting CO2 environments. We found that female silversides grew faster than males, but there was no interaction between CO2 and sex, indicating that males and females were similarly affected by high pCO2. Because Atlantic silversides are known to exhibit temperature-dependent sex determination, we also analyzed sex ratios, revealing no evidence for CO2-dependent sex determination in this species. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2020) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2020-12-25.

Liberia's 14-year civil war left much of the country's infrastructure shambles. The country's 170 megawatt power generation capacity and national grid were completely destroyed. In Monrovia, just 0.1 percent of households had access to electricity. According to the 2008 National Census, access to piped water fell from 15 percent of the population in 1986 to less than 3 percent in 2008. The national road network was left in severe disrepair. Peace brought many positive developments. The Freeport of Monrovia is now privately managed and has resumed normal operations. Essential rehabilitation work has been carried out, and the port's performance now matches that of neighboring ports along the West African coast. Liberia has also successfully liberalized its mobile telephone markets, with access surging to 40 percent in 2009, at some of the lowest prices in Africa. Despite the potential for private investment, Liberia will likely need more than a decade to reach the illustrative infrastructure targets outlined in this report. Under business-as-usual assumptions for spending and efficiency, it would take at least 40 years for Liberia to reach these goals. Yet with a combination of increased finance, improved efficiency, and cost-reducing innovations, it should be possible to significantly reduce that time.

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.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).