Gasoline Prices in Philippines decreased to 1.04 USD/Liter in July from 1.06 USD/Liter in June of 2025. This dataset provides the latest reported value for - Philippines Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Monthly average retail prices for gasoline and fuel oil for Canada, selected provincial cities, Whitehorse and Yellowknife. Prices are presented for the current month and previous four months. Includes fuel type and the price in cents per litre.

Gasoline Prices in Poland decreased to 1.62 USD/Liter in July from 1.63 USD/Liter in June of 2025. This dataset provides the latest reported value for - Poland Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Gasoline Prices in Saudi Arabia remained unchanged at 0.62 USD/Liter in July. This dataset provides the latest reported value for - Saudi Arabia Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Historical water and gas chemistry data from geothermal areas are important for detecting long-term patterns, informing geothermal energy exploration, development, and use, and for contextualizing more recent data. The U.S. Geological Survey has published water and gas chemistry data from geothermal areas in the western United States, which is primarily available as scanned PDF files. This makes the data difficult to access or include in large-scale data analysis. This data release provides digitized and reformatted data from 20 previously published U.S. Geological Survey Open-File reports and journal articles, representing 1867 water chemistry samples and 313 gas chemistry samples. All data have been standardized to the same units, geographic coordinates, and file format. Description of sample site location was improved. Many reports do not report geographic location coordinates; those that do are frequently inaccurate, as latitude and longitude were interpolated from a map, or in some cases, estimated in the field before the common use of global positioning systems (GPS). Collection dates for individual samples range from 1930 to 2005, although most samples were collected between the years 1970 and 2000. Samples are primarily from California, Oregon, and Washington, although some reports include data from sites in Montana, Idaho, Nevada, Utah, Arizona, and New Mexico. Attributes for both water and gas chemistry are: Sample name, Sample ID, Type, Collection date, Collection time, Reported location, Reported latitude, Reported longitude, Reported Easting, Reported Northing, Location description, Region, State, County, Latitude, Longitude, Location resolution, Location error, Elevation, Source, Author comment, and Digitizer comment. Attributes for water chemistry are: Well depth, Collection depth, Discharge, Temperature, pH (field), pH (lab), pH, Aluminum (Al), Arsenic (As), Boron (B), Barium (Ba), Bromide (Br), Calcium (Ca), Chloride (Cl), Carbonate (CO3), Alkalinity as carbonate (CO3), Cesium (Cs), Copper (Cu), Dissolved Organic Carbon as Carbon (DIC as C), Fluoride (F), Iron (Fe), Hydrogen sulfide (H2S), Bicarbonate (HCO3), Alkalinity as bicarbonate (HCO3), Carbonic acid (H2CO3), Mercury (Hg), Iodide (I), Potassium (K), Lithium (Li), Magnesium (Mg), Manganese (Mn), Molybdenum (Mo), total Nitrogen (N), Sodium (Na), Ammonium (NH4), Nickel (Ni), Nitrate (NO3), total Phosphorus (P), Lead (Pb), Phosphate (PO4), Rubidium (Rb), Silica (SiO2), Sulfate (SO4), Strontium (Sr), Uranium (U), Vanadium (V), Zinc (Zn), Reported cations, Reported anions, Cations, Anions, Reported total dissolved solids, Salinity, Charge balance, Specific conductance, isotopic composition of hydrogen (Delta 2H), isotopic composition of oxygen in water (Delta 18O (H2O)), Oxygen shift, isotopic composition of oxygen in sulfate (Delta 18O (SO4)), isotopic composition of carbon (Delta 13C), isotopic composition of carbon in dissolved inorganic carbon (Delta 13C (DIC)), Tritium (3H), and 14C. Attributes for gas chemistry are: Temperature, Total gas, argon (Ar), oxygen and argon (O2 + Ar), ethane (C2H6), methane (CH4), carbon dioxide (CO2), hydrogen (H2), hydrogen sulfide (H2S), helium (He), nitrogen (N2), ammonia (NH3), oxygen (O2), dissolved argon (Ar dissolved), dissolved methane (CH4 dissolved), dissolved carbon dioxide (CO2 dissolved), dissolved hydrogen (H2 dissolved), dissolved helium (He dissolved), dissolved nitrogen (N2 dissolved), dissolved ammonia (NH3 dissolved), dissolved oxygen (O2 dissolved), isotopic ratio of helium (3He/4He), isotopic ratio of helium corrected for the atmospheric isotopic composition of helium (3He/4He corrected), isotopic composition of nitrogen (Delta 15N), and isotopic composition of carbon in carbon dioxide (Delta 13C (CO2)).

