Name: Historical Anthropogenic Pollutant Emissions
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Historical Anthropogenic Pollutant Emissions

Global Trends 1750-2021

By [source]

About this dataset

This dataset provides an in-depth look into anthropogenic emissions of pollutants from 1750 to 2021. It covers a variety of different sources, areas, and categories including emissions from all continents and entities as well as specific emissions based on the IPCC 2006 PRIMAP standards. With this data users can easily see trends, analyze changes over time, and gain insights into emission rates and behaviors across entities and regions to better understand climate change. Comprehending human impacts on the environment is crucial to understanding various issues like global warming--this dataset provides reliable numerical evidence to get started!

More Datasets

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How to use the dataset

This dataset provides a comprehensive and detailed analysis of historical anthropogenic emissions of pollutants for PRIMAP-hist 1750-2021. To begin analyzing this data, it can be helpful to first look at the data by continent or entity. This can provide important insight into how different regions are contributing to global emissions patterns, trends, and impacts on climate change.

For more in depth analysis of pollutant levels within continents or entities, the dataset offers several columns whose values can be summarized and graphed. These include 'ISO3', which is the 3 digit country code used by most standards organizations; 'Entity', which represents an individual region (or combination thereof) within a continent; and 'IPCC2006_PRIMAP', which outlines primary pollutant categories as defined by the Intergovernmental Panel on Climate Change in their 2006 report. Utilizing these categories of data will help visualize how certain regions are prioritizing reductions in various types of carbon-containing particles that are known to have damaging effects on climate change when emitted in large quantities (such as carbon dioxide, methane, etc.).

By combining these areas with additional related datasets from other sources, such as energy consumption patterns or economic output per capita/area metrics from government records around the globe, one could further create detailed reports about regional activity levels versus pollution outputs for equitable comparison across nations over time.

Ultimately this dataset serves as a valuable resource for discovering insights about pollutant emissions over time across many countries - so feel free to explore its depths both alone and alongside other related material!

Research Ideas

Identifying correlations between levels of individual pollutants and corresponding climate change impacts over time

Studying differences in pollution rates among geographical regions and organizations to inform policy decisions

Determining how emissions from different sources have contributed to overall anthropogenic changes in atmospheric composition for better management of climate change initiatives

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

Acknowledgements

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

Tables

Guetschow Et Al 2022 Primap Hist V2–4–11 Oct 2022

@kaggle.thedevastator_historical_anthropogenic_pollutant_emissions.guetschow_et_al_2022_primap_hist_v2_4_11_oct_2022

