This statistic displays the distribution of carbon dioxide emissions worldwide as of 2015, based on select regions. As of this year, some six percent of the global carbon dioxide emissions originated from the Middle East. Carbon dioxide can be removed from the atmosphere through reforestation, soil improvements, and other carbon sinks.

Greenhouse gas emissions from the food system are unsurprisingly highest in countries with large populations, as well as large agricultural producers. In 2015, China accounted for 13.5 percent of global greenhouse gas emissions from food in 2015, followed by Indonesia which accounted for 8.82 percent. The ten largest emitters accounted for more than half of global emissions from food. However, this statistic does not take into account the emissions produced by consumption. For example, many rich countries cause emissions in poorer countries through deforestation for food that is then exported.

The world produced an estimated *** billion metric tons of carbon dioxide equivalent (GtCO₂e) between 2015 and 2024. The biggest contributor to this total was the global power sector, which released almost *** GtCO₂e into the atmosphere during this period. Manufacturing followed, with cumulative emissions amounting to just over 100 GtCO₂e. Together, these two sectors were responsible for almost half the cumulative emissions produced between 2015 and 2024.

The CAIT Country GHG emissions collection applies a consistent methodology to create a six-gas, multi-sector, and internationally comparable data set for 186 countries.

CAIT enables data analysis by allowing users to quickly narrow down by year, gas, country/state, and sector. Automatic calculations for percent changes from prior year, per capita, and per GDP are also available. Users are presented with clear and customizable data visualizations that can be readily shared through unique URLs or embedded for further use online.

Data for Land-Use and Forestry indicator are provided by the Food and Agriculture Organization of the United Nations (FAO). WRI has been granted a non-exclusive, non-transferrable right to publish these data. Therefore, if users wish to republish this dataset in whole or in part, they should contact FAO directly at copyright@fao.org

Data sources: - Boden, T.A., G. Marland, and R.J. Andres. 2015. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi 10.3334/CDIAC/00001_V2015 Available online at:http://cdiac.ornl.gov/trends/emis/overview_2011.html . - Food and Agriculture Organization of the United Nations (FAO). 2014. FAOSTAT Emissions Database. Rome, Italy: FAO. Available at: http://faostat3.fao.org/download/G1/*/E - International Energy Agency (IEA). 2014. CO2 Emissions from Fuel Combustion (2014 edition). Paris, France: OECD/IEA. Available online at:http://data.iea.org/ieastore/statslisting.asp. © OECD/IEA, [2014]. - World Bank. 2014. World Development Indicators 2014. Washington, DC. Available at: http://data.worldbank.org/ Last Accessed May 18th, 2015 - U.S. Energy Information Administration (EIA). 2014. International Energy Statistics Washington, DC: U.S. Department of Energy. Available online at:http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=90&pid=44&aid=8 - U.S. Environmental Protection Agency (EPA). 2012. “Global Non-CO2 GHG Emissions: 1990-2030.” Washington, DC: EPA. Available at: http://www.epa.gov/climatechange/EPAactivities/economics/nonco2projections.html.

Analysis of ‘CAIT - Country Greenhouse Gas Emissions Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/57c6dff7c751df24c697bae5 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

The CAIT Country GHG emissions collection applies a consistent methodology to create a six-gas, multi-sector, and internationally comparable data set for 186 countries.

CAIT enables data analysis by allowing users to quickly narrow down by year, gas, country/state, and sector. Automatic calculations for percent changes from prior year, per capita, and per GDP are also available. Users are presented with clear and customizable data visualizations that can be readily shared through unique URLs or embedded for further use online.

Data for Land-Use and Forestry indicator are provided by the Food and Agriculture Organization of the United Nations (FAO). WRI has been granted a non-exclusive, non-transferrable right to publish these data. Therefore, if users wish to republish this dataset in whole or in part, they should contact FAO directly at copyright@fao.org

Data sources: - Boden, T.A., G. Marland, and R.J. Andres. 2015. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi 10.3334/CDIAC/00001_V2015 Available online at:http://cdiac.ornl.gov/trends/emis/overview_2011.html . - Food and Agriculture Organization of the United Nations (FAO). 2014. FAOSTAT Emissions Database. Rome, Italy: FAO. Available at: http://faostat3.fao.org/download/G1/*/E - International Energy Agency (IEA). 2014. CO2 Emissions from Fuel Combustion (2014 edition). Paris, France: OECD/IEA. Available online at:http://data.iea.org/ieastore/statslisting.asp. © OECD/IEA, [2014]. - World Bank. 2014. World Development Indicators 2014. Washington, DC. Available at: http://data.worldbank.org/ Last Accessed May 18th, 2015 - U.S. Energy Information Administration (EIA). 2014. International Energy Statistics Washington, DC: U.S. Department of Energy. Available online at:http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=90&pid=44&aid=8 - U.S. Environmental Protection Agency (EPA). 2012. “Global Non-CO2 GHG Emissions: 1990-2030.” Washington, DC: EPA. Available at: http://www.epa.gov/climatechange/EPAactivities/economics/nonco2projections.html.

