Publication Detail

NCST Research Report: Truck Choice Modeling: Understanding California's Transition to Zero-Emission Vehicle Trucks Taking into Account Truck Technologies, Costs, and Fleet Decision Behavior

UCD-ITS-RR-17-36

Research Report

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Suggested Citation:
Miller, Marshall, Qian Wang, Lewis Fulton (2017) NCST Research Report: Truck Choice Modeling: Understanding California's Transition to Zero-Emission Vehicle Trucks Taking into Account Truck Technologies, Costs, and Fleet Decision Behavior. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-17-36

This report presents the results of a project to develop a truck vehicle/fuel decision choice model for California and to use that model to make initial projections of truck sales by technology out to 2050. The report also describes the linkage of this model to a broader scenarios model of road transportation energy use in California to 2050. A separate report provides our detailed assumptions about truck technologies, fuels, and projections to 2050 that are inputs to this choice modeling effort.

The need for low carbon trucking in California, as in other states and countries of the world, is outlined in IPCC reports and the Paris Agreement. An 80% reduction in energy-related CO2 emissions worldwide is targeted in that agreement. For trucks to contribute anywhere near this level of reduction, new, zero emissions technologies, such as electric and hydrogen fuel cell trucks, would need to be adopted at a large scale and at a rapid pace, both unprecedented for trucks anywhere in the world to date.

Many truck models create new technology market penetration scenarios through minimizing cost or in an ad-hoc manner. This model utilizes a fleet decision choice process based on real world factors identified through discussions with trucking fleets. These factors include capital and operating costs, uncertainty (risk), model availability, refueling inconvenience, green PR (perceived benefit of environmentally beneficial technologies), and various incentives. We have developed a spreadsheet structured as a nested multinomial logit model that monetizes these factors to calculate a generalized cost. We have attempted to estimate the value of these factors to different types of fleets using a series of interviews, initial survey work, a truck choice workshop, and finally expert judgment and “basic logic” on how various factors might be valued now and in the future.

The factors drive the choice analysis and are highly uncertain and likely highly variant across fleet types and even fleets within a type (early adopter, late adopter, in-between), so we use a scenario approach to explore how this uncertainty could affect our results and projections. We created four scenarios and variants: 1) a business as usual (BAU), 2) a zero-emission vehicle (ZEV) mandate requiring the market share of ZEVs to reach 25% by 2050 (ZEV scenario 1a), 3) the same scenario but with a low penalty assumed for refueling time and (ZEV scenario 1b) 4) a ZEV mandate requiring the market share of ZEVs to reach 50% by 2050 (ZEV scenario 2). We also look at some policies that could help to spur sales growth among ZEV technologies in order to reach specific targets.