This project represents the data used in “Influences of potential oil and gas development and future climate on sage-grouse declines and redistribution.” The data sets describe greater sage-grouse (Centrocercus urophasianus) population change, summarized in different boundaries within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Population changes were based on different scenarios of oil and gas development intensities, projected climate models, and initial sage-grouse population estimates. Description of data sets pertaining to this project: Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with and without effects of climate change. 1. Greater sage-grouse population change (percent change) over 50-years in a high oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 2. Greater sage-grouse population change (percent change) in a low oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 3. Greater sage-grouse population change (percent change) over 50-years in a low oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 4. Greater sage-grouse population change (percent change) in a moderate oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 5. Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with no effects of climate change (2006-2062) The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically , the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for medium oil and gas development, high population size, and no climate component. The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

Onshore and offshore, oil and gas, transmission pipelines under the following Acts: Offshore Commonwealth waters - Offshore Petroleum and Greenhouse Gas storage Act 2006 Onshore - Pipelines Act 2005 WARNING! This is a working dataset and it contains missing and incorrect information. If you have queries about the data please call 03 9027 4436 to discuss. Location accuracies of the pipelines vary as much as +/- 200m

Gasoline Prices in Kenya increased to 1.44 USD/Liter in July from 1.37 USD/Liter in June of 2025. This dataset provides - Kenya Gasoline Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

PLEASE NOTE: These data have been updated. See Related Links for new data. Geodatabase of the Commonwealth Offshore Petroleum and Greenhouse Gas Storage Act 2006 - An Act about petroleum exploration and recovery, and the injection and storage of greenhouse gas substances, in offshore areas, and for other purposes.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

This project represents the data used in “Influences of potential oil and gas development and future climate on sage-grouse declines and redistribution.” The data sets describe greater sage-grouse (Centrocercus urophasianus) population change, summarized in different boundaries within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Population changes were based on different scenarios of oil and gas development intensities, projected climate models, and initial sage-grouse population estimates. Description of data sets pertaining to this project: Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with and without effects of climate change. 1. Greater sage-grouse population change (percent change) over 50-years in a high oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 2. Greater sage-grouse population change (percent change) in a low oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 3. Greater sage-grouse population change (percent change) over 50-years in a low oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 4. Greater sage-grouse population change (percent change) in a moderate oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 5. Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with no effects of climate change (2006-2062) The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

Recommended citation

Gütschow, J.; Busch, D.; Pflüger, M. (2024): The PRIMAP-hist national historical emissions time series v2.6.1 (1750-2023). zenodo. doi:10.5281/zenodo.15016289.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Content

• Use of the dataset and full description
• Abstract
• Support
• Sources
• Files included in the dataset
• Notes
• Data format description (columns)
• References
• Changelog

Abstract

The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1750 to 2023, and almost all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture are available. The "country reported data priority" (CR) scenario of the PRIMAP-hist datset prioritizes data that individual countries report to the UNFCCC.

For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "National Inventory Submissions". Until 2023 data was submitted in the "Common Reporting Format" (CRF). Since 2024 the new "Common Reporting Tables" (CRT) are used. For developing countries, non-AnnexI in terms of the UNFCCC, we use the "Biannial Transparency Reports" (BTR) which mostly come with data also using the "Common Reporting Tables". We also use older data available through the UNFCCC DI portal (di.unfccc.int) and additional country submissions from "Biannial Update Reports" (BUR), "National Communications" (NC), and "National Inventory Reports" (NIR) read from pdf and where available xls(x) or csv files. For a list of these submissions please see below. For South Korea the 2023 official GHG inventory has not yet been submitted to the UNFCCC but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC. We have mostly replaced the official data that has not been submitted to the UNFCCC used in v2.6 as countries have now submitted their data in CRT format, but had to make some exceptions as the CRT data was not usable for all countries.

