Impacts of Policy on Electric Vehicle Diffusion

Usher, J; Higgens, A; Ross, K; Dunstan, C; Paevere, P

Impacts of Policy on Electric Vehicle Diffusion

Usher, J Higgens, A Ross, K

Dunstan, C Paevere, P

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Publisher:: ATRF
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 37th ATRF, 2015
Issue Date:: 2015-11-15

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Field	Value	Language
dc.contributor.author	Usher, J	en_US
dc.contributor.author	Higgens, A	en_US
dc.contributor.author	Ross, K https://orcid.org/0000-0002-3445-6079	en_US
dc.contributor.author	Dunstan, C	en_US
dc.contributor.author	Paevere, P	en_US
dc.date	2015-09-30	en_US
dc.date.issued	2015-11-15	en_US
dc.identifier.citation	Proceedings of the 37th ATRF, 2015	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43868
dc.description.abstract	Selection and design of appropriate government policies to support electric vehicle (EV) adoption can be aided by modelling the future impact of policy instruments relative to a given baseline estimate. This paper highlights the innovative application of a diffusion model to analyse complex impacts of EV policy instruments on future incremental EV uptake. Several versions of four key policy instruments are tested in the model: linking electric vehicle sales to Renewable Electricity Purchases (RE-EV), financial subsidies, smart charging incentives and a common cost metric to educate consumers about the lifetime costs of EVs. Market share between battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs) and internal combustion engine vehicles (ICEVs) were forecasted out to the year 2034 across all 1.5 million households in the state of Victoria, Australia. The RE-EV scenario had the strongest performance in terms of economic and societal indicators. Non-subsidy policy instruments can also support uptake of EVs, especially in the case of encouraging BEV adoption. We found feebate scenarios were more effective policies than rebates. Rebate and feebate scenarios applied within the 2014-2019 timeframe compared better than those with longer timeframes. Our analyses showed how combined policy scenarios not only further improved EV uptake but also allowed government to fund rebates through feebate income.	en_US
dc.publisher	ATRF	en_US
dc.relation.ispartof	Proceedings of the 37th ATRF	en_US
dc.relation.ispartof	Australasian Transport Research Forum	en_US
dc.title	Impacts of Policy on Electric Vehicle Diffusion	en_US
dc.type	Conference Proceeding
utslib.location.activity	Sydney, Australia	en_US
utslib.for	1605 Policy and Administration	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
pubs.organisational-group	/University of Technology Sydney/Strength - ISF - Institute for Sustainable Futures
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2015-10-02	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2015-09-30	en_US

Abstract:

Selection and design of appropriate government policies to support electric vehicle (EV) adoption can be aided by modelling the future impact of policy instruments relative to a given baseline estimate. This paper highlights the innovative application of a diffusion model to analyse complex impacts of EV policy instruments on future incremental EV uptake. Several versions of four key policy instruments are tested in the model: linking electric vehicle sales to Renewable Electricity Purchases (RE-EV), financial subsidies, smart charging incentives and a common cost metric to educate consumers about the lifetime costs of EVs. Market share between battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs) and internal combustion engine vehicles (ICEVs) were forecasted out to the year 2034 across all 1.5 million households in the state of Victoria, Australia. The RE-EV scenario had the strongest performance in terms of economic and societal indicators. Non-subsidy policy instruments can also support uptake of EVs, especially in the case of encouraging BEV adoption. We found feebate scenarios were more effective policies than rebates. Rebate and feebate scenarios applied within the 2014-2019 timeframe compared better than those with longer timeframes. Our analyses showed how combined policy scenarios not only further improved EV uptake but also allowed government to fund rebates through feebate income.

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http://hdl.handle.net/10453/43868