Bayesian Methods for Enhanced Greenhouse Gas Emissions Inventories

Developing effective mitigation strategies for greenhouse gas reduction hinges on accurate emissions and metadata tracking to identify the most impactful reduction opportunities. Given that emissions cannot be perfectly and ubiquitously observed, constructing inventories entails fusing data from multiple sources that are of varying levels of fidelity, quality, and completeness. This proposal suggests that Bayesian models, powered by modern probabilistic programming frameworks, can integrate multiple data sources data into posterior emissions estimates while also accounting for incompleteness and leveraging data from less granular spatiotemporal scales. A preliminary analysis combining country-level steel production data and facility-level activity data shows promise for estimating emissions reduction potential when there is a population of facilities that have not been directly observed