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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18183144 |
