Aerial LiDAR-Based, Source-Resolved Methane Emissions Inventory: Permian Basin Case Study for Benchmarking U.S....

Reducing methane emissions can slow near-term warming, yet building accurate inventories to inform mitigation efforts and track progress toward reduction targets remains challenging. We present a 2024 source-resolved methane inventory for the Permian Basin, built from quarterly aerial LiDAR scans. We combined public infrastructure records with machine learning identification of non-producing sites to define the facility population and generate sampling plans and then deployed Bridger Photonics' Gas Mapping LiDAR to scan 51,785 sites across four quarters. Sources were localized within 2 m and attributed to equipment acquired by aerial photography during scans. We detail a Monte Carlo framework that propagates quantification, extrapolation, sampling, and detection sensitivity uncertainty and weights spatial extrapolation by observed equipment counts, avoiding bias from over- or under-sampling of large facilities. The workflow yields a source-resolved inventory down to 0.4 kg/h with quarterly temporal resolution. Total annual basin emissions were 5,133 kt CH₄ after adding gathering pipelines and subthreshold emissions from prior studies. Emissions were seasonal, with the winter up to 17% higher than the summer. The basin-wide methane loss rate was 3.13%. Texas emitted 4,038 kt CH₄ with a 3.6% loss rate, while New Mexico emitted 1,095 kt CH₄ with a 2.1% loss rate. At the operator level, most large operators outperformed the basin average intensity by a wide margin.