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.

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.

Global per capita carbon dioxide emissions averaged 4.7 metric tons in 2023. This represented a slight increase in comparison to the previous year. Qatar has the largest per capita CO₂ emissions worldwide, at more than 37 metric tons per person.

Emissions by Country

Quantifying Sources and Emission Levels

By [source]

About this dataset

This dataset provides an in-depth look into the global CO2 emissions at the country-level, allowing for a better understanding of how much each country contributes to the global cumulative human impact on climate. It contains information on total emissions as well as from coal, oil, gas, cement production and flaring, and other sources. The data also provides a breakdown of per capita CO2 emission per country - showing which countries are leading in pollution levels and identifying potential areas where reduction efforts should be concentrated. This dataset is essential for anyone who wants to get informed about their own environmental footprint or conduct research on international development trends

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How to use the dataset

This dataset provides a country-level survey of global fossil CO2 emissions, including total emissions, emissions from coal, oil, gas, cement, flaring and other sources as well as per capita emissions.

For researchers looking to quantify global CO2 emission levels by country over time and understand the sources of these emissions this dataset can be a valuable resource.

The data is organized using the following columns: Country (the name of the country), ISO 3166-1 alpha-3 (the three letter code for the country), Year (the year of survey data), Total (the total amount of CO2 emitted by the country in that year), Coal (amount of CO2 emitted by coal in that year), Oil (amount emitted by oil) , Gas (amount emitted by gas) , Cement( amount emitted by cement) , Flaring(flaring emission levels ) and Other( other forms such as industrial processes ). In addition there is also one extra column Per Capita which provides an insight into how much personal carbon dioxide emission is present in each Country per individual .

To make use of these columns you can aggregate sum up Total column for a specific region or help define how much each source contributes to Total column such as how many percent it accounts for out of 100 or construct dashboard visualizations to explore what sources are responsible for higher level emission across different countries similar clusters or examine whether individual countries Focusing on Flaring — emissions associated with burning off natural gas while drilling—can improve overall Fossil Fuel Carbon Emission profiles better understanding of certain types nuclear power plants etc.

The main purpose behind this dataset was to facilitate government bodies private organizations universities NGO's research agencies alike applying analytical techniques tracking environment changes linked with influence cross regions providing resources needed analyze process monitor developing directed ways managing efficient ways get detailed comprehensive verified information

With insights gleaned from this dataset one can begin identify strategies efforts pollutant mitigation climate change combat etc while making decisions centered around sustainable developments with continent wide unified plans policy implementations keep an eye out evidences regional discrepancies being displayed improving quality life might certainly seem likely assure task easy quickly done “Global Fossil Carbon Dioxide Emissions:Country Level Survey 2002 2022 could exactly what us

Research Ideas

• Using the per capita emissions data, develop a reporting system to track countries' progress in meeting carbon emission targets and give policy recommendations for how countries can reach those targets more quickly.
• Analyze the correlation between different fossil fuel sources and CO2 emissions to understand how best to reduce CO2 emissions at a country-level.
• Create an interactive map showing global CO2 levels over time that allows users to visualize trends by country or region across all fossil fuel sources

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: GCB2022v27_MtCO2_flat.csv | Column name | Description ...

Qatar has the highest per capita carbon dioxide emissions worldwide, at **** metric tons per person. Many countries in the Middle East had high levels emissions, especially when compared to countries in Africa. Greenhouse gas emissions worldwide Some of the Middle East’s largest oil producing countries, including Qatar, the United Arab Emirates, and Saudi Arabia are among the world’s largest carbon dioxide (CO₂) emitters per capita. Countries such as the United States, Australia and Canada also show disproportionately high levels of emission per inhabitant. Despite a relatively low population for its size, Canada’s CO₂ emissions have recently surpassed *** million metric tons, and the country is now amongst the largest producers of CO₂ emissions worldwide. Rising emissions Global greenhouse gas emissions have been on the rise since the industrial revolution began approximately 200 years ago. Over the past half-century CO₂ emissions have skyrocketed, and climbed to a record high in recent years. Yet, emissions fell considerably in 2020 as a result of the COVID-19 pandemic, which caused disruptions to transportation and industrial activities.

Catholic_CO2_Footprint_Beta_FullSees_MinusTop10Burhans, Molly A., Cheney, David M., Gerlt, R.. . “Catholic_CO2_Footprint_Beta_FullSees_MinusTop10”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.DEVELOPED AS A POPUP LAYERMethodologyThis 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/

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 United States has one of the highest road transportation emissions per capita worldwide. The average American emitted roughly 4,500 kilograms of CO2 from road transport in 2018, which was slightly more than in neighboring Canada. The U.S. emits the largest volume of CO2 emissions from road transportation worldwide, followed by China. However, China's emissions per capita are considerably lower than in the U.S.. Low income countries, such as those is South Asia and Africa, have far lower road transportation emissions per capita than in higher income countries. For example, per capita road transportation emissions in Eritrea averaged 63 kg CO2/capita in 2018. This was approximately 70 times less than the per capita emissions in the U.S..