Gaps in the country reported data are filled using third party data such as CDIAC, EI (fossil CO2), Andrew cement emissions data (cement), FAOSTAT (agriculture), and EDGAR 2024 (all sectors for CO2, CH4, N2O, HFCs, PFCs, SF6, NF3, except energy CO2). Lower priority data are harmonized to higher priority data in the gap-filling process.

For the third party priority time series gaps in the third party data are filled from country reported data sources.

Data for earlier years which are not available in the above mentioned sources are sourced from EDGAR-HYDE, CEDS, and RCP (N2O only) historical emissions.

The v2.4 release of PRIMAP-hist reduced the time-lag from 2 to 1 years for the October release. Thus the present version 2.6.1 includes data for 2023. For energy CO2 growth rates from the EI Statistical Review of World Energy are used to extend the country reported (CR) or CDIAC (TP) data to 2023. For CO2 from cement production Andrew cement data are used. For other gases and sectors we use EDGAR 2024 data. In a few cases we have to rely on numerical methods to estimate emissions for 2023.

Version 2.6.1 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care as they are constructed from different sources using different methodologies and are not harmonized.

The PRIMAP-hist v2.6.1 dataset is an updated version of

Gütschow, J.; Pflüger, M.; Busch, D. (2024): The PRIMAP-hist national historical emissions time series v2.6 (1750-2023). zenodo. doi:10.5281/zenodo.13752654.

The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset. Detailed per country information is available from the detailed changelog which is available on the primap.org website and on zenodo.

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Please notify us (johannes.guetschow@climate-resource.com) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.

Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: PRIMAP2 on GitHub. The data are published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the PRIMAP2 documentation.

We have also included files with more than three significant digits. These files are mainly aimed at people doing policy analysis using the country reported data scenario (HISTCR). Using the high precision data they can avoid questions on discrepancies with the reported data. The uncertainties of emissions data do not justify the additional significant digits and they might give a false sense of accuracy, so please use this version of the dataset with extra care.

Support

If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there. Please use the tag "v2.6.1" in any issue you create regarding this dataset.

If you need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@climate-resource.com.

Climate Resource makes this data available CC BY 4.0 licence. Free support is limited to simple questions and non-commercial users. We also provide additional data, and data support services to clients wanting more frequent updates, additional metadata or to integrate these datasets into their workflows. Get in touch at contact@climate-resource.com if you are interested.

Sources

• Global CO2 emissions from cement production v250226 data, paper: Andrew
  (2025), Andrew (2019)
• EI Statistical Review of World Energy website: Energy Institute (2024)
• CDIAC data: Hefner and Marland (2023), data: Hefner (2024), paper: Gilfillan and Marland (2021)
• CEDS: data: Hoesly et al. (2020), paper: Hoesly et al. (2018)
• EDGAR 2024: data/website: European Commission, European Commision, JRC (2024), report: European Commission. Joint Research Centre & IEA. (2024)
• EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001)
• FAOSTAT database data: Food and Agriculture Organization of the United Nations (2024)
• RCP historical data data, paper: Meinshausen et al. (2011)
• UNFCCC National Communications and National Inventory Reports for developing countries available from the UNFCCC DI portal <a

A cells polygon feature class was created by the U. S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Illinois. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. Data were retrieved from the Illinois State Geological Survey (ISGS) oil and gas wells database. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2006.

Geoscience Australia's Oracle organic geochemical database comprises analytical results for samples relevant to petroleum exploration, including source rocks, crude oils and natural gases collected across the Australian continent. The data comprises organic chemical analyses of hydrocarbon-bearing earth materials as well as including connectivity to some inorganic analyses. These data enable petroleum fluids to be typed into families and correlated to their source rock, from which depositional environment, age, and migration distances can be determined, and hence the extent of the total petroleum system can be mapped. This comprehensive data set is useful to government for evidence-based decision making on natural resources and the petroleum industry for de-risking conventional and unconventional petroleum exploration programs.

