Dataset: Fostering the use of zero and near zero emission vehicles in freight operations

This study explored different incentives programs in California and reviewed the literature to identify other potential types of incentives to foster a change. Based on the review, the team developed a stated preference survey to collect information from fleets and carrier companies about their economics, and their vehicle purchase preferences, and to test their behavioral perceptions towards those types of incentives. The team deployed the survey in two different waves targeting, first the members of the largest transportation association in California, and second, to a sample of carrier companies. However, the response rate was very small which limited the type of analyses that could be conducted with the data. Alternatively, the team developed a multi-criteria decision making (MCDM) tool using a Spherical Fuzzy Analytical Hierarchy Process based on experts’ knowledge. The model provides insights about the most appropriate options for different uses (e.g., last mile, long-haul distribution). This study considered diesel, compressed (renewable) natural gas (CNG/RNG), hybrid electric (HE), battery electric (BE) and fuel-cell hydrogen (H2) vehicles. The model evaluates the alternatives using five criteria: economic; business, incentives & market-related; environmental & regulatory; infrastructure; and safety & vehicle performance factors. It also considers twenty-one sub-criteria, e.g., total cost of ownership, payback period, brand image, financial & non-financial incentives, and public/private fueling/ charging infrastructure availability.

  1. Stated-Preference Data: Collected through and online (Qualtrics) survey. The data was collected in two deployments during the first semester (1st deployment) and the summer of 2019 (2nd deployment). The team received approval (exemption from IRB). The data has no business identifier.
  2. Expert Assessment Data: The team developed a collected data from three experts about pairwise comparisons among determinants and factors for vehicle technology assessment. The data helped implement a Multi-Criteria Decision-Making (MCDM) tool based on  a Spherical Fuzzy Analytical Hierarchy Process model.