Evaluating China's fossil-fuel CO<sub>2</sub> emissions from a comprehensive dataset of nine inventories
<p><span id="page11372"/>China's fossil-fuel <span class="inline-formula">CO<sub>2</sub></span> (<span class="inline-formula">FFCO<sub>2</sub></span>) emissions accounted for approximately 28 %...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-10-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/20/11371/2020/acp-20-11371-2020.pdf |
| Summary: | <p><span id="page11372"/>China's fossil-fuel <span class="inline-formula">CO<sub>2</sub></span> (<span class="inline-formula">FFCO<sub>2</sub></span>) emissions accounted for approximately 28 %
of the global total <span class="inline-formula">FFCO<sub>2</sub></span> in 2016. An accurate estimate of China's <span class="inline-formula">FFCO<sub>2</sub></span>
emissions is a prerequisite for global and regional carbon budget analyses and the monitoring of
carbon emission reduction efforts. However, significant uncertainties and discrepancies exist in
estimations of China's <span class="inline-formula">FFCO<sub>2</sub></span> emissions due to a lack of detailed traceable emission
factors (EFs) and multiple statistical data sources. Here, we evaluated China's <span class="inline-formula">FFCO<sub>2</sub></span> emissions from nine published global and regional emission datasets. These datasets show that the
total emissions increased from 3.4 (3.0–3.7) in 2000 to 9.8 (9.2–10.4) <span class="inline-formula">Gt</span>
<span class="inline-formula">CO<sub>2</sub></span> <span class="inline-formula">yr<sup>−1</sup></span> in 2016. The variations in these estimates were largely due to the
different EF (0.491–0.746 <span class="inline-formula">t</span> C per t of coal) and activity data. The large-scale patterns
of gridded emissions showed a reasonable agreement, with high emissions being concentrated in major city clusters, and the standard deviation mostly ranged from 10 % to 40 % at the provincial level. However, patterns beyond the provincial scale varied significantly, with the top 5 % of
the grid level accounting for 50 %–90 % of total emissions in these datasets. Our findings highlight the significance of using locally measured EF for Chinese coal. To reduce
uncertainty, we recommend using physical <span class="inline-formula">CO<sub>2</sub></span> measurements and use these values for
dataset validation, key input data sharing (e.g., point sources), and finer-resolution validations at various levels.</p> |
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| ISSN: | 1680-7316 1680-7324 |