Evaluating Fuel Tax Revenue Impacts of Electric Vehicle Adoption in Virginia Counties

Application of a Bivariate Linear Mixed Count Model

Wenjian Jia, Zhiqiu Jiang, T. Donna Chen, Rajesh Paleti

Research output: Contribution to journalArticle

Abstract

Increasing electric vehicle (EV) shares and fuel economy pose challenges to a fuel tax-based transportation funding scheme. This paper evaluates such fuel tax revenue impacts using Virginia as a case study. First, a bivariate count model is developed using vehicle registration data in 132 counties from 2012 to 2016. Model results indicate strong correlation between presence of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) on a county basis. Counties with higher percent of males are associated with higher BEV (but not PHEV) counts. In contrast, higher average commute time is predicted to increase the number of PHEVs in each county, but not BEVs. Greater population density, population over 65, population with graduate degrees, and household size are found to increase PHEV and BEV counts, whereas more households with children is associated with fewer EVs. The analysis forecasts 0.6–10% statewide EV adoption by 2025, with an adoption rate of 2.4% in the most likely scenario. Nine scenarios, combining different predictions of EV adoption and fuel economy improvement, project 2025 statewide fuel tax revenue to decrease by 5–19%, relative to 2016 receipts. Furthermore, model results suggest that, on average, a light-duty vehicle in a rural area will pay 28% more in fuel taxes than its urban counterpart by 2025. The framework proposed here provides a reference for other regions to conduct similar analysis using public agency data in the vehicle electrification era.

Original languageEnglish (US)
JournalTransportation Research Record
DOIs
StatePublished - Jan 1 2019

Fingerprint

Plug-in hybrid vehicles
Electric vehicles
Taxation
Fuel economy
Electric batteries
Battery electric vehicles

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering

Cite this

@article{706d4a2124f24731ad9ecfc3c66125b4,
title = "Evaluating Fuel Tax Revenue Impacts of Electric Vehicle Adoption in Virginia Counties: Application of a Bivariate Linear Mixed Count Model",
abstract = "Increasing electric vehicle (EV) shares and fuel economy pose challenges to a fuel tax-based transportation funding scheme. This paper evaluates such fuel tax revenue impacts using Virginia as a case study. First, a bivariate count model is developed using vehicle registration data in 132 counties from 2012 to 2016. Model results indicate strong correlation between presence of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) on a county basis. Counties with higher percent of males are associated with higher BEV (but not PHEV) counts. In contrast, higher average commute time is predicted to increase the number of PHEVs in each county, but not BEVs. Greater population density, population over 65, population with graduate degrees, and household size are found to increase PHEV and BEV counts, whereas more households with children is associated with fewer EVs. The analysis forecasts 0.6–10{\%} statewide EV adoption by 2025, with an adoption rate of 2.4{\%} in the most likely scenario. Nine scenarios, combining different predictions of EV adoption and fuel economy improvement, project 2025 statewide fuel tax revenue to decrease by 5–19{\%}, relative to 2016 receipts. Furthermore, model results suggest that, on average, a light-duty vehicle in a rural area will pay 28{\%} more in fuel taxes than its urban counterpart by 2025. The framework proposed here provides a reference for other regions to conduct similar analysis using public agency data in the vehicle electrification era.",
author = "Wenjian Jia and Zhiqiu Jiang and Chen, {T. Donna} and Rajesh Paleti",
year = "2019",
month = "1",
day = "1",
doi = "10.1177/0361198119844973",
language = "English (US)",
journal = "Transportation Research Record",
issn = "0361-1981",
publisher = "US National Research Council",

}

TY - JOUR

T1 - Evaluating Fuel Tax Revenue Impacts of Electric Vehicle Adoption in Virginia Counties

T2 - Application of a Bivariate Linear Mixed Count Model

AU - Jia, Wenjian

AU - Jiang, Zhiqiu

AU - Chen, T. Donna

AU - Paleti, Rajesh

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Increasing electric vehicle (EV) shares and fuel economy pose challenges to a fuel tax-based transportation funding scheme. This paper evaluates such fuel tax revenue impacts using Virginia as a case study. First, a bivariate count model is developed using vehicle registration data in 132 counties from 2012 to 2016. Model results indicate strong correlation between presence of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) on a county basis. Counties with higher percent of males are associated with higher BEV (but not PHEV) counts. In contrast, higher average commute time is predicted to increase the number of PHEVs in each county, but not BEVs. Greater population density, population over 65, population with graduate degrees, and household size are found to increase PHEV and BEV counts, whereas more households with children is associated with fewer EVs. The analysis forecasts 0.6–10% statewide EV adoption by 2025, with an adoption rate of 2.4% in the most likely scenario. Nine scenarios, combining different predictions of EV adoption and fuel economy improvement, project 2025 statewide fuel tax revenue to decrease by 5–19%, relative to 2016 receipts. Furthermore, model results suggest that, on average, a light-duty vehicle in a rural area will pay 28% more in fuel taxes than its urban counterpart by 2025. The framework proposed here provides a reference for other regions to conduct similar analysis using public agency data in the vehicle electrification era.

AB - Increasing electric vehicle (EV) shares and fuel economy pose challenges to a fuel tax-based transportation funding scheme. This paper evaluates such fuel tax revenue impacts using Virginia as a case study. First, a bivariate count model is developed using vehicle registration data in 132 counties from 2012 to 2016. Model results indicate strong correlation between presence of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) on a county basis. Counties with higher percent of males are associated with higher BEV (but not PHEV) counts. In contrast, higher average commute time is predicted to increase the number of PHEVs in each county, but not BEVs. Greater population density, population over 65, population with graduate degrees, and household size are found to increase PHEV and BEV counts, whereas more households with children is associated with fewer EVs. The analysis forecasts 0.6–10% statewide EV adoption by 2025, with an adoption rate of 2.4% in the most likely scenario. Nine scenarios, combining different predictions of EV adoption and fuel economy improvement, project 2025 statewide fuel tax revenue to decrease by 5–19%, relative to 2016 receipts. Furthermore, model results suggest that, on average, a light-duty vehicle in a rural area will pay 28% more in fuel taxes than its urban counterpart by 2025. The framework proposed here provides a reference for other regions to conduct similar analysis using public agency data in the vehicle electrification era.

UR - http://www.scopus.com/inward/record.url?scp=85065666455&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065666455&partnerID=8YFLogxK

U2 - 10.1177/0361198119844973

DO - 10.1177/0361198119844973

M3 - Article

JO - Transportation Research Record

JF - Transportation Research Record

SN - 0361-1981

ER -