Seminar
Day 2 (8 Sep 2022), Session 7, APPRAISAL 2, 16:30 - 18:30
Status
Accepted, awaiting documents
Submitted by / Abstract owner
Le Wen
Authors
Le Wen and Mingyue (Selena) Sheng
Short abstract
This study aims to provide informative policy implications in accelerating the pace of EV uptake by providing a realistic signal given solar PV's great renewable energy potential for a more sustainable mobility and energy system.
Abstract
Electric vehicles (EVs) are gaining momentum globally with the potential of emission reduction in the transportation sector by using renewable energy sources. As a country with committed climate change actions, New Zealand encourages effective policy formulations to promote the uptake of EVs and other low-emission forms of transport (Ministry of Transport, 2021). However, despite the growing sales of EVs and the improved charging infrastructure, the overall percentage of EVs is minuscule: EVs do not even make one per cent of the total vehicle fleet (over 4.5 million vehicles in 2019) in New Zealand (Ministry of Transport, 2019).
The resistance to EV adoption is characterised by both economic uncertainty and socio-technical factors (Berkeley et al., 2018). The economic barriers associated with low EV uptake rate include the upfront vehicle cost, vehicle type, and fuel economy (Musti and Kockelman, 2011). Advancements in EV technology and battery over recent years are lowering both the purchasing cost and the entire ownership cost of EVs (Schmidt et al., 2017). However, many consumers still hold negative perceptions around limited driving range and lengthy charging times (Daziano and Chiew, 2013). Several policy implications are required to accelerate EV adoption by bringing down the barriers in shifting towards complete electrification of the fleet. The increasing investment in public charging infrastructure over the years helps eliminate the range anxiety, and rising photovoltaic (PV) installed at households helps lower the charging cost. Most studies consider the barriers influencing consumer behaviour towards new transport technologies focused on the economic, social, and socio-demographic factors in new technology adoption (Browne et al., 2012; Wikström et al., 2016). Research has looked into how EVs can be charged with PV power and the integration of solar power in the electricity network (van der Kam et al., 2018). However, there is a lack of research on the spillover effect of PV solar power on one's EV purchase decision.
In this paper, we employ negative binomial models with embedded spatial lagged variables to test three hypotheses based on potential solar effects, spatial autocorrelation, and marginal effects. The solar potential effects hypothesis is to test the availability of PV panels on EV uptake. The hypothesis on spatial autocorrelation is to test the "neighbourhood effect" of EV-charging infrastructure on EV uptake. And the hypothesis on marginal effects aims to examine if EV adoption by technology enthusiasts during the early-adopter phase could affect subsequent adoption once the technology becomes more widely spread.
This article aims to provide informative policy implications in accelerating the pace of EV uptake by providing a realistic signal given solar's great renewable energy potential. To the best of our knowledge, this is the first economic paper to address the solar possibility in the context of New Zealand.
Refs:
Berkeley, N., Jarvis, D., & Jones, A. (2018). Analysing the take up of battery electric vehicles: An investigation of barriers amongst drivers in the UK. Transportation Research Part D: Transport and Environment, 63, 466–481. https://doi.org/10.1016/j.trd.2018.06.016
Browne, D., O’Mahony, M., & Caulfield, B. (2012). How should barriers to alternative fuels and vehicles be classified and potential policies to promote innovative technologies be evaluated? Journal of Cleaner Production, 35, 140–151. https://doi.org/10.1016/j.jclepro.2012.05.019
Daziano, R. A., & Chiew, E. (2013). On the effect of the prior of Bayes estimators of the willingness to pay for electric-vehicle driving range. Transportation Research Part D: Transport and Environment, 21, 7–13. https://doi.org/10.1016/j.trd.2013.02.005
Ministry of Transport. (2019). Fleet statistics Vehicle fleet. https://www.transport.govt.nz/statistics-and-insights/fleet-statistics/vehicle-fleet/
Ministry of Transport. (2021). Electric Vehicles Programme. https://www.transport.govt.nz/area-of-interest/environment-and-climate-change/electric-vehicles-programme/
Musti, S., & Kockelman, K. M. (2011). Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas. Transportation Research Part A: Policy and Practice, 45(8), 707–720. https://doi.org/10.1016/j.tra.2011.04.011
Schmidt, O., Hawkes, A., Gambhir, A., & Staffell, I. (2017). The future cost of electrical energy storage based on experience rates. Nature Energy, 2(8), 17110. https://doi.org/10.1038/nenergy.2017.110
van der Kam, M. J., Meelen, A. A. H., van Sark, W. G. J. H. M., & Alkemade, F. (2018). Diffusion of solar photovoltaic systems and electric vehicles among Dutch consumers: Implications for the energy transition. Energy Research & Social Science, 46, 68–85. https://doi.org/10.1016/j.erss.2018.06.003
Wikström, M., Hansson, L., & Alvfors, P. (2016). Investigating barriers for plug-in electric vehicle deployment in fleets. Transportation Research Part D: Transport and Environment, 49, 59–67. https://doi.org/10.1016/j.trd.2016.08.008
Programme committee
Transport Economics, Finance and Appraisal
Topic
The future of cities: emerging new travel and land use patterns
