the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Apr 2025
CHN-CH4: A Gridded (0.1°×0.1°) Anthropogenic Methane Emission Inventory of China from 1990 to 2020
Abstract. China is the largest emitter of global methane emissions, contributing about 10 % to anthropogenic climate change based on existing methane inventories. However, significant uncertainties in these statistics limit the accuracy at both national and sub-national scales. The lack of continuous gridded emissions inventories also constrains the inverse analysis of atmospheric observations. To address these, we present CHN-CH4, a spatially aggregated 0.1°×0.1° anthropogenic methane emission inventory for mainland China from 1990 to 2020. CHN-CH4 offers the country with new temporal coverage and details, by means of national statistical yearbooks and remote sensing products. Over the three decades, mainland China emitted 1156.689 [884.857–1413.315] Tg of methane, with the highest emission occurred in the last decade. But this decade also marked the beginning of a decreasing trend, from 45.017 [33.329–55.738] Tg in 2010 to 43.351 [32.089–52.679] Tg in 2020. As important priors, CHN-CH4 enables robust comparisons between estimated emissions and atmospheric observations, thereby improving the accuracy of inverse modelling, which is crucial for effective tracking of methane emissions. By providing a reliable and detailed emissions inventory, CHN-CH4 would be a valuable tool in accelerating the global effort to achieve equitable methane emission reduction goals, as well as supporting China’s climate policy.
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Status: open (until 30 May 2025)
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RC1: 'Comment on essd-2025-178', Anonymous Referee #1, 02 May 2025
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The authors create a high resolution bottom-up inventory of methane emissions in China, offering a valuable dataset for the community. My comments are concerned primarily with (1) clarifying methodology and (2) strengthening evaluation against other datasets. I support publication after these comments are addressed.
Major comments:
- Figure 3: It is not clear to me what is being compared in the scatter plots d-i. Are these all individual grid cells in China for a given year (2000/2019)? For 2000-2009, is this a decadal average or all data for each year? It is also clear that there is a density plot, but there is no colorbar showing how many points are combined at the yellow saturation. Perhaps this would be better shown on a log-log plot so lower emissions grid cells are more visible and easier to compare.
- Lines 307-311: How do you know EDGAR is overestimating these emissions? Bottom-up estimation is complicated and can have errors, and top-down quantification similarly has errors; our uncertainties are generally quite large. In particular, I disagree with line 311; I think this paper would greatly benefit from a more detailed comparison with EDGAR at sector, regional, and national levels. Disagreement between your estimates is expected, but it is important to understand why your inventory disagrees.
- In addition to your scatterplots in Figure 3, I’d also like to see a spatial comparison with other inventories, especially EDGAR and also perhaps some leading sectoral inventories like GFEI for fossil fuels (especially coal). It would also be interesting to see spatial comparisons with a top-down posterior estimates. Do your maps generally agree on the spatial patterns of emissions, or are there significant differences? This could perhaps be shown in a difference plot like Figure 5.
- On that note, what version of EDGAR do you use for your comparison? Table 1 suggests V8 but it isn’t clear from the citations. How do your comparisons look with different versions of EDGAR? V8 for example tends to have more emissions from point sources.
- I would like more information and discussion of the extrapolation and interpolation methodologies used in this paper. It seems like the authors are dependent on simple linear regression for e.g. landfills (255-261), regressing against GDP to predict landfills before yearbook data begin in 2003. I see that on a national basis the correlation in Figure S2 is strong, but is this also true at the provincial level? Similarly, could the authors clarify how missing data are handled, across all sectors? Are the methods the authors use to handle these data in line with previous work?
Minor comments:
- Could you describe the methodology in the left portion of Figure 1 in more detail (at bottom left)? I don’t understand what t1 and t2 are, for example.
- Abstract (and elsewhere, e.g. line 358 and Table 5): Do you really have 3 decimal points of confidence in your estimates? I recommend rounding.
- From your dataset, I see that your data is at annual temporal resolution, but this is not clear in the text of the paper. Could you clarify this?
- Throughout, e.g. lines 182-183, 224, 310: Are the years 1980-2010 correct here or do you mean 1990-2020? If 1980-2010 is correct, how do you handle 2010-2020?
- Line 234: EF for coal combustion is missing
- Line 260-261: The authors grid landfill data using GDP. I am not familiar with landfills in China but I wonder if this a reasonable assumption? Perhaps the authors could compare with estimates that incorporate more spatial information, like this one: Bofeng Cai et al. CH4 mitigation potentials from China landfills and related environmental co-benefits. Adv.4,eaar8400(2018).DOI:10.1126/sciadv.aar8400
- Figure captions in general should be more descriptive of figure content.
Citation: https://doi.org/10.5194/essd-2025-178-RC1 -
RC2: 'Comment on essd-2025-178', Anonymous Referee #2, 22 May 2025
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Review of Guo et al.
Guo et al presented (1990-2020) long-term, high-resolution emission inventory for mainland China. Building long-term methane emission inventory is hard work and the efforts by the authors are quite commendable. I also appreciate that the carbon community will have one more regional inventory to use/evaluate. A unique advantage of this work is the authors used statistical yearbook and remote sensing data to improve the temporal coverage.
My major concerns are on the spatial distributions. For the spatial distributions, the authors took them from existing inventories for some source sectors (FAO inventory for livestock, EDGAR inventory for coal, oil, gas, to some extent). Therefore, the authors found a better consistency with EDGAR than PKUv2, which is thus as expected. I was wondering if the authors can explicitly show maps between your results and EDGAR, and discuss in detail the extent to which your product has improved in spatial accuracy compared to existing inventories (e.g., EDGAR, as the authors stated in the Introduction, which is part of the motivation of this work). If the authors used spatial distribution from existing inventories, which are known to have spatial bias, the novelty of this work and the accuracy of this product demand more clarifications. I would suggest that the authors elaborate (in both text and figures) on this point. Doing so would improve the clarity and benefit the future readers and users of your product.
Another concern is the remote sensing dataset. The authors highlighted in the abstract and Fig. 1 that satellite remote sensing are substantially used in their work. But I failed to find such use in a clear way. For example, for rice paddies, the authors claimed that ‘Due to the limitations of existing satellite products, which do not cover the entire period from 1990 to 2020, we used two datasets for gridded rice cultivation areas annually: CCD-Rice for the period 1990-2016 (Shen et al., 2024) and ChinaCP for the 115 period 2017-2020 (Qiu et al., 2022).’ Therefore, satellite data is not used at least in rice paddy identification. Please clarify in detail how satellite remote sensing is used for the source sectors.
Minor comments.
Line 14: accumulative methane emissions are not very meaningful here. I suggest using the annual average instead.
Line 29: livestock is part of agricultural activities. Re-phrase it here.
Sect. 2.2.1： I was curious if the authors included abandoned coal mines, as Qiang Liu et al., (2024), https://www.nature.com/articles/s41558-024-02004-3, highlighted the big role of it.
Sect. 2.2.2： Can the authors elaborate on how you assign emission to midstream and downstream emissions? I believe it’s missing from this section right now.
Lines 115: I think we should consider uncertainties from both rice area and emission factors. Currently the authors only considered emission factor uncertainties, which is not comprehensive.
Citation: https://doi.org/10.5194/essd-2025-178-RC2
Data sets
CHN-CH4: A Gridded (0.1°×0.1°) Anthropogenic Methane Emission Inventory of China from 1990 to 2020 Fengxiang Guo, Fan Dai, Peng Gong, and Yuyu Zhou https://doi.org/10.5281/zenodo.15107383
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