--- Original source retains full ownership of the source dataset ---

Greenhouse gas emissions from oil and gas refining were highest in the United States and China in 2023. The two countries emitted the equivalent of *** and *** million metric tons of carbon dioxide from refining activities that year, respectively, making up almost ** percent of global oil and gas refining emissions.

This statistic displays the distribution of energy-related carbon dioxide emissions worldwide in 2015, based on sector. During this year, some 22 percent of the global annual energy-related emissions were from the transportation sector. Carbon dioxide can be removed from the atmosphere through reforestation, soil improvements, and other carbon sinks.

Global oil and gas refining emissions were estimated at *** million metric tons of carbon dioxide equivalent (MtCO₂e) in 2023 – a year-on-year increase of *** percent. Emissions from this sector plummeted **** percent in 2020 as the COVID-19 pandemic hit global oil and gas demand. In total, oil and gas refining has emitted almost **** GtCO₂e between 2015 and 2023.

HU: CO2 Emissions: Kg per USD of GDP 2015 Price data was reported at 0.318 kg in 2020. This records a decrease from the previous number of 0.321 kg for 2019. HU: CO2 Emissions: Kg per USD of GDP 2015 Price data is updated yearly, averaging 0.492 kg from Dec 1990 (Median) to 2020, with 31 observations. The data reached an all-time high of 0.820 kg in 1991 and a record low of 0.318 kg in 2020. HU: CO2 Emissions: Kg per USD of GDP 2015 Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hungary – Table HU.World Bank.WDI: Environmental: Gas Emissions and Air Pollution. Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.;Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source.;Weighted average;

Palau PW: CO2 Emissions: Kg per USD of(GDP) Gross Domestic Product2015 Price data was reported at 0.603 kg in 2020. This records a decrease from the previous number of 0.766 kg for 2019. Palau PW: CO2 Emissions: Kg per USD of(GDP) Gross Domestic Product2015 Price data is updated yearly, averaging 0.831 kg from Dec 1990 (Median) to 2020, with 31 observations. The data reached an all-time high of 1.067 kg in 1993 and a record low of 0.000 kg in 1991. Palau PW: CO2 Emissions: Kg per USD of(GDP) Gross Domestic Product2015 Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Palau – Table PW.World Bank.WDI: Environmental: Gas Emissions and Air Pollution. Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.;Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source.;Weighted average;

Details are in : https://doi.org/10.1016/j.ecmx.2022.100299

The Kingdom of Saudi Arabia (KSA) is among the countries that committed to taking measures to cut greenhouse gas emissions in accordance with the 2015 Paris Climate Agreement. KSA has rolled out the 2030 Vision aiming at creating a more diverse and sustainable economy that cascaded into a series of initiatives, including the circular carbon economy, Saudi green initiative, and the national renewable energy program. Furthermore, KSA has recently announced an ambitious goal to reach net-zero goal by 2060. In its updated nationally determined contribution (NDC), the Kingdom committed to reducing its carbon emissions by 278 million tons of CO2eq (equivalent) annually by 2030. This ambition is more than a two-fold increase versus the previously announced target (130 million tons of CO2eq). With no current plans to change its hydrocarbon production rates, this reduction in emissions would be achieved mainly through diversifying its energy mix, increasing the efficiency of industrial processes, and deploying carbon capture utilization and storage (CCUS). To achieve this goal, it is vital to establish a detailed register for CO2 emissions from stationary industrial sources to design optimum and effective CCUS applications. This register includes details about the emission source locations, rates, and characteristics. For the first time, this paper provides a country-wide extensive study that maps out CO2 emissions from stationary industrial emitters associated with the leading six industries in the country, which are electricity generation, desalination, oil refining, cement, petrochemicals, and iron & steel. Moreover, CO2 concentrations within the emitted flue gas from these resources are estimated, which is crucial to determine the capture cost. This study aims to provide a vital resource for researchers and policymakers who seek to reduce greenhouse gas emissions by promoting renewable energy, improving the efficiency of existing fossil-fuel-based industries, and evaluating the potential of CCUS in KSA.

Russia released an estimated 1.16 billion metric tons of carbon dioxide equivalent (GtCO₂e) from oil and gas production and transprotation in 2023, making it the biggest carbon polluter in this sector. The six countries shown accounted for 60 percent of global emissions from oil and gas production and transport in 2023.