The data are produced by a wide range of analytical techniques. For example, source rocks are evaluated for their bulk compositional characteristics by programmed pyrolysis, pyrolysis-gas chromatography and organic petrology. Natural gases are analysed for their molecular and isotopic content by gas chromatography (GC) and gas chromatography-temperature conversion-mass spectrometry (GC-TC-IRMS). Crude oils and the extracts of source rocks are analysed for their bulk properties (API gravity; elemental analysis) and their molecular (biomarkers) and isotopic (carbon and hydrogen) content by GC, gas chromatography-mass spectrometry (GCMS) and GC-TC-IRMS.

The sample data originate from physical samples, well completion reports, and destructive analysis reports provided by the petroleum industry under the Offshore Petroleum and Greenhouse Gas Storage Act (OPGGSA) 2006 and previous Petroleum (submerged Lands) Act (PSLA) 1967. The sample data are also sourced from geological sampling programs in Australia by Geoscience Australia and its predecessor organisation's Australian Geological Survey Organisation (AGSO) and Bureau of Mineral Resources (BMR), and from the state and territory geological organisations. Geoscience Australia generates data from its own laboratories. Other open file data from publications, university theses and books are also included

Value: The organic geochemistry database enables digital discoverability and accessibility to key petroleum geochemical datasets. It delivers open file, raw petroleum-related analytical results to web map services and web feature services in Geoscience Australia’s portal. Derived datasets and value-add products are created based on calculated values and geological interpretations to provide information on the subsurface petroleum prospectivity of the Australian continent. For example, the ‘Oils of Australia’ series and the ‘characterisation of natural gas’ reports document the location, source and maturity of Australia’s petroleum resources. Details of the total petroleum systems of selected basins studied under the Exploring for the Future project can be found in the Petroleum Systems Summaries Tool in Geoscience Australia’s portal. Related Geoscience Australia Records and published papers can be obtained from eCat.

Scope: The collection initially comprised organic geochemical and petrological data on organic-rich sedimentary rocks, crude oils and natural gas from petroleum wells drilled in the onshore and offshore Australian continent. Over time, other sample types (ground water, fluid inclusions, mineral veins, bitumen) from other borehole types (minerals, stratigraphic – including the Integrated Ocean Drilling Program), marine dredge samples and field sites (outcrop, mines, surface seepage samples) have been analysed for their hydrocarbon content and are captured in the database. Results for many of the oil and gas samples held in the Australian National Offshore Wells Data Collection are included in this database.

The 2025 annual OPEC basket price stood at ***** U.S. dollars per barrel as of June. This would be lower than the 2024 average, which amounted to ***** U.S. dollars. The abbreviation OPEC stands for Organization of the Petroleum Exporting Countries and includes Algeria, Angola, Congo, Equatorial Guinea, Gabon, Iraq, Iran, Kuwait, Libya, Nigeria, Saudi Arabia, Venezuela, and the United Arab Emirates. The aim of the OPEC is to coordinate the oil policies of its member states. It was founded in 1960 in Baghdad, Iraq. The OPEC Reference Basket The OPEC crude oil price is defined by the price of the so-called OPEC (Reference) basket. This basket is an average of prices of the various petroleum blends that are produced by the OPEC members. Some of these oil blends are, for example: Saharan Blend from Algeria, Basra Light from Iraq, Arab Light from Saudi Arabia, BCF 17 from Venezuela, et cetera. By increasing and decreasing its oil production, OPEC tries to keep the price between a given maxima and minima. Benchmark crude oil The OPEC basket is one of the most important benchmarks for crude oil prices worldwide. Other significant benchmarks are UK Brent, West Texas Intermediate (WTI), and Dubai Crude (Fateh). Because there are many types and grades of oil, such benchmarks are indispensable for referencing them on the global oil market. The 2025 fall in prices was the result of weakened demand outlooks exacerbated by extensive U.S. trade tariffs.

This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

© MarineCadastre.gov This layer is a component of BOEMRE Layers.

