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.

India: Carbon dioxide emissions per capita: The latest value from 2023 is 2.05 metric tons of carbon dioxide equivalent per capita, an increase from 1.92 metric tons of carbon dioxide equivalent per capita in 2022. In comparison, the world average is 4.76 metric tons of carbon dioxide equivalent per capita, based on data from 189 countries. Historically, the average for India from 1970 to 2023 is 0.97 metric tons of carbon dioxide equivalent per capita. The minimum value, 0.38 metric tons of carbon dioxide equivalent per capita, was reached in 1971 while the maximum of 2.05 metric tons of carbon dioxide equivalent per capita was recorded in 2023.

Saudi Arabia is the largest per capita emitter of fossil carbon dioxide (CO₂) among G20 countries. In 2023, emissions in the Middle Eastern country amounted to just over ** metric tons per person (tCO₂/cap). Meanwhile, India had the lowest per capita CO₂ emissions, at around *** tCO₂/cap.

Population vs emissions Despite being the most populated G20 country, India has the lowest per capita CO₂ emissions of them all. This is mainly due to India’s economy being largely agrarian. Additionally, per capita energy consumption in the South Asian country is relatively low compared to many developed nations. On the other hand, Canada, which has a small population size of roughly ** million, had the second-largest emissions per capita in the G20. The North American country’s oil and gas industry is a key factor for this. Emissions from oil and gas production Other major oil and gas producers, such as Saudi Arabia, the U.S., and Russia, are also among the biggest per capita emitters globally. Russia alone accounted for nearly a ******* of global GHG emissions from oil and gas production in 2022, while the U.S. contributed almost ** percent.

India Consumption Based Emissions of CO2 per Capita data was reported at 1.739 Tonne in 2021. This records an increase from the previous number of 1.610 Tonne for 2020. India Consumption Based Emissions of CO2 per Capita data is updated yearly, averaging 0.978 Tonne from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 1.772 Tonne in 2018 and a record low of 0.661 Tonne in 1990. India Consumption Based Emissions of CO2 per Capita data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s India – Table IN.OWID.ESG: Environmental: CO2 and Greenhouse Gas Emissions: Annual.

Over the past two decades, many developing countries have experienced significant growth in per capita emissions. This is particularly the case in Asia. Fueled by rapid industrialization, per capita emissions in Vietnam have increased by almost 400 percent since 2000. Per capita emissions in China, India, and Indonesia have also increased substantially during this period. In comparison, per capita emissions in developed countries such as the United Kingdom have halved since 2000. Per capita emissions The growth in per capita emissions in China has coincided with the country becoming the world’s biggest emitter. However, despite the vast amounts of carbon dioxide China releases every year, its per capita emissions are far lower than in many other countries, at just eight metric tons per person. In comparison, the average American produces nearly 15 metric tons of carbon dioxide a year. This is three times higher than the average per capita emissions worldwide. Emissions in oil producing countries Per capita emissions are noticeably higher in oil producing countries. In the Middle East region, Qatar and Kuwait average more than 25 metric tons of CO₂ per inhabitant. People in more populous oil producing countries, such as Canada and Australia, average roughly 15 metric tons of carbon dioxide a year.

India Production Based Emissions of CO2 per Capita: Coal data was reported at 1.280 Tonne in 2021. This records an increase from the previous number of 1.137 Tonne for 2020. India Production Based Emissions of CO2 per Capita: Coal data is updated yearly, averaging 0.139 Tonne from Dec 1858 (Median) to 2021, with 153 observations. The data reached an all-time high of 1.280 Tonne in 2021 and a record low of 0.002 Tonne in 1865. India Production Based Emissions of CO2 per Capita: Coal data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s India – Table IN.OWID.ESG: Environmental: CO2 and Greenhouse Gas Emissions: Annual.

The average for 2023 based on 19 countries was 7.91 metric tons of carbon dioxide equivalent per capita. The highest value was in Saudi Arabia: 18.73 metric tons of carbon dioxide equivalent per capita and the lowest value was in India: 2.05 metric tons of carbon dioxide equivalent per capita. The indicator is available from 1970 to 2023. Below is a chart for all countries where data are available.

Per capita carbon dioxide emissions in the United States were estimated at 14 metric tons (tCO₂) in 2024. Under a business-as-usual scenario based on laws and regulations as of December 2024 under evolutionary technological growth assumptions, U.S. per capita emissions would fall to 9.2 tCO₂ by 2050. Since 1990, U.S. per capita emissions have reduced by roughly 30 percent. Americans have a large carbon footprint Although per capita emissions have fallen in the U.S., they are still far higher than other countries. This is especially the case when compared to other major GHG emitters like China and India. In 2023, per capita GHG emissions in the U.S. were 17.2 tCO₂e, roughly 2.5 times the global average. Which state has the largest carbon footprint? The U.S. state with the largest carbon footprint is Wyoming. In 2022, energy-related per capita CO₂ emissions in Wyoming were 96.6 tCO₂, roughly six times the national average. This is because of the states polluting coal industry.