16.61 MB
29,580 rows
278 columns

CREATE TABLE guetschow_et_al_2022_primap_hist_v2_4_11_oct_2022 (
  "source" VARCHAR,
  "scenario_primap_hist" VARCHAR  -- Scenario (PRIMAP-hist),
  "area_iso3" VARCHAR  -- Area (ISO3),
  "entity" VARCHAR,
  "unit" VARCHAR,
  "category_ipcc2006_primap" VARCHAR  -- Category (IPCC2006 PRIMAP),
  "n_1750" DOUBLE  -- 1750,
  "n_1751" DOUBLE  -- 1751,
  "n_1752" DOUBLE  -- 1752,
  "n_1753" DOUBLE  -- 1753,
  "n_1754" DOUBLE  -- 1754,
  "n_1755" DOUBLE  -- 1755,
  "n_1756" DOUBLE  -- 1756,
  "n_1757" DOUBLE  -- 1757,
  "n_1758" DOUBLE  -- 1758,
  "n_1759" DOUBLE  -- 1759,
  "n_1760" DOUBLE  -- 1760,
  "n_1761" DOUBLE  -- 1761,
  "n_1762" DOUBLE  -- 1762,
  "n_1763" DOUBLE  -- 1763,
  "n_1764" DOUBLE  -- 1764,
  "n_1765" DOUBLE  -- 1765,
  "n_1766" DOUBLE  -- 1766,
  "n_1767" DOUBLE  -- 1767,
  "n_1768" DOUBLE  -- 1768,
  "n_1769" DOUBLE  -- 1769,
  "n_1770" DOUBLE  -- 1770,
  "n_1771" DOUBLE  -- 1771,
  "n_1772" DOUBLE  -- 1772,
  "n_1773" DOUBLE  -- 1773,
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  "n_1775" DOUBLE  -- 1775,
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  "n_1788" DOUBLE  -- 1788,
  "n_1789" DOUBLE  -- 1789,
  "n_1790" DOUBLE  -- 1790,
  "n_1791" DOUBLE  -- 1791,
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  "n_1793" DOUBLE  -- 1793,
  "n_1794" DOUBLE  -- 1794,
  "n_1795" DOUBLE  -- 1795,
  "n_1796" DOUBLE  -- 1796,
  "n_1797" DOUBLE  -- 1797,
  "n_1798" DOUBLE  -- 1798,
  "n_1799" DOUBLE  -- 1799,
  "n_1800" DOUBLE  -- 1800,
  "n_1801" DOUBLE  -- 1801,
  "n_1802" DOUBLE  -- 1802,
  "n_1803" DOUBLE  -- 1803,
  "n_1804" DOUBLE  -- 1804,
  "n_1805" DOUBLE  -- 1805,
  "n_1806" DOUBLE  -- 1806,
  "n_1807" DOUBLE  -- 1807,
  "n_1808" DOUBLE  -- 1808,
  "n_1809" DOUBLE  -- 1809,
  "n_1810" DOUBLE  -- 1810,
  "n_1811" DOUBLE  -- 1811,
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  "n_1813" DOUBLE  -- 1813,
  "n_1814" DOUBLE  -- 1814,
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  "n_1816" DOUBLE  -- 1816,
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  "n_1820" DOUBLE  -- 1820,
  "n_1821" DOUBLE  -- 1821,
  "n_1822" DOUBLE  -- 1822,
  "n_1823" DOUBLE  -- 1823,
  "n_1824" DOUBLE  -- 1824,
  "n_1825" DOUBLE  -- 1825,
  "n_1826" DOUBLE  -- 1826,
  "n_1827" DOUBLE  -- 1827,
  "n_1828" DOUBLE  -- 1828,
  "n_1829" DOUBLE  -- 1829,
  "n_1830" DOUBLE  -- 1830,
  "n_1831" DOUBLE  -- 1831,
  "n_1832" DOUBLE  -- 1832,
  "n_1833" DOUBLE  -- 1833,
  "n_1834" DOUBLE  -- 1834,
  "n_1835" DOUBLE  -- 1835,
  "n_1836" DOUBLE  -- 1836,
  "n_1837" DOUBLE  -- 1837,
  "n_1838" DOUBLE  -- 1838,
  "n_1839" DOUBLE  -- 1839,
  "n_1840" DOUBLE  -- 1840,
  "n_1841" DOUBLE  -- 1841,
  "n_1842" DOUBLE  -- 1842,
  "n_1843" DOUBLE  -- 1843
);

Guetschow Et Al 2022 Primap Hist V2–4 No Extrap 11 Oct 2022

@kaggle.thedevastator_historical_anthropogenic_pollutant_emissions.guetschow_et_al_2022_primap_hist_v2_4_no_extrap_11_oct_2022

