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.

Qatar has the highest per capita carbon dioxide emissions worldwide, at 42.6 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 500 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.

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.

The average for 2020 based on 38 countries was 6.22 metric tons. The highest value was in Australia: 14.78 metric tons and the lowest value was in Costa Rica: 1.36 metric tons. The indicator is available from 1990 to 2020. Below is a chart for all countries where data are available.

The average for 2020 based on 27 countries was 5.54 metric tons. The highest value was in Luxembourg: 12.46 metric tons and the lowest value was in Malta: 3.13 metric tons. The indicator is available from 1990 to 2020. Below is a chart for all countries where data are available.

Per capita carbon dioxide emissions in the United States were estimated at 14.6 metric tons (tCO₂) in 2022. Under a business-as-usual scenario based on laws and regulations as of November 2022 under evolutionary technological growth assumptions, U.S. per capita emissions would fall to 10.6 tCO₂. 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 2021, per capita GHG emissions in the U.S. were 17.6 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 2020, energy-related per capita CO₂ emissions in Wyoming were 96.4 tCO₂, roughly six times the national average. This is because of the states polluting coal industry.

Australia had the highest per capita greenhouse gas emissions of all OECD member countries in 2023, at 22 metric tons of carbon dioxide equivalent (tCO₂e). Canadians were the second-worst carbon polluters that year, with average emissions of just over 20 tCO₂e/cap.13 of the 38 OECD member countries had per capita emissions below the global average in 2023, with Costa Rica, the most recent OECD member, averaging just below four tCO₂e/cap.

Kenya KE: CO2 Emissions: Metric Tons per Capita data was reported at 0.310 Metric Ton in 2014. This records an increase from the previous number of 0.297 Metric Ton for 2013. Kenya KE: CO2 Emissions: Metric Tons per Capita data is updated yearly, averaging 0.287 Metric Ton from Dec 1960 (Median) to 2014, with 55 observations. The data reached an all-time high of 0.386 Metric Ton in 1981 and a record low of 0.192 Metric Ton in 1985. Kenya KE: CO2 Emissions: Metric Tons per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Environment: Pollution. Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.; ; Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States.; Weighted average;

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 17 metric tons per person (tCO₂/cap). Meanwhile, India had the lowest per capita CO₂ emissions, at around 2.1 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 38 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 quarter of global GHG emissions from oil and gas production in 2022, while the U.S. contributed almost 20 percent.

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/

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/

Brazil BR: CO2 Emissions: Metric Tons per Capita data was reported at 1.943 Metric Ton in 2020. This records a decrease from the previous number of 2.051 Metric Ton for 2019. Brazil BR: CO2 Emissions: Metric Tons per Capita data is updated yearly, averaging 1.784 Metric Ton from Dec 1990 (Median) to 2020, with 31 observations. The data reached an all-time high of 2.515 Metric Ton in 2014 and a record low of 1.313 Metric Ton in 1990. Brazil BR: CO2 Emissions: Metric Tons per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Environmental: Gas Emissions and Air Pollution. Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.;Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions;Weighted average;

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.

Iran: Carbon dioxide emissions per capita: The latest value from 2020 is 7.06 metric tons, a decline from 7.22 metric tons in 2019. In comparison, the world average is 3.84 metric tons, based on data from 185 countries. Historically, the average for Iran from 1990 to 2020 is 6 metric tons. The minimum value, 3.56 metric tons, was reached in 1990 while the maximum of 7.57 metric tons was recorded in 2014.

Angola AO: CO2 Emissions: Metric Tons per Capita data was reported at 0.593 Metric Ton in 2020. This records a decrease from the previous number of 0.754 Metric Ton for 2019. Angola AO: CO2 Emissions: Metric Tons per Capita data is updated yearly, averaging 0.925 Metric Ton from Dec 1990 (Median) to 2020, with 31 observations. The data reached an all-time high of 1.125 Metric Ton in 2015 and a record low of 0.544 Metric Ton in 1992. Angola AO: CO2 Emissions: Metric Tons per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Angola – Table AO.World Bank.WDI: Environmental: Gas Emissions and Air Pollution. Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.;Emissions data are sourced from Climate Watch Historical GHG Emissions (1990-2020). 2023. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org/ghg-emissions;Weighted average;

