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Comparison of EDGAR, ODIAC, and MEIC Grid CO 2 Emission Inventories: Historical and Current Versions.

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Title: Comparison of EDGAR, ODIAC, and MEIC Grid CO 2 Emission Inventories: Historical and Current Versions.
Authors: Jin, Chunlin1 (AUTHOR) chunlinj@cumt.edu.cn, Jiang, Xingxing2,3 (AUTHOR), Han, Youmei1,3 (AUTHOR)
Source: Remote Sensing. Sep2026, Vol. 18 Issue 18, p3144. 24p.
Subjects: Emission inventories, Spatial resolution, Carbon emissions, Geography, Climate change mitigation
Geographic Terms: China
Abstract: Highlights: What are the main findings? Global CO2 emission inventories exhibit minor total global deviations but prominent regional and grid-scale discrepancies, with MEIC-China showing far larger version uncertainty than iteratively optimized EDGAR and ODIAC global datasets. Grid scale exerts a robust nonlinear effect on inventory differences, with emission discrepancies showing apparent transitions near 2°, while finer grids amplify spatial divergence among different inventory products. What are the implications of the main findings? The near-2° scale feature provides potential resolution guidance: fine grids suit hotspot-oriented refined carbon analysis, while grids above 2° ensure efficient and stable macro-scale global and regional carbon research. Diverse inventory strengths and inherent biases necessitate scenario-based dataset adoption and multi-inventory synthesis, which improves the reliability of carbon emission assessment and supports localized precision carbon management. High-resolution gridded CO2 emission inventories underpin carbon cycle research and facilitate bridging global and national climate mitigation targets with regional emission assessments and site-specific mitigation actions. Major datasets including ODIAC, EDGAR, MEIC-global and MEIC-China adopt inconsistent spatial proxies and accounting frameworks, causing notable discrepancies. This study compares historical and latest versions at the global, top five emitting national, and grid scales, analyzing cross-version/inter-inventory variations and grid scale effects. The results reveal global total emission deviations of only 7.97% and 2.03%, yet pronounced regional heterogeneity. Maximum discrepancies reach 17.58% in Russia and 44.68% in Japan; grid-scale deviations over 50% exceed 35%. Post-v2022 ODIAC and post-v8.0 EDGAR have reduced version uncertainty, whereas MEIC-China shows large inter-version differences of 14.52% and 17.88% from long update cycles. Grid scale exerts nonlinear impacts on discrepancies, with the most pronounced transitions observed around 2°: discrepancies tend to amplify at resolutions finer than 2°, whereas discrepancies gradually diminish and appear to stabilize at resolutions coarser than 2°. This work clarifies core inventory characteristics, informing data selection, fusion and precision carbon management. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Comparison of EDGAR, ODIAC, and MEIC Grid CO 2 Emission Inventories: Historical and Current Versions.
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  Data: <searchLink fieldCode="AR" term="%22Jin%2C+Chunlin%22">Jin, Chunlin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chunlinj@cumt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Xingxing%22">Jiang, Xingxing</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Youmei%22">Han, Youmei</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2026, Vol. 18 Issue 18, p3144. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Emission+inventories%22">Emission inventories</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink><br /><searchLink fieldCode="DE" term="%22Geography%22">Geography</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change+mitigation%22">Climate change mitigation</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? Global CO2 emission inventories exhibit minor total global deviations but prominent regional and grid-scale discrepancies, with MEIC-China showing far larger version uncertainty than iteratively optimized EDGAR and ODIAC global datasets. Grid scale exerts a robust nonlinear effect on inventory differences, with emission discrepancies showing apparent transitions near 2°, while finer grids amplify spatial divergence among different inventory products. What are the implications of the main findings? The near-2° scale feature provides potential resolution guidance: fine grids suit hotspot-oriented refined carbon analysis, while grids above 2° ensure efficient and stable macro-scale global and regional carbon research. Diverse inventory strengths and inherent biases necessitate scenario-based dataset adoption and multi-inventory synthesis, which improves the reliability of carbon emission assessment and supports localized precision carbon management. High-resolution gridded CO2 emission inventories underpin carbon cycle research and facilitate bridging global and national climate mitigation targets with regional emission assessments and site-specific mitigation actions. Major datasets including ODIAC, EDGAR, MEIC-global and MEIC-China adopt inconsistent spatial proxies and accounting frameworks, causing notable discrepancies. This study compares historical and latest versions at the global, top five emitting national, and grid scales, analyzing cross-version/inter-inventory variations and grid scale effects. The results reveal global total emission deviations of only 7.97% and 2.03%, yet pronounced regional heterogeneity. Maximum discrepancies reach 17.58% in Russia and 44.68% in Japan; grid-scale deviations over 50% exceed 35%. Post-v2022 ODIAC and post-v8.0 EDGAR have reduced version uncertainty, whereas MEIC-China shows large inter-version differences of 14.52% and 17.88% from long update cycles. Grid scale exerts nonlinear impacts on discrepancies, with the most pronounced transitions observed around 2°: discrepancies tend to amplify at resolutions finer than 2°, whereas discrepancies gradually diminish and appear to stabilize at resolutions coarser than 2°. This work clarifies core inventory characteristics, informing data selection, fusion and precision carbon management. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18183144
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        Text: English
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      – SubjectFull: Emission inventories
        Type: general
      – SubjectFull: Spatial resolution
        Type: general
      – SubjectFull: Carbon emissions
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      – SubjectFull: Geography
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      – SubjectFull: Climate change mitigation
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Comparison of EDGAR, ODIAC, and MEIC Grid CO 2 Emission Inventories: Historical and Current Versions.
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            NameFull: Jin, Chunlin
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            NameFull: Jiang, Xingxing
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            NameFull: Han, Youmei
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              M: 09
              Text: Sep2026
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              Y: 2026
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