This work presents a simulation-based modeling of the equivalent greenhouse gas (GHG) of plugin hybrid electric vehicles (PHEVs) for real driving patterns obtained from monitoring of real vehicles in public survey data sets such as the California Household Travel Survey (CHTS). Aim of the work is to highlight differences in attainable GHG reduction by adopting a PHEV instead of a conventional vehicle (CV) for different driving patterns obtained from real-world sub-populations of vehicles. Modeling of the equivalent GHG for a trip made by a PHEV can be challenging since it not only depends on the vehicle design and driving pattern of the trip in question, but also on: i) all electric range (AER) of the PHEV, ii) “well to tank” (W2T) equivalent GHG of the electricity used to charge the battery, as well as, iii) battery depletion in previous trips since the last charging event. To overcome some of these modeling challenges, previous work on an energy re-allocation model for the estimation of GHG equivalent of different charging behaviors is adopted. Full set of recorded vehicle trips in CHTS (approx. 65 thousand trips) are analyzed for two PHEV models (short and long AER), for different charging behaviors and grid conditions. Comparisons are made for GHG reduction if the same trips were done by an equivalent-sized CV, for different sub-populations of vehicles ranging from “more city-like” driving patterns to “less city-like”. Results show that certain sub-populations of vehicles could reduce their GHG by 1.7 to 3 times more than other sub-populations.