@article{li_wang_xu_liu_dai_lan_2024, title={Bioplastic derived from corn stover: Life cycle assessment and artificial intelligence-based analysis of uncertainty and variability}, volume={946}, ISSN={["1879-1026"]}, url={http://dx.doi.org/10.1016/j.scitotenv.2024.174349}, DOI={10.1016/j.scitotenv.2024.174349}, abstractNote={Exploring feasible and renewable alternatives to reduce dependency on traditional fossil-based plastics is critical for sustainable development. These alternatives can be produced from biomass, which may have large uncertainties and variabilities in the feedstock composition and system parameters. This study develops a modeling framework that integrates cradle-to-grave life cycle assessment (LCA) with a rigorous process model and artificial intelligence (AI) models to conduct uncertainty and variability analyses, which are highly time-consuming to conduct using only the process model. This modeling framework examines polylactic acid (PLA) produced from corn stover in the U.S. An analysis of uncertainty and variability was conducted by performing a Monte Carlo simulation to show the detailed result distributions. Our Monte Carlo simulation results show that the mean life-cycle Global Warming Potential (GWP) of 1 kg PLA is 4.3 kgCO}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Li, Junwei and Wang, Yinqiao and Xu, Chuan and Liu, Sipan and Dai, Jiayi and Lan, Kai}, year={2024}, month={Oct} }