Estimating Facility-Level Greenhouse Gas Emissions in U.S. Hospitals with Machine Learning Imputation of Incomplete Self-Reported Data

The U.S. healthcare industry has the largest per-capita carbon footprint compared with those of other countries. Despite growing commitments and efforts to decarbonization hospitals face challenges in reducing emissions in part because hospital-level estimates of greenhouse gas (GHG) emissions are limited or unavailable. This study estimates hospital-level GHG emissions from 38 sources across seven operational sectors for 283 hospitals, representing approximately 4% of U.S. hospitals in 2020, that voluntarily participated in the Practice Greenhealth Environmental Excellence Award program. Using our previously developed and validated machine learning–based imputation approach based on gradient boosting machines, this study addresses incomplete self-reported operational and activity data to enable large-scale facility-level emissions accounting. Total GHG emissions from these hospitals were estimated at 9.3 megatonnes (Mt) carbon dioxide (CO 2 ) equivalent, corresponding to nearly 2% of estimated U.S. healthcare emissions. Energy use, food services, and waste management were the largest contributors. While total emissions increased with hospital size, GHG emissions per staffed bed declined as hospital size increased, suggesting greater resource efficiency in larger facilities. Smaller hospitals exhibited both higher emissions intensity and a greater prevalence of missing activity data. These findings demonstrate that hospital-level GHG accounting can identify priority areas for targeted mitigation and reveal substantial variation in emissions across facilities, indicating opportunities for emissions reduction without compromising healthcare quality. The results underscore the need for standardized data collection to improve the accuracy of hospital-level GHG emissions estimates and to support scalable decarbonization strategies.