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Large methane mitigation potential through prioritized closure of gas-rich coal mines

Abstract

Large-scale closure of coal mines is required for China to achieve carbon neutrality. However, what this means for methane emissions, particularly for abandoned mine methane (AMM), is highly uncertain. Here we construct a detailed and dynamic coal mine database to estimate China’s coal methane emissions during 2011–2019 and evaluate future emission trajectories based on different mine closure policies. We find that AMM emissions have been largely underestimated, which leads to an increased proportion of AMM in China’s total coal methane emissions, and are expected to become the dominant source by 2035. We develop a coal mine closure strategy prioritizing high-gas-content mines. Compared with the current closure strategy based on mine scale, this strategy could reduce cumulative methane emissions by 67 Tg (26%) to 2050, potentially reaching 100 Tg (39%) with improved methane recovery and utilization practices.

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Fig. 1: China’s AMM emissions during the period 2011–2019.
Fig. 2: Temporal and spatial change in coal methane emissions over 2011–2019.
Fig. 3: A conceptual illustration of the near- and long-term aggregate impacts on methane emissions of switching coal production from high-gas-content to low-gas-content mines.
Fig. 4: Prioritized early closure of high-gas-content mines to enhance methane mitigation.

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Data availability

Source data are provided with this paper, which can be accessed in Zenodo62 and GitHub (https://github.com/Marquezliu-lq/China_Coal_Mine_Methane). Raw data of China’s coal mines are publicly available at State Administration of Coal Mine Safety48, China’s National Energy Administration49, Global Energy Monitor50 and China Coal Trade Data51. Other data are available from the corresponding author upon reasonable request.

Code availability

The scripts for calculating coal methane emissions in China and simulating future mine closure policies are available at Zenodo62 and GitHub (https://github.com/Marquezliu-lq/China_Coal_Mine_Methane).

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Acknowledgements

This work was supported by the National Key R&D Program of China (no. 2022YFE0209200), the National Natural Science Foundation of China (72140003,42141020), Tsinghua University Initiative Scientific Research Program and Energy Foundation China.

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Authors and Affiliations

Authors

Contributions

Conceptualization, Q.L. and F.T.; methodology, Q.L. and F.T.; investigation, Q.L. and F.T.; writing, Q.L., F.T., C.P.N., Y.Z. and L.W.; funding acquisition, F.T.; resources, F.T.; supervision, F.T.

Corresponding author

Correspondence to Fei Teng.

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The authors declare no competing interests.

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Nature Climate Change thanks Tim Arnold, Bo Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of AMM emissions.

The AMM emissions calculated in this work are compared with that from national GHGs inventory6,7 and Gao et al.16. The orange boxes indicate the mean values of this study’s 2,000 Monte Carlo simulations of AMM emissions for each year, with the colored band representing the 95% confidence interval. Our estimation of AMM is 2 to 10 times higher than those previous studies.

Extended Data Fig. 2 Gridded emissions of AMM from 2011 to 2019.

The number of abandoned underground mines increased from 424 to 11,758, resulting the AMM emissions distributed widely around China. The administrative boundaries were provided by National Catalogue Service for Geographic Information (http://www.webmap.cn/).

Extended Data Fig. 3 Consistency of bottom-up and top-down studies.

We independently calculate the correlation coefficient between bottom-up studies (emissions from Edgar V5.05 are excluded as they are not independent from CEDS9) and top-down analyses. Our estimate is much more consistent with all top-down studies compared to previous bottom-up studies in terms of correlation, except for CEDS (or Edgar V5.0). It is not surprising that CEDS (or Edgar V5.0) has slightly higher correlation than our study, as it was used as prior information by all of these top-down analyses.

Extended Data Fig. 4 Gridded emissions of CMM from 2011 to 2019.

The number of CMM sources has sharply decreased from 10,423 to 2,932. In 2011, the CMM sources were distributed widely around China but were only concentrated in several provinces in 2019. The administrative boundaries were provided by National Catalogue Service for Geographic Information (http://www.webmap.cn/).

Extended Data Fig. 5 Changes of coal methane emissions at the regional scale.

The rise in northwest China is mainly due to increased coal production, while in northern China, AMM is the major driver. In northeast, east, and south China, the decrease of CMM drives the change of overall coal methane emissions.

Extended Data Fig. 6 Emission and reduction trajectories of each sub-source.

(a), Reference scenario. (b), Enhanced recovery and utilization. (c), EF-driven closure. (d), EF-driven closure and enhanced utilization. The share of CMM emissions decrease while AMM emissions continue to increase. The proportion of AMM surpasses CMM by 2035 and 2024, respectively, under the strategies of scale-driven and EF-driven closures.

Extended Data Fig. 7 Correlations of EFs, production costs, and scales of mines in 2019.

The correlation coefficient of coal mine EFs and costs (ρ(Cost, EF)) is higher than that of coal mine EFs and scales (ρ(Cost, Scale)). Thereby, the EF-driven strategy shows a more rapid decrease in annual cost index than the scale-driven strategy.

Supplementary information

Supplementary Information

Supplementary Data Source, Methods, Figs. 1–20 and Tables 1–10.

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Liu, Q., Teng, F., Nielsen, C.P. et al. Large methane mitigation potential through prioritized closure of gas-rich coal mines. Nat. Clim. Chang. 14, 652–658 (2024). https://doi.org/10.1038/s41558-024-02004-3

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