This Map Service contains many of the primary data types created by both the Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) within the Department of Interior (DOI) for the purpose of managing offshore federal real estate leases for oil, gas, minerals, renewable energy, sand and gravel. These data layers are being made available as REST mapping services for the purpose of web viewing and map overlay viewing in GIS systems. Due to re-projection issues which occur when converting multiple UTM zone data to a single national or regional projected space, and line type changes that occur when converting from UTM to geographic projections, these data layers should not be used for official or legal purposes. Only the original data found within BOEM/BSEE’s official internal database, federal register notices or official paper or pdf map products may be considered as the official information or mapping products used by BOEM or BSEE. A variety of data layers are represented within this REST service are described further below. These and other cadastre information the BOEM and BSEE produces are generated in accordance with 30 Code of Federal Regulations (CFR) 256.8 to support Federal land ownership and mineral resource management.

For more information – Contact: Branch Chief, Mapping and Boundary Branch, BOEM, 381 Elden Street, Herndon, VA 20170. Telephone (703) 787-1312; Email: mapping.boundary.branch@boem.gov

The REST services for National Level Data can be found here: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE/MMC_Layers/MapServer

REST services for regional level data can be found by clicking on the region of interest from the following URL: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE

Individual Regional Data or in depth metadata for download can be obtained in ESRI Shape file format by clicking on the region of interest from the following URL: http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx

Currently the following layers are available from this REST location:

OCS Drilling Platforms -Locations of structures at and beneath the water surface used for the purpose of exploration and resource extraction. Only platforms in federal Outer Continental Shelf (OCS) waters are included. A database of platforms and rigs is maintained by BSEE.

OCS Oil and Natural Gas Wells -Existing wells drilled for exploration or extraction of oil and/or gas products. Additional information includes the lease number, well name, spud date, the well class, surface area/block number, and statistics on well status summary. Only wells found in federal Outer Continental Shelf (OCS) waters are included. Wells information is updated daily. Additional files are available on well completions and well tests. A database of wells is maintained by BSEE.

OCS Oil & Gas Pipelines -This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

Unofficial State Lateral Boundaries - The approximate location of the boundary between two states seaward of the coastline and terminating at the Submerged Lands Act Boundary. Because most State boundary locations have not been officially described beyond the coast, are disputed between states or in some cases the coastal land boundary description is not available, these lines serve as an approximation that was used to determine a starting point for creation of BOEM’s OCS Administrative Boundaries. GIS files are not available for this layer due to its unofficial status.

BOEM OCS Administrative Boundaries - Outer Continental Shelf (OCS) Administrative Boundaries Extending from the Submerged Lands Act Boundary seaward to the Limit of the United States OCS (The U.S. 200 nautical mile Limit, or other marine boundary)For additional details please see the January 3, 2006 Federal Register Notice.

BOEM Limit of OCSLA ‘8(g)’ zone - The Outer Continental Shelf Lands Act '8(g) Zone' lies between the Submerged Lands Act (SLA) boundary line and a line projected 3 nautical miles seaward of the SLA boundary line. Within this zone, oil and gas revenues are shared with the coastal state(s). The official version of the ‘8(g)’ Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction described below.

Submerged Lands Act Boundary - The SLA boundary defines the seaward limit of a state's submerged lands and the landward boundary of federally managed OCS lands. The official version of the SLA Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction Diagrams described below.

Atlantic Wildlife Survey Tracklines(2005-2012) - These data depict tracklines of wildlife surveys conducted in the Mid-Atlantic region since 2005. The tracklines are comprised of aerial and shipboard surveys. These data are intended to be used as a working compendium to inform the diverse number of groups that conduct surveys in the Mid-Atlantic region.The tracklines as depicted in this dataset have been derived from source tracklines and transects. The tracklines have been simplified (modified from their original form) due to the large size of the Mid-Atlantic region and the limited ability to map all areas simultaneously.The tracklines are to be used as a general reference and should not be considered definitive or authoritative. This data can be downloaded from http://www.boem.gov/uploadedFiles/BOEM/Renewable_Energy_Program/Mapping_and_Data/ATL_WILDLIFE_SURVEYS.zip