15.53 MB
28,112 rows
278 columns

CREATE TABLE guetschow_et_al_2022_primap_hist_v2_4_no_extrap_11_oct_2022 (
  "source" VARCHAR,
  "scenario_primap_hist" VARCHAR  -- Scenario (PRIMAP-hist),
  "area_iso3" VARCHAR  -- Area (ISO3),
  "entity" VARCHAR,
  "unit" VARCHAR,
  "category_ipcc2006_primap" VARCHAR  -- Category (IPCC2006 PRIMAP),
  "n_1750" DOUBLE  -- 1750,
  "n_1751" DOUBLE  -- 1751,
  "n_1752" DOUBLE  -- 1752,
  "n_1753" DOUBLE  -- 1753,
  "n_1754" DOUBLE  -- 1754,
  "n_1755" DOUBLE  -- 1755,
  "n_1756" DOUBLE  -- 1756,
  "n_1757" DOUBLE  -- 1757,
  "n_1758" DOUBLE  -- 1758,
  "n_1759" DOUBLE  -- 1759,
  "n_1760" DOUBLE  -- 1760,
  "n_1761" DOUBLE  -- 1761,
  "n_1762" DOUBLE  -- 1762,
  "n_1763" DOUBLE  -- 1763,
  "n_1764" DOUBLE  -- 1764,
  "n_1765" DOUBLE  -- 1765,
  "n_1766" DOUBLE  -- 1766,
  "n_1767" DOUBLE  -- 1767,
  "n_1768" DOUBLE  -- 1768,
  "n_1769" DOUBLE  -- 1769,
  "n_1770" DOUBLE  -- 1770,
  "n_1771" DOUBLE  -- 1771,
  "n_1772" DOUBLE  -- 1772,
  "n_1773" DOUBLE  -- 1773,
  "n_1774" DOUBLE  -- 1774,
  "n_1775" DOUBLE  -- 1775,
  "n_1776" DOUBLE  -- 1776,
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  "n_1783" DOUBLE  -- 1783,
  "n_1784" DOUBLE  -- 1784,
  "n_1785" DOUBLE  -- 1785,
  "n_1786" DOUBLE  -- 1786,
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  "n_1788" DOUBLE  -- 1788,
  "n_1789" DOUBLE  -- 1789,
  "n_1790" DOUBLE  -- 1790,
  "n_1791" DOUBLE  -- 1791,
  "n_1792" DOUBLE  -- 1792,
  "n_1793" DOUBLE  -- 1793,
  "n_1794" DOUBLE  -- 1794,
  "n_1795" DOUBLE  -- 1795,
  "n_1796" DOUBLE  -- 1796,
  "n_1797" DOUBLE  -- 1797,
  "n_1798" DOUBLE  -- 1798,
  "n_1799" DOUBLE  -- 1799,
  "n_1800" DOUBLE  -- 1800,
  "n_1801" DOUBLE  -- 1801,
  "n_1802" DOUBLE  -- 1802,
  "n_1803" DOUBLE  -- 1803,
  "n_1804" DOUBLE  -- 1804,
  "n_1805" DOUBLE  -- 1805,
  "n_1806" DOUBLE  -- 1806,
  "n_1807" DOUBLE  -- 1807,
  "n_1808" DOUBLE  -- 1808,
  "n_1809" DOUBLE  -- 1809,
  "n_1810" DOUBLE  -- 1810,
  "n_1811" DOUBLE  -- 1811,
  "n_1812" DOUBLE  -- 1812,
  "n_1813" DOUBLE  -- 1813,
  "n_1814" DOUBLE  -- 1814,
  "n_1815" DOUBLE  -- 1815,
  "n_1816" DOUBLE  -- 1816,
  "n_1817" DOUBLE  -- 1817,
  "n_1818" DOUBLE  -- 1818,
  "n_1819" DOUBLE  -- 1819,
  "n_1820" DOUBLE  -- 1820,
  "n_1821" DOUBLE  -- 1821,
  "n_1822" DOUBLE  -- 1822,
  "n_1823" DOUBLE  -- 1823,
  "n_1824" DOUBLE  -- 1824,
  "n_1825" DOUBLE  -- 1825,
  "n_1826" DOUBLE  -- 1826,
  "n_1827" DOUBLE  -- 1827,
  "n_1828" DOUBLE  -- 1828,
  "n_1829" DOUBLE  -- 1829,
  "n_1830" DOUBLE  -- 1830,
  "n_1831" DOUBLE  -- 1831,
  "n_1832" DOUBLE  -- 1832,
  "n_1833" DOUBLE  -- 1833,
  "n_1834" DOUBLE  -- 1834,
  "n_1835" DOUBLE  -- 1835,
  "n_1836" DOUBLE  -- 1836,
  "n_1837" DOUBLE  -- 1837,
  "n_1838" DOUBLE  -- 1838,
  "n_1839" DOUBLE  -- 1839,
  "n_1840" DOUBLE  -- 1840,
  "n_1841" DOUBLE  -- 1841,
  "n_1842" DOUBLE  -- 1842,
  "n_1843" DOUBLE  -- 1843
);

