Our QMRV program tests multiple emissions measurement methods, models and technologies in an effort to provide more accurate, timely and measurement-informed emissions inventories of our own and our supply chain’s GHG emissions. This improved data will help to inform our climate strategies and mitigation programs, as well as those of our supply chain partners and other stakeholders. As part of this project, we are working with supply chain and academic partners to develop and employ multiscale, multitechnology measurement methodologies including ground, drone, aerial and satellite, along with robust assessments of operational and maintenance practices, to develop more accurate inventories of facilities. For example, we are pairing continuous monitoring with snapshot data collection to reconcile measurements with inventory estimates and account for intermittent emission events. Transparently sharing key results with program participants and the public is a key element of our QMRV work intended to communicate learnings as widely as possible.
In 2022, we completed the initial QMRV program with our upstream natural gas producers. We initiated the QMRV program with midstream partners, which includes QMRV assessments of our own midstream assets. We also initiated the QMRV program at our own Sabine Pass Liquefaction and Corpus Christi Liquefaction facilities.
Key findings and actionable insights
In 2022, we published the first peer-reviewed study from our QMRV work with upstream producers. This study helps to illustrate cost-effective emissions reduction pathways and the value of better measuring and reporting to drive effective emissions management.
Though we will continue to expand and refine our work, initial key findings demonstrate significant variations in emissions by site and daily operational variations, which make inventory- and estimate-based emissions measurements insufficient for developing target-based policies such as methane fees, methane border adjustments or low-leakage certification frameworks. More accurate, multiscale measurements combining both snapshot and continuous emissions monitoring data are needed to improve data accuracy.
Independent verification of measurements and quantified emissions using transparent, peer-reviewed approaches are vital for building the credibility of emissions data and emissions reduction activities, and for building trust with the broader public.
These findings led us to co-found and sponsor the Energy and Emissions Modeling Data Lab (EEMDL) in 2022. The EEMDL is a new, multidisciplinary research and education initiative led by the University of Texas at Austin in collaboration with Colorado State University and the Colorado School of Mines. EEMDL’s goal is to provide reliable, science-based, transparent, measurement-informed GHG assessments by developing publicly available models and data sets.