the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Apr 2026
Shifting Drivers of China's Methane Emissions amid Economic Growth and Mitigation between 2019–2024
Abstract. Amid the accelerated growth in global atmospheric methane (CH4) concentrations after 2019, identifying key emitting regions, quantifying their contributions, and elucidating the underlying drivers have become pressing needs. However, limited monitoring capacity and complex inversion systems have constrained the timely and accurate assessments of regional CH4 emissions. Here, we construct a regional atmospheric inversion framework using the Local Ensemble Transform Kalman Filter (LETKF), constrained by satellite CH4 observations. Applied to East Asia at 0.5° × 0.625° resolution, this system produces weekly CH4 flux estimates for China during 2019–2024. We show that China’s CH4 emissions increased from 61.1 (56.2–66.7) Tg in 2019 to 66.8 (61.5–73.0) Tg in 2024. The livestock sector contributed nearly half of the growth, while rising waste and oil-gas emissions and northward expansion of rice cultivation shifted China’s emissions growth to previously low-emitting Northwest and Northeast regions. Our framework demonstrates the feasibility of near-real-time, regional-scale emissions monitoring, offering a transferable tool for other high-emitting countries.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-6284', Anonymous Referee #1, 01 May 2026
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RC2: 'Comment on egusphere-2025-6284', Anonymous Referee #2, 21 May 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-6284/egusphere-2025-6284-RC2-supplement.pdf
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- 1
Li et al. present a high-resolution atmospheric inversion to track changes in China's methane emissions between 2019 and 2024. Their analysis highlights that livestock, waste, oil and gas emissions, and the northward expansion of rice paddies have emerged as new drivers of the country's recent emission increases over the past six years, in contrast to the dominant drivers identified in the 2010s. Overall, the study is executed and presented with very high quality, and the findings are new and have important implications. I only have a few questions and suggestions below; addressing them may further improve the manuscript.
1. The inversion reveals complex spatial patterns in emission changes, which is particularly noteworthy for rice emissions, where the pattern appears somewhat like a checkerboard. My question is whether these signals are real or possibly inversion artefacts, especially given that observational coverage in southern China is limited. Do the authors recommend interpreting these fine-scale spatial patterns, or are the results more robust when aggregated by region or at the national level?
2. The sectoral attribution relies on the spatial distribution of the prior emission inventory, which can be challenging at the fine scale resolved in this study. For livestock, the authors use EDGAR v8, a global inventory that does not necessarily incorporate the best available information for China. How confident are the authors that the spatial distribution of livestock emissions in EDGAR v8 is accurate enough to conclude that the attributed changes are indeed driven by livestock? For example, does EDGAR v8 capture the pig-reallocation policies mentioned in the discussion? Based on Section 3.3.1 and Fig. S3, the authors suggest that increasing livestock emissions are driven by rising pig populations—a potentially important insight for mitigation. Does the spatial distribution shown in Fig. S7 support this finding? Notably, Figs. S6 and S7 provide critical spatial information and could be considered for inclusion in the main text. Some discussion based on on-the-ground information may better convince readers about the sectoral attribution.
3. I appreciate that the authors performed a thorough evaluation of the inversion, as presented in Fig. 1. One suggestion: grid-level correlation is useful, but spatial correlation (i.e., improvement in spatial patterns) would be even more informative, as it relates more directly to the spatial distribution of emissions.
4. In Fig. 2, ocean regions are masked out. Does the analysis include offshore methane emissions?
5. L468, a recent vehicle-based measurement study by Sun et al. (2026) reported an increase in CH4 emissions from wastewater treatment plants, but their estimated increase is relatively small (<0.1 Tg yr-1 between 2019 and 2023). It thus appears that the expansion of total urban treatment capacity alone would not explain the magnitude of the increase derived in this study. Nevertheless, it would be helpful to discuss this sector in the context of published results.
6. L525, a study by Liang et al. (2024), also using satellite observations, reported a similar estimate of rice emissions for 2021 (0.85 Tg yr-1). However, Liang et al. noted that the prior spatial distribution of rice emissions in EDGAR is incorrect. Does the test of an alternative rice emission inventory (the GRPI by Chen et al., 2025) show any sensitivity of the sectoral attribution in Northeast China to the prior rice emission distribution? I also note that in Fig. 5, the use of GRPI does lead to relatively large variation in mean rice emissions. I wonder whether this variation is mainly due to spatial distribution or systematic bias.
Sun et al., Measurement-based assessment reveals key drivers and mitigation potential of methane emissions from China's wastewater treatment. Sci. Adv., 2026
Liang et al., Satellite-Based Monitoring of Methane Emissions from China's Rice Hub. Environ. Sci. Technol., 2024
Chen et al., Global Rice Paddy Inventory (GRPI): a high-resolution inventory of methane emissions from rice agriculture based on Landsat satellite inundation data, Earth's Future, 2025