Guetschow Et Al 2022 Primap Hist V2–4 No Extrap No Rou 5512d08b

@kaggle.thedevastator_historical_anthropogenic_pollutant_emissions.guetschow_et_al_2022_primap_hist_v2_4_no_extrap_no_rou_5512d08b

37.18 MB
34,604 rows
278 columns

CREATE TABLE guetschow_et_al_2022_primap_hist_v2_4_no_extrap_no_rou_5512d08b (
  "source" VARCHAR,
  "scenario_primap_hist" VARCHAR  -- Scenario (PRIMAP-hist),
  "area_iso3" VARCHAR  -- Area (ISO3),
  "entity" VARCHAR,
  "unit" VARCHAR,
  "category_ipcc2006_primap" VARCHAR  -- Category (IPCC2006 PRIMAP),
  "n_1750" DOUBLE  -- 1750,
  "n_1751" DOUBLE  -- 1751,
  "n_1752" DOUBLE  -- 1752,
  "n_1753" DOUBLE  -- 1753,
  "n_1754" DOUBLE  -- 1754,
  "n_1755" DOUBLE  -- 1755,
  "n_1756" DOUBLE  -- 1756,
  "n_1757" DOUBLE  -- 1757,
  "n_1758" DOUBLE  -- 1758,
  "n_1759" DOUBLE  -- 1759,
  "n_1760" DOUBLE  -- 1760,
  "n_1761" DOUBLE  -- 1761,
  "n_1762" DOUBLE  -- 1762,
  "n_1763" DOUBLE  -- 1763,
  "n_1764" DOUBLE  -- 1764,
  "n_1765" DOUBLE  -- 1765,
  "n_1766" DOUBLE  -- 1766,
  "n_1767" DOUBLE  -- 1767,
  "n_1768" DOUBLE  -- 1768,
  "n_1769" DOUBLE  -- 1769,
  "n_1770" DOUBLE  -- 1770,
  "n_1771" DOUBLE  -- 1771,
  "n_1772" DOUBLE  -- 1772,
  "n_1773" DOUBLE  -- 1773,
  "n_1774" DOUBLE  -- 1774,
  "n_1775" DOUBLE  -- 1775,
  "n_1776" DOUBLE  -- 1776,
  "n_1777" DOUBLE  -- 1777,
  "n_1778" DOUBLE  -- 1778,
  "n_1779" DOUBLE  -- 1779,
  "n_1780" DOUBLE  -- 1780,
  "n_1781" DOUBLE  -- 1781,
  "n_1782" DOUBLE  -- 1782,
  "n_1783" DOUBLE  -- 1783,
  "n_1784" DOUBLE  -- 1784,
  "n_1785" DOUBLE  -- 1785,
  "n_1786" DOUBLE  -- 1786,