Spain ES: CO2 Emissions: Kg per USD of(GDP) Gross Domestic Product2015 Price data was reported at 0.199 kg in 2018. This records a decrease from the previous number of 0.208 kg for 2017. Spain ES: CO2 Emissions: Kg per USD of(GDP) Gross Domestic Product2015 Price data is updated yearly, averaging 0.294 kg from Dec 1960 to 2018, with 59 observations. The data reached an all-time high of 0.390 kg in 1980 and a record low of 0.199 kg in 2018. Spain ES: CO2 Emissions: Kg per USD of(GDP) Gross Domestic Product2015 Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Spain – Table ES.World Bank.WDI: Environment: Pollution. Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.; ; Data for up to 1990 are sourced from Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data from 1990 are CAIT data: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source.; Weighted average;

Global carbon dioxide emissions from fossil fuels and industry totaled 37.01 billion metric tons (GtCO₂) in 2023. Emissions are projected to have risen 1.08 percent in 2024 to reach a record high of 37.41 GtCO₂. Since 1990, global CO₂ emissions have increased by more than 60 percent. Who are the biggest emitters? The biggest contributor to global GHG emissions is China, followed by the United States. China wasn't always the world's biggest emitter, but rapid economic growth and industrialization in recent decades have seen emissions there soar. Since 1990, CO₂ emissions in China have increased by almost 450 percent. By comparison, U.S. CO₂ emissions have fallen by 6.1 percent. Nevertheless, the North American country remains the biggest carbon polluter in history. Global events cause emissions to drop The outbreak of COVID-19 caused global CO₂ emissions to plummet some 5.5 percent in 2020 as a result of lockdowns and other restrictions. However, this wasn't the only time in recent history when a major global event caused emissions reductions. For example, the global recession resulted in CO₂ levels to fall by almost two percent in 2009, while the recession in the early 1980s also had a notable impact on emissions. On a percentage basis, the largest annual reduction was at the end of the Second World War in 1945, when emissions decreased by 17 percent.

Laos LA: CO2 Emissions: Kg per USD of(GDP) Gross Domestic Product2015 Price data was reported at 1.032 kg in 2020. This records a decrease from the previous number of 1.037 kg for 2019. Laos LA: CO2 Emissions: Kg per USD of(GDP) Gross Domestic Product2015 Price data is updated yearly, averaging 0.198 kg from Dec 1990 (Median) to 2020, with 31 observations. The data reached an all-time high of 1.155 kg in 2017 and a record low of 0.181 kg in 2000. Laos LA: CO2 Emissions: Kg per USD of(GDP) Gross Domestic Product2015 Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank.WDI: Environmental: Gas Emissions and Air Pollution. Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.;Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source.;Weighted average;

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

AT: CO2 Emissions from Liquid Fuel Consumption: % of Total data was reported at 49.397 % in 2016. This records an increase from the previous number of 48.940 % for 2015. AT: CO2 Emissions from Liquid Fuel Consumption: % of Total data is updated yearly, averaging 49.570 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 56.141 % in 1979 and a record low of 27.329 % in 1960. AT: CO2 Emissions from Liquid Fuel Consumption: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Austria – Table AT.World Bank.WDI: Environmental: Gas Emissions and Air Pollution. Carbon dioxide emissions from liquid fuel consumption refer mainly to emissions from use of petroleum-derived fuels as an energy source.;Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States.;Weighted average;

The data in these Appendices to the Global Anthropogenic Emissions of Non-CO2 Greenhouse Gases (1990-2020) report provide historical and projected estimates of emissions from over 90 countries and 8 regions for all major non-CO2 greenhouse gas emission sources. The gases included in this data set are methane (CH4), nitrous oxide (N2O), and the high global warming potential (high GWP) gases (hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6)). See the full report at https://www.epa.gov/climatechange/economics/international.html.

This is an updated version of Gütschow et al. (2017, http://doi.org/10.5880/pik.2017.001). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its first update. For a detailed description of the changes please consult the CHANGELOG included in the data description document. This 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 2015 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) 1996 categories. For CO2‚‚ from energy and industry time series for subsectors are available. List of datasets included in this data publication:(1) PRIMAP-hist_v1.2_14-Dec-2017.csv: With numerical extrapolation of all time series to 2014. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v1.2_14-Dec-2017.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v1.2_data-format-description: including CHANGELOG(4) PRIMAP-hist_v1.2_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:- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2017B)- UNFCCC Biennal Update Reports: UNFCCC (2016)- UNFCCC Common Reporting Format (CRF): UNFCCC (2016), UNFCCC (2017)- BP Statistical Review of World Energy: BP (2017)- CDIAC: Boden et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2016)- Houghton land use CO2: Houghton (2008)- RCP historical data: Meinshausen et al. (2011)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- HYDE land cover data: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011)- SAGE Global Potential Vegetation Dataset: Ramankutty and Foley (1999)- FAO Country Boundaries: Food and Agriculture Organization of the United Nations (2015)