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Vehicle Ownership and Income Growth, Worldwide: 1960-2030

Open access
Dargay, Joyce, Gately, Dermot, Sommer, Martin·2007·The Energy Journal

Abstract

The speed of vehicle ownership expansion in emerging market and developing countries has important implications for transport and environmental policies, as well as the global oil market. The literature remains divided on the issue of whether the vehicle ownership rates will ever catch up to the levels common in the advanced economies. This paper contributes to the debate by building a model that explicitly models the vehicle saturation level as a function of observable country characteristics: urbanization and population density. Our model is estimated on the basis of pooled time-series (1960-2002) and crosssection data for 45 countries that include 75 percent of the world’s population. We project that the total vehicle stock will increase from about 800 million in 2002 to more than two billion units in 2030. By this time, 56% of the world’s vehicles will be owned by non-OECD countries, compared with 24% in 2002. In particular, China’s vehicle stock will increase nearly twenty-fold, to 390 million in 2030. This fast speed of vehicle ownership expansion implies rapid growth in oil demand.

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Metadata

Authors
Dargay, J., Gately, D., & Sommer, M.
Venue
The Energy Journal
Published
2007-10
Volume
28
Issue
4
Pages
143-170
ISSN
  • 0195-6574 (ISSN-L)
  • 1944-9089
OpenAlex
W1983804881
Cited by
825
References
0
FWCI
16.40
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Vehicle Ownership and Income Growth, Worldwide: 1960-2030. https://app.cochat.ai/papers/10.5547/issn0195-6574-ej-vol28-no4-7
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Dargay, J., Gately, D., & Sommer, M. (2007). Vehicle Ownership and Income Growth, Worldwide: 1960-2030. The Energy Journal. https://doi.org/10.5547/issn0195-6574-ej-vol28-no4-7

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.