How Well Do We Know the Future of CO2Emissions? Projecting Fleet Emissions from Light Duty Vehicle Technology Drivers.
While the UK has committed to reduce CO2emissions to 80% of 1990 levels by 2050, transport accounts for nearly a fourth of all emissions and the degree to which decarbonization can occur is highly uncertain. We present a new methodology using vehicle and powertrain parameters within a Bayesian framework to determine the impact of engineering vehicle improvements on fuel consumption and CO2emissions. Our results show how design changes in vehicle parameters (e.g., mass, engine size, and compression ratio) result in fuel consumption improvements from a fleet-wide mean of 5.6 L/100 km in 2014 to 3.0 L/100 km by 2030. The change in vehicle efficiency coupled with increases in vehicle numbers and fleet-wide activity result in a total fleet-wide reduction of 41 ± 10% in 2030, relative to 2012. Concerted internal combustion engine improvements result in a 48 ± 10% reduction of CO2emissions, while efforts to increase the number of diesel vehicles within the fleet had little additional effect. Increasing plug-in and all-electric vehicles reduced CO2emissions by less (42 ± 10% reduction) than concerted internal combustion engines improvements. However, if the grid decarbonizes, electric vehicles reduce emissions by 45 ± 9% with further reduction potential to 2050.
Energy Efficient Cities Initiative, University of Cambridge Department of Engineering, Trumpington Street, Cambridge CB2 1PZ, United Kingdom.
Niall P D
NP
Martin
Energy Efficient Cities Initiative, University of Cambridge Department of Engineering, Trumpington Street, Cambridge CB2 1PZ, United Kingdom.
Justin D K
JD
Bishop
Energy Efficient Cities Initiative, University of Cambridge Department of Engineering, Trumpington Street, Cambridge CB2 1PZ, United Kingdom.|Department of Civil, Environmental or Geo-Engineering, University of Minnesota , Minneapolis, Minnesota 55455-0116, United States.
Adam M
0000-0003-2915-3273
AM
Boies
D000393:Air Pollutants; D001499:Bayes Theorem; D005742:Gasoline; D018986:Motor Vehicles; D013672:Technology; D001335:Vehicle Emissions
D000393:Air Pollutants; D005742:Gasoline; D001335:Vehicle Emissions