2015
DOI: 10.1016/j.tranpol.2014.10.003
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Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy

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Cited by 59 publications
(37 citation statements)
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References 23 publications
(27 reference statements)
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“…The results are not only related to the statistical fit of the model but might generate some preliminary policy implications. Usually, one of the topics in the car choice literature is the identification of which car alternative is the most preferred (see the work for the Italian car market done by Valeri and Danielis (2015) using the same database we used in this research). Considering the intensive rate of car use, the high number of cars owned by the Italian families, and the important role that the automotive sector has in Italy, we investigated if car use and expertise play a role in the process of buying a new car, with special attention to the AFVs.…”
Section: Results Assessments and Conclusionmentioning
confidence: 99%
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“…The results are not only related to the statistical fit of the model but might generate some preliminary policy implications. Usually, one of the topics in the car choice literature is the identification of which car alternative is the most preferred (see the work for the Italian car market done by Valeri and Danielis (2015) using the same database we used in this research). Considering the intensive rate of car use, the high number of cars owned by the Italian families, and the important role that the automotive sector has in Italy, we investigated if car use and expertise play a role in the process of buying a new car, with special attention to the AFVs.…”
Section: Results Assessments and Conclusionmentioning
confidence: 99%
“…A total of four trained interviewers randomly administered 121 interviews in the first semester of 2013. 1 An example of a SP task and further details of the survey are reported by Valeri and Danielis (2015). The SP includes five attributes that describe car alternatives: purchase price, expressed in Euros (€); annual operating cost (gasoline, insurance, tax, maintenance), expressed in Euros (€); acceleration, expressed in seconds needed to go from 0 to 100 km/h; range expressed in kilometres (km); and refueling distance, expressed as the distance required to get the closest refueling or recharging station (km).…”
Section: Data Collectionmentioning
confidence: 99%
“…Rasouli and Timmermans (2013) found that heterogeneity is particularly high when the price of EV is much higher than CV. Several studies discovered an income effect, namely that people with high incomes are less pricesensitive than others (Achtnicht et al, 2012;Hackbarth & Madlener, 2013;Hess et al, 2012;Mabit & Fosgerau, 2011;Molin et al, 2012;Potoglou & Kanaroglou, 2007;Valeri & Danielis, 2015), while Jensen et al (2013) found this effect to be insignificant. Preferred car size also plays a role in price sensitivity: Jensen et al (2013) concluded that buyers of smaller cars have a higher marginal utility of price.…”
Section: Financial Attributes Refer To Various Types Of Monetary Costmentioning
confidence: 99%
“…Again, people with higher incomes place lower importance on fuel cost (Helveston et al, 2015;Valeri & Danielis, 2015). However, Chinese respondents with higher income (2013) are more sensitive to high fuel costs (Helveston et al, 2015).…”
Section: Financial Attributes Refer To Various Types Of Monetary Costmentioning
confidence: 99%
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