BOEM OCS Protraction Diagrams & Leasing Maps - This data set contains a national scale spatial footprint of the outer boundaries of the Bureau of Ocean Energy Management’s (BOEM’s) Official Protraction Diagrams (OPDs) and Leasing Maps (LMs). It is updated as needed. OPDs and LMs are mapping products produced and used by the BOEM to delimit areas available for potential offshore mineral leases, determine the State/Federal offshore boundaries, and determine the limits of revenue sharing and other boundaries to be considered for leasing offshore waters. This dataset shows only the outline of the maps that are available from BOEM.Only the most recently published paper or pdf versions of the OPDs or LMs should be used for official or legal purposes. The pdf maps can be found by going to the following link and selecting the appropriate region of interest. http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx Both OPDs and LMs are further subdivided into individual Outer Continental Shelf(OCS) blocks which are available as a separate layer. Some OCS blocks that also contain other boundary information are known as Supplemental Official Block Diagrams (SOBDs.) Further information on the historic development of OPD's can be found in OCS Report MMS 99-0006: Boundary Development on the Outer Continental Shelf: http://www.boemre.gov/itd/pubs/1999/99-0006.PDF Also see the metadata for each of the individual GIS data layers available for download. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs), serve as the legal definition for BOEM offshore boundary coordinates and area descriptions.

BOEM OCS Lease Blocks - Outer Continental Shelf (OCS) lease blocks serve as the legal definition for BOEM offshore boundary coordinates used to define small geographic areas within an Official Protraction Diagram (OPD) for leasing and administrative purposes. OCS blocks relate back to individual Official Protraction Diagrams and are not uniquely numbered. Only the most recently published paper or pdf

This dataset contains CO2 Emissions by sectors for 2020. Follow datasource.kapsarc.org for timely data to advance energy economics research. Notes:Note: The IEA Greenhouse gas emissions from energy product replaces the IEA CO2 emissions from fuel combustion product, with expanded content. Similarly, the Greenhuose gas emissions from energy highlights replaces the IEA CO2 emissions from fuel combustion highlights. This extract from the Greenhouse Gas Emissions from Energy 2022 database contains an extensive selection of GHG emissions data for over 190 countries and regions. Emissions data are based on the IEA World Energy Balances 2022 and on the 2006 IPCC Guidelines for Greenhouse Gas Inventories.

Land authorizations for areas on which well or facility activities can occur. This dataset contains spatial data collected on or after October 30, 2006. The spatial data includes approved and post-construction land areas associated with well or facility activities. This dataset is updated nightly.

Contents

Use of the dataset and full description

Support

Abstract

Sources

Files included in the dataset

Data format description (columns)

Missing Data

References

Use of the dataset and full description

A full description of a previous version of the dataset can be found in:

Jeffery, M. L., Gütschow, J., Gieseke, R., and Gebel, R.: PRIMAP-crf: UNFCCC CRF data in IPCC 2006 categories, Earth Syst. Sci. Data, 10, 1427-1438, https://doi.org/10.5194/essd-10-1427-2018, 2018

An update of the description paper for is under preparation.

If you use this dataset, we would appreciate a brief notification to the lead author (johannes.guetschow@pik-potsdam.de) with a description of how the data was used. This information can help to guide the production of future updates to the dataset.

New versions of the UNFCCC CRF data are released annually with an additional year of data. Some countries also submit revised versions of their data through the year. Where possible, the PRIMAP-crf data will be updated accordingly and a revised dataset released. Data releases with an additional year of data are indicated in the naming of the data - the year of data publication is indicated by the dataset name, e.g. PRIMAP-crf-2021 data includes data first released by countries in 2021. Inclusion of subsequent data revisions from the same year are indicated by the version number, for example PRIMAP-crf-2020-v2 includes all CRF2020 data published until 12th January 2021.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above).

Since version 2020v2 we use the data formats developed for the PRIMAP2 climate policy analysis suite: PRIMAP2 on GitHub. The data is published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the PRIMAP2 documentation

Support

If you need support in using the dataset or have any other questions regarding the dataset, please contact Dr. Johannes Gütschow at johannes.guetschow@pik-potsdam.de.

If you wish to use the .csv file in excel but the data does not appear to display correctly, you need to set the delimiter character. To do so:

highlight the first column

Under the 'Data' tab, select 'Text to columns'

In the first pop-up window, select 'Delimited'

In the second pop-up, select 'comma' separated values

No selection needed in the third pop-up, click Finish and the data should display correctly.