Qatar had the highest per capita carbon dioxide emissions worldwide in 2023, at 42.6 metric tons per person. Per capita emissions in Qatar have remained relatively stable in recent years, but have fallen notably since the mid-2000s.In 1985, the tiny Dutch Caribbean island of Curaçao had the highest CO₂ emissions per capita in the world, at 90 metric tons, but by 2020, this figure had fallen to just 10 metric tons per person. Greenhouse gas emissions worldwide Several of the Middle East’s largest oil producing countries, including Qatar, the United Arab Emirates, and Saudi Arabia, are among the world’s largest per capita CO₂ emitters. Per capita emissions in Saudi Arabia have experienced a growth of roughly 75 percent since 1990. Canada is another major oil producing country with high CO₂ emissions per capita. Rising emissions Global greenhouse gas emissions have been on the rise since the industrial revolution began approximately 200 years ago. However, over the past 50 years CO₂ emissions have skyrocketed, and are projected to have reached a new record high in 2024.

Thematic area - Climate change

Name of Indicator - Greenhouse gas emission

DPSIR - Pressure

Indicator type - B – performance indicator

Definition of the indicator

The indicator shows the quantities of greenhouse gas emissions into atmosphere on national level. The emissions are presented by greenhouse gas type. The indicator provides information on emissions in the following sectors: energy, industrial processes and solvents, agriculture, waste and net removals from land use, land use change and forestry (LULUCF). Annual aggregated GHG per capita, per km2 and per unit of GDP.

Units - Mt/year CO2 equivalent

Policy relevance of the indicator:

The Republic of Moldova is a non-Annex I Party to the United Nations Framework Convention on Climate Change (ratified in 1995). In 2003 Moldova ratified the Kyoto Protocol. Government of the of the Republic of Moldova adopted Environment Strategy for the period 2014-2023 (Government Decision #301 from 24.04.2014) and Strategy on adaptation to climate change till 2020 and it’s Action Plan (Government Decision #1009 from 10.12.2014).

Targets:

According to Copenhagen Agreement, Republic of Moldova aims to reduce, to not less than 25% compared to the base year (1990), the total national level of greenhouse gas emissions by 2020, by implementing economic mechanisms focused on global climate change mitigation, in accordance with the principles and provisions of the United Nations Framework Convention on Climate Change.

The Environmental Protection Strategy for the years 2014-2023 and the Action Plan for its implementation states that a 20 % GHG emissions reduction compared to the base line scenario has to be reached in the Republic of Moldova by 2020.

Republic of Moldova’s iNDC states to reduce unconditional, by 2030, total emissions of national greenhouse gas emissions net, with no less than 67% compared to 1990, in support of the global effort on the trend of increasing global average temperature by 2100 in limit of up to 2 ° C. The objective of reducing emissions could increase up to 78% conditionally - according to an overall agreement that would address important issues such as financial resources with low costs, technology transfer and technical cooperation.

Key question - What is the average trend of GHG emissions for the whole period?

Specific question - What are the emission changes by sectors, by GHG, per capita, per km2, per unit of GDP?

Assessment

The base year for Republic of Moldova is 1990.

The inventory data presents that for base year the total emissions of GHG in CO2 equivalent are 43,42 without net removals from LULUCF sector and 37,53 aggregated emissions including emissions/removals from LULUCF.

For 1991-2013 (the last Inventory data) the net GHG emissions without/with removals decrease respectively from 43,42/37,53 Mt/year CO2 equivalent to 12,84/12,74 Mt/year CO2 equivalent compared with base year. This constitutes a reducing of GHG emissions with 30% and respectively 33% comparing with base year. Figure 1 presents the trend of the aggregated emissions (without and with LULUCF sector).

Table 1 presents the aggregated emissions (without and with LULUCF sector), the main GHG emissions and the share of the total emissions compare with the base year.

The analysis of the inventory presents that for the base year the big share of GHG type has CO2 emission (81%), followed by CH4 emissions (11%) and N2O emissions (7%). The trend is the same for the next years. So, in 2013 the share of CO2 emissions continue to be the highest (65%), CH4 emissions are the second with 21% and the third one are N2O emissions with 13% share from total emissions. The difference between 1990 and 2013 is the share from total emissions between these GHG. During 1990-2010 the share of CO2 emissions decreases, while the share of CH4 and NO2 increase. Nevertheless, during 1990-2013 the emissions of GHG decrease: CO2 emissions with 23,6%, CH4 with 55,3% and N2O with 52,1% (see Figure 2).