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in the USA. Power Plant emissions from all power plants in the United Staes were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information http://carma.org/region/detail/202

Per capita carbon dioxide (CO₂) emissions in India have soared in recent decades, climbing from 0.4 metric tons per person in 1970 to a high of 2.07 metric tons per person in 2023. Total CO₂ emissions in India also reached a record high in 2023. Greenhouse gas emissions in India India is the third-largest CO₂ emitter globally, behind only China and the United States. Among the various economic sectors of the country, the power sector accounts for the largest share of greenhouse gas emissions in India, followed by agriculture. Together, these two sectors were responsible for more than half of India's total emissions in 2023. Coal emissions One of the main reasons for India's high emissions is the country's reliance on coal, the most polluting of fossil fuels. India's CO₂ emissions from coal totaled roughly two billion metric tons in 2023, a near sixfold increase from 1990 levels.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in the Poland. Power Plant emissions from all power plants in the Poland were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information

Top 10 Dioceses with Highest Carbon FootprintBurhans, Molly A., Cheney, David M., Gerlt, Robert, Thompson, Helen. “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/

In 2023, China was the biggest carbon polluter in the world by far, having released 11.9 billion metric tons of carbon dioxide (GtCO₂). Although the U.S. was the second-biggest emitter, with 4.9 GtCO₂ in 2023, its CO₂ emissions have declined by 13 percent since 2010. By comparison, China’s CO₂ emissions have increased by more than 38 percent in the same period. Cumulative emissions Although China is currently the world's largest carbon polluter, the U.S. has released far more historical carbon dioxide emissions, at more than 400 GtCO₂ since 1750. The wide gap between the two countries is because China's emissions have mostly been produced in the past two decades. Combined, the U.S. and China account for roughly 40 percent of cumulative CO₂ emissions since the Industrial Revolution began. Sources of emissions One of the largest sources of global CO₂ emissions is the power sector, with electricity produced by coal-fired power plants a significant contributor. In China, emissions from coal-fired electricity generation have soared since the turn of the century, and reached 5.2 GtCO₂ in 2023.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in Hungary. Power Plant emissions from all power plants in Hungary were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in Portugal. Power Plant emissions from all power plants in Portugal were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in Chile. Power Plant emissions from all power plants in Chile were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information

The average Canadian produced **** metric tons of carbon dioxide in 2023. Although per capita emissions in Canada have fallen almost ** percent since peak emissions in 1980, Canadians still had the second-highest CO₂ emissions per inhabitant of all G20 countries in 2023.

Climate Risk Datasets: NARCliM 1.0 Climate risk datasets derived from Global Climate Models (GCMs) typically have a coarse spatial resolution (around 100-200km grid cells), whereas water models require data with much finer spatial resolution. The NSW and Australian Regional Climate Modelling (NARCliM) project addresses this by using dynamical downscaling on a subset of available Coupled Model Intercomparison Project (CMIP) Phase 3 GCMs (Evans et al., 2014). In the first phase of this project (NARCliM 1.0), four CMIP3 GCMs were remodelled onto finer resolution grids using three different regional climate models (RCMs). This process produced a suite of 12 modelled results for three distinct 20-year climate periods: 1990-2009, 2020-2039, and 2060-2079.

NARCliM 1.0 utilized the SRES A2 emissions scenario, which describes a highly heterogeneous world characterized by self-reliant regions with well-preserved local identities and a continuously increasing global population (projected to reach 15 billion by 2100). Economic development in this scenario is regionally focused, and per capita economic growth and technological change are more fragmented and slower compared to other emissions scenarios. Notably, potent greenhouse gases such as hydrofluorocarbons and land-use emissions increase rapidly in the latter half of the century, resulting in the highest overall emissions among the SRES scenarios. This scenario is projected to lead to an approximate 2.2-degree Celsius increase in average global temperature by 2079 (relative to the 1980-1999 baseline).

Average Monthly Scaling Factors from NARCliM 1.0 To inform the Regional Water modelling, future climate projections were combined with 10,000 years of long-term paleo-stochastic climate data. This approach aimed to stress-test our water systems under long-term climate variability and projected changes. For this reason, we selected the Global Climate Model (GCM) and Regional Climate Model (RCM) combination that projected the lowest rainfall compared to the present day.

We calculated monthly rainfall ratios by comparing projected rainfall data (2060-79) to current rainfall data (1990-2009) over 20-year periods at each climate station used in the water models. This calculation utilized bias-corrected NARCliM datasets, and the resulting ratios were then multiplied by the stochastic dataset to factor it. Similarly, potential evapotranspiration results from the same NARCliM scenario were used to factor the stochastically generated potential evapotranspiration results.

The resulting rainfall and evapotranspiration data, representing the dry future climate scenario used in the regional water strategy modelling, was derived from the CSIRO’s Mk 3.0 global climate model, downscaled by three RCMs under NARCliM 1.0. This allowed us to understand how proposed options in the draft strategies would perform under the most severe drought conditions in a future characterized by a slow global response to curbing carbon emissions. It is important to note that this is not the only future climate scenario being considered.

We apply this method of generating stochastic data from historic and paleoclimate datasets, and then factoring it using NARCliM 1.0 outputs, to NSW’s inland regions and north coast. For the south-coast region, our analysis is based on changes to East Coast Low seasonal frequency, informed by NARCliM 1.0. These changes were initially applied using available East Coast Low synoptic data. We then generated stochastic data from this altered dataset (DPIE, 2023).

Note: If you would like to ask a question, make any suggestions, or tell us how you are using this dataset, please visit the NSW Water Hub which has an online forum you can join.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in the Czech Republic. Power Plant emissions from all power plants in the Czech Republic were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in Finland. Power Plant emissions from all power plants in Finland were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information