A comparison of estimates of global carbon dioxide emissions from fossil carbon sources
Since the first estimate of global CO2 emissions was published in 1894, important progress has been made in the development of estimation methods while the number of available datasets has grown. The existence of parallel efforts should lead to improved accuracy and understanding of emissions estima...
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| Published in | Earth system science data Vol. 12; no. 2; pp. 1437 - 1465 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Katlenburg-Lindau
Copernicus GmbH
29.06.2020
Copernicus Publications |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Since the first estimate of global CO2 emissions was
published in 1894, important progress has been made in the development of
estimation methods while the number of available datasets has grown. The
existence of parallel efforts should lead to improved accuracy and
understanding of emissions estimates, but there remains significant
deviation between estimates and relatively poor understanding of the reasons
for this. Here I describe the most important global emissions datasets
available today and – by way of global, large-emitter, and case examples – quantitatively compare their estimates, exploring the reasons for
differences. In many cases differences in emissions come down to differences
in system boundaries: which emissions sources are included and which are
omitted. With minimal work in harmonising these system boundaries across
datasets, the range of estimates of global emissions drops to 5 %, and
further work on harmonisation would likely result in an even lower range,
without changing the data. Some potential errors were found, and some
discrepancies remain unexplained, but it is shown to be inappropriate to
conclude that uncertainty in emissions is high simply because estimates
exhibit a wide range. While “true” emissions cannot be known, by comparing
different datasets methodically, differences that result from system
boundaries and allocation approaches can be highlighted and set aside to
enable identification of true differences, and potential errors. This must
be an important way forward in improving global datasets of CO2
emissions. Data used to generate Figs. 3–18 are available at
https://doi.org/10.5281/zenodo.3687042 (Andrew, 2020). |
|---|---|
| AbstractList | Since the first estimate of global CO.sub.2 emissions was published in 1894, important progress has been made in the development of estimation methods while the number of available datasets has grown. The existence of parallel efforts should lead to improved accuracy and understanding of emissions estimates, but there remains significant deviation between estimates and relatively poor understanding of the reasons for this. Here I describe the most important global emissions datasets available today and - by way of global, large-emitter, and case examples - quantitatively compare their estimates, exploring the reasons for differences. In many cases differences in emissions come down to differences in system boundaries: which emissions sources are included and which are omitted. With minimal work in harmonising these system boundaries across datasets, the range of estimates of global emissions drops to 5 %, and further work on harmonisation would likely result in an even lower range, without changing the data. Some potential errors were found, and some discrepancies remain unexplained, but it is shown to be inappropriate to conclude that uncertainty in emissions is high simply because estimates exhibit a wide range. While "true" emissions cannot be known, by comparing different datasets methodically, differences that result from system boundaries and allocation approaches can be highlighted and set aside to enable identification of true differences, and potential errors. This must be an important way forward in improving global datasets of CO.sub.2 emissions. Data used to generate Figs. 3-18 are available at Since the first estimate of global CO2 emissions was published in 1894, important progress has been made in the development of estimation methods while the number of available datasets has grown. The existence of parallel efforts should lead to improved accuracy and understanding of emissions estimates, but there remains significant deviation between estimates and relatively poor understanding of the reasons for this. Here I describe the most important global emissions datasets available today and – by way of global, large-emitter, and case examples – quantitatively compare their estimates, exploring the reasons for differences. In many cases differences in emissions come down to differences in system boundaries: which emissions sources are included and which are omitted. With minimal work in harmonising these system boundaries across datasets, the range of estimates of global emissions drops to 5 %, and further work on harmonisation would likely result in an even lower range, without changing the data. Some potential errors were found, and some discrepancies remain unexplained, but it is shown to be inappropriate to conclude that uncertainty in emissions is high simply because estimates exhibit a wide range. While “true” emissions cannot be known, by comparing different datasets methodically, differences that result from system boundaries and allocation approaches can be highlighted and set aside to enable identification of true differences, and potential errors. This must be an important way forward in improving global datasets of CO2 emissions. Data used to generate Figs. 3–18 are available at https://doi.org/10.5281/zenodo.3687042 (Andrew, 2020). Since the first estimate of global CO2 emissions was published in 1894, important progress has been made in the development of estimation methods while the number of available datasets has grown. The existence of parallel efforts should lead to improved accuracy and understanding of emissions estimates, but there remains significant deviation between estimates and relatively poor understanding of the reasons for this. Here I describe the most important global emissions datasets available today and – by way of global, large-emitter, and case examples – quantitatively compare their estimates, exploring the reasons for differences. In many cases differences in emissions come down to differences in system boundaries: which emissions sources are included and which are omitted. With minimal work in harmonising these system boundaries across datasets, the range of estimates of global emissions drops to 5 %, and further work on harmonisation would likely result in an even lower range, without changing the data. Some potential errors were found, and some discrepancies remain unexplained, but it is shown to be inappropriate to conclude that uncertainty in emissions is high simply because estimates exhibit a wide range. While “true” emissions cannot be known, by comparing different datasets methodically, differences that result from system boundaries and allocation approaches can be highlighted and set aside to enable identification of true differences, and potential errors. This must be an important way forward in improving global datasets of CO2 emissions. Data used to generate Figs. 3–18 are available at https://doi.org/10.5281/zenodo.3687042 (Andrew, 2020). Since the first estimate of global CO2 emissions was published in 1894, important progress has been made in the development of estimation methods while the number of available datasets has grown. The existence of parallel efforts should lead to improved accuracy and understanding of emissions estimates, but there remains significant deviation between estimates and relatively poor understanding of the reasons for this. Here I describe the most important global emissions datasets available today and – by way of global, large-emitter, and case examples – quantitatively compare their estimates, exploring the reasons for differences. In many cases differences in emissions come down to differences in system boundaries: which emissions sources are included and which are omitted. With minimal work in harmonising these system boundaries across datasets, the range of estimates of global emissions drops to 5 %, and further work on harmonisation would likely result in an even lower range, without changing the data. Some potential errors were found, and some discrepancies remain unexplained, but it is shown to be inappropriate to conclude that uncertainty in emissions is high simply because estimates exhibit a wide range. While “true” emissions cannot be known, by comparing different datasets methodically, differences that result from system boundaries and allocation approaches can be highlighted and set aside to enable identification of true differences, and potential errors. This must be an important way forward in improving global datasets of CO2 emissions. Data used to generate Figs. 3–18 are available at10.5281/zenodo.3687042 (Andrew, 2020). |
| Audience | Academic |
| Author | Andrew, Robbie M. |
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| Copyright | COPYRIGHT 2020 Copernicus GmbH 2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | Since the first estimate of global CO2 emissions was
published in 1894, important progress has been made in the development of
estimation methods while the... Since the first estimate of global CO.sub.2 emissions was published in 1894, important progress has been made in the development of estimation methods while... Since the first estimate of global CO2 emissions was published in 1894, important progress has been made in the development of estimation methods while the... |
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| SubjectTerms | Boundaries Carbon cycle Carbon dioxide Carbon dioxide emissions Carbon sources Coal Datasets Emissions Emitters Energy Errors Estimates Fossil fuels Fossils International agreements |
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| Title | A comparison of estimates of global carbon dioxide emissions from fossil carbon sources |
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