Compressed and liquefied natural gas (CNG and LNG, respectively) were subjected to life-cycle inventory analysis (LCI), focusing on carbon dioxide and methane emissions. The resulting inventory values represented the cumulative emissions from the extraction, processing, transportation, and storage of CNG and LNG as alternative automotive fuels. The analysis also included the effects of secondary energy inputs, such as electricity, into the natural gas life cycle. However, the assessment departed from the standard LCI methodology by the incorporation of data uncertainty propagation modeling using fuzzy possibility theory. This procedure addressed the data quality issues recognized as major factors in determining the validity of inventory models. The possibilistic simulation was carried out using a modified version of Argonne National Laboratory's GREET 1.5a fuel-cycle inventory model. The results of the possibilistic uncertainty propagation were comparable to those of conventional sensitivity analysis and probabilistic modeling.