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.

Since the industrial revolution, the volume of carbon dioxide (CO2) emitted into the air by human activity has grown exponentially, especially since the second half of the 20th century.

Graph and download economic data for Total Carbon Dioxide Emissions From All Sectors, All Fuels for Florida (EMISSCO2TOTVTTTOFLA) from 1970 to 2021 about carbon dioxide emissions, fuels, sector, FL, and USA.

The average American was responsible for emitting 13.8 metric tons of carbon dioxide (tCO₂) in 2023. U.S. per capita fossil CO₂ emissions have fallen by more than 30 percent since 1990. Global per capita emission comparisons Despite per capita emissions in the U.S. falling notably in recent decades, they remain roughly three times above global average per capita CO₂ emissions. In fact, the average American emits more CO₂ in one day than the average Somalian does throughout the entire year. Additionally, while China is now the world’s biggest emitter, the average Chinese citizen’s annual carbon footprint is roughly half the average American’s. Which U.S. state has the largest carbon footprint? Per capita energy-related CO₂ emissions in the U.S. vary greatly by state. Wyoming was the biggest CO₂ emitter per capita in 2022, with 97 tCO₂ per person. The least-populated state’s high per capita emissions are mainly due to its heavily polluting coal industry. In contrast, New Yorkers had the one of the smallest carbon footprints in 2022, at less than nine tCO₂ per person.

Japan Air Pollutant Emissions: Greenhouse Gases: Tonnes of CO2 Equivalent: Industry: Human Health and Social Work Activities data was reported at 9,322,607.000 Tonne in 2020. This records an increase from the previous number of 9,211,272.000 Tonne for 2019. Japan Air Pollutant Emissions: Greenhouse Gases: Tonnes of CO2 Equivalent: Industry: Human Health and Social Work Activities data is updated yearly, averaging 9,266,939.500 Tonne from Dec 1991 (Median) to 2020, with 30 observations. The data reached an all-time high of 11,359,409.000 Tonne in 2002 and a record low of 5,719,344.000 Tonne in 1991. Japan Air Pollutant Emissions: Greenhouse Gases: Tonnes of CO2 Equivalent: Industry: Human Health and Social Work Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Japan – Table JP.OECD.ESG: Environmental: Air Pollutant Emissions: Greenhouse Gases: by Industry: OECD Member: Annual.

In 2023, global carbon dioxide emissions from fossil fuel combustion and industrial processes reached a record high of 37.8 billion metric tons (GtCO₂). Global CO₂ emissions are projected to have reached record levels in 2024. The world has pumped more than 1,800 GtCO₂ into the atmosphere since the industrial revolution began, though almost 45 percent has been produced since 2000. What is carbon dioxide? CO₂ is a colorless, naturally occurring gas that is released after people and animals inhale oxygen. It is a greenhouse gas, meaning it absorbs and releases thermal radiation which in turn creates the “greenhouse effect”. In addition to other greenhouse gases, CO₂ is also a major contributor to the ability of the Earth to maintain a habitable temperature. Without CO₂ and other greenhouse gases, Earth would be too cold to live on. However, while CO₂ alone is not a harmful gas, the abundance of it is what causes climate change. The increased use of electricity, transportation, and deforestation in human society have resulted in the increased emissions of CO₂, which in turn has seen a rise in earth’s temperature. In fact, around 70 percent of global warming since 1851 is attributable to CO₂ emissions from human activities. Who are the largest emitters worldwide? China is the biggest carbon polluter worldwide, having released almost 12 GtCO₂ in 2023. This was more than the combined emissions of the United States and India, the second and third-largest emitters that year, respectively.

Due to the gradual threat of climate change to human living environments, the significant use of fossil fuels by humans has led to an increasing concentration of carbon dioxide in the atmosphere, becoming the main cause of global warming. Without effective control of carbon dioxide emissions, the situation could become even more severe. Taiwan Water Corporation actively promotes various energy-saving and carbon reduction policies in coordination with the government, advocating water conservation and raising awareness of the necessary CO2 emissions per cubic meter of water used by consumers. The goal is to promote the concept that water conservation can also reduce carbon emissions and mitigate the impact of global warming on the people of the country. This dataset provides the annual CO2 emissions per cubic meter of water used by Taiwan Water Corporation.

This scatter chart displays urban population (people) against greenhouse gas emissions (CO2, CH4, N2O, HFCs, PFCs, SF6) (Mt of CO2 equivalent) in New Zealand. The data is about countries per year.

This dataset is about countries per year in Costa Rica. It has 64 rows. It features 4 columns: country, greenhouse gas emissions (CO2, CH4, N2O, HFCs, PFCs, SF6), and individuals using the Internet.

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/

The 2016 version of this database presents a time series recording 1° latitude by 1° longitude CO2 emissions in units of million metric tons of carbon per year from anthropogenic sources for 1751-2013. Detailed geographic information on CO2 emissions can be critical in understanding the pattern of the atmospheric and biospheric response to these emissions. Global, regional, and national annual estimates for 1751 through 2013 were published earlier (Boden et al. 2016). Those national, annual CO2 emission estimates were based on statistics about fossil-fuel burning, cement manufacturing and gas flaring in oil fields as well as energy production, consumption, and trade data, using the methods of Marland and Rotty (1984). The national annual estimates were combined with gridded 1° data on political units and 1984 human populations to create the new gridded CO2 emission time series. The same population distribution was used for each of the years as proxy for the emission distribution within each country. The implied assumption for that procedure was that per capita energy use and fuel mixes are uniform over a political unit. The consequence of this first-order procedure is that the spatial changes observed over time are solely due to changes in national energy consumption and nation-based fuel mix. Increases in fossil-fuel CO2 emissions over time are apparent for most areas. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp058/ndp058_v2016.html

