『Abstract
Estimation of fuel (gasoline and diesel) consumption for vehicles
in China under different long-term energy policy scenarios is
presented here. The fuel economy of different vehicle types is
subject to variation of government regulations; hence fuel consumption
of passenger cars (PCs), light trucks (Lts), heavy trucks (Hts),
buses and motor cycles (MCs) are calculated with respect to (i)
the number of vehicles, (ii) distance traveled, and (iii) fuel
economy. On the other hand, the consumption rate of alternative
energy sources (i.e. ethanol, methanol, biomass-diesel and CNG)
is not evaluated here. The number of vehicles is evaluated using
the economic elastic coefficient method, relating to per capita
gross domestic product (GDP) from 1997 to 2007. The long-range
Energy Alternatives Planning (LEAP) system software is employed
to develop a simple model to project fuel consumption in China
until 2030 under these scenarios. Three energy consumption decrease
scenarios are designed to estimate the reduction of fuel consumption:
(i) ‘business as usual’ (BAU); (ii)‘advanced fuel economy’ (AFE);
and (iii) ‘alternative energy replacement’ (AER). It is shown
that fuel consumption is predicted to reach 992.28 Mtoe (million
tons oil equivalent ) with the BAU scenario by 2030. In the AFE
and AER scenarios, fuel consumption is predicted to be 734.68
and 699.36 Mtoe, respectively, by 2030. In the AER scenario, fuel
consumption in 2030 will be reduced by 391.92 (39.50％) and 134.29
(18.28％) Mtoe in comparison to the BAU and AFE scenarios, respectively.
In conclusion, our models indicate that the energy conservation
policies introduced by governmental institutions are potentially
viable, as long as they are effectively implemented.
Keywords: Energy policy; Fuel consumption; Vehicle numbers』
1. Introduction
2. Methodology
2.1. Vehicle number projection
2.2. Average number of kilometers traveled by vehicles
2.3. Scenario designs
2.3.1. BAU scenario
2.3.2. AFE scenario
2.3.3. AER scenario
2.4. Fuel economy
3. Results and discussion
3.1. Vehicle number prediction
3.2. Fuel economy
3.3. Fuel consumptions
4. Conclusion
Acknowledgements
Reference