Re-estimating CO₂ emission factors for gasoline passenger cars adding driving behaviour characteristics——A case study of Beijing

The transportation sector is one of the largest sources of energy consumption and CO₂ emissions. The most important and difficult step in estimating transportation CO₂ emissions is to accurately estimate vehicle CO₂ emission factors (EFs). Most of the conventional methods draw less attention to driving behaviours. Based on a questionnaire survey, this study built a three-layer modified progressive regression model which included driving behaviours, and integrated vehicle and traffic characteristics. The results showed that for gasoline passenger cars with 7 seats or fewer in Beijing，EFs were significantly affected by engine displacement (D), vehicle age (G), producing country, the proportion of annual total mileage on national, provincial and municipal roads except freeways (P), waiting mode for a red light when the waiting time is more than 1 min (W), and whether the windows were open while driving faster than 60 km/h (E). The influence order is: D (.908)>W(−.199)>E(.080)>P(.063)>producing country (−.048, .042, <.001 for J, A and C respectively). More than 60 s flame out waiting time and closing windows while velocity more than 60 km/h can reduce 23.38 g CO₂/km and 8.93 g CO₂/km, respectively. The method to estimate vehicle EFs in this study can be used in other countries, and can provide scientific support for policymakers to implement traffic-control and CO₂-reduction measures. - Highlights: • We build 3 layers multiple regression model to re-estimate the EFs of passenger cars. • The estimation integrate Characteristics of Driving behaviours, Vehicle and Traffic. • We estimate the EFs regression equation and elastic coefficient of each factor. • More than 60 s Flameout waiting time can reduce 23.38 g CO₂ emission/km. • Closing windows while the vehicle velocity more than 60 km/h can reduce 8.93 g CO₂/km.