Jake Nusynowitz, Lily Jamneshan, Nana Aisha Garba, Marquita Samuels, Prasad Bhoite, Sarah E Stumbar
Medical Students' Knowledge and Perceptions of Mpox in a High Incidence Region: Implications for Clinical Preparedness
PRiMER (Leawood, Kan.)
life cycle assessment lca plays crucial role green manufacturing uncover critical aspects alleviating environmental burdens due manufacturing processes however scarcity life cycle inventory lci data manufacturing processes considerable challenge paper proposes novel approach extrapolate lci data manufacturing processes taking advantage lci data ecoinvent datasets decision treebased supervised machine learning models namely decision tree random forest gradient boosting adaptive boosting developed extrapolate data ghg emissions ie carbon dioxide nitrous oxide methane water vapor initially correlation analysis conducted derive influential factors ghg quantities resulting manufacturing activities first collected data preprocessed split train test sets 70 30 respectively second fivefold crossvalidation method applied tune hyperparameters models models retrained using best hyperparameters evaluated using test set results reveal gradient boosting model superior predictive performance extrapolating ghg emission data average coefficients determination r2 test set 095 moreover model predictions involve relatively low values average root mean squared error average mean percentage error test set correlation feature importance analyses emphasized workpiece material manufacturing technology considerable effect natural resource consumption ie energy material water inflows process meanwhile energy consumption water usage raw aluminum depletion influential factors ghg emissions eventually case study extrapolate inflows outflows new manufacturing activities conducted using validated models proposed graboost model provides computational supplementary approach estimate extrapolate ghg emissions different manufacturing processes lci data incomplete dont exist within lci databases