Abstract

PRIMAP-crf is a processed version of data reported by countries to the United Nations Framework Convention on Climate Change (UNFCCC) in the Common Reporting Format (CRF). The processing has three key aspects: 1) Data from individual countries and years are combined into one file. 2) Data is reorganised to follow the IPCC 2006 hierarchical categorisation. 3) 'Baskets' of gases are calculated according to different global warming potential estimates from each of the three most recent IPCC reports.

Sources

The original CRF data is all freely available via the UNFCCC website https://unfccc.int/ghg-inventories-annex-i-parties/2021. Please consider also citing this source in any work that you produce using PRIMAP-crf.

This dataset includes all 2021 CRF data available as of 19th April, 2021. For later data updates, please check the PRIMAP-crf page of the Paris Reality Check website https://www.pik-potsdam.de/paris-reality-check/primap-crf/ or updates to the Zenodo repository.

Files included in the dataset

Guetschow-et-al-2021-PRIMAP-crf_2021-v1.csv : primary data file with data in IPCC 2006 categories in PRIMAP2 interchange format Guetschow-et-al-2021-PRIMAP-crf_2021-v1.yaml : metadata in PRIMAP2 interchange format for the primary csv data file Guetschow-et-al-2021-PRIMAP-crf_2021-v1.nc : primary data file with data in IPCC 2006 categories in PRIMAP2 NetCDF format (metadata included) Guetschow-et-al-2021-PRIMAP-crf96_2021-v1.csv : additional data file with data in IPCC 1996 categories in PRIMAP2 interchange format Guetschow-et-al-2021-PRIMAP-crf96_2021-v1.yaml : metadata in PRIMAP2 interchange format for the additional csv data file Guetschow-et-al-2021-PRIMAP-crf96_2021-v1.nc : additional data file with data in IPCC 1996 categories in PRIMAP2 NetCDF format (metadata included) PRIMAP-crf-IPCC2006-category-codes.csv : definitions of IPCC 2006 category codes used in PRIMAP-crf primap-crf-data-description-2021v1.pdf : data description document

Data format description (columns)

The PRIMAP-crf data in the comma-separated values (CSV) files is formatted consistently with the PRIMAP2 interchange format.

The data contained in each column is as follows:

source

Name of the data source. Here: PRIMAP-crf.

scenario (PRIMAP)

The scenario refers to the year of the UNFCCC submissions (in this case 2021), and the revision number (here v1). 2021v1 includes all 2021 data released until 19th April 2021. Previous versions are available for the emissions data reported in 2017 (Jeffery et al., 2018), 2018 (Gütschow et al., 2019), 2019 (Gütschow et al., 2020), 2020 (Gütschow et al., 2021)

provenance

Provenance of the data. Here: "measured" as it is an original source.

country (ISO3)

ISO 3166 three-letter country codes.

Additionally, the European Union is included as the sum of its 28 pre-Brexit member states with the code "EU28" and as the sum of it's 27 post-Brexit member states with the code "EU27BX". The EU data is the sum of the data of it's member states, not the data officially reported to the UNFCCC by the EU.

category (IPCC2006) (or category (IPCC1996))

IPCC (Intergovernmental Panel on Climate Change) 1996 or 2006 category codes. Please see the accompanying file PRIMAP-crf-IPCC2006-category-codes.csv for a definition of codes used for IPCC 2006 categories.

Data for 1996 categories are shared for the top level categories only, as defined below.

entity

The gases and gas baskets. Where a global warming potential (GWP) is used it is given in parentheses. GWP weighted data is only provided for the gas baskets KYOTOGHG, FGASES, HFCS, PFCS, OTHERHFCS, and OTHERPFCS. We use 100 year global warming potentials from either IPCC Second Assessment Report (SARGWP100), Assessment Report 4 (AR4GWP100), Assessment Report 5 (AR5GPW100), or Assessment Report 5 with carbon-cycle feedbacks (AR5CCFGWP100). Where no global warming potential is specified, quantities are given in absolute weights of the gas.