Halocarbons emissions (HFCs, PFCs) and sulphur hexafluoride (SF6) emissions have been registered in the Republic of Moldova starting with 1995. This year is considered as a reference year for F-gases (HFCs, PFCs and SF6). Evolution of these emissions denotes a steady trend towards increase in the last years, though their share in the total national emissions structure is insignificant.

The observed sectors in inventory are energy sector, industrial process, solvent and other product use, agriculture, land use, land use change, forestry and waste. The total GHG emissions by sectors are presented in Table 2 and the trend is presented in Figure 3. In general, Energy Sector has the greatest contribution to national GHG emissions, with an average share of 70% in 1990 and 65% in 2013 (see Figure 4 and Figure 5). Agriculture Sector was the second sector contributor with an average share of 10%, followed by Industrial Processes with average share of 4% for 1990. The trend of the share of different sectors for 2013 has changed and Industrial Processes has been replaced by Waste Sector with a share of 12% from the total emissions.

Figure 6 shows that starting with 1992 till 2004 there was a reduction of total GHG emissions from the Waste Sector. This trend is explained by the economic decline that occurred in the Republic of Moldova during the period under review, by a significant drop in the wellbeing of population, and respectively, capacity to generate solid and other types of wastes. At the same time, starting with 2005, there has been a clear growing trend of direct GHG emissions from the Waste Sector.

The main indicator for the assessment of the GHG emissions in the international aspects are GHG per capita. The emission of GHG per capita decrease from 9,95 tons CO2 equivalent in 1990 to 3,16 tons CO2 equivalent in 2013. The lower level was during 2007 – 2.18 tons CO2 equivalent per capita (see Figure 7). For comparison the average European level of this indicator is 9.4 tons CO2 equivalent per capita in 2013. The emission of the GHG are directly linked with economic growth of the country, because with increasing of economic activity the consumption of energy and resources increase to. For the period 1990 to 2013 aggregated GHG emissions per unit of GDP decrease from 4.39 tons CO2 equivalent to 1.91 tons CO2 equivalent. Between 1990 to 2007 emissions of GDP in the most European countries decrease for more than 30%. The trend in the aggregated GHG emissions per km2 is the same as the trends of GHG emission per capita and per GDP (see Figure 7).

Key messages: For the period 1990 to 2013: • the total emission throughout the inventory have decrease with 30%. • the emissions of the GHG per capita decrease with 32%. • the energy sector has the greatest contribution to national GHG emissions.

Trend - positive.

Data coverage - 1990-2013

Data source - Republic of Moldova’s Third National Communication to United Nation Framework Convention on Climate Change (UNFCCC), Ministry of Environment.

Methodology To calculate GHG emissions as well as GHG inventories, the methodology provided by UNFCCC/IPCC is used. Methodology is based on the calculation of GHGs as a product from the rate of activity for individual sectors and emission factors. The national inventory is structured to match the reporting requirement of the UNFCCC and is divided into six main sectors: (1) Energy, (2) Industrial Processes, (3) Solvents and Other Products Use, (4) Agriculture, (5) Land Use, Land-Use Change and Forestry and (6) Waste. Emissions of direct (CO2, CH4, N2O, HFCs, PFCs and SF6) and indirect (NOx, CO, NMVOC, SO2) greenhouse gases were estimated based on methodologies contained in the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.

Reporting obligations - UNFCCC

Greece: Carbon dioxide emissions per capita: The latest value from 2020 is 4.77 metric tons, a decline from 5.6 metric tons in 2019. In comparison, the world average is 3.84 metric tons, based on data from 185 countries. Historically, the average for Greece from 1990 to 2020 is 7.68 metric tons. The minimum value, 4.77 metric tons, was reached in 2020 while the maximum of 9.44 metric tons was recorded in 2007.

Togo: Carbon dioxide emissions per capita: The latest value from 2020 is 0.29 metric tons, a decline from 0.3 metric tons in 2019. In comparison, the world average is 3.84 metric tons, based on data from 185 countries. Historically, the average for Togo from 1990 to 2020 is 0.27 metric tons. The minimum value, 0.16 metric tons, was reached in 1993 while the maximum of 0.44 metric tons was recorded in 2009.

Turkey: Carbon dioxide emissions per capita: The latest value from 2020 is 4.89 metric tons, an increase from 4.83 metric tons in 2019. In comparison, the world average is 3.84 metric tons, based on data from 185 countries. Historically, the average for Turkey from 1990 to 2020 is 3.7 metric tons. The minimum value, 2.56 metric tons, was reached in 1990 while the maximum of 5.21 metric tons was recorded in 2017.