  "n_1787" DOUBLE  -- 1787,
  "n_1788" DOUBLE  -- 1788,
  "n_1789" DOUBLE  -- 1789,
  "n_1790" DOUBLE  -- 1790,
  "n_1791" DOUBLE  -- 1791,
  "n_1792" DOUBLE  -- 1792,
  "n_1793" DOUBLE  -- 1793,
  "n_1794" DOUBLE  -- 1794,
  "n_1795" DOUBLE  -- 1795,
  "n_1796" DOUBLE  -- 1796,
  "n_1797" DOUBLE  -- 1797,
  "n_1798" DOUBLE  -- 1798,
  "n_1799" DOUBLE  -- 1799,
  "n_1800" DOUBLE  -- 1800,
  "n_1801" DOUBLE  -- 1801,
  "n_1802" DOUBLE  -- 1802,
  "n_1803" DOUBLE  -- 1803,
  "n_1804" DOUBLE  -- 1804,
  "n_1805" DOUBLE  -- 1805,
  "n_1806" DOUBLE  -- 1806,
  "n_1807" DOUBLE  -- 1807,
  "n_1808" DOUBLE  -- 1808,
  "n_1809" DOUBLE  -- 1809,
  "n_1810" DOUBLE  -- 1810,
  "n_1811" DOUBLE  -- 1811,
  "n_1812" DOUBLE  -- 1812,
  "n_1813" DOUBLE  -- 1813,
  "n_1814" DOUBLE  -- 1814,
  "n_1815" DOUBLE  -- 1815,
  "n_1816" DOUBLE  -- 1816,
  "n_1817" DOUBLE  -- 1817,
  "n_1818" DOUBLE  -- 1818,
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  "n_1821" DOUBLE  -- 1821,
  "n_1822" DOUBLE  -- 1822,
  "n_1823" DOUBLE  -- 1823,
  "n_1824" DOUBLE  -- 1824,
  "n_1825" DOUBLE  -- 1825,
  "n_1826" DOUBLE  -- 1826,
  "n_1827" DOUBLE  -- 1827,
  "n_1828" DOUBLE  -- 1828,
  "n_1829" DOUBLE  -- 1829,
  "n_1830" DOUBLE  -- 1830,
  "n_1831" DOUBLE  -- 1831,
  "n_1832" DOUBLE  -- 1832,
  "n_1833" DOUBLE  -- 1833,
  "n_1834" DOUBLE  -- 1834,
  "n_1835" DOUBLE  -- 1835,
  "n_1836" DOUBLE  -- 1836,
  "n_1837" DOUBLE  -- 1837,
  "n_1838" DOUBLE  -- 1838,
  "n_1839" DOUBLE  -- 1839,
  "n_1840" DOUBLE  -- 1840,
  "n_1841" DOUBLE  -- 1841,
  "n_1842" DOUBLE  -- 1842,
  "n_1843" DOUBLE  -- 1843
);