| Code      | Description                   |
| Code      | Description                   |
| :------------- | :----------------------------------------------- |
| CH4      | Methane                     |
| CO2      | Carbon Dioxide                  |
| N2O      | Nitrous Oxide                  |
|        |                         |
| SF6      | Sulfur Hexafluoride               |
| NF3      | Nitrogen Trifluoride               |
|        |                         |
| HFC125     | Pentafluoroethane, HFC-125            |
| HFC134     | Tetrafluoroethane, HFC-134            |
| HFC134A    | Tetrafluoroethane, HFC-134a           |
| HFC143     | Trifluoroethane, HFC-143             |
| HFC143A    | Trifluoroethane, HFC-143a            |
| HFC152A    | 1,1-Difluoroethane, HFC-152a           |
| HFC227EA    | Heptafluoropropane, HFC-227a           |
| HFC23     | Trifluoromethane, HFC-23             |
| HFC236FA    | 1,1,1,3,3,3-hexafluoropropane, HFC-236fa     |
| HFC245CA    | 1,1,2,2,3-pentafluoropropane, HFC-245ca     |
| HFC245FA    | Enovate, HFC-245fa                |
| HFC32     | Difluoromethane, HFC-32             |
| HFC365MFC   | 1,1,1,3,3-pentafluorobutane, HFC-365mfc     |
| HFC41     | Fluoromethane, HFC-41              |
| HFC4310    | 1,1,1,2,3,4,4,5,5,5-decafluoropentane, HFC-43-10 |
| OTHERHFCS   | Unspecified mix of HFCs (GWP as in reporting)  |
| HFCS      | Hydrofluorocarbons (SAR)             |
|        |                         |
| C2F6      | Hexafluoroethane, C2F6              |
| C3F8      | Octafluorpropane, C3F8              |
| C4F10     | Perfluorobutane, C4F10              |
| C5F12     | Dodecafluoropentane, C5F12            |
| C6F14     | Perfluorohexane, C6F14              |
| CC4F8     | Octafluorocyclobutane, cC4F8           |
| CF4      | Tetrafluoromethane, CF4             |
| OTHERPFCS   | Unspecified mix of PFCs (GWP as in reporting)  |
| PFCS      | Perflurocarbons (SAR)              |
|        |                         |
| FGASES     | Fluorinated Gases (SAR)             |
|        |                         |
| KYOTOGHG    | Kyoto greenhouse gases (SAR)           |
|        |                         |
| NMVOC     | Non-Methane Volatile Organic Compounds      |
| NOX      | Nitrogen Oxide                  |
| SO2      | Sulfur dioxide                  |
| CO       | Carbon Monoxide                 |

Gas names

unit

Units are either

This is an updated version of Gütschow et al. (2019, http://doi.org/10.5880/pik.2019.001). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its previous updates. For a detailed description of the changes please consult the CHANGELOG included in the data description document. The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2017, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Agriculture is available. Version 2.1 of the PRIMAP-hist dataset does not include emissions from Land use, land use change and forestry (LULUCF). List of datasets included in this data publication:(1) PRIMAP-hist_v2.1_09-Nov-2019.csv: With numerical extrapolation of all time series to 2017. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v2.1_09-Nov-2019.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v2.1_data-format-description: including CHANGELOG(4) PRIMAP-hist_v2.1_updated_figures: updated figures of those published in Gütschow et al. (2016)(all files are also included in the .zip folder) When using this dataset or one of its updates, please also cite the data description article (Gütschow et al., 2016, http://doi.org/10.5194/essd-8-571-2016) to which this data are supplement to. Please consider also citing the relevant original sources. SOURCES:- Global CO2 emissions from cement production v4: Andrew (2019)- BP Statistical Review of World Energy: BP (2019)- CDIAC: Boden et al. (2017)- EDGAR version 4.3.2: JRC and PBL (2017), Janssens-Maenhout et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2019)- RCP historical data: Meinshausen et al. (2011)- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2019)- UNFCCC Biennal Update Reports: UNFCCC (2019)- UNFCCC Common Reporting Format (CRF): UNFCCC (2018), UNFCCC (2019), Jeffery et al. (2018) Full references are available in the data description document.