VMT reduction and potential environmental effects with a tradable credits scheme: a simulation case study of Great Britain

ABSTRACT

We investigate the influence of a tradable credits scheme (TCS) on travel demand and vehicle emissions, based on the vehicle miles travelled (VMT). With a microeconomic quantitative analysis scheme, a constant elasticity of substitution (CES) function is used as an approach to model the annual mileage for different travel purposes. An illustration is given for the effects of a TCS on emission mitigation based on historical data for Great Britain. A scenario analysis demonstrates that a TCS can achieve a target for reducing the number of private trips. Besides a movement of trips from the private car mode to public modes, there is also some trip restraint, with individuals choosing not to take some trips. Compared with Fowkes et al.’s research on road pricing in London, the research illustrates that a TCS can be designed to have similar effects to a road pricing scheme. We also demonstrate that a TCS could bring emission changes arising from changes in VMT.

KEYWORDS:

• Tradable credits scheme
• emission
• mode choice
• vehicle miles travelled
• climate change assessment

Acknowledgements

The work described in this article was jointly supported by the National Natural Science Foundation of China (71422010, 71361130016), the EU Marie Curie IIF (MOPED, 300674) and the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2016ZQ002), Beijing Jiaotong University. The content is solely the responsibility of the authors and does not necessarily represent the views of the funding sources. Any remaining errors or shortcomings are our own. Any views or conclusions expressed in this paper do not represent those of funding sources.

Disclosure statement

An earlier version of this paper has been communicated at the 94th Transportation Research Record Annual Meeting, 11–14 January 2015 (Paper number:15-4554). No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71361130016; 71422010]; EU Marie Curie IIF (MOPED) [300674] and the State Key Laboratory of Rail Traffic Control and Safety [Contract No. RCS2016ZQ002], Beijing Jiaotong University.