Modelling Heavy Vehicle Reforms in Australia using VURMTAX
Liu, Xianglong ORCID: https://orcid.org/0000-0001-9437-4182, Nassios, Jason
ORCID: https://orcid.org/0000-0002-0250-2608 and Dixon, Janine
ORCID: https://orcid.org/0000-0002-9514-5450
(2026)
Modelling Heavy Vehicle Reforms in Australia using VURMTAX.
Working Paper.
Victoria University, Melbourne, Victoria.
Abstract
This report uses VURMTAX, a dynamic multi-regional computable general equilibrium model of Australia, to assess the economy-wide effects of two heavy vehicle reform scenarios: (1) increasing heavy vehicle road access; and, (2) establishing a National Automated Access System (NAAS). The first scenario is modelled as a nationwide multifactor productivity improvement in the Road Freight sector, together with an increase in road wear costs, reflecting additional public expenditure required to maintain road infrastructure quality. The second scenario is modelled as a Road Freight multifactor productivity improvement applying to all Australian states and territories except Western Australia and the Northern Territory. Policy shock inputs for both scenarios are provided by the Productivity Commission based on estimates of BITRE (forthcoming). This report draws on database and model development work that disaggregates the use of different commercial vehicle classes across industries and regions within the economy, thereby improving the representation of heavy vehicles in VURMTAX. Results indicate that both reforms generate positive economy-wide gains, including higher real GDP, household consumption, investment, and output in road freight-using industries, although the size and distribution of economic impacts vary across regions and sectors in each scenario.
| Item type | Monograph (Working Paper) |
| URI | https://vuir.vu.edu.au/id/eprint/50334 |
| Official URL | https://www.copsmodels.com/elecpapr/g-368.htm |
| ISBN | 978192165477 |
| Subjects | Current > Division/Research > Centre of Policy Studies (CoPS) |
| Download/View statistics | View download statistics for this item |
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