Quantifying methane emissions from metallurgical coal production at mine-level using empirical measurements in a Bayesian inference framework

While thermal coal is being phased out, metallurgical coal is likely to remain essential for steel production for several more decades. Reducing the climate impact of ongoing coal mining requires accurate quantification and mitigation of methane emissions. Reliable emission estimates are crucial for designing effective mitigation strategies and for reporting under frameworks such as the UNFCCC and the Global Methane Pledge.

 Traditional approaches to estimating coal mine methane emissions rely on generalized models, such as Langmuir isotherms, which consider only coal rank and mine depth. These first-order approximations fail to capture the considerable variability in emissions across individual mines, mining methods, production regimes, and operational practices such as ventilation and methane drainage. Recent advances in satellite remote sensing now allow for inversion-based measurement of methane emissions at the scale of individual ventilation shafts and drainage stations. The International Methane Emissions Observatory (IMEO) Steel Methane Programme (SMP) leverages these observations by integrating satellite measurements with aircraft campaigns, published studies, and a comprehensive bottom-up inventory. The SMP applies a Bayesian inference framework to effectively integrate incomplete and heterogeneous data, delivering the first empirically grounded global dataset of methane emission estimates from metallurgical coal mines. Supported by a transparent deterministic methodology, the SMP framework will produce a publicly accessible database of coal mine methane emissions, alongside IMEO’s best estimate of annual mine-level emissions. By providing a transparent, empirically grounded framework, this work also establishes a scalable approach that can be applied to thermal coal production and integrated into global greenhouse gas monitoring initiatives.