ausblenden:
Schlagwörter:
Bayesian network; Expert elicitation; Vehicle emissions; CO2 abatement costs; Electric vehicles
Zusammenfassung:
We formulate and elicit Bayesian Belief Networks (BBNs) for assessing possible characteristics of the 2030 German new passenger car fleet, including market shares of different vehicle types, CO2 emissions, user costs, and CO2 abatement costs for internal combustion engine vehicles including hybrid electric vehicles (ICE); plug-in hybrid electric vehicles (PHEV); and battery electric vehicles (BEV). Seven technology and environmental experts from the German Original Equipment Manufacturers (OEM) sector were elicited for key relationships and conditional probability values in the model, yielding seven distinct BBNs able to predict how different future technology, economic and policy scenarios will influence model projections. The 2030 scenarios include differing amounts of technological advancement in battery development, regulation, and fuel and electricity greenhouse gas intensities. Across the expert models, 2030 baseline fleet greenhouse gas emissions are predicted to be at 50–65% of 2008 new fleet emissions. They can be further reduced to 40–50% of the emissions of the 2008 new fleet through a combination of a higher share of renewables in the electricity mix, a larger share of biofuels in the fuel mix, and a stricter regulation of car CO2 emissions in the European Union. The experts' BBNs predict that the 2030 ICE will have lower user costs per kilometer than PHEV or BEV for most scenarios, and that ICE will remain the dominant vehicle type in the 2030 German new fleet. According to all of the experts' BBNs, CO2 abatement costs are negative for the 2030 ICE in all scenarios, but can be positive or negative for PHEV and BEV, depending on the expert model and scenario assumed. Critical areas where expert models agree and differ serve to highlight where reductions in uncertainty regarding future technology, economic, environmental and regulatory relationships are most needed to improve our ability to predict and anticipate future vehicle fleet composition and vehicle performance.