Guetschow Et Al 2022 Primap Hist V2–4 No Rounding 11 Oct 2022

@kaggle.thedevastator_historical_anthropogenic_pollutant_emissions.guetschow_et_al_2022_primap_hist_v2_4_no_rounding_11_oct_2022

40.38 MB
36,390 rows
278 columns

CREATE TABLE guetschow_et_al_2022_primap_hist_v2_4_no_rounding_11_oct_2022 (
  "source" VARCHAR,
  "scenario_primap_hist" VARCHAR  -- Scenario (PRIMAP-hist),
  "area_iso3" VARCHAR  -- Area (ISO3),
  "entity" VARCHAR,
  "unit" VARCHAR,
  "category_ipcc2006_primap" VARCHAR  -- Category (IPCC2006 PRIMAP),
  "n_1750" DOUBLE  -- 1750,
  "n_1751" DOUBLE  -- 1751,
  "n_1752" DOUBLE  -- 1752,
  "n_1753" DOUBLE  -- 1753,
  "n_1754" DOUBLE  -- 1754,
  "n_1755" DOUBLE  -- 1755,
  "n_1756" DOUBLE  -- 1756,
  "n_1757" DOUBLE  -- 1757,
  "n_1758" DOUBLE  -- 1758,
  "n_1759" DOUBLE  -- 1759,
  "n_1760" DOUBLE  -- 1760,
  "n_1761" DOUBLE  -- 1761,
  "n_1762" DOUBLE  -- 1762,
  "n_1763" DOUBLE  -- 1763,
  "n_1764" DOUBLE  -- 1764,
  "n_1765" DOUBLE  -- 1765,
  "n_1766" DOUBLE  -- 1766,
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  "n_1768" DOUBLE  -- 1768,
  "n_1769" DOUBLE  -- 1769,
  "n_1770" DOUBLE  -- 1770,
  "n_1771" DOUBLE  -- 1771,
  "n_1772" DOUBLE  -- 1772,
  "n_1773" DOUBLE  -- 1773,
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  "n_1775" DOUBLE  -- 1775,
  "n_1776" DOUBLE  -- 1776,
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  "n_1778" DOUBLE  -- 1778,
  "n_1779" DOUBLE  -- 1779,
  "n_1780" DOUBLE  -- 1780,
  "n_1781" DOUBLE  -- 1781,
  "n_1782" DOUBLE  -- 1782,
  "n_1783" DOUBLE  -- 1783,
  "n_1784" DOUBLE  -- 1784,
  "n_1785" DOUBLE  -- 1785,
  "n_1786" DOUBLE  -- 1786,
  "n_1787" DOUBLE  -- 1787,
  "n_1788" DOUBLE  -- 1788,
  "n_1789" DOUBLE  -- 1789,
  "n_1790" DOUBLE  -- 1790,
  "n_1791" DOUBLE  -- 1791,
  "n_1792" DOUBLE  -- 1792,
  "n_1793" DOUBLE  -- 1793,
  "n_1794" DOUBLE  -- 1794,
  "n_1795" DOUBLE  -- 1795,
  "n_1796" DOUBLE  -- 1796,
  "n_1797" DOUBLE  -- 1797,
  "n_1798" DOUBLE  -- 1798,
  "n_1799" DOUBLE  -- 1799,
  "n_1800" DOUBLE  -- 1800,
  "n_1801" DOUBLE  -- 1801,
  "n_1802" DOUBLE  -- 1802,
  "n_1803" DOUBLE  -- 1803,
  "n_1804" DOUBLE  -- 1804,
  "n_1805" DOUBLE  -- 1805,
  "n_1806" DOUBLE  -- 1806,
  "n_1807" DOUBLE  -- 1807,
  "n_1808" DOUBLE  -- 1808,
  "n_1809" DOUBLE  -- 1809,
  "n_1810" DOUBLE  -- 1810,
  "n_1811" DOUBLE  -- 1811,
  "n_1812" DOUBLE  -- 1812,
  "n_1813" DOUBLE  -- 1813,
  "n_1814" DOUBLE  -- 1814,
  "n_1815" DOUBLE  -- 1815,
  "n_1816" DOUBLE  -- 1816,
  "n_1817" DOUBLE  -- 1817,
  "n_1818" DOUBLE  -- 1818,
  "n_1819" DOUBLE  -- 1819,
  "n_1820" DOUBLE  -- 1820,
  "n_1821" DOUBLE  -- 1821,
  "n_1822" DOUBLE  -- 1822,
  "n_1823" DOUBLE  -- 1823,
  "n_1824" DOUBLE  -- 1824,
  "n_1825" DOUBLE  -- 1825,
  "n_1826" DOUBLE  -- 1826,
  "n_1827" DOUBLE  -- 1827,
  "n_1828" DOUBLE  -- 1828,
  "n_1829" DOUBLE  -- 1829,
  "n_1830" DOUBLE  -- 1830,
  "n_1831" DOUBLE  -- 1831,
  "n_1832" DOUBLE  -- 1832,
  "n_1833" DOUBLE  -- 1833,
  "n_1834" DOUBLE  -- 1834,
  "n_1835" DOUBLE  -- 1835,
  "n_1836" DOUBLE  -- 1836,
  "n_1837" DOUBLE  -- 1837,
  "n_1838" DOUBLE  -- 1838,
  "n_1839" DOUBLE  -- 1839,
  "n_1840" DOUBLE  -- 1840,
  "n_1841" DOUBLE  -- 1841,
  "n_1842" DOUBLE  -- 1842,
  "n_1843" DOUBLE  -- 1843
);