India Production Based Emissions of CO2 per Capita: Cement data was reported at 0.106 Tonne in 2021. This records an increase from the previous number of 0.088 Tonne for 2020. India Production Based Emissions of CO2 per Capita: Cement data is updated yearly, averaging 0.012 Tonne from Dec 1928 (Median) to 2021, with 94 observations. The data reached an all-time high of 0.106 Tonne in 2021 and a record low of 0.000 Tonne in 1940. India Production Based Emissions of CO2 per Capita: Cement data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s India – Table IN.OWID.ESG: Environmental: CO2 and Greenhouse Gas Emissions: Annual.

Data on India's CO2 Emissions - total emissions, emission per capita, emission by energy source, industry-wise emissions, and comparison with global peers.

China's per capita carbon dioxide emissions more than tripled between 2000 and 2023, to reach *** metric tons (tCO₂). Despite this rapid growth, China's per capita emissions remained below those produced in the U.S. Meanwhile, per capita emissions in India, which is now the world's most populous country, averaged *** tCO₂ in 2023. India has the lowest per capita emissions in the G20.

India IN: CO2 Productivity: CO2 Emissions from Air Transport per Capita data was reported at 12.970 Tonne in 2022. This records an increase from the previous number of 9.160 Tonne for 2021. India IN: CO2 Productivity: CO2 Emissions from Air Transport per Capita data is updated yearly, averaging 11.270 Tonne from Dec 2013 (Median) to 2022, with 10 observations. The data reached an all-time high of 15.230 Tonne in 2018 and a record low of 7.010 Tonne in 2020. India IN: CO2 Productivity: CO2 Emissions from Air Transport per Capita 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 India – Table IN.OECD.GGI: Environmental: CO2 Productivity: Non OECD Member: Annual.

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.

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/

India’s fossil carbon dioxide emissions grew almost ***** percent in 2023, to a new high of ***** billion metric tons (GtCO₂). India is one of the world’s fastest-growing economies, and has experienced rapid industrialization in recent decades. As a result, the country’s emissions from fossil fuel use and industrial purposes have almost ******* since the turn of the century to become the third-highest globally. Contributions to historical emissions Although India is currently the world’s *****-largest emitter, the South Asian country’s historical emissions are far lower than other major GHG polluters. Since the Industrial Revolution began, India’s fossil fuel use has emitted around ** GtCO₂ into the atmosphere, ranking *** worldwide. In comparison, the U.S. and China have emitted around *** and *** GtCO₂, respectively. India’s contributions to global warming were estimated at only around **** percent as of 2023. Per capita emissions As well as having lower cumulative emissions, India’s per capita GHG emissions are also the lowest among the world’s top emitters. With just ***** metric tons of CO₂ equivalent emitted per person (tCO₂e/cap) in 2023, this was less than **** the world average of *** tCO₂e/cap. India was the only G20 economy with per capita emissions below the global average.

Inde: Carbon dioxide emissions per capita: Pour cet indicateur, La Banque mondiale fournit des données pour la Inde de 1970 à 2023. La valeur moyenne pour Inde pendant cette période était de 0.97 metric tons avec un minimum de 0.38 metric tons en 1971 et un maximum de 2.05 metric tons en 2023.

India CO2 Emissions: Tonnes of CO2 Equivalent per Capita per Year data was reported at 2.074 Tonne in 2023. This records an increase from the previous number of 1.942 Tonne for 2022. India CO2 Emissions: Tonnes of CO2 Equivalent per Capita per Year data is updated yearly, averaging 0.866 Tonne from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 2.074 Tonne in 2023 and a record low of 0.374 Tonne in 1973. India CO2 Emissions: Tonnes of CO2 Equivalent per Capita per Year data remains active status in CEIC and is reported by European Commission’s Directorate-General for Joint Research Centre. The data is categorized under Global Database’s India – Table IN.DG JRC.EDGAR: Environmental: Greenhouse Gas Emissions: CO2 Emissions: Annual.

Per capita carbon dioxide emissions from coal use vary greatly around the world. Countries such as South Africa, Australia, and China – the latter by far the biggest emitter from coal combustion – produce more than **** metric tons of carbon dioxide (tCO₂) per person. Meanwhile, India, which is the second-largest contributor to global coal use emissions, had per capita coal emissions of less than *** tCO₂.

Per capita CO₂ emissions from domestic commercial passenger flights vary greatly worldwide. The United States had the highest emissions worldwide in 2024, with the average American emitting an estimated *** kilograms of CO₂ from internal passenger flights, roughly time times above the global average. Australia and Norway followed, with average per capita emissions of *** and *** kilograms, respectively. In comparison, average emissions from internal flights in India were just **** kilograms per capita.

Catholic_CO2_Footprint_Beta_FullSees_Top10Burhans, Molly A., Cheney, David M., Gerlt, R.. . “Catholic_CO2_Footprint_Beta_FullSees_Top10”. 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/