Predicting the climate impact of healthcare facilities using gradient boosting machines

Abstract

Health care accounts for 9–10% of greenhouse gas (GHG) emissions in the United States. Strategies for monitoring these emissions at the hospital level are needed to decarbonize the sector. However, data collection to estimate emissions is challenging. We explored the potential of gradient boosting machines (GBM) to impute missing data on resource consumption in the 2020 survey of a consortium of 283 hospitals participating in Practice Greenhealth. GBM imputed missing values for selected variables in order to predict electricity use (R2 = 0.82) and beef consumption (R2 = 0.82) and anesthetic gas desflurane use (R2 = 0.51), using administrative and financial data readily available for most hospitals. After imputing missing consumption data, estimated GHG emissions associated with these three examples totaled over 3 million metric tons of CO2 equivalent emissions (MTCO2e). Specifically, electricity consumption had the largest total carbon footprint (2.4 MTCO2e), followed by beef (0.6 million MTCO2e) and desflurane consumption (0.03 million MTCO2e) across the 283 hospitals. The approach should be applicable to other sources of hospital GHGs in order to estimate total emissions of individual hospitals and to refine survey questions to help develop better intervention strategies.

Keywords

• Healthcare facility
• Climate change
• Machine learning
• Missing data imputation
• Sustainability