Costa Rica Air Pollutant Emissions: Carbon Dioxide: Tonnes of CO2 Equivalent: Industry: Human Health Activities data was reported at 100,592.401 Tonne in 2021. This records an increase from the previous number of 74,292.215 Tonne for 2020. Costa Rica Air Pollutant Emissions: Carbon Dioxide: Tonnes of CO2 Equivalent: Industry: Human Health Activities data is updated yearly, averaging 82,150.187 Tonne from Dec 2017 (Median) to 2021, with 5 observations. The data reached an all-time high of 100,592.401 Tonne in 2021 and a record low of 74,292.215 Tonne in 2020. Costa Rica Air Pollutant Emissions: Carbon Dioxide: Tonnes of CO2 Equivalent: Industry: Human Health Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Costa Rica – Table CR.OECD.ESG: Environmental: Air Pollutant Emissions: Carbon Dioxide: by Industry: OECD Member: Annual.

This dataset is about countries per year in Mauritius. It has 64 rows. It features 4 columns: country, greenhouse gas emissions (CO2, CH4, N2O, HFCs, PFCs, SF6), and individuals using the Internet.

This dataset is about countries per year in Antigua and Barbuda. It has 64 rows. It features 4 columns: country, greenhouse gas emissions (CO2, CH4, N2O, HFCs, PFCs, SF6), and individuals using the Internet.

Japan Air Pollutant Emissions: Carbon Dioxide: Tonnes of CO2 Equivalent: Industry: Human Health Activities data was reported at 3,397,787.000 Tonne in 2020. This records an increase from the previous number of 3,295,602.000 Tonne for 2019. Japan Air Pollutant Emissions: Carbon Dioxide: Tonnes of CO2 Equivalent: Industry: Human Health Activities data is updated yearly, averaging 4,183,918.000 Tonne from Dec 1991 (Median) to 2020, with 30 observations. The data reached an all-time high of 6,317,218.000 Tonne in 2000 and a record low of 3,295,602.000 Tonne in 2019. Japan Air Pollutant Emissions: Carbon Dioxide: Tonnes of CO2 Equivalent: Industry: Human Health Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Japan – Table JP.OECD.ESG: Environmental: Air Pollutant Emissions: Carbon Dioxide: by Industry: OECD Member: Annual.

The 2015 version of this database presents a time series recording 1° latitude by 1° longitude CO2 emissions in units of million metric tons of carbon per year from anthropogenic sources for 1751-2011. Detailed geographic information on CO2 emissions can be critical in understanding the pattern of the atmospheric and biospheric response to these emissions. Global, regional, and national annual estimates for 1751 through 2011 were published earlier (Boden et al. 2015). Those national, annual CO2 emission estimates were based on statistics about fossil-fuel burning, cement manufacturing and gas flaring in oil fields as well as energy production, consumption, and trade data, using the methods of Marland and Rotty (1984). The national annual estimates were combined with gridded 1° data on political units and 1984 human populations to create the new gridded CO2 emission time series. The same population distribution was used for each of the years as proxy for the emission distribution within each country. The implied assumption for that procedure was that per capita energy use and fuel mixes are uniform over a political unit. The consequence of this first-order procedure is that the spatial changes observed over time are solely due to changes in national energy consumption and nation-based fuel mix. Increases in fossil-fuel CO2 emissions over time are apparent for most areas.

Air Pollutant Emissions: Carbon Dioxide from Biomass: Tonnes of CO2 Equivalent: Industry: Human Health Activities data was reported at 582.130 Tonne in 2021. This records a decrease from the previous number of 12,491.950 Tonne for 2020. Air Pollutant Emissions: Carbon Dioxide from Biomass: Tonnes of CO2 Equivalent: Industry: Human Health Activities data is updated yearly, averaging 12,386.120 Tonne from Dec 2008 (Median) to 2021, with 14 observations. The data reached an all-time high of 13,639.340 Tonne in 2013 and a record low of 582.130 Tonne in 2021. Air Pollutant Emissions: Carbon Dioxide from Biomass: Tonnes of CO2 Equivalent: Industry: Human Health Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.ESG: Environmental: Air Pollutant Emissions: Carbon Dioxide From Biomass: by Industry: OECD Member: Annual.

The Asia-Pacific region produced 18.9 billion metric tons of carbon dioxide (GtCO₂) from energy use in 2023. China's CO₂ emissions are by far the highest in the Asia-Pacific region, at more than 10 GtCO₂ per year. The second most polluting region in 2023 was North America, where 5.9 GtCO₂ were generated, the majority of which came from the U.S. Global CO₂ emissions growth Global CO₂ emissions from energy consumption have more than doubled since 1970, and reached a record high in 2023. The rise in emissions is mainly due to rapidly growing economies and increasing energy demand in developing regions. This is especially the case in the Asia-Pacific region, where emissions have almost tripled since the turn of the century. The Middle East has also seen a dramatic rise in emissions, going from producing the lowest CO₂ emissions worldwide in 1965, to the fourth-highest as of 2023. Atmospheric carbon dioxide concentrations The increased burning of fossil fuels - as well as deforestation and other human activities - has seen atmospheric CO₂ concentrations surge in recent decades. In 2023, global atmospheric concentrations of CO₂ reached a record high of 421.08 parts per million, which is roughly 50 percent higher than